Identification of C3 As a Therapeutic Target for Diabetic Nephropathy By

Home , ARHGDIB, GLG1, P2RY13, P2RY14, TRIM22

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OPEN Identifcation of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis ShuMei Tang, XiuFen Wang, TianCi Deng, HuiPeng Ge & XiangCheng Xiao*

The pathogenesis of diabetic nephropathy is not completely understood, and the efects of existing treatments are not satisfactory. Various public platforms already contain extensive data for deeper bioinformatics analysis. From the GSE30529 dataset based on diabetic nephropathy tubular samples, we identifed 345 genes through diferential expression analysis and weighted gene coexpression correlation network analysis. GO annotations mainly included neutrophil activation, regulation of immune efector process, positive regulation of cytokine production and neutrophil-mediated immunity. KEGG pathways mostly included phagosome, complement and coagulation cascades, cell adhesion molecules and the AGE-RAGE signalling pathway in diabetic complications. Additional datasets were analysed to understand the mechanisms of diferential gene expression from an epigenetic perspective. Diferentially expressed miRNAs were obtained to construct a miRNA-mRNA network from the miRNA profles in the GSE57674 dataset. The miR-1237-3p/SH2B3, miR-1238-5p/ ZNF652 and miR-766-3p/TGFBI axes may be involved in diabetic nephropathy. The methylation levels of the 345 genes were also tested based on the gene methylation profles of the GSE121820 dataset. The top 20 hub genes in the PPI network were discerned using the CytoHubba tool. Correlation analysis with GFR showed that SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA, SERPING1, EGF and KNG1 may be involved in diabetic nephropathy. Eight small molecule compounds were identifed as potential therapeutic drugs using Connectivity Map.

It is estimated that a total of 451 million people sufered from diabetes by 2017, and the number is speculated to be 693 million by 2045 1. As one of the most serious microvascular complications, diabetic nephropathy (DN) has been a major cause of end-stage renal disease (ESRD) in many countries. Te congregation of advanced glycation end-products, oxidative stress and activation of protein kinase C are the major pathogeneses of DN. A new viewpoint holds that tubular injury plays an important and even initial role 2. Current treatment strategies for DN aim at controlling blood glucose and blood pressure levels and inhibiting the RAS system to reduce albuminuria and delay the progression of DN3. However, considering the high incidence of DN-related ESRD, the efect is not entirely satisfactory. Terefore, there is a critical need to identify new therapeutic targets and improve clinical management. High-throughput sequencing technology ofers an efective method to study disease-related genes and provides promising medication goals in many felds4. To date, several studies have screened genes or miRNAs involved in DN 5–9. Integrating these data could overcome the heterogeneity of studies and provide more accurate information. Tis study identifed target genes that may improve the understanding of the molecular mechanisms of DN and provide a resource to build new hypotheses for further follow-up studies. We suggest that the complement system may serve as a therapeutic target in DN. Results Diferential expression analysis of genes in the GSE30529 dataset. Diferential expression analysis of genes in the GSE30529 dataset5 was performed to obtain diferentially expressed genes (DEGs) that may be involved in DN. First, the GSE30529 dataset was subjected to quality examination to detect batch efects and determine the principal component of the dataset that contributed the most to the variance. Te boxplot showed that the overall gene expression levels of the samples in the GSE30529 dataset were approximately the same

Department of Nephrology, XiangYa Hospital, Central South University, XiangYa Road NO 87, Changsha 41008, Hunan, China. *email: [email protected]

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a b

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group c d Control Diabetic Nephropathy

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IGJ 1 IGLC1

IGLV1-44 0 FCER1A 2.5 -1 HDAC9

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TNFAIP8 threshold SPARC -2.5 Up Down NMI NoSignifi PLK2

024 -log10(adj.P.Val) KDELC1

Figure 1. Diferential expression analysis of GSE30529. (a) Boxplot of GSE30529. (b) PCA of GSE30529. Te two main components contributed 25.7% and 25.4%. (c) Volcano map of DEGs. A total of 386 upregulated DEGs and 71 downregulated DEGs were identifed between the DN group and the control group with the criteria of |log2 FC| greater than 1 and adjusted P value less than 0.05. (d) Heatmap of the top 25 DEGs.

(Fig. 1a), suggesting that there was no batch efect. In addition, the two main components contributed 25.7% and 25.4% in principal component analysis (PCA) (Fig. 1b), suggesting that there are obviously diferent components between the DN group and the control group. Tese diferent components may be biologically signifcant DEGs. Afer the quality inspection, diferential expression analysis was performed by the limma package10 to acquire DEGs with the criteria of |log2-fold change (FC)| greater than 1 and adjusted p value less than 0.05. As a result, 386 upregulated DEGs and 71 downregulated DEGs were identifed between the DN group and the control group. Te volcano map in Fig. 1c displays the general distribution of these genes, and the top 25 DEGs (PART1, IGJ, IGLC1, IGLV1-44, FCER1A, HDAC9, VCAN, TNC, PDLIM1, PXDN, C3, LTF, CXCL6, MMP7, LYZ, MID1, TRIM22, PTPRE, MARCKSL1, QPCT, TNFAIP8, SPARC, NMI, PLK2 and KDELC1) are shown in the hierarchical clustering heatmap in Fig. 1d.

Weighted gene coexpression network analysis of the GSE30529 dataset. Compared with dif- ferential analysis that focuses on the diferential expression of genes, the advantage of weighted gene coexpression network analysis (WGCNA) is that it uses expression correlation information between multiple genes to identify genes of interest. Terefore, we applied two analytical methods to screen the target genes. Similarly, we

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Sample clustering to detect outliers Scale independence Mean connectivity 100

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"GSM757019" "GSM757020" 2 Scale Free Topology Model Fit,signed R^ 2 "GSM757029"

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Figure 2. WGCNA of GSE30529. (a) Sample clustering of GSE30529. (b) Analysis of sof-thresholding powers to ft the scale-free topology model and the mean connectivity of the sof-thresholding powers; 10 was chosen as the value to construct a scale-free network. (c) Dendrogram of the gene modules. Te branches represent diferent gene modules, and each leaf represents a gene in the cluster dendrogram. (d) Clustering and heatmap of 22 gene modules. (e) Heatmap of the weighted gene coexpression correlations of all genes.

performed sample cluster analysis frst to learn sample similarity. Te results showed that there were 3 outliers (Fig. 2a); therefore, three samples (GSM757025, GSM757027 and GSM757034) were removed. When perform- ing WGCNA, to construct a scale-free network, the scale-free topological ftting index reaches 0.85 and the mean connectivity reaches 100 by setting the sof threshold power value to 10 (Fig. 2b). Based on the weighted gene coexpression correlation, hierarchical clustering analysis was carried out to obtain diferent gene modules, which are represented by branches of the clustering tree and diferent colours. A total of 22 modules were found in the network, with module sizes ranging from 30 to 10,000 and merge cut hights of 0.25 (Fig. 2c). Te 22 modules were divided into two clusters in general according to the relationships between the modules (Fig. 2d). In addition, the weighted coexpression correlations of all genes were displayed in a heatmap plot (Fig. 2e). Finally, 3,538 highly related genes were selected in the TOM matrix with a threshold greater than 0.1. Te results of the two analyses can be combined to obtain more accurate targets. Terefore, a list of 345 target genes was obtained, and these genes may play a regulatory role in DN (Fig. 3a).

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Enrichment KEGG a b Phagosome Staphylococcus aureus infection Pertussis Complement and coagulation cascades Tuberculosis Amoebiasis Cell adhesion molecules (CAMs) Type I diabetes mellitus Leishmaniasis Allograft rejection Graft-versus-host disease Autoimmune thyroid disease Viral myocarditis Human papillomavirus infection ECM-receptor interaction Focal adhesion Rheumatoid arthritis Influenza A Systemic lupus erythematosus 112 345 3193 Asthma Legionellosis Epstein-Barr virus infection Antigen processing and presentation Toxoplasmosis Natural killer cell mediated cytotoxicity Hematopoietic cell lineage Pathogenic Escherichia coli infection Platelet activation Chemokine signaling pathway Fc gamma R-mediated phagocytosis AGE-RAGE signaling pathway in diabetic complications Malaria NF-kappa B signaling pathway p.adjust Osteoclast differentiation Small cell lung cancer 0.002 Human cytomegalovirus infection 0.004 Kaposi sarcoma-associated herpesvirus infection Viral protein interaction with cytokine and cytokine receptor 0.006 Chagas disease (American trypanosomiasis) 0.008 Toll-like receptor signaling pathway

SERPINA3 CXCL 01020 LTF LYZ

CCR2 ANXA1 C3

CLU

POSTN LU 6 HLA-DPA1 Q M c PC CD53 MNDA T HL CC A L -DPB 19 SEL PY FC CA L 1 G R AN R3 D CR X B ISPLD2A2 ITGB CCL 2 PL 5 A TY C8 RO BP SLP CXCL1I CORO 1A CTSS ITGAM FGL2 FABP5 FCGR2A A2M GPNMB STAT1 T2 RNASE PECAM1 4 VSIG -F HLA RHOA2 ILF AB31 R FG GM LA-E H P1 XB T2 BS LYN FCN1 14 P3 CD S A C IL7R SYK

V FCGR2B 1 logFC T FCER1G

IMPDH2 1 ITGA DOCK2 RPS3 RF LPCA I

IL33 ADA -1 3 WNT5A CD83 AOC1

GO Terms

neutrophil activation regulation of immune effector process positive regulation of cytokine production neutrophil mediated immunity

neutrophil degranulation neutrophil activation involved in immune responseT cell activation extracellular structure organization

extracellular matrix organization response to interferon-gamma leukocyte cell-cell adhesionleukocyte proliferation

Figure 3. Enrichment analysis. (a) Venn diagram of the DEG list and highly related gene list. A total of 345 target genes were obtained. (b, c) GO annotation and KEGG pathway enrichment analysis. GO annotations mainly included neutrophil activation, regulation of immune efector process, positive regulation of cytokine production and neutrophil-mediated immunity. KEGG pathways mostly included phagosome, complement and coagulation cascades, cell adhesion molecules and ECM-receptor interaction and focal adhesion.

Functional enrichment analysis of the target genes. Te pathogenesis of diabetic nephropathy is very complex, and understanding the functions of the target genes could guide the direction of new research. Functional enrichment analysis of the target genes was performed with the clusterProfler package 11 to explore the Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in which target genes are involved. Te top 12 GO terms were identifed and mainly included neutrophil activation, regulation of immune efector process, positive regulation of cytokine production and neutrophil-mediated immunity (Fig. 3b). Te KEGG pathways mostly included phagosome, complement and coagulation cascades, cell adhesion molecules (CAMs), ECM-receptor interaction and focal adhesion (Fig. 3c). Te AGE-RAGE signalling pathway in diabetic complications was also found. It is interesting that the immune system seems to play an important role.

Potential epigenetic regulatory mechanism. It has now been recognized that the occurrence and development of DN are the result of complex interactions between genetic and environmental factors. Envi-

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group Cluster Dendrogram 0.6 a b Co n

10 0 Diabetic Nephropathy 0

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Con1 Con3 0.2 Diabetic Nephropathy1 0 Diabetic Nephropathy5 Diabetic Nephropathy9 PC2 (15.68%) Diabetic Nephropathy6 Diabetic Nephropathy7 Diabetic Nephropathy8

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Figure 4. Diferential expression analysis of GSE51674. (a) Cluster dendrogram of GSE51674. (b) Principal component analysis of GSE30529. Te two main components contributed 62.71% and 15.68%. (c) Volcano map of diferentially expressed miRNAs. Sixty-seven upregulated miRNAs and 16 downregulated miRNAs were identifed between the DN group and the control group with the criteria of |log2 FC| greater than 3 and adjusted p value less than 0.01. (d) Heatmap of the downregulated DEGs.

ronmental signals could change intracellular pathways through chromatin modifers and regulate gene expression patterns leading to diabetes and its complications 12. Afer determining the target genes, we studied more datasets to understand the potential mechanisms of the diferential expression of the target genes, including the GSE51674 dataset9, which contains miRNA profles, and the GSE121820 dataset, which contains DNA methylation profles. Generally, gene expression could be inhibited by miRNAs via base pairing with mRNA. Diferential expression analysis was performed on the miRNA profles of the GSE51674 dataset 9. Similarly, quality examinations of GSE51674 were performed. Tere were no very heterogeneous samples in the sample cluster dendrogram (Fig. 4a). PCA showed that the two main components contributed 62.71% and 15.68%, respectively (Fig. 4b). Next, 16 downregulated miRNAs and 67 upregulated miRNAs were found with the criteria of |log2 FC| greater than 3 and adjusted p value less than 0.01 (Fig. 4c). Te 16 downregulated miRNAs are shown in the hierarchical clustering heatmap in Fig. 4d. To construct a downregulated miRNA-mRNA network, the TargetScan, miRWalk, miRBase and miRTarBase databases 13–16 were used for target gene prediction of the miRNAs. Eighty-eight downregulated miRNA-mRNA pairs were obtained according to the miRNA target webtools (Fig. 5a). Among them, TGFBI, SH2B3 and ZNF652 were upregulated in the GSE30529 dataset (Fig. 5b). Terefore, the miR-1237-3p/

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SSSSTR3TR3 a b TM C7 GGTF3C4 TF3 C4 4 ZZNF107NF107 MMCTS1 SSMAD7MAD7 XXRCC2RCC22 hsa-miR-191-5p TGFBI KLF13 CC6oC6orf106rf106 TBBC1DC1D24

CHD4 MMGMGATAT5 LLRRC59RRC59 RRAD51BD B hsa-hsa miR-766-3636 p CDK6 ZZNF101NF101 GGMEB2B22 PHC2C2 GGABRB2ABRB2 CCYP1AYP1A2 NNIPAL33 TFPI PPEX11BB MDM2 hsa-hsah miR-766-5p656 SSHISA6HISA6 TFDP2 CBL SSLC38ALC38AA7 GJC1 GGPR132PR132 LLRPAP1RPAP1 SET CRTC2CRTC2 RAB5BRA B SLLLC25A337 NOVANOVA2 RRPL13AA TTMEM33MEM333 hsa-mmiR-12228-3p NFAT55 GLG1 ZCC3H13H122B RNF41 TTNRC6BNRC6BB MMTFMT ORAI2 ARRRHGAPRHG P1P ZZNFZNF554554 MMORN4MO 4 ETS1

KSR2 hsa-miR-18825 hsa-miR-12383 -5p hsaaa-miR-940949 CYP8B1 ZCZC3H43H4 PODXPODXL WAC MMECPECP2 PPPP2CBPP2CBB DFFA

TMMMEM170EM170A SSHOCHOC2 ZNF652 IRGQ AARPP19P 9 ZZNF468NF4688 hsa-miR-425-3p hsa-miR-1234-3p

TUB hsa-miR-33b-3p hsa-miR-425-5p APAF1 TPM3

CCCND2CND2 SCN4SCN4B RRBMS3BMS3 SPIB CCREBRFREBRF A1CF IKZF3 GPR107 SH2B3 hsa-miR-12383 -3p hsa-miR-12282 1 hsa-sa mmiR-12337-3p373 3p hsa-miR-1225-3p

TPP53INP53INP2 SPOP NFYA TRRMT1MT100B TTPD52LPD52L2 FFAM53CAM53C GAATAD2TAD2A

Figure 5. miRNA-mRNA network. (a) Venn plot of four prediction results. (b) miRNA-mRNA network. In this network, TGFBI, SH2B3 and ZNF652 in red were upregulated in GSE30529.

SH2B3, miR-1238-5p/ZNF652 and miR-766-3p/TGFBI axes may be involved in diabetic nephropathy. Similar work was carried out on the upregulated miRNAs, but their predicted genes did not overlap with the target genes from GSE30529. DNA methylation is the main epigenetic form of gene expression regulation. To understand the methylation level changes of the target genes, the GSE121820 dataset was downloaded as a validation dataset. Among 345 target genes, 227 genes had methylation diferences between the DN group and the control group (Supplemental Table 1).

PPI network and identifcation of hub genes. First, the list of target genes was exported to the STRING database. By setting the interaction confdence score at the highest level at 0.9, a protein–protein interaction (PPI) network was constructed, which contained 190 nodes and 680 edges (Fig. 6a). Each node represents a protein, and an edge represents an interaction between proteins. Te size and gradient colour of the nodes are adjusted by the degree, while the thickness and gradient colour of the edge are adjusted by the interaction score. To search for important nodes in the networks, all nodes were ranked by the 12 topological analysis methods provided by CytoHubba. Each algorithm computed all node scores, and then 1–50 points were assigned based on the rank. According to all points, the top 20 nodes (KNG1, C3, FN1, SYK, HLA-E, EGF, ITGB2, CXCL1, CXCL8, ITGAV, LYN, VWF, RHOA, HLA-DQA1, ITGAM, SERPING1, P2RY13, ANXA1, P2RY14 and FCER1G) were identifed (Fig. 6b). Because the products of genes were at the core of the PPI network, these hub genes were considered potential therapeutic targets.

Clinical data validation and drug prediction. To verify the potential roles of the hub genes in DN, clinical data including two datasets (Woroniecka and Schmid) from Nephroseq were obtained, and Pearson correlation analysis was performed between the hub genes and clinical data. Te gene expression of SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA and SERPING1 in DN tubule samples was negatively related to GFR, suggesting a pathogenic role of the upregulated genes (Fig. 7a, c, e). Conversely, the gene expression of EGF and KNG1 in DN tubule samples was positively related to GFR, suggesting a protective role of the downregulated genes (Fig. 7b, d, f). Given that the efectiveness of existing treatment strategies is not entirely satisfactory, it is necessary to pro- pose new strategies and develop new therapeutic methods. Connectivity Map 17 was used to compare the DEG list with the database reference dataset, and a correlation score (− 100 to 100) was obtained. Negative numbers indicate that the DEG list and the reference gene expression spectrum may be opposite; that is, the expression spectrum of drug disturbance is negatively correlated with the expression spectrum of disease disturbance. Twenty-three upregulated DEGs (logFC greater than 2.5) and 13 downregulated DEGs (logFC less than 1.5) were

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Cell ID molecule HEPG2 HA1E A375 HT29 PC3 HCC515 A549 VCAP MCF7 Summary VEGF-receptor-2-kinase-inhibitor-IV NaN − 97.65 NaN NaN − 98.72 − 36.65 NaN 39.31 NaN − 95.76 GPR158 − 99.67 − 99.54 − 87.54 − 85.11 − 95.37 − 96.24 NaN 0.00 − 78.44 − 99.00 CYP51A1 − 96.01 − 25.96 − 98.71 66.44 − 91.52 NaN − 99.55 NaN 0.00 − 99.38 PTMS NaN 0.00 − 98.98 − 93.33 − 78.99 − 96.77 − 98.10 − 35.22 0.00 − 97.04 Withaferin-A 0.00 0.00 − 92.00 − 14.15 − 97.00 − 98.40 0.00 − 90.66 − 96.15 − 95.84 Digoxin NaN 0.00 − 98.81 0.00 − 76.95 − 97.80 0.00 − 91.20 − 97.67 − 96.35 Digoxin 0.00 0.00 − 97.41 0.00 − 96.72 − 83.92 − 93.40 − 98.91 − 87.77 − 95.77 Ouabain − 91.46 − 25.32 − 96.72 − 88.99 − 89.54 − 53.35 − 97.20 0.00 − 92.89 − 94.13 PHF15 64.27 − 81.91 − 95.04 0.00 − 96.29 NaN − 96.54 NaN 0.00 − 95.50

Table 1. Small molecular compounds identifed by connectivity map.

exported to Connectivity Map to search for potential drugs. Small molecule compounds with an average coef- fcient of less than − 90 were sorted according to the correlation score of the reference gene expression spectrum. As a result, 8 small molecule compounds were identifed as potential therapeutic drugs (Table 1). Discussion As one of the microvascular complications of diabetes, DN is the main cause of ESRD. Existing treatments are not sufcient to control the development of disease. New treatment strategies are needed. High-throughput omics data have been widely used to study the mechanisms of disease and predict possible therapeutic targets. We performed diferential expression analysis and WGCNA of GSE30529 and obtained 345 target genes. GO annotations mainly included neutrophil activation, regulation of immune efector process, positive regulation of cytokine production and neutrophil-mediated immunity. KEGG pathways mostly included phagosome, complement and coagulation cascades, cell adhesion molecules (CAMs), ECM-receptor interaction, focal adhesion and AGE-RAGE signalling pathway in diabetic complications. Te results supported that the immune response may be involved in DN. Cytokine release and extracellular matrix deposition may be subsequent events and continue with the development of disease. We also studied additional datasets to understand the potential mechanisms of the diferential expression of the target genes. Te miRNA-mRNA network suggested that the miR-766-3p/ TGFBI, miR-1238-5p/ZNF652 and miR-1237-3p/SH2B3 axes may be involved in diabetic nephropathy and that most target genes have diferences in DNA methylation levels between the DN group and the control group. Next, a PPI network was established, and the 20 hub genes were identifed. Furthermore, correlation analysis with clinical data demonstrated the disease-promoting efect of SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA and SERPING1, which were upregulated in DN tubule samples. In contrast, EGF and KNG1, which were downregulated in DN tubule samples, were suggested to have protective efects in DN. To date, there have been some reports about hub genes and DN. Spleen tyrosine kinase (SYK) was reported to mediate high glucose-induced TGF-β1 and IL-1β secretion18,19. In a diabetic animal model, C-X-C motif chemokine ligand 1 (CXCL1) was found to possibly serve as a proinfammatory mediator 20,21. In addition, VWF was reported to be involved in intrarenal thrombosis leading to the deterioration of renal function22. Purvis et al. observed higher circulating plasma levels of ANXA1 in T1D and T2D patients, whereas the exogenous sup- plementation of ANXA1 improves insulin resistance and prevents the progression of subsequent microvascular complications in mice 23,24. Previous studies have demonstrated that statins prevent DN by reducing the activity of Ras homolog family member A (RhoA) protein activation25–28. Another study reported that the activation of RhoA/ROCK may regulate the NF-κB signalling pathway29. In addition, sinomenine, kaempferol, catalpol and rutin have been shown to have protective efects through the RhoA/ROCK signalling pathway 30–33. EGF was considered a urine biomarker in two studies34,35. Recently, the newest report about cytosine methylation diferences in kidney tubule samples supported this viewpoint 36. In addition, one large-scale linkage study revealed polymorphisms in kininogen 1 (KNG1) associated with DN in European populations 37. C3 was the gene of interest through diferential expression analysis and WGCNA. Te KEGG pathways of the target genes also included the complement and coagulation cascade. In addition, the selection of the core genes in the PPI network also indicated that C3 was centrally located. Tese results may prove that complement C3 serves as a therapeutic target in diabetic nephropathy. Te results are consistent with knowledge that the complement system participates in DN. Te development of diabetes is intimately linked to low-grade infammation38. High levels of infammatory markers such as C-reactive protein and adiponectin proved this viewpoint 39,40. Infamma- tion might promote the occurrence and development of diabetic complications such as DN. However, the underlying mechanisms of the initiation of low-grade infammation are still poorly understood. Increasing research evidence has proven that the innate immune system is closely involved in diabetes 41. Simultaneously, the roles for pattern recognition receptors (PRRs) associated with DN have been discussed42,43. Te complement system is not only involved in innate immune defence by PRRs (mannose-binding lectin and fcolin) but also considered an important proinfammatory factor. Several studies have pointed out that the complement system is involved in the pathogenesis of DN and might be a therapeutic target 44–46. Signifcant diferences in complement system component levels in both plasma and urine were found between DN patients and diabetic patients. In addition, Li et al. highlighted the relatively more important impact of C3a, C5a and sC5b-9 in the development of DN 47. Sun et al. demonstrated that more severe kidney damage was associated with the deposition of C1q and C3c in

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S100A4 CGA CES2 ACKR1 RBP4 STRA6 a PLAC8 A OC 1 ANXA2 SLPI ADCY7CXCLC 6 RNASET2 MED17 LYZ LPL MNDA ILF2 APOC3 CX3CR1 P2RY13 ASB9 DOCK2 DCK FABP5 CXCLC 8 IFI16 QPCT APOH CCL19 UBE2J1 ADA CFH CCR2 ANXA1 LTF PYCARD FCGR2B PVALB SORD P2RY14 KRT7 FTCD HTR2B CXCL1 AARRPC1B GAD1 CCL5 CLEC7A CD48 CXCL9 PLA2G4A CFB DCXR IGJ KRT19MTHFD2 HRH11 IL10RA GPR18 IGLL5 GHR PLCB4 C3 VSIG4 FCER1A EVI2A CTSS F11 KNG1 LYN C1QA CSF2RB TLR7 EVI2B CD2 C7 CLUCCLLULU FCGR2A PDIA6 C1QB FCER1G C1S LCP2 IFI44L FSTL1 PROC SERPING1 HLA-DPB1 HLA-DRA HLA-DMA TIMP1 HCLS1 CASP4 ACTN1 MX1 VWF SRGN ITGAM C1RC1C R SYK STAT1 HLA-DQA1H HLA-F HLA-DPA11 RCN11 FN1 A2M TUBA1A TNC ITGB2 IRF1 IRF8 CASP1 FCGR3B LPGAT1 HRGRGR CTSK HLA-E HLA-B BST2B VCANANN PECAM1 TUBA1B TYROBP PAPSS1 EGF LPCAT1 PRC1 TGFBIBIB CD53 CD3D GBP2 RHOA IFITM3 PDGFRA TRIM2222 HLA-C MMP7 FAS ARHGDIB CHST15 LAMC2 ITGAV THY1 PSMB9 IFITM2 IL7R PSMB8 NCF2 CD14 NMI CASP3 SELPLG DSE TLR1 PSMB10 KDELR3 COL15A1 BIRC3 CD163 CD1C DCN VIM SPRY1Y1 MRC1 LTB LAMA4 LY96 SELL COL4A1 WNT5A COL4A2 COL1A2 PRKCB COMP LY86 COL6A3 PDK2 MELK FZD7 CD69 COL3A1 GZMK FZD2 RRM2 LUM POSTN GZMA b KIAA0101 KNG1 50 C3 40

FN1 30 SYK 20 HLA-E 10 EGF ITGB2 0 CXCL1 CXCL8 ITGAV LYN VWF RHOA HLA-DQA1 ITGAM SERPING1 P2RY13 ANXA1 P2RY14

Betweennes s BottleNeck Closenes s ClusteringCofficient Degree DMNC EcCentricit y EPC MC C MN C Radialit y Stress FCER1G

Figure 6. PPI network. (a) PPI network of combined genes. Tere are 190 nodes and 680 edges. Te size and gradient colour of nodes are adjusted by degree. Te thickness and gradient colour of the edge are adjusted by the interaction score. (b) Heatmap of the CytoHubba analysis score.

renal histopathology assessment 48. Furthermore, a large-scale cohort study substantiated that diabetic patients with high plasma levels of C3 are more prone to kidney damage than the general population49. Another study indicated that the serum levels of C3 may help to diferentiate DN patients from diabetic patients without kidney damage 50. Blockade of C3a and C5a receptors in a T1DM model indicated a potential protective efect on renal fbrosis by improving endothelial-to-myofbroblast transition through the Wnt/β-catenin signalling pathway51. Similarly, blockade of C3a receptors in rats with T2DM improved renal morphology and function by inhibiting cytokine release and TGFβ/Smad3 signalling 52. However, the best approach for targeting the complement system to prevent the development of DN still needs to be explored. Terefore, 8 potential small molecule compounds were identifed by the Connectivity Map database in our study. In summary, our study has important signifcance in understanding the underlying mechanisms of DN and is helpful for developing new treatment strategies for DN. However, further molecular biological experiments are needed to verify the association between the identifed genes and DN.

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a 6 b 6 c 6

SYK 4 e

e 4 4 ue u l l a

CXCL1 Valu Va on on i LYN

2 2 essi 2 ressio nV C3 pr press VWF KNG1 xp Ex Ex E

2 CXCL1

ANXA1 EGF g2 Log Lo 0 Log2 0 0 3.5 4.0 4.5 5.05.5 3.54.0 4.55.0 5.5 4567

-2 -2 -2 Log2 GFR (MDRD) (ml/min/1.73m2) (from Woroniecka) Log2 GFR (MDRD) (ml/min/1.73m2) (from Woroniecka) Log2 GFR (MDRD) (ml/min/1.73m2) (fromSchmid)

6 dfe 6 6

C3 EGF

e 4

e 4 e 4 KNG1

HLA-E al u Valu V Valu n on

RHOA io s s

essi 2 EGF 2 ession

r 2 re r p SERPING1 p p Ex Ex Ex 2 g Log2 Log2 Lo 0 0 0 4567 4567 4567

-2 -2 Log2 GFR (MDRD) at Time 0 (ml/min/1.73m2) (fromSchmid) -2 Log2 GFR (CG) (ccs/min) (form Schmid) Log2 GFR (CG) (ccs/min) (form Schmid)

Figure 7. Pearson correlation analyses of GFR and target genes. (a) Te gene expression of SYK (p = 0.0022, r = − 0.8437), CXCL1 (p = 0.0016, r = − 0.8554), LYN (p = 0.0269, r = − 0.6911), VWF (p = 0.0452, r = − 0.6423) and ANXA1 (p = 0.0211, r = − 0.7111) was negatively related to GFR. (b) Te gene expression of EGF (p = 0.0027, r = 0.8349) and KNG1 (p = 0.0073, r = 0.7838) was positively correlated with GFR. (c) Te gene expression of C3 (p = 0.0459, r = − 0.6109) and CXCL1 (p = 0.0061, r = − 0.7645) was negatively correlated with GFR. (d) Te gene expression of EGF (p = 0.0037, r = 0.7919) was positively related to GFR. (e) Te gene expression of C3 (p = 0.0171, r = − 0.6970), HLA-E (p = 0.0132, r = − 0.7161), RHOA (p = 0.0439, r = − 0.6154) and SERPING1 (p = 0.0091, r = − 0.7409) was negatively correlated with GFR. (f) EGF (p = 0.0121, r = 0.7221) and KNG1 (p = 0.0153, r = 0.7053) were positively related to GFR.

Materials and methods Data download. Te GSE30529 (expression profling by array)5 and GSE51674 (non-coding RNA profling by array)9 datasets were downloaded by the GEOquery package 53 in R sofware version 3.6.2. GSE121820_ T2DN-CTL (methylation profling by genome tiling array, unpublished) was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Te GSE30529 dataset based on the GPL571 platform includes 10 DN tubule samples and 12 control samples. Te GSE51674 dataset based on the GPL10656 platform includes 6 DN tissue samples and 4 control samples. Te GSE121820 dataset based on the GPL5082 platform contains 10 T2 DN blood samples and 10 control samples.

Data processing. All diferential analyses were performed by the limma package10. Adjusted p values less than 0.05 and |log2-fold change (FC)| greater than 1 were considered statistically signifcant in the diferential analysis of GSE30529. Adjusted p values less than 0.01 and |log2 FC| greater than 3 were considered statistically signifcant in the diferential analysis of GSE51674. In addition, the TargetScan, miRWalk, miRBase and miRTar- Base databases 13–16 were used for the target gene prediction of the diferentially expressed miRNAs. Weighted gene coexpression network analysis (WGCNA) allows biologically meaningful module information mining based on pairwise correlations between genes in high-throughput data using the WGCNA package 54. Te WGCNA workfow consists of gene coexpression network construction, module identifcation, module rela- tionship analysis and the identifcation of highly related genes. Te gene coexpression network was constructed with the fltering principle that the sof threshold makes the network more consistent with a scale-free topology. Te modules were identifed with the criterion of module size 30–10,000, merge cut height equal to 0.25 and verbose equal to 3. Highly related genes were obtained with thresholds greater than 0.1 in the topological overlap matrix (TOM).

Functional enrichment analysis and hub gene screening. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed with the clusterProfler package11. Te STRING database55 (version 11.0, https://string-db.org/) was used to search for interactions between the candidate proteins based on laboratory data, other databases, text mining and pre- dictive bioinformatics data. Cytoscape sofware was used to visualize the protein–protein interaction (PPI) network and perform network analysis. CytoHubba, a built-in tool in Cytoscape, uses 12 methods to explore important nodes in biological networks, such as the Degree method (Deg), Maximum Neighborhood Compo- nent (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC), Closeness, EcCentricity, Radiality, BottleNeck, Stress, Betweenness, Edge Percolated Component (EPC) and ClusteringCofcient56.

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Clinical data analysis and drug analysis. Te Nephroseq v5 analysis engine (https://v5.nephroseq.org) provides access to gene expression signatures and clinical features. Pearson correlation analysis was performed between genes and GFR5,57. Unpaired Student’s t test was used to compare two groups. P values less than 0.05 were considered statistically signifcant. Nonsignifcant results are not displayed. Connectivity Map17, an online database that relates disease, genes, and drugs based on similar or opposite gene expression signatures, was used for potential drug prediction. Data availability Te GSE30529, GSE51674 and GSE121820 datasets are available in GEO database (https://www.ncbi.nlm.nih. gov/geo/). Te R script data used to support the fndings of this study are included within the supplementary information fle.

Received: 2 March 2020; Accepted: 30 July 2020

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X.W., T.D. and H.G.: takes responsibility for all aspects of the reliability and freedom from bias of the data presented. X.X.: takes responsibility for full text evaluation and guidance, fnal approval of the version to be submitted. All authors read and approved the fnal manuscript.

Competing interests Te authors declare no competing interests.

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