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. 2023 Apr 19:11:e15155.
doi: 10.7717/peerj.15155. eCollection 2023.

Analysing transcriptomic signatures and identifying potential genes for the protective effect of inactivated COVID-19 vaccines

Hongquan Chen #  1   2 , Lu Zhang #  1   2 , Chen Xu  1   2 , Xiaoyun Shen  3 , Jiazhou Lou  1   2 , Shengjun Wu  1   2
Affiliations

Analysing transcriptomic signatures and identifying potential genes for the protective effect of inactivated COVID-19 vaccines

Hongquan Chen et al. PeerJ. .

Abstract

Inactivated vaccines are one of the most effective strategies for controlling the coronavirus disease 2019 (COVID-19) pandemic. However, the response genes for the protective effect of inactivated vaccines are still unclear. Herein, we analysed the neutralization antibody responses elicited by vaccine serum and carried out transcriptome sequencing of RNAs isolated from the PBMCs of 29 medical staff receiving two doses of the CoronaVac vaccine. The results showed that SARS-CoV-2 neutralization antibody titers varied considerably among individuals, and revealed that many innate immune pathways were activated after vaccination. Furthermore, the blue module revealed that NRAS, YWHAB, SMARCA5, PPP1CC and CDC5L may be correlated with the protective effect of the inactivated vaccine. Additionally, MAPK1, CDC42, PPP2CA, EP300, YWHAZ and NRAS were demonstrated as the hub genes having a significant association with vaccines. These findings provide a basis for understanding the molecular mechanism of the host immune response induced by inactivated vaccines.

Keywords: Inactivated vaccine; Neutralization antibody; RNA-seq; SARS-CoV-2; Transcriptome profile.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. The differentially expressed genes (DEGs) in vaccinated individuals (V), patients (P) and healthy group (H).
(A) The neutralization antibody (NAT) level of vaccinated individuals; DEGs across vaccinated individuals (n = 29), healthy donors (n = 3), and infected patients (n = 3). (B) Volcano plot on DEGs in the comparison of different groups. Red dot represents upregulated DEGs with P-value (two-sided unpaired Wald test) < 0.05 and Log2 > 1.0 and blue indicates downregulated DEGs with P-value < 0.05 and Log2 < −1.0.
Figure 2
Figure 2. Protein–protein interaction (PPI) network of gene signature.
(A) The PPI network extracted from all upregulated DEGs between vaccine and healthy group by MCODE. (B) The PPI network extracted from all downregulated DEGs between vaccine and healthy group by MCODE. (C) The PPI network extracted from all upregulated DEGs between patients and healthy group by MCODE. (D) The PPI network extracted from all downregulated DEGs between patients and healthy group by MCODE.
Figure 3
Figure 3. GO and KEGG enrichment analyses of DEGs in PBMCs transcriptome.
(A) GO enrichment analysis by biological process (BP) and cellular component (CC) between healthy and vaccination group. (B) GO enrichment analysis by biological process (BP) and molecular function (MF) between healthy and patients group. (C) KEGG pathway analysis between healthy and vaccination group. (D) KEGG pathway analysis between healthy and patients group.
Figure 4
Figure 4. WGCNA of DEGs revealed the gene-network modules.
(A) Scale-free fit index (left) and mean connectivity (right) for various soft-thresholding powers. (B) Clustering dendrogram of DEGs together with assigned module colors. (C) Heatmap of the correlation between modules and clinical traits (including SARS-CoV-2 infection status and vaccination). (D) Cluster of module feature vectors. (E) Blue module.
Figure 5
Figure 5. Analysis of blue module and identification of genes.
(A) PPI network of genes in blue module of vaccinated group with —MM_R— > 0.95, —GS— >0.9 and removing the points that degree < 3. Node diameter and color intensity represents the degree of connectivity with vaccine. (B) PPI network of top 100 hub genes co-owned in blue modules by vaccinated and patient groups. Node diameter and color intensity represents the degree of connectivity. The thickness of edge indicates the strength of connectivity for each gene.
See this image and copyright information in PMC

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