Introduction: Accumulating evidence has disclosed that IgA nephropathy (IgAN) could present shortly after the second dose of COVID-19 mRNA vaccine. However, the undying mechanism remains unclear and we aimed to investigate the potential molecular mechanisms. Methods: We downloaded gene expression datasets of COVID-19 mRNA vaccination (GSE201535) and IgAN (GSE104948). Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to identify co-expression modules related to the second dose of COVID-19 mRNA vaccination and IgAN. Differentially expressed genes (DEGs) were screened, and a transcription factor (TF)-miRNA regulatory network and protein-drug interaction were constructed for the shared genes. Results: WGCNA identified one module associated with the second dose of COVID-19 mRNA vaccine and four modules associated with IgAN. Gene ontology (GO) analyses revealed enrichment of cell cycle-related processes for the COVID-19 mRNA vaccine hub genes and immune effector processes for the IgAN hub genes. We identified 74 DEGs for the second dose of COVID-19 mRNA vaccine and 574 DEGs for IgAN. Intersection analysis with COVID-19 vaccine-related genes led to the identification of two shared genes, TOP2A and CEP55. The TF-miRNA network analysis showed that hsa-miR-144 and ATF1 might regulate the shared hub genes. Conclusions: This study provides insights into the common pathogenesis of COVID-19 mRNA vaccination and IgAN. The identified pivotal genes may offer new directions for further mechanistic studies of IgAN secondary to COVID-19 mRNA vaccination.

Coronavirus disease 2019 (COVID-19) is a severe acute respiratory syndrome that has threatened the healthcare systems all over the world and vaccination plays a critical role in curbing the COVID-19 pandemic [1]. The approved mRNA-based COVID-19 vaccines, e.g., BNT162b2 produced by Pfizer-BioNTech demonstrated that mRNA-based vaccines could rapidly elicit potent immune responses and respond to the viral pandemic [2‒4]. According to the controlled clinical trial, the mRNA vaccines have been found to be safe and severe reactions have been rare [1, 5]. However, cases of glomerulonephritis have been reported after the mass-scale vaccination, especially IgA nephropathy (IgAN) [6‒16].

IgAN is the most common primary glomerulonephritis worldwide and primarily affects patients between 20 and 40 years old [17]. To date, the pathological evaluation of invasive renal biopsy is the only diagnostic method for IgAN, which is characterized by IgA deposition in the intraglomerular mesangial regions [18]. IgAN is a significant cause of chronic kidney disease and about 20–40% of patients could progress to end-stage renal disease within 20 years after being diagnosed [19‒21]. It was reported that quite a lot of patients suffer from IgAN, including new onset and relapse after the inoculation of COVID-19 vaccine, especially the second dose [6, 7, 9, 11‒15]. New onset or relapsing IgAN patients after COVID-19 vaccination referred to the group of non-kidney disease history people or IgAN patients (remission or partial remission) who received COVID-19 vaccine in the previous 3 months and suffered from IgAN [9, 22]. O-glycosylated galactose-deficient IgA1 (Gd-IgA1) production and antiglycan IgA/IgG autoantibodies against it were the important pathophysiology triggers of IgAN [23]. Moreover, the IgG FC region glycosylation of COVID-19 vaccine received people could predict the vaccine responsiveness [24]. Thus, aberrant glycosylation is a common feature of them. Furthering understanding of the molecular pathomechanism that why secondary IgAN occurs after COVID-19 vaccine injection could allow for a better understanding of this phenomenon.

In this study, we aimed to identify the pivotal genes associated with the pathogenesis of IgAN secondary to COVID-19 mRNA vaccination. The transcriptional datasets were downloaded from the GEO database. Integrated bioinformatics was used to identify common hub genes in the second dose of COVID-19 mRNA vaccine and IgAN. Finally, we screened out 2 important hub genes and further constructed the transcription factor (TF)-miRNA regulatory network and protein-drug network. Our study might be the first study to explore the share gene signatures between COVID-19 mRNA vaccine and IgAN using a systems biology approach.

Gene Expression Dataset Processing

The study design was summarized in Figure 1. We extracted the gene expression data of Peripheral Blood Mononuclear Cells (PBMCs) from 15 controls (day 0 post the second dose of BNT162b2 COVID-19 mRNA vaccine) and 29 vaccinated samples (day 1–10 post second dose of BNT162b2 COVID-19 mRNA vaccine) (GSE201535) from the Gene Expression Omnibus (GEO) dataset. Normalization of the counts by log(x + 1) using Sangerbox [25]. Meanwhile, the gene expression data of glomeruli from 21 controls (living donors) and 27 IgAN were collected (GSE104948). Finally, the keyword “COVID-19 vaccine” was used to search the GeneCards database (https://www.genecards.org/) to screen the associated genes.

Fig. 1.

Workflow diagram of this study.

Fig. 1.

Workflow diagram of this study.

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Weighted Gene Co-Expression Network Analysis

The R package weighted gene co-expression network analysis (WGCNA) [26] was used to evaluate the GSE201535 and GSE104948 expression matrix. We conducted WGCNA to obtain the COVID-19 vaccine and IgAN-associated modules. Hcluster function was performed for the sample clustering dendrogram and appropriate soft powers value (ranged from 1 to 20) was selected to determine the average connectivity and independence of various modules. The minModuleSize of the gene dendrogram was set as 30. Then, we measured the association between sample traits and modules via gene significance values (GS) and module membership values (MM). |GS| >0.3 combined |MM| >0.7 was the screening criteria to filter hub genes [27].

Differentially Expressed Gene Analysis

GSE201535 counts matrix and GSE104948 expression matrix were used to analyze the differentially expressed genes (DEGs) via the limma online via Sangerbox [25]. |Fold Change| >1.5 and the FDR <0.05 were used as the screening criterion.

Identification of Shared Hub Genes in COVID-19 Vaccine and IgAN

We selected the modules that were highly correlated with the COVID-19 vaccine and IgAN. The selected hub genes were intersected with DEGs and COVID-19 vaccine-related genes in the GeneCard using jvenn [28].

Gene Ontology and KEGG Enrichment Analysis

Gene ontology (GO) and KEGG enrichment analysis were performed online using SangerBox to analyze the potential function of selected hub genes and DEGs [25].

Construction of TF-miRNA and Protein-Drug Regulatory Network

The TF-miRNA and protein-drug regulatory network were constructed by using the NetworkAnalyst platform [29]. The TF-miRNA regulatory network was collected from the RegNetwork repository that incorporated in the NetworkAnalyst platform and the protein-drug regulatory network was collected from the DrugBank database (Version 5.0) included in the NetworkAnalyst platform.

ROC Curves of the Hub Genes

The ROC curves of the hub genes were constructed. The area under the ROC curve (AUC) was calculated to evaluate the diagnostic performance of the shared hub genes on COVID-19 vaccine and IgAN using SangerBox [25].

Construction of Weighted Gene Co-Expression Network

We exacted the expression data of PBMCs from 15 controls (day 0 post the second dose of BNT162b2 COVID-19 mRNA vaccine) and 29 vaccinated samples (day 1–10 post second dose of BNT162b2 COVID-19 mRNA vaccine) (GSE201535) and conducted WGCCNA to identify the significant modules related to the second dose of COVID-19 mRNA vaccine. The hierarchical clustering revealed the potential differences between the second dose of COVID-19 mRNA vaccine and controls (Fig. 2a). The soft threshold was set to 16 and the scale-free topology fitting index reached 0.89 (Fig. 2b, c). The gene expression was divided into 53 modules via WGCNA calculation (Fig. 2d). One module “purple” was highly associated with the second dose of COVID-19 mRNA vaccine and was selected as related module (Fig. 2e). We chose |GS| >0.3 and |MM| >0.7 as the screening criteria for the hub genes and 146 genes were found. The GO biological process analysis revealed that these hub genes were enriched in the cell cycle, mitotic cell cycle, etc., pathway (Fig. 3a). The KEGG analysis also revealed the similar enrichment pathways (Fig. 3b).

Fig. 2.

WGCNA analysis in GSE201535 and key module identification. a The sample-trait clustering heatmap. b The scale-free topology model fit index analysis for soft threshold powers and the mean connectivity analysis for soft threshold powers. c Scale-free topology fitting graph. d Module-Clinical Trait Relationships of module genes. e Module-trait correlation heatmap.

Fig. 2.

WGCNA analysis in GSE201535 and key module identification. a The sample-trait clustering heatmap. b The scale-free topology model fit index analysis for soft threshold powers and the mean connectivity analysis for soft threshold powers. c Scale-free topology fitting graph. d Module-Clinical Trait Relationships of module genes. e Module-trait correlation heatmap.

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Fig. 3.

Functional analysis of the hub genes for GSE201535. a The GO biological process analysis of the selected hub genes. b The KEGG analysis of the selected hub genes.

Fig. 3.

Functional analysis of the hub genes for GSE201535. a The GO biological process analysis of the selected hub genes. b The KEGG analysis of the selected hub genes.

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Meanwhile, we conducted WGCNA of the glomerular expression data from GSE104948 containing 21 controls (living donors) and 27 IgAN (Fig. 4a–d). Four modules “brown,” “yellow,” “black,” and “darkred” were highly associated with IgAN. Eight hundred and six genes were selected as the hub genes of these modules. The GO biological process analysis demonstrated that the immune effector process, cell activation, myeloid leukocyte activation, etc., were enriched (Fig. 5a). The KEGG analysis demonstrated that phagosome, pertussis, leishmaniasis, etc., were enriched (Fig. 5b).

Fig. 4.

WGCNA analysis in GSE104948 and key module identification. a The sample-trait clustering heatmap. b The scale-free topology model fit index analysis for soft threshold powers and the mean connectivity analysis for soft threshold powers. c Scale-free topology fitting graph. d Module-Clinical Trait Relationships of module genes. e Module-trait correlation heatmap.

Fig. 4.

WGCNA analysis in GSE104948 and key module identification. a The sample-trait clustering heatmap. b The scale-free topology model fit index analysis for soft threshold powers and the mean connectivity analysis for soft threshold powers. c Scale-free topology fitting graph. d Module-Clinical Trait Relationships of module genes. e Module-trait correlation heatmap.

Close modal
Fig. 5.

Functional analysis of the hub genes for GSE104948. a The GO biological process analysis of the selected hub genes. b The KEGG analysis of the selected hub genes.

Fig. 5.

Functional analysis of the hub genes for GSE104948. a The GO biological process analysis of the selected hub genes. b The KEGG analysis of the selected hub genes.

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Identification of DEGs and Shared Genes between COVID-19 mRNA Vaccine and IgAN

For the GSE201535 dataset, a total of 74 DEGs were identified with 72 genes up-regulated and 2 genes down-regulated (Fig. 6a, b). The GO biological process and KEGG analysis indicated the enrichment of the cell cycle for these DEGs (Fig. 6c, d). Based on the GSE104948 dataset, a total of 574 DEGs were identified with 369 genes up-regulated and 205 genes down-regulated (Fig. 7a, b). The GO biological process analysis revealed the enrichment in defense response, cell activation, response to biotic stimulus, etc. for the DGEs. The KEGG analysis indicated the enrichment in phagosome, complement and coagulation cascades, viral protein interaction with cytokine and cytokine receptor, etc., for the DGEs (Fig. 7c, d).

Fig. 6.

Differential expression profiling for GSE201535. a The volcano map of GSE201535. b The heatmap of GSE201535. c The GO biological process analysis of the selected DEGs. d The KEGG analysis of the selected DEGs.

Fig. 6.

Differential expression profiling for GSE201535. a The volcano map of GSE201535. b The heatmap of GSE201535. c The GO biological process analysis of the selected DEGs. d The KEGG analysis of the selected DEGs.

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Fig. 7.

Differential expression profiling for GSE104948. a The volcano map of GSE104948. b The heatmap of GSE104948. c The GO biological process analysis of the selected DEGs. d The KEGG analysis of the selected DEGs.

Fig. 7.

Differential expression profiling for GSE104948. a The volcano map of GSE104948. b The heatmap of GSE104948. c The GO biological process analysis of the selected DEGs. d The KEGG analysis of the selected DEGs.

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Next, by taking the intersection of DEGs of the GSE201535 dataset, DEGs of the GSE104948 dataset, hub genes of the GSE201535 dataset, hub genes of the GSE104948 dataset, and associated genes with COVID-19 Vaccine in the GeneCards dataset, there were two shared genes selected (topoisomerase II alpha [TOP2A] and Centrosomal protein 55 [CEP55]), which were visualized by Venn diagrams (Fig. 8a).

Fig. 8.

Network analysis of the shared hub genes. a Venn diagram screening for shared hub genes. b Network for TF-miRNA interaction with shared hub genes. c Network for protein-drug interaction with shared hub genes.

Fig. 8.

Network analysis of the shared hub genes. a Venn diagram screening for shared hub genes. b Network for TF-miRNA interaction with shared hub genes. c Network for protein-drug interaction with shared hub genes.

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TF-miRNA and Protein-Drug Regulatory Network Analysis

TF-miRNA regulatory network was constructed using NetworkAnalyst online, predicting the interaction of miRNA and TF with the two hub genes (Fig. 8b). This constructed network included 56 nodes and 56 edges and 28 miRNAs and 26 TF genes interacted with TOP2A and CEP55. Among them, ATF1 and has-miR-144 were the shared factor of these two hub genes (Fig. 8b).

The protein-drug regulatory network was also constructed using NetworkAnalyst online, predicting the potential drug targeting the hub genes. There were 33 kinds of drugs were predicted to interacted with one hub gene: TOP2A (Fig. 8c).

ROC Curves of Hub Genes

We assessed the diagnostic efficacy of TOP2A and CEP55 by plotting ROC curves using SangerBox online. For the GSE201535 dataset, TOP2A (AUC: 0.90) and CEP55 (AUC: 0.85) exhibited good diagnostic efficiency for differentiating the samples with COVID-19 vaccine from controls (Fig. 9a). For the GSE104948 dataset, TOP2A (AUC: 0.94) and CEP55 (AUC: 0.90) also exhibited good diagnostic efficiency for differentiating the patients with IgAN from living donors (Fig. 9b).

Fig. 9.

Diagnostic efficiency for the shared hub genes. a The ROC curve of the diagnostic efficacy of TOP2A and CEP55 in GSE201535. b The ROC curve of the diagnostic efficacy of TOP2A and CEP55 in GSE104948.

Fig. 9.

Diagnostic efficiency for the shared hub genes. a The ROC curve of the diagnostic efficacy of TOP2A and CEP55 in GSE201535. b The ROC curve of the diagnostic efficacy of TOP2A and CEP55 in GSE104948.

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IgAN is a worldwide primary glomerular disease and a “four-hit” process was postulated as the pathophysiology of it, including the production of O-glycosylated Gd-IgA1, the formation of antiglycan IgA/IgG autoantibodies recognizing Gd-IgA1, the formation of polymeric IgA1 immune complexes, and the deposition of immune complex in the mesangial area of kidney and activation of complement system [23]. Recently, several case reports have indicated a temporal relationship between kidney diseases, particularly IgAN, and COVID-19 vaccines [6‒16]. In 2021, cases of macrohaematuria were reported 2 days after receiving the COVID-19 vaccine [30]. Since then, an increasing number of new onset or relapsing cases of IgAN have been reported after COVID-19 vaccination and IgAN was the most common type of glomerulonephritis after this vaccination [6, 15, 31‒34]. Moreover, the majority of reported IgAN occurred after the second dose of the mRNA vaccine such as BNT162b (Pfizer) and mRNA-1273 (Moderna) [6, 7, 9, 11‒15]. Patients previously suffering from IgAN could be regarded as individuals with latent immunological disorders predisposed to IgAN, so the relapse to some extent infers the pathogenic effect of vaccines on susceptible populations. This influence might also contain cumulative dosage effect because most patients developed symptoms after the second dose (79.2%), while only 20.8% patients developed clinical symptoms after the first dose [9]. However, the potential mechanism underlying remains unclear. In this study, we investigated the shared hub genes of the second dose of COVID-19 vaccine and IgAN using WGCNA and DGE analysis, resulting in the identification of TOP2A and CEP55.

DNA TOP2A is expressed in proliferating cells and encodes a topoisomerase enzyme that facilitates the passage of double-stranded DNA through transient breaks [35]. The role of TOP2A in COVID-19 has been partially reported, showing upregulation in the blood cells of COVID-19 patients and suggesting its potential as a therapeutic target [36, 37]. Interestingly, our study revealed TOP2A as the key hub gene in the PBMCs of individuals who received the second dose of COVID-19 mRNA vaccine, indicating a potential similarity between COVID-19 infection (live virus) and vaccination (spike protein of the virus) to some extent. Previous research on TOP2A primarily focused on kidney cancer, where its upregulation was associated with prognosis and considered a potential target for mRNA vaccine development [38‒46]. TOP2A was upregulated in kidney cancer, could predict the prognosis, and might be a tumor target to guide mRNA vaccine development [40, 47]. Recently, a study proposed that TOP2A might serve as the target of transferrin receptor1, an IgA receptor, to play an important role in IgAN [48]. Additionally, TOP2A was proposed as a crucial biomarker in adriamycin-induced chronic glomerulonephritis rat models, possibly involved in inflammation development [49]. The traditional medicine Qi Teng Xiao Zhuo granules were found to regulate TOP2A expression by modulating the cell cycle in the chronic glomerulonephritis rat model [50]. However, there are no studies reporting the role of TOP2A in IgAN.

CEP55 is a key protein involved in cytokinesis during cell division and is specifically expressed in testis and various types of cancers but not in normal cells [51‒53]. It has been proposed as a candidate for peptide vaccine therapy in cancers like colorectal carcinoma and breast carcinoma [52, 54, 55]. In the context of nephropathy, studies on CEP55 have primarily focused on kidney cancer [41, 44, 56‒60]. Additionally, frameshift variants or deletions of CEP55 have been associated with polycystic kidneys [61, 62]. However, there are no studies reporting the role of CEP55 in IgAN. According to the researches, CEP55 could active NF-KB pathway while this pathway was reported to be a predictive factor for the poor prognosis of IgAN patients [63, 64]. We hypothesized that CEP55 may play an important role in IgAN via NF-KB pathway. Interestingly, the ESCRT- and ALIX-binding region from CEP55 integrated in the COVID-19 vaccine could enable longer-lasting protection against SARS-CoV-2 viruses [65]. Therefore, we proposed that CEP55 might play an important role both in IgAN and COVID-19 vaccination.

Our study has certain limitations. First, the expression of the shared hub genes should be validated in PBMCs from individuals who received the second dose of COVID-19 vaccine and in kidney sections of IgAN patients. Second, we need to collect peripheral blood of patients with IgAN and those who received the second dose of COVID-19 vaccine for biological verification of our results. Third, blood samples need to be collected from IgAN patients who developed the condition after the second dose of COVID-19 vaccine to further validate the importance of the hub genes.

In summary, our bioinformatics analysis revealed TOP2A and CEP55, both associated with the cell cycle, as potential hub genes linking COVID-19 mRNA vaccination and IgAN. This study provides a potential direction for further exploration of the molecular mechanisms underlying IgAN secondary to COVID-19 mRNA vaccination.

The authors would like to express our appreciation to “Henan Provincial People’s Hospital” for their effort.

In the public database used in this study, each participant signed an informed consent form during the collection of raw data for each study and the study complied with the Declaration of Helsinki. The protocol was reviewed and approved by the Ethics Committee of the Henan Provincial People’s Hospital, approval number [No. 2021-21].

The authors report there are no competing interests to declare.

This work was supported by the Henan Provincial People’ Hospital.

Wang Luoyi and Mao Zhaomin designed study and analyzed the results. Fengmin Shao and Lirong Zhang contributed to the interpretation of the results and wrote the paper.

All data analyzed during this study are included in this published article.

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