Humoral immunity and transcriptome differences of COVID-19 inactivated vacciane and protein subunit vaccine as third booster dose in human

Abstract

Under the background of the severe human health and world economic burden caused by COVID-19, the attenuation of vaccine protection efficacy, and the prevalence and immune escape of emerging variants of concern (VOCs), the third dose of booster immunization has been put on the agenda. Systems biology approaches can help us gain new perspectives on the characterization of immune responses and the identification of factors underlying vaccine-induced immune efficacy. We analyzed the antibody signature and transcriptional responses of participants vaccinated with COVID-19 inactivated vaccine and protein subunit vaccine as a third booster dose. The results from the antibody indicated that the third booster dose was effective, and that heterologous vaccination with the protein subunit vaccine as a booster dose induced stronger humoral immune responses than the homologous vaccination with inactivated vaccine, and might be more effective against VOCs. In transcriptomic analysis, protein subunit vaccine induced more differentially expressed genes that were significantly associated with many important innate immune pathways. Both the homologous and heterologous boosters could increase the effectiveness against COVID-19, and compared with the inactivated vaccine, the protein subunit vaccine, mediated a stronger humoral immune response and had a more significant correlation with the innate immune function module, which provided certain data support for the third booster immunization strategy.

Keywords: COVID-19; humoral immunity; third booster vaccine; transcriptome analysis; variants of concern.

Figure 1

The details of this study design. (A) Participants’ vaccination and blood sample collection information. (B) Serum samples were used for antibody testing and PBMCs were used for transcriptome analysis. (C) Gender and age characteristics of participants.

Figure 2

Humoral immune responses against wild, beta, delta, and omicron (BA.2.3) SARS-CoV-2 variants among participants. Neutralizing antibody levels against four lineages of SARS-CoV-2 in the IV_group (A) and PSV_group (B) before the third dose and 7 days, 14 days, and 28 days after vaccination. (C) IgG antibodies against SARS-CoV-2 in the IV_group and the PSV_group before the third dose and 7 days, 14 days, and 28 days after the vaccination. The geometric mean titer (GMT) and the fold difference in neutralizing antibodies between VOCs and wild-type are labeled. Geometric mean and SEM are depicted; statistical significances were analyzed by ordinary one-way ANOVA and two-sided paired t-tests with logtransformation.

Figure 3

Volcano plot of global gene expression changes induced by the third dose of inactivated vaccine after 7 days (A), 14 days (B) and 28 days (C) and the number of differentially expressed genes at three time points (D). Volcano plot of global gene expression changes induced by the third dose of protein subunit vaccine after 7 days (E), 14 days (F) and 28 days (G) and the number of differentially expressed genes at three time points (H). Red dots indicate significant upregulation; blue dots indicate significant downregulation (p-value < 0.1 and 2^logFC_cutoff).

Figure 4

Hierarchical cluster analysis of DEGs in two vaccine groups. (A) The expression profile of 233 up-regulated DEGs and 71 down-regulated DEGs in IV_group. (B) The expression profile of 244 up-regulated DEGs and 188 down-regulated DEGs in PSV_group. Each row represents mRNA and each column represents a sample. Red indicates higher expression and blue indicates low expression in vaccination groups. Venn diagrams of up-regulated (C) and down-regulated (D) DEGs determined in IV_group and PSV_group.

Figure 5

The up-regulated transcriptional changes induced by the third dose of inactivated vaccine and protein subunit vaccine. The PPI network of up-regulated gene sets in IV_group-specific (A), IV_group shared with PSV_group (B) and PSV_group-specific (C) groups. The PPI network extracted from a-c by MCODE (D–F). GO terms of the three core up-regulated gene sets extracted by MCODE (G). The expression level verification of CHRNB2 (H, I), HCRTR1 (J, K) and ZBED2 (L, M).

Figure 6

The down-regulated transcriptional changes induced by the third dose of inactivated vaccine and protein subunit vaccine. The PPI network of down-regulated gene sets in IV_group-specific (A), IV_group shared with PSV_group (B) and PSV_group-specific (C) groups. The PPI network extracted from a-c by MCODE (D–F). GO terms of the three core down-regulated gene sets extracted by MCODE (G). The expression level verification of CXCL2 (H, I), CXCL8 (J, K) and IL1β (L, M).

Figure 7

WGCNA of the PBMCs transcriptome. (A, B) Analysis of network topology for various soft-thresholding powers. The left panel shows the scale-free fit index (y-axis) as a function of the soft-thresholding power (x-axis). The right panel displays the mean connectivity (degree, y-axis) as a function of the soft-thresholding power (x-axis). (C, D) 3D cluster map of genes, based on differences in topological overlap, and assigned module colors. (E) Heatmap depicts the Topological Overlap Matrix (TOM) of genes selected for weighted co-expression network analysis. Light color represents lower overlap and red represents higher overlap. (F) Module-trait associations: Each row corresponds to a module eigengene and each column to a trait. Each cell contains the corresponding correlation and p-value. (G) Scatter diagram for MM vs GS in the greenyellow (eigengene value = 0.48). (H) KEGG signaling pathway enriched by greenyellow gene module.