Genome-Wide Characterization of Transcriptional Patterns in High and Low Antibody Responders to Rubella Vaccination

<div><p></p><p>Immune responses to current rubella vaccines demonstrate significant inter-individual variability. We performed mRNA-Seq profiling on PBMCs from high and low antibody responders to rubella vaccination to delineate transcriptional differences upon viral stimulation. Generalized linear models were used to assess the per gene fold change (FC) for stimulated versus unstimulated samples or the interaction between outcome and stimulation. Model results were evaluated by both FC and p-value. Pathway analysis and self-contained gene set tests were performed for assessment of gene group effects.</p><p>Of 17,566 detected genes, we identified 1,080 highly significant differentially expressed genes upon viral stimulation (p<1.00E⁻¹⁵, FDR<1.00E⁻¹⁴), including various immune function and inflammation-related genes, genes involved in cell signaling, cell regulation and transcription, and genes with unknown function. Analysis by immune outcome and stimulation status identified 27 genes (p≤0.0006 and FDR≤0.30) that responded differently to viral stimulation in high vs. low antibody responders, including major histocompatibility complex (MHC) class I genes (<i>HLA-A</i>, <i>HLA-B</i> and <i>B2M</i> with p = 0.0001, p = 0.0005 and p = 0.0002, respectively), and two genes related to innate immunity and inflammation (<i>EMR3</i> and <i>MEFV</i> with p = 1.46E⁻⁰⁸ and p = 0.0004, respectively). Pathway and gene set analysis also revealed transcriptional differences in antigen presentation and innate/inflammatory gene sets and pathways between high and low responders. Using mRNA-Seq genome-wide transcriptional profiling, we identified antigen presentation and innate/inflammatory genes that may assist in explaining rubella vaccine-induced immune response variations. Such information may provide new scientific insights into vaccine-induced immunity useful in rational vaccine development and immune response monitoring.</p></div

Significant pathways differentially expressed in rubella vaccine recipients.

a<p>All presented pathways passed the FDR<0.05.</p

Dotplots of mRNA-Seq gene expression counts for A (EMR3, EGF-like module containing, mucin-like, gene) and B (MEFV, Mediterranean fever, gene), demonstrating differences in gene expression in high antibody responders compared to low antibody responders to rubella vaccination.

<p>Lines indicate the mean value of the counts within groups. Vertical axis is log₂(gene counts). HU-gene counts for unstimulated PBMCs of high responders; HS-gene counts for rubella virus-stimulated PBMCs of high responders; LU-gene counts for unstimulated PBMCs of low responders; LS-gene counts for rubella virus-stimulated PBMCs of low responders.</p

Differential response to rubella virus stimulation in high vs. low antibody responders to rubella vaccination.

a<p>Gene symbol and gene description are provided for gene identification.</p>b<p>Fold change for the interaction (HS/HU)/(LS/LU).</p>c<p>P-value and false discovery rate for the interaction.</p>d<p>Fold change in High responders, stimulated vs. unstimulated samples (HS/HU).</p>e<p>Fold change in Low responders, stimulated vs. unstimulated samples (LS/LU).</p

Analysis of mRNA-Seq reads/transcripts, mapping to Rubella virus genome.

<p>Quantification of viral transcripts in the high and low antibody responder groups was done using the Bowtie alignment tool, with alignment of reads to the Rubella virus strain Wistar RA 27/3, complete genome GenBank: FJ211588.1. <b>A</b> Mapping of rubella virus (RV)-specific reads in high antibody responders compared to low antibody responders to rubella vaccination; <b>B</b> Mapping of RV-specific reads across different rubella virus proteins. Bars represent mean ± SD.</p

Overall response to rubella virus stimulation in PBMC samples of vaccines.

a<p>Gene symbol and gene description are provided for gene identification, information on immune function-related genes is provided in italic font.</p>b<p>Fold change for overall response to stimulation analysis (all stimulated samples vs. all unstimulated samples; Stim/Unstim, S/U).</p>c<p>P-value and false discovery rate (FDR) for the overall analysis.</p>d<p>Fold change for High responders, stimulated vs. unstimulated samples (HS/HU).</p>e<p>Fold change for Low responders, stimulated vs. unstimulated samples (LS/LU).</p

Gene sets with the highest significance based on the interaction model (p₁) and overall gene expression (stimulated vs. unstimulated, p₂) model.

a<p>p₁ is the p-value from gene set-level analysis, analyzing interaction in high versus low antibody responders.</p>b<p>p₂ is the p-value from gene set-level analysis, analyzing overall all stimulated compared to all unstimulated samples.</p

Local functional relationship networks of M4.2 (gene set, for gene annotation, please see Table 5) genes MGAM, ALPL, LOC728519, ANXA3, CR1, TLR5, CA4, BMX, PGLYRP1, OPLAH, LRG1, C19orf59, KREMEN1 for the context of Global Immune Network (ImmuNet tool http://tsb.mssm.edu/primeportal/?q=immuneNET).

<p>The functional relationship network was generated via Bayesian integration of diverse functional genomic data using a gold standard specific to immune system. The top 20 genes connected to the query set with connection weight higher than 0.339 are displayed. Darker lines indicate stronger functional relationships.</p