Immune responses and transcription landscape of adults with the third dose of homologous and heterologous booster vaccines of COVID-19

Abstract

Background: Heterologous booster vaccines are more effective than homologous booster vaccines in combating the coronavirus disease 2019 (COVID-19) outbreak. However, our understanding of homologous and heterologous booster vaccines for COVID-19 remains limited.

Methods: We recruited 34 healthy participants from two cohorts who were primed with two-dose inactivated COVID-19 vaccine before, vaccinated with COVID-19 inactivated vaccine and adenovirus-vectored vaccine (intramuscular and aerosol inhalation of Ad5-nCoV) as a third booster dose. We assessed the immune responses of participants before and 14 days after vaccination, including levels of neutralizing antibodies, IgG, and cytokines, and quantified the transcriptional profile of peripheral blood mononuclear cells (PBMCs).

Results: The Ad5-nCoV group showed a significantly higher neutralizing antibody geometric mean titer (GMT) compared to the ICV group after 14 days of heterologous boosting. The intramuscular Ad5-nCoV group had a GMT of 191.8 (95% CI 129.0, 285.1) compared to 38.1 (95% CI 23.1, 62.8) in the ICV₁ group (p<0.0001). The aerosolized Ad5-nCoV group had a GMT of 738.4 (95% CI 250.9-2173.0) compared to 244.0 (95% CI 135.0, 441.2) in the ICV₂ group (p=0.0434). Participants in the aerosolized Ad5-nCoV group had median IFN-γ+ spot counts of 36.5 (IQR 15.3-58.8) per 10⁶ PBMCs, whereas, both intramuscular Ad5-nCoV and CoronaVac immunization as the third dose showed lower responses. This suggests that a third dose of booster Ad5-nCoV vaccine (especially aerosolized inhalation) as a heterologous vaccine booster induces stronger humoral and cellular immune responses, which may be more potent against VOCs than the use of inactivated vaccine homologs. In transcriptomic analyses, both aerosolized inhalation/intramuscular injection of the Ad5-nCoV vaccine and inactivated vaccine induced a large number of differentially expressed genes that were significantly associated with several important innate immune pathways including inflammatory responses, regulation of the defense response, and regulation of cytokine production. In addition, we identified crucial molecular modules of protective immunity that are significantly correlated with vaccine type and neutralizing antibodies level.

Conclusion: This study demonstrated that inhalation/intramuscular injection of the Ad5-nCoV vaccine-mediated stronger humoral and cellular immune responses compared with the inactivated vaccine, and correlated significantly with innate immune function modules, supporting a heterologous booster immunization strategy.

Keywords: Ad5-nCoV; COVID-19; aerosol inhalation; immunity; intramuscular injection; transcriptome.

Figure 1

Study schematic.

Figure 2

Neutralizing antibodies against wild-type SARS-CoV-2 and RBD-specific antibody and PBMC transcriptomic bioinformatics analysis of intramuscular Ad5-nCoV participants from cohort 1. (A) Neutralizing antibodies against wild-type SARS-CoV-2 and RBD-specific antibodies of intramuscular Ad5-nCov (IM-Ad5-nCov) and inactivated vaccine (ICV) participants. (B) Principal component analysis of PBMC transcripts in intramuscular Ad5-nCov participants. (C) Volcano plots showing differentially expressed genes (DEGs) before and after IM-Ad5-nCov or ICV vaccination. Blue, downregulated; red, upregulated. (D) Venn diagram showing DEGs before and after IM-Ad5-nCov and ICV vaccination. (E) Heatmap of DEGs before and after IM-Ad5-nCov and ICV vaccination. (F) Cluster analysis of Gene Ontology (GO) enrichment. Different colors in the tree diagram represent different enrichment modules. (G) Enrichment plots by Gene Set Enrichment Analysis (GSEA). ***p<0.001, ****p<0.0001.

Figure 3

Neutralizing antibodies against wild-type SARS-CoV-2 and RBD-specific antibody and PBMC transcriptomic bioinformatics analysis of aerosolized inhaled Ad5-nCoV participants from cohort 2. (A) Neutralizing antibodies against wild-type SARS-CoV-2 and RBD-specific antibodies of aerosolized inhaled Ad5-nCov (IH-Ad5-nCov) and inactivated vaccine (ICV) participants. (B) Principal component analysis of PBMC transcripts in aerosolized inhaled Ad5-nCov participants. (C) Volcano plots showing differentially expressed genes (DEGs) before and after IH-Ad5-nCov or ICV vaccination. Blue, downregulated; red, upregulated. (D) Venn diagram showing DEGs before and after IH-Ad5-nCov and ICV vaccination. (E) Heatmap of DEGs before and after IH-Ad5-nCov and ICV vaccination. (F) Cluster analysis of Gene Ontology (GO) enrichment. Different colors in the tree diagram represent different enrichment modules. (G) Enrichment plots by Gene Set Enrichment Analysis (GSEA). **p<0.01, ns represents not significant.

Figure 4

WGCNA analyses the transcriptome of participants receiving aerosol inhalation of Ad5-nCov (IH-Ad5-nCov), intramuscular injection of Ad5-nCov (IM-Ad5-nCov), and inactivated vaccine (ICV) to identify modules associated with clinical features and pathway enrichment. (A) Soft-thresholding calculation; Left: scale-free fit indices using various soft-thresholding powers; Right: mean connectivity using various soft-thresholding powers. (B) Heatmap of module-trait relationships. Each row corresponds to a module, and a column corresponds to a clinical trait. Each cell includes the corresponding correlation and p-value. (C) Cluster dendrograms of the coexpression network modules are ordered by hierarchical clustering of genes based on a 1-TOM matrix. Each module is colored differently. (D) Pathway enrichment analysis of modules (blue) significantly and positively associated with vaccination, wild-type SARS-CoV-2 neutralizing antibodies, and RBD-specific antibodies. (E) Relationships among these enrichment terms are displayed as a network (Metascape). Each term is represented by a circular node whose size is proportional to the number of input genes under the term and whose color represents its clustering identity (i.e., nodes of the same color belong to the same cluster). An edge connects terms with a similarity greater than 0.3 (the thickness of the edge represents the similarity score). The network was visualized with Cytoscape using a ‘force-directed’ layout, with the edges bundled for clarity. One term from each cluster was selected, and its term description was displayed as a label.