Diabetic kidney disease (DKD) has been a leading cause of chronic kidney disease (CKD) since 2011, surpassing glomerulonephritis in China (Zhang et al., 2016). Diabetes accounts for 30–50 % of all CKD cases and affects 285 million (6.4%) adults worldwide (Webster et al., 2017). Furthermore, the number of patients suffering from DKD has been increasing.

Given the mortality and morbidity of DKD, numerous studies have sought to determine the pathogenesis of DKD and promote pharmaceutical development aiming to slow and even reverse the progression of DKD. It is widely acknowledged that hemodynamic alterations, metabolic derangement, immune dysregulation, and filtration barrier damage cause kidney damage in DKD (Bonner et al., 2020). Furthermore, increasing evidence has shown that the renal tubulointerstitium acts as an initiator and major determinant of DKD pathogenesis (Nath, 1992; Tang et al., 2011; Tang and Lai, 2012; Gilbert, 2017; Zeni et al., 2017). As a consequence of decreased O₂ delivery, mitochondrial dysfunction, increased O₂ consumption, and non-ischemic pathways, renal tubule cells and the interstitium are damaged by apoptosis and fibrosis (Gilbert, 2017). Accordingly, some clinical trials, including the use of ARB/ACEI (IDNT (Rodby et al., 2000), RENAAL (Brenner et al., 2001)), SGLT-2 inhibitor (EMPAREG (Zinman et al., 2015), CANVAS (Neal et al., 2017), and CREDENCE (Perkovic et al., 2019), and GLP-1 analogs LEADER (Marso et al., 2016), REWIND (Gerstein et al., 2019) have demonstrated the effectiveness of slowing the progression of DKD. The role of inflammation in DKD progression has received wide attention in recent years (Yang and Mou, 2017; Hickey and Martin, 2018; Matoba et al., 2019; Tang and Yiu, 2020). However, no major clinical trials have targeted immune disorders in DKD because the underlying mechanisms are not well-understood.

Currently, the development of high-throughput technologies and online bioinformatics databases has enabled researchers to explore disease-related genes and the underlying mechanisms of diseases. As a widely used bioinformatics analysis method, weighted gene co-expression network analysis (WGCNA) clustered genes with similar expression patterns and provided trait-related gene information relying on expression data values. Although many studies have focused on exploring the genome expression of DKD, none has focused on the tubulointerstitium of DKD samples, and most bioinformatics studies lacked verification.

In this study, we selected a subset of microarray gene expression profiles from GSE104954, which was uploaded to the Gene Expression Omnibus database in a previous study (Grayson et al., 2018). Using WGCNA-based methods, we first identified VCAN as a hub gene with an essential role in the immune response during DKD progression. We then validated that versican was differentially expressed in kidney tissues from patients with DKD and in kidney living donors (LDs).,

DKD poses great burdens to the national health system because of its high morbidity and major expenditure. Once it has progressed to the dominant albuminuria stage, DKD continues to progress to end-stage renal disease inexorably. Furthermore, because of the global increase in CKD caused by diabetes mellitus each year, we examined the pathogenesis of DKD through bioinformatics analysis.

To better understand the pathogenesis of DKD, we compared the transcriptome profiles between DKD and LD. Overall, 563 DEGs were identified. According to GO and KEGG analysis, these genes were mostly enriched in immune-related biological processes, such as immune system process, immune response, defense response, and complement and coagulation cascades. These processes may be relevant to various infections and were deduced from enrichment analysis of pathways, such as viral protein interaction with cytokine and cytokine receptor, Staphylococcus aureus infection, and pertussis malaria. Most processes occur extracellularly and on the cell surface from adhesion interactions, such as cell adhesion, biological adhesion, signaling receptor binding, cell adhesion molecules, and cytokine-cytokine receptor interaction. Additionally, some vesicle-relevant processes are involved in the progression of DKD, such as the phagosome and extracellular exosome. IPA showed that some immune-relevant pathways were activated, including acute phase response signaling, complement system, and the role of pattern recognition receptors, such as in recognition of bacteria and viruses, leukocyte extravasation signaling, dendritic cell maturation, systemic lupus erythematosus in B cell signaling pathway, interleukin-8 signaling, and the Th1 and Th2 pathways.

The immune response exerted a major influence on the progression of DKD. This result agrees with studies reporting the involvement of immune system components in DKD progression. A wide range of proinflammatory molecules, including cytokines, receptors, chemokines, cell adhesive molecules, and transcription factors, participate in the progression of DKD (Navarro-Gonzalez et al., 2011). Infiltration of a large quantity of immune cells also accelerates the progression; these cells include macrophages, dendritic cells, T lymphocytes, B lymphocytes, neutrophils, and mast cells (Nguyen et al., 2006; Tesch, 2010; Zheng et al., 2012; Yang and Mou, 2017). Thus, immune cell infiltration, proinflammatory cytokines, inflammasome activation, immune complex formation, and complement activation function together to induce kidney injury (Hickey and Martin, 2018). Additionally, some pharmaceuticals developed for DKD targeting inflammatory mediators have been reported as effective to varying degrees (Pichler et al., 2017).

To further examine the relationship between traits and gene expression, we performed WGCNA. The lightcyan module was selected to further study the module with the highest correlation between traits and gene expression. After integrating genes from DEG analysis and WGCNA, we selected several hub genes with high fold-changes and high trait relevance. Among them, VCAN showed the largest difference between the DKD and LD groups.

Versican, translated from the VCAN gene, is a component of the extracellular matrix and plays a role in modulating cell adhesion, proliferation, migration, apoptosis, and ECM assembly. It also functions in inflammation modulation through its interaction with immune cell receptors and chemokines. It exists in four isoforms, namely V0, V1, V2, and V3. These isoforms differ in two alternative splicing glycosaminoglycan domains, which are the chondroitin sulfate (CS) attachment domain (Foulcer et al., 2014). V0, V1, and V3 are found in most tissues. V0 and V1 are major isoforms that accumulate in disease tissues, and it appears that these two isoforms play a role in the inflammation process. V3 is thought to inhibit the pro-inflammatory function of V0/V1 because of its lack of CS domains. V2 is only expressed in the central neuron system (Wight et al., 2014a,b; Wight, 2017). In previous studies of CKD, versican has been discovered as a predictor of disease progression, but there is no study focusing on the influence of versican on DKD tubulointerstitium (Rudnicki et al., 2012; Han et al., 2019; Taylor et al., 2020).

Versican exerts its proinflammatory influence by affecting the adhesion of myeloid and lymphoid cells (Gill et al., 2010; Wight et al., 2014a,b). Adhesion of leukocytes, including activated T-lymphocytes and monocytes, is modulated by versican (Potter-Perigo et al., 2010; Evanko et al., 2012). Moreover, versican participates in extracellular matrix assembly and remodeling. Versican interacts with various other molecules, including hyaluronic acid, tenascin-R, fibulin-1, and fibrillin (LeBaron et al., 1992; Aspberg et al., 1999; Isogai et al., 2002). By controlling extracellular matrix molecule organization, versican modulates cell invasion. Therefore, versican can modulate inflammation cell infiltration in disease tissues. Versican also influences inflammation by modulating cytokine release, such as by inducing the secretion of tumor necrosis factor-α and interleukin-6 in macrophages (Wight et al., 2014a). A pro-inflammatory phenotype is prone to tubulointerstitial remodeling and fibrosis (Hijmans et al., 2017).

We then perform correlation analysis between DEGs and immune cell types as well as between VCAN transcription data and immune cell types. Immune scores in the DKD group were markedly higher than those in the LD group. From another perspective, proinflammatory cells, such as monocytes, dendritic cells, and CD8⁺ Tem (T effector memory) cells increased, whereas anti-inflammation cells, such as Tregs, decreased. Among cells showing differential expression between DKD and LD, the transcription levels of VCAN demonstrated a high correlation with proinflammatory cells, such as classical dendritic cells, natural killer cells, mast cells, monocytes, and CD8⁺ Tem cells. Therefore, versican may reveal inflammation states by indicating immune cell infiltration in DKD. Furthermore, versican may mediate DKD inflammatory injury by influencing the distribution of these immune cells.

There were several limitations to our study. First, our analysis was restricted by the number of samples enrolled, as transcriptome data on DKD tubulointerstitium were limited, and most were tested using different platforms. Therefore, it was difficult to integrate these data. Second, as mentioned above, the transcriptional level of VCAN is related to pro-immune cell expression in DKD tissue; however, it was difficult to draw the conclusion that versican modulated immune cell infiltration, and we will work on these issues in our future studies.

We identified VCAN as a hub gene in DKD tubulointerstitial injury by integrating DEG analysis and WGCNA. This result was further validated in kidney tissue from patients with DKD and LDs. Moreover, versican was predicted to play a role in immune injury according to the enrichment of functions and signaling pathways. The level of versican was correlated with the assembly of immune cells in the kidney during DKD progression.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE104954; https://www.ncbi.nlm.nih.gov/, GSE30122.

The experiment protocols were approved by the Research Ethics Committee of The First Affiliated Hospital, College of Medicine, Zhejiang University. The patients/participants provided their written informed consent to participate in this study.

QX and HJ conceived and designed the project. QX conducted the data analysis and wrote the manuscript. All authors edited and revised the manuscript, and agreed to be accountable for the content of the work.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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