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. 2019 Aug 20;116(34):17121-17126.
doi: 10.1073/pnas.1822046116. Epub 2019 Aug 9.

Long noncoding RNAs are involved in multiple immunological pathways in response to vaccination

Diógenes S de Lima  1 , Lucas E Cardozo  1 , Vinicius Maracaja-Coutinho  2 , Andreas Suhrbier  3 , Karim Mane  4 , David Jeffries  4 , Eduardo L V Silveira  1 , Paulo P Amaral  5 , Rino Rappuoli  6   7 , Thushan I de Silva  4   8   9 , Helder I Nakaya  10   11
Affiliations

Long noncoding RNAs are involved in multiple immunological pathways in response to vaccination

Diógenes S de Lima et al. Proc Natl Acad Sci U S A. .

Abstract

Understanding the mechanisms of vaccine-elicited protection contributes to the development of new vaccines. The emerging field of systems vaccinology provides detailed information on host responses to vaccination and has been successfully applied to study the molecular mechanisms of several vaccines. Long noncoding RNAs (lncRNAs) are crucially involved in multiple biological processes, but their role in vaccine-induced immunity has not been explored. We performed an analysis of over 2,000 blood transcriptome samples from 17 vaccine cohorts to identify lncRNAs potentially involved with antibody responses to influenza and yellow fever vaccines. We have created an online database where all results from this analysis can be accessed easily. We found that lncRNAs participate in distinct immunological pathways related to vaccine-elicited responses. Among them, we showed that the expression of lncRNA FAM30A was high in B cells and correlates with the expression of immunoglobulin genes located in its genomic vicinity. We also identified altered expression of these lncRNAs in RNA-sequencing (RNA-seq) data from a cohort of children following immunization with intranasal live attenuated influenza vaccine, suggesting a common role across several diverse vaccines. Taken together, these findings provide evidence that lncRNAs have a significant impact on immune responses induced by vaccination.

Keywords: long noncoding RNAs; systems biology; transcriptome; vaccination.

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Conflict of interest statement

Conflict of interest statement: R.R. is a full-time employee of GlaxoSmithKline group of companies.

Figures

Fig. 1.
Fig. 1.
Experimental design of meta-analysis of 17 vaccine cohorts. (A) Graphic representation of sampling regimen. Blood samples were collected on days 0, 1, 3, 7, 14, and 28 after vaccination with IV or YF-17D. Numbers in circles represent the number of samples used from each cohort after processing. The Influenza season is shown in the name of the IV cohort (07 = 2007/2008, 08 = 2008/2009, etc.). (B) Summary of computational analyses performed in this work. Blood transcriptome from cohorts was assessed with commercial microarray platforms. After remapping of probes, each cohort was subjected to analysis of differential expression, correlation to antibody titers, correlation to neighboring genes, and coexpression analysis. (C) Characterization of lncRNA classes represented in each microarray platform.
Fig. 2.
Fig. 2.
Transcriptome analysis of cohorts immunized with inactivated IV. Cumulative sum of differentially expressed lncRNAs (A) and protein-coding genes (B) (y-axis) in one or more datasets (x-axis). Transcripts were considered differentially expressed if limma P values were lower than 0.05 in at least three cohorts (for lncRNAs) or four cohorts (for protein-coding genes). (C) Forest plots of representative lncRNAs and TNFRSF17 with reiterated differential expression. Log2 fold changes with their corresponding 95% confidence interval (x-axis) are plotted for each cohort (y-axis). Red vertical lines and shaded regions represent log2 fold-change summaries and their 95% confidence intervals, respectively. (D) Heatmap depicting gene expression of human immune cells from ref. . Logs (FPKM) of genes are scaled around zero. Columns represent samples; rows represent genes.
Fig. 3.
Fig. 3.
FAM30A expression correlates to antibody production and to gene segments within the IgH locus. (A) Correlations between FAM30A and antibody titers with corresponding 95% confidence interval (x-axis) are plotted for each cohort (y-axis). Red vertical line and shaded region represent the correlation summary and its 95% confidence interval, respectively. (B) FAM30A expression is higher in B cells according to ref. . (C) Fold-change correlation between FAM30A and gene segments within the IgH locus at day 7 after inactivated IV. Each circle represents Pearson correlation coefficient in different IV cohorts. Meta-analysis P values are represented below cohort identifiers.
Fig. 4.
Fig. 4.
Influenza vaccination consensus network. (A) The consensus network was constructed by intersecting CEMiTool results from all IV cohorts and prioritizing frequently detected edges. Inference of communities was performed using a spin glass clustering algorithm. Graph colors are based on community assignment. Each community is represented by a rectangle containing its name and the number of genes and lncRNAs (in parentheses). (B) Overrepresentation analysis of selected communities using blood transcriptional modules (BTMs). False discovery rates (FDR) (x-axis) are plotted for each BTM. (C) Gene set enrichment analysis (GSEA) performed with network communities (rows) and mean fold changes of all vaccinees from each cohort as ranks (columns). Heat map represents normalized enrichment scores (NESs) of communities whose FDR < 0.05. (D) Some PRKCQ-AS1 connections within CM5. The colors of nodes represent log2 fold-change summaries on day 1 relative to baseline.
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