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. 2013 May 1;8(5):e62149.
doi: 10.1371/journal.pone.0062149. Print 2013.

Genome-wide characterization of transcriptional patterns in high and low antibody responders to rubella vaccination

Iana H Haralambieva  1 , Ann L Oberg, Inna G Ovsyannikova, Richard B Kennedy, Diane E Grill, Sumit Middha, Brian M Bot, Vivian W Wang, David I Smith, Robert M Jacobson, Gregory A Poland
Affiliations

Genome-wide characterization of transcriptional patterns in high and low antibody responders to rubella vaccination

Iana H Haralambieva et al. PLoS One. .

Abstract

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. 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 (HLA-A, HLA-B and B2M with p = 0.0001, p = 0.0005 and p = 0.0002, respectively), and two genes related to innate immunity and inflammation (EMR3 and MEFV 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.

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Conflict of interest statement

Competing Interests: Dr. GAP is the chair of a Safety Evaluation Committee for investigational vaccine trials being conducted by Merck Research Laboratories. Dr. GAP offers consultative advice on vaccine development to Merck & Co. Inc., CSL Biotherapies, Avianax, Sanofi Pasteur, Dynavax, Novartis Vaccines and Therapeutics, and PAXVAX Inc. Dr. RMJ is a member of a Data Monitoring Committee for two Merck vaccine studies as well as a Safety Review Committee for another Merck vaccine study. He also recently served as a principal investigator for three vaccine studies including one by Novartis and two by Pfizer. BMB is employed by Sage Bionetworks, which is a commercial company. This does not alter the authors' adherence to all PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. 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.
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.
Figure 2
Figure 2. 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).
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.
Figure 3
Figure 3. Analysis of mRNA-Seq reads/transcripts, mapping to Rubella virus genome.
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. A Mapping of rubella virus (RV)-specific reads in high antibody responders compared to low antibody responders to rubella vaccination; B Mapping of RV-specific reads across different rubella virus proteins. Bars represent mean ± SD.
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