Bioinformatic Analyses of Renal Ischaemia-Reperfusion Injury Models: Identification of Key Genes Involved in the Development of Kidney Disease Open Access

Acute kidney injury (AKI) is associated with high healthcare costs, morbidity and mortality in the clinic and has become an important public health concern worldwide [1]. Recently, AKI has been suggested to lead to long-term outcomes, including the development and progression of chronic kidney disease (CKD) and end-stage renal disease (ESRD), even in those patients who recover [2, 3]. Although the needed to eliminate the short- and long-term outcomes of AKI is urgent, no effective therapy exists to prevent the development of these renal diseases.

Renal ischaemia-reperfusion injury (IRI) is one of the major causes of AKI in hospital settings [4]. The complicated cellular pathophysiology in IRI development, including cell adhesion, proliferation and cytokine secretion, is associated with inflammation, the innate immune response, and injury to tubular, endothelial and vascular components [5, 6]. Many factors are thought to relate to the development of renal IRI. Yu TM et al. [7] demonstrated that RANTES (regulated on activation, normal T-cell expressed and secreted), produced by renal tubular cells, is a major chemokine in the development of renal IRI. Furthermore, an inhibitor of the complement C1 fraction (C1INH) prevented inflammatory reactions, neutrophil migration and the development of fibrosis in a murine model of renal IRI [8]. In addition, microRNAs (miRNAs), non-coding RNAs that regulate their target mRNAs via a post-transcriptional repression process, have a potential effect in renal IRI development [9]. miR-21 is upregulated to protect against apoptosis of tubular epithelial cells during the development of renal IRI [10]. On the other hand, increased miR-24 promotes apoptosis of endothelial and tubular epithelial cells [11]. While multiple signalling pathways have been reported, the molecular mechanisms underlying the development of renal IRI are still unclear.

In the present study, the differentially expressed genes (DEGs) were screened from the GSE27274 dataset between sham controls and two groups of rats subjected to different time-points of reperfusion injury. By performing functional enrichment, protein-protein interaction (PPI) and miRNA-target gene network analyses, several candidate genes were demonstrated to be potential targets or biomarkers for diagnosing or treating the development of renal IRI.

Gene expression profile data from GSE27274 were obtained from the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database. The dataset was based on the GPL6101 Illumina platform ratRef-12 v1.0 expression beadchip and deposited by Krishnamoorthy A et al. [12]. Included in the dataset were 24 samples from bilateral ischaemia-reperfusion (IR) kidney injury and its sham controls. Renal cortex and medulla samples obtained at 24 h or 120 h of reperfusion were used for further analysis. To clarify the genes and pathways in the whole kidney, cortex and medulla samples from the corresponding rats were pooled.

The raw array data were normalized by the Robust Multiarray Average (RMA) method [13]. The mean value of multiple probes for a single gene was calculated. The linear models for microarray data (limma) package [14] in R were used to analyse the DEGs from renal IRI compared with those from sham controls, and the |log Fold Change (FC)| > 1.5 and p-values < 0.05 were considered the threshold as previously described [15].

The online functional annotation tool DAVID (Database for Annotation, Visualization and Integrated Discovery) was used to understand the biological meaning of genes [16]. The up- and downregulated DEGs in renal IRI were used to perform GO biological process (BP), cellular components (CC), molecular function (MF) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the DAVID functional classification tool. The cut-off values for the GO analysis were set at p < 0.05 and enrichment score more than 2 and for the KEGG Pathway analysis at p < 0.05.

The Search Tool for the Retrieval of Interacting Genes (STRING) [17] database is a global resource for predicting PPIs. In the present study, DEGs were uploaded to the STRING online tool, and combined scores > 0.4 were selected as significant interactions. PPI networks were visualized by Cytoscape® version 3.4.0 software [18].

The WEB-based Gene Set Analysis Toolkit (WebGestalt) [19] is an online integrated data mining system. The regulatory miRNAs of both up- and downregulated DEGs were predicted by using WebGestalt, and the thresholds were set at p value < 0.05 and enriched genes > 2. Up- and downregulated DEGs and the miRNA network were visualized with Cytoscape.

The DEGs between sham controls and the samples obtained at 24 h (IR24) or 120 h (IR120) of reperfusion after renal ischaemia are shown in Fig. 1. Compared with the expression profiles from sham controls, a total of 504 DEGs (182 up- and 322 downregulated genes) were found in IR24 rats. Moreover, 111 DEGs (66 up- and 45 downregulated genes) were screened in IR120 rats. In total, 80 DEGs were common to both the IR24 and IR120 samples, with 41 upregulated and 39 downregulated genes (Fig. 2).

To identify the functional enrichment in the development of renal IRI, GO enrichment analyses were performed for up- and downregulated common DEGs (Table 1). Upregulated common DEGs were significantly enriched in 27 GO terms for BP. The BP terms with the smallest P value in the two different reperfusion groups included inflammatory response, cell adhesion, innate immune response and cell proliferation. Downregulated common DEGs were enriched in amino-acid betaine catabolic processes, transmembrane transport, homocysteine and one-carbon metabolic processes.

Furthermore, significantly enriched pathways of up- and downregulated common DEGs were obtained by utilizing DAVID. As shown in Table 2, the main enriched KEGG pathways of upregulated DEGs were toll-like receptor signalling, complement and coagulation cascades and ECM-receptor interaction. Downregulated DEGs were enriched in glycine, serine, threonine, cysteine and methionine metabolic pathways.

Based on the STRING database web tool, PPI networks of the 80 common DEGs were constructed with a combined score of > 0.4. As shown in Fig. 3, the PPI had 36 nodes and 54 edges. Genes with the highest interaction degrees encoded tissue inhibitor of matrix metalloproteinase-1 (Timp1; degree=10), angiotensinogen (Agt; degree=8), dimethylglycine dehydrogenase (Dmgdh; degree=7) and secreted phosphoprotein 1 (also known as osteopontin; Spp1; degree=7).

In AKI and CKD, microRNAs (miRNAs) are one of the most common epigenetic factors regulating gene expression [10, 20]. The online tool WebGestalt was used to retrieve potential regulatory miRNAs associated with DEGs, and the miRNA-DEG network was visualized with Cytoscape. As shown in Fig. 4A, the network of miRNAs and upregulated DEGs contained 17 nodes and 19 edges. Adamts1, Hspa2, Fn1 and Abcg1 had the highest degrees of 5, 3, 3 and 3, respectively. miR-142-5p was demonstrated to have an important role in regulating the highest number of upregulated DEGs, including Hspa2, Abcg1, Adamts1 and Tpbg. On the other hand, the network of miRNA and downregulated DEGs contained 12 nodes and 9 edges (Fig. 4B). Mat2a and Timp3 showed the highest degrees of 3 and 2, respectively. Moreover, miR-181a, miR-181b, miR-181c and miR-181d were found to regulate 3 of the target genes, Mat2a, Timp3 and lgsf11.

In the present study, 80 common DEGs were extracted at 24 h (IR24) and 120 h (IR120) of reperfusion after renal IRI in rats. Spp1, Timp1 and Dmgdh demonstrated higher degrees in the PPI networks and may be potential key genes in the development of renal IRI. Interestingly, the upregulated DEGs were enriched in BPs and pathways associated with inflammation. On the other hand, the downregulated DEGs were enriched in the catabolic process and metabolic pathways of the amino-acid betaine. Finally, miR-181 was suggested to have a key role in renal IRI development.

Damaged renal cells secrete proinflammatory cytokines to promote inflammation, which in turn injures renal cells in AKI [1]. Inflammation has therefore been considered one of the most important mechanisms contributing to the pathophysiology of AKI, including renal IRI [21]. In this study, the upregulated DEG Spp1 was a key gene in the PPI networks and was enriched in BP terms and pathways associated with inflammation. Spp1, also known as osteopontin, is a secreted glycoprotein that acts as a proinflammatory cytokine and is highly expressed in the kidney [22]. Although Spp1 was demonstrated to prevent renal injury at 24 h after reperfusion of IRI in female mice [23], other investigators observed promotion of inflammation by Spp1 via enhanced infiltration of natural killer (NK) cells in IRI male mice [24, 25]. Furthermore, Spp1 also led to renal inflammation in chronic aldosterone-treated mice and in ApoE-deficient mice on a high-cholesterol diet [26, 27]. Although the role of Spp1 in the early stage of IRI is controversial depending upon sexual differences, it may at least have a potential role in promoting renal inflammation in males during IRI development.

Timp1 is the other hub gene identified by the PPI network analysis of upregulated DEGs. The protein encoded by Timp1 is a member of tissue inhibitors of metalloproteinases (TIMPS), which are regulators of matrix metalloproteinases (MMPs) [28]. In addition to matrix degradation, TIMP1 also participates in inflammation, apoptosis and cell growth [29]. Although Timp1 deficiency failed to ameliorate renal interstitial fibrosis in a murine model of unilateral urethral obstruction (UUO) due to compensatory increases in TIMP2, TIMP3 and PAI-1 mediating renal fibrosis [30], over-expression of TIMP1 deteriorated interstitial fibrosis by promoting the inflammatory response in the UUO model [31]. On the other hand, Timp1 was also considered a urinary biomarker in cisplatin- and sepsis-induced AKI [32, 33]. Accordingly, TIMP1 may not be a direct target for the treatment of renal fibrosis, but it may become a potential biomarker for study of the development of renal IRI.

The third hub gene identified by the PPI network analysis of downregulated DEGs is Dmgdh, which was also enriched in BP terms related to the catabolic process and metabolic pathways of the amino-acid betaine. Dimethylglycine dehydrogenase, encoded by Dmgdh, is a mitochondrial matrix enzyme involved in the metabolism of choline to glycine [34]. Glycine was reported to possess anti-inflammatory and immunomodulatory roles against ischaemia-induced renal injury [35]. In fact, glycine protected against mild unilateral IRI (15 min ischaemia) [36]. Conversely, in severe bilateral IRI (40 min ischaemia) in rats, glycine exacerbated oxidative stress and renal cell injury through binding and activating N-methyl-D-aspartate (NMDA) receptors [37]. These two contrasting findings in glycine-treated rats may be attributed to sexual difference, strain background or degree of renal ischaemia. Overall, Dmgdh might at least play a role in mild IRI.

Up- and downregulated networks of miRNA-targeted DEGs were predicted and constructed. miR-142-5p in upregulated network was calculated to regulate Adamts1, Hspa2, Abcg1 and Tpbg. In the downregulated network, miR-181a, miR-181b, miR-181c and miR-181d were predicted to regulate Mat2a, Timp3 and Igsf11. Among these miRNAs, miR-142-5p was identified as a candidate biomarker of renal fibrosis [38]. miR-181a was documented to mediate cell apoptosis in cisplatin-treated HK-2 cells [39]. Furthermore, among these miRNA-targeted genes, Timp3-encoded protein has been demonstrated not only to protect against inflammation, apoptosis and tubulointerstitial fibrosis in the early stage of renal injury following UUO in mice [40] but also to inhibit breakdown enzymes of capillary basement membrane, including ADAMTS1 (a disintegrin and metalloproteinase with thrombospondin motif-1, Adamts1-encoded protein) [41]. ADAMTS1, a secreted metalloproteinase with angiogenesis inhibition, is considered as a mediator of renal vascular stability. Its expression was increased in proximal tubular cells following post-ischaemia of renal IRI and in pericytes after UUO administration [41, 42]. Therefore, miR-142-5p, miR-181a and their target genes may have critical roles in the development of renal IRI.

Although Hmox1, complement/coagulation cascades and the metabolic pathway of glycerophospholipid were previously reported to be potential targets for the early stage of renal IRI [43], our findings may provide novel targets or biomarkers for further research, diagnosis and treatment of renal IRI. Certainly, animal experiments and clinical studies are needed to confirm their significance in the development of renal IRI.

The present study pointed out that the Spp1, Timp1, and Dmgdh genes, with their associated BP terms and pathways, have potential roles in the development of renal IRI. Furthermore, miR-142-5p and miR-181a might be key miRNAs that modulate renal IRI development.

This work was supported by independent scientific research project of Wuhan University, Wuhan, Hubei, China (Grant number: 2042017kf0106) and from the National Natural Science Foundation of China (NSFC) (No. 81172793, 81370800, 81500516).

The authors declare no competing interests.