Demographic and immune characteristics of the study subjects.

<p>Demographic and immune characteristics of the study subjects.</p

Quantile-quantile plot of p-values for KIR alleles and haplotypes associated with neutralizing antibodies.

<p>The p-values result from regression of neutralizing antibody titer on the dose of each allele or haplotype, along with adjusting covariates that include eigenvectors to adjust for population stratification. The observed p-values were close to the diagonal line, suggesting that the observed p-values had a distribution that would be expected when the null hypothesis of no association is true.</p

A large population-based association study between HLA and KIR genotypes and measles vaccine antibody responses

<div><p>Human antibody response to measles vaccine is highly variable in the population. Host genes contribute to inter-individual antibody response variation. The killer cell immunoglobulin-like receptors (KIR) are recognized to interact with HLA molecules and possibly influence humoral immune response to viral antigens. To expand on and improve our previous work with HLA genes, and to explore the genetic contribution of KIR genes to the inter-individual variability in measles vaccine-induced antibody responses, we performed a large population-based study in 2,506 healthy immunized subjects (ages 11 to 41 years) to identify HLA and KIR associations with measles vaccine-induced neutralizing antibodies. After correcting for the large number of statistical tests of allele effects on measles-specific neutralizing antibody titers, no statistically significant associations were found for either HLA or KIR loci. However, suggestive associations worthy of follow-up in other cohorts include B*57:01, DQB1*06:02, and DRB1*15:05 alleles. Specifically, the B*57:01 allele (1,040 mIU/mL; p = 0.0002) was suggestive of an association with lower measles antibody titer. In contrast, the DQB1*06:02 (1,349 mIU/mL; p = 0.0004) and DRB1*15:05 (2,547 mIU/mL; p = 0.0004) alleles were suggestive of an association with higher measles antibodies. Notably, the associations with KIR genotypes were strongly nonsignificant, suggesting that KIR loci in terms of copy number and haplotypes are not likely to play a major role in antibody response to measles vaccination. These findings refine our knowledge of the role of HLA and KIR alleles in measles vaccine-induced immunity.</p></div

HLA allelic associations with measles-specific neutralizing antibody titers in the pooled data of 2,506 subjects and the three separate cohorts: Rochester, San Diego, and US.

<p>Results are presented for alleles that have p-values < 0.001 for association with neutralizing antibody titers.</p

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

<div><p>Introduction//Background</p><p>The 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.</p><p>Methods</p><p>We 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.</p><p>Results</p><p>We 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).</p><p>Conclusion</p><p>Our 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.</p></div

Whole Transcriptome Profiling Identifies CD93 and Other Plasma Cell Survival Factor Genes Associated with Measles-Specific Antibody Response after Vaccination

<div><p>Background</p><p>There are insufficient system-wide transcriptomic (or other) data that help explain the observed inter-individual variability in antibody titers after measles vaccination in otherwise healthy individuals.</p><p>Methods</p><p>We performed a transcriptome(mRNA-Seq)-profiling study after <i>in vitro</i> viral stimulation of PBMCs from 30 measles vaccine recipients, selected from a cohort of 764 schoolchildren, based on the highest and lowest antibody titers. We used regression and network biology modeling to define markers associated with neutralizing antibody response.</p><p>Results</p><p>We identified 39 differentially expressed genes that demonstrate significant differences between the high and low antibody responder groups (p-value≤0.0002, q-value≤0.092), including the top gene <i>CD93</i> (p<1.0E^-13, q<1.0E^-09), encoding a receptor required for antigen-driven B-cell differentiation, maintenance of immunoglobulin production and preservation of plasma cells in the bone marrow. Network biology modeling highlighted plasma cell survival (<i>CD93</i>, <i>IL6</i>, <i>CXCL12</i>), chemokine/cytokine activity and cell-cell communication/adhesion/migration as biological processes associated with the observed differential response in the two responder groups.</p><p>Conclusion</p><p>We identified genes and pathways that explain in part, and are associated with, neutralizing antibody titers after measles vaccination. This new knowledge could assist in the identification of biomarkers and predictive signatures of protective immunity that may be useful in the design of new vaccine candidates and in clinical studies.</p></div

Differential miRNA expression in B cells is associated with inter-individual differences in humoral immune response to measles vaccination

<div><p>Background</p><p>MicroRNAs are important mediators of post-transcriptional regulation of gene expression through RNA degradation and translational repression, and are emerging biomarkers of immune system activation/response after vaccination.</p><p>Methods</p><p>We performed Next Generation Sequencing (mRNA-Seq) of intracellular miRNAs in measles virus-stimulated B and CD4⁺ T cells from high and low antibody responders to measles vaccine. Negative binomial generalized estimating equation (GEE) models were used for miRNA assessment and the DIANA tool was used for gene/target prediction and pathway enrichment analysis.</p><p>Results</p><p>We identified a set of B cell-specific miRNAs (e.g., miR-151a-5p, miR-223, miR-29, miR-15a-5p, miR-199a-3p, miR-103a, and miR-15a/16 cluster) and biological processes/pathways, including regulation of adherens junction proteins, Fc-receptor signaling pathway, phosphatidylinositol-mediated signaling pathway, growth factor signaling pathway/pathways, transcriptional regulation, apoptosis and virus-related processes, significantly associated with neutralizing antibody titers after measles vaccination. No CD4⁺ T cell-specific miRNA expression differences between high and low antibody responders were found.</p><p>Conclusion</p><p>Our study demonstrates that miRNA expression directly or indirectly influences humoral immunity to measles vaccination and suggests that B cell-specific miRNAs may serve as useful predictive biomarkers of vaccine humoral immune response.</p></div

Genes whose expression is highly correlated with cis-acting CpGs show functional enrichment.

<p><b>A)</b> Genes with significant association (p < 1E^-4) indicate 32 GO terms enriched at the p < 0.01 level and annotating at least 3 genes, across time points. Color intensity is used to signify statistical significance. Genes are mapped to network biology resources (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0152034#sec002" target="_blank">Methods</a>) and the associations at <b>B)</b> baseline, <b>C)</b> during early and <b>D)</b> late time periods shown, represented in the same location in all panels; (for brevity, only genes within the largest connected components are shown). We color genes in the network that have a significant association at each time period (baseline teal, early green, late orange). The network layout is manually adjusted and edges bundled to improve legibility. See the online version for sufficient resolution to view gene names.</p

B cell-specific miRNAs demonstrating differences in expression between high and low antibody responders to measles vaccination (interaction analysis, q-value < 0.2).

<p>B cell-specific miRNAs demonstrating differences in expression between high and low antibody responders to measles vaccination (interaction analysis, q-value < 0.2).</p

Interrelationships between genes associating with each humoral immune outcome.

<p><b>A)</b> A Venn diagram summarizes the number of genes associated with each outcome across time points. A “core” set of 54 genes are identified using at least two outcomes. We divide genes only associated with one outcome into two groups; those that share network links with genes in the core (inner number), and those that do not (outer number). <b>B)</b> The fraction of genes with direct links to each other (gene-gene connectivity), within and between each outcome-specific set, is compared to observations from random gene sets of the same size. The six comparisons between HAI, B-cell ELISPOT, and gene expression are shown in the same relative position as the Venn diagram with a colored vertical bar indicating the gene-gene connectivity observed in our study and the distribution of connectivity from randomly generated genesets in gray. <b>C)</b> A visualization of the network is shown using the subset of links with greatest confidence and laid out similarly to the other panels. The extent of gene-gene connectivity is apparent from the number of genes (represented by colored circles) with known direct interactions (gray lines crossing between groups).</p