Integrative microRNA-gene expression network analysis in genetic hypercalciuric stone-forming rat kidney

Introduction

Kidney stones are commonly found in children and adults (Coe, Evan & Worcester, 2005), and can becaused by multiple factors. Idiopathic hypercalciuria (IH) is an important risk factor for calcium urolithiasis (Worcester et al., 2013; Yoon et al., 2013). Patients with IH have normal serum Ca²⁺, and increased urinary calcium excretion. But the pathophysiological process and molecular mechanism of IH are still unclear. The genetic hypercalciuric stone-forming (GHS) rat, has many pathophysiological characteristics identical to that of IH patients, such as normal serum Ca²⁺, hypercalciuria, elevated intestinal Ca²⁺ resorption and a tendency to lose calcium from the bone (Frick et al., 2013; Frick, Krieger & Bushinsky, 2015), which is regarded as an ideal animal model of calcium urolithiasis.

MicroRNAs (miRNAs) are a group of small, non-coding RNAs that regulate protein-coding gene function at the post-transcriptional level by binding to complementary sites on target mRNAs in the 3′UTR (Ambros, 2004). Meanwhile, miRNAs have been shown to regulate a wide range of biological processes including cell growth, metabolism, differentiation, proliferation and apoptosis, which have important implications in diseases processes (Ambros, 2001). Dysregulation of miRNAs has been associated with many human diseases. However, miRNA expression patterns and their biological functions in urolithiasis remain unknown. Understanding the relevance of miRNA and mRNA expression patterns in GHS rat kidneys is important to better elucidate the relationship between pathophysiological process and genes.

In this study we therefore conducted mRNA and miRNA expression profiling of three pairs of GHS and normal Sprague-Dawley (SD) rat kidneys. A subset of differentially expressed genes was validated by qPCR in 12 pairs of kidneys. Bioinformatic analysis was further performed to construct an integrative regulatory network of altered miRNA-mRNA transcriptsin GHS rat kidney.

Materials and Methods

Microarray

Three GHS rats and three SD rats were randomly selected for microarray analysis. The Affymetrix GeneChip miRNA 4.0 Array and Affymetrix Gene 1.0 Array for rats were used to compare miRNA and mRNA expression profiles, respectively, in GHS and control rat kidneys. Microarray analysis was performed by GMINIX Informatics Ltd. Co, Shanghai, China. The data have been deposited in the NCBI Gene Expression Omnibus and are accessible through GEO Series accession number GSE75543.

Results

Serum calcium and phosphorus levels, and urine calcium excretion

Serum calcium and phosphorus levels were not significantly altered in GHS compared with SD control rats (Figs. 2A and 2B). GHS rats did, however, excrete significantly more urine calcium (mg/24 h) than SD control rats (Fig. 2C).

miRNA target gene prediction

Target mRNAs for differentially expressed miRNAs were predicted using the miRanda database. Since miRNAs negatively regulate gene expression, upregulated miRNAs result in downregulated target mRNAs, and vice versa. A total of 29,164 miRNA-mRNA pairs (based on 19 dysregulated miRNAs and their 10,521 target mRNAs) were predicted (Table S4).

Integrated analysis of dysregulated miRNAs and mRNAs

The set of intersecting mRNAs between the predicted target mRNAs and differentially expressed mRNAs were selected (Table S5) and those that were negatively correlated with their predicted miRNA matches were used for downstream GOanalysis and KEGG pathway analysis.

There were 417 upregulated and 286 downregulated GO terms (P < 0.05), with the most significant GO terms including negative regulation of apoptotic process (GO:0043066), response to lipopolysaccharide (GO:0032496) and inflammatory response (GO:0006954). Table 2 shows the top 15 up- and downregulated GO terms, with further detail in Tables S6 and S7.

Table 2:

GO-dysregulated.

The top 15 up- and downregulated GO terms.

GO-ID	GO-name	Enrichment	P-value
Upregulated GOs by GO analysis
GO:0043066	Negative regulation of apoptotic process	5.274431709	5.53034E−12
GO:0032496	Response to lipopolysaccharide	8.108054973	1.51933E−11
GO:0006954	Inflammatory response	8.099023444	2.11218E−10
GO:0007165	Signal transduction	5.49328365	2.53815E−10
GO:0071347	Cellular response to interleukin-1	16.88289078	1.09298E−08
GO:0045944	Positive regulation of transcription from RNA polymerase II promoter	3.652056561	1.27896E−08
GO:0014070	Response to organic cyclic compound	6.26379716	3.29652E−08
GO:0008285	Negative regulation of cell proliferation	5.262199724	5.94266E−08
GO:0033590	Response to cobalamin	90.04208417	6.01314E−08
GO:0042493	Response to drug	4.287718294	6.34324E−08
GO:0006468	Protein phosphorylation	4.846457788	8.88401E−08
GO:0042542	Response to hydrogen peroxide	12.4673655	1.76279E−07
GO:0009617	Response to bacterium	19.09983604	2.66E−07
GO:0001889	Liver development	8.612721094	2.86336E−07
GO:0001666	Response to hypoxia	5.58112092	4.40694E−07
Downregulated GOs by GO analysis
GO:0045893	Positive regulation of transcription, DNA-dependent	3.77523152	3.05206E−07
GO:0045892	Negative regulation of transcription, DNA-dependent	4.062477396	3.32485E−07
GO:0016568	Chromatin modification	13.26523231	5.79738E−07
GO:0043065	Positive regulation of apoptotic process	5.038731654	6.66823E−07
GO:0008150	Biological process	2.365931438	1.86723E−06
GO:0016572	Histone phosphorylation	46.4283131	3.08593E−06
GO:0045944	Positive regulation of transcription from RNA polymerase II promoter	2.954529015	4.94117E−06
GO:0006355	Regulation of transcription, DNA-dependent	2.982729366	6.6796E−06
GO:0003407	Neural retina development	36.11091019	1.08935E−05
GO:0001525	Angiogenesis	5.879901494	1.29214E−05
GO:0008284	Positive regulation of cell proliferation	3.559902873	3.26349E−05
GO:0006950	Response to stress	5.762379285	4.3047E−05
GO:0006915	Apoptotic process	3.631264712	4.68891E−05
GO:0007165	Signal transduction	3.448256676	8.82113E−05
GO:0043066	Negative regulation of apoptotic process	3.059606407	0.000134769

DOI: 10.7717/peerj.1884/table-2

According to KEGG pathway analysis, 93 KEGG pathways were significantly enriched, of which 75 were upregulated and 18 downregulated at P < 0.05 (Tables S8 and S9). The most highly enriched pathways included the Pantothenate and CoA biosynthesis and synthesis and degradation of ketone bodies pathways. The top up- and downregulated pathways are shown in Fig. 3. The darker bars indicate pathways that reported to be related to urolithasis in previous studies.

Next, to illustrate the role of key miRNAs in the regulation of kidney mRNAs in GHS rats, a miRNA-mRNA-Network was built based on the subset of significantly differentially expressed mRNAs that were also members of significantly enriched GO terms and KEGG pathways (Table S10, Fig. 4). In total, 223 mRNAs and 19 miRNAs were included in the network, where box nodes represent miRNAs, and circular nodes represent mRNAs. The degree of connectivity, which represents the number of genes regulated by a given miRNA, is indicated by the size of the node with a higher degree of connectivity represented by larger nodes (Table S11). Rno-miR-674-5p, rno-miR-672-5p, rno-miR-138-5p, and rno-miR-21-3p had high degrees of connectivity and may play crucial roles in this regulatory network. Meanwhile, Sema5a, Lpin2, Gcnt1, Masp1, Olr1 and Traf3 were the most common miRNA targets. Table 3 presented a subset of significantly regulated miRNA-mRNA hybrids.

Table 3:

miRNA-mRNA hybrid.

miRNAs and target mRNAs in GHS rat kidneys.

miRNA	Target mRNAs
rno-miR-138-1-3p	Cxcl16, Cyp4a1, Dnm3, Ehhadh, Hbegf, Hspa4l, Inhbb, Masp1, Mmp9, Nr4a3, Rprm, Sema5a, Steap2
rno-miR-138-5p	Aass, Adh1, Ak7, Bhmt, Calcr, Cdh2, Csf2rb, Cxcl1, F3, Ggcx, Gna12, Gpd1, Gsk3a, Gstt1, Hbegf, Hmgcl, Hspa4l, Icam1, Inhbb, Lpin2, Nr4a3, Pnpla2, Rela, Sds, Sema4b, Sema4c, Sema5a, Spry4, Tmprss13, Zfp36
rno-miR-196c-3p	Agpat9, Cebpb, Dusp16, Hbegf, Inhbb, Masp1, Nfkbia, Nfkbiz, Olr1, Pvrl1, Rel, Sema4c, Sorbs1, Tnfrsf21
rno-miR-201-3p	Amt, Bcl10, Crem, Ctsd, Cubn, Ddit4, Dusp16, Entpd3, Got2, Gpx1, Hbegf, Il23a, Lpin2, Map2k3, Masp1, Nr4a3, Ppp2r1a, Sema5a, Sept8, Tmprss13, Vdr
rno-miR-203a-3p	Angptl4, B3gnt5, Bcl10, Bhmt, Crem, Cxcl1, Cxcl11, Cyp1b1, Dpys, Hbegf, Il2ra, Inhbb, Lpin2, Nr4a3, Olr1, Qdpr, S1pr1, Sema4g, Sema5a, Serpine1, Sorbs1, Steap2, Tpm3, Vcam1, Xbp1
rno-miR-206-3p	Amt, Angpt4, Angptl4, Cdh2, Crem, Cyp4a1, Dnm3, Ehhadh, Gadd45b, Icam1, Il6, Map3k8, Mchr1, Ncoa4, Nr1h3, Nr4a3, Olr1, Pex11a, Pla2g15, Sema4g, Sema5a, Sgk1, Spry4, Tpm3, Zfp36
rno-miR-484	Angptl4, B4galnt1, Bcl10, Chrd, Cox4i1, Cxcl16, Ddit4, Flt3, Foxo3, Gadd45b, Gcat, Gpd1, Hyal2, Mgat5, Ncoa4, Nfkbiz, P2rx7, Pex11a, Pim1, Ppara, Rprm, Runx1, S1pr1, Scarb1, Sele, Sema4g, Sema5a, Sema6b, Sorbs1, Tnfrsf21, Tpm3
rno-miR-184	Alg2, Dnm1, Lpcat1, Ndst1, Ocln, Prom1, Ube2n, Wwp1
rno-miR-21-3p	Ace, Adam10, Adh6, Cacnb2, Cdc14b, Cycs, Dut, Egf, Ganc, Ggps1, Itpr2, Mboat2, Mecom, Ocln, Plau, Pld1, Rgn, Rps6ka5, Snca, Traf3, Whsc1
rno-miR-672-5p	Agtr1b, Alg2, Creb3l2, Dnm1, Gcnt1, Ggps1, Itpr2, Lpcat1, Ndst1, Prom1, Rbl2, Scp2, Sh3gl2, Sin3a, Smad6, Stat1, Traf3
rno-miR-674-5p	Adam10, Adh6, Agtr1b, Alg2, Capn1, Dgkz, Dnmt1, Ganc, Ggps1, Hdac7, Itpr2, Mavs, Mef2c, Ndufs4, Rbl1, Rbl2

DOI: 10.7717/peerj.1884/table-3

Moreover, to investigate the regulatory relationships between these genes and their potential role in hypercalciuria, we performed a signal-net analysis based on significantly regulated KEGG pathways (Fig. 5, Table S12). Signal-net analysis has shown that NF-kappa B signal pathway, including the members of NF-kappa B1, RelA and NF-kappa B2 et al., might play a core role in the gene regulatory network. And NF-kappa B1 and RelA had the highest degrees of connectivity of 19 and 18, respectively.

Discussion

IH typically manifests as hypercalciuria with normal serum Ca²⁺, increased intestinal Ca²⁺ absorption, bone resorption and decreased bone mineral density in addition to decreased renal tubule Ca²⁺ reabsorption (Yoon et al., 2013). Thus, IH is one of the most important risk factors for calcium urolithiasis. However, the pathogenesis of IH is not yet fully understood. GHS rats exhibit many features of human IH including increased intestinal Ca²⁺ absorption, increased bone resorption, decreased renal tubule Ca²⁺ reabsorption and high levels of vitamin D receptor (VDR) protein in Ca²⁺-transporting organs (Frick et al., 2013; Frick, Krieger & Bushinsky, 2015). The research of Tsuruoka, Bushinsky & Schwartz (1997) strongly suggests that decreased tubular Ca²⁺ reabsorption plays a key role in hypercalciuria. In general, GHS rats represent an ideal animal model for idiopathic hypercalciuria urolithiasis.

MicroRNAs regulate various disease processes by inhibiting the expression of their target mRNAs. Understanding the relevance of miRNA and mRNA expression patterns in GHS rat kidneys is important to better elucidate the relationship between pathophysiological process and genes. In the present study, we used bioinformatics methods to determine the potential role of differentially expressed miRNAs and mRNAs in GHS rat kidneys, and identified specific miRNAs and possible negative regulative mRNAs that may affect the development of hypercalciuria.

We used a multi-step approach to identify mRNA targets of dysregulated miRNAs in GHS rat kidneys whereby we first identified mRNAs (N = 2, 418) and miRNAs (N = 19) that were significant differentially expressed between GHS and control SD rats; next, potential miRNA-mRNA pairs were predicted for the 19 miRNAs using miRanda, resulting in 29,164 miRNA-mRNA pairs; the intersecting set of predicted target mRNAs and differentially expressed mRNAs were then selected for further analysis.

We found a significant enrichment in over 700 GO terms, including inflammatory response (GO:0006954) and response to hypoxia (GO:0001666). Interestingly, local hypoxic conditions and inflammatory injuries have been linked to the initiation of urolithiasis (Cao et al., 2006), whereas antioxidants protect renal tubular cells from cellular injury and decrease the formation of calcium oxalate stones (Itoh et al., 2005). Moreover, the GO term response to calcium ion (GO:0051592) was enriched (based on differentially expressed genes such as IL-6 and VDR). Increased levels of VDR protein in the kidney are regarded as a common feature in both GHS rats and IH individuals (Frick, Krieger & Bushinsky, 2015), and VDR intimately affects urolithiasis formation (Arcidiacono et al., 2014; Zhang, Nie & Jiang, 2013). Glybochko et al. (2010) found a significant increase in serum IL-6 in nephrolithiasis patients compared with healthy individuals, and Hasna et al. (2015) showed that IL-6 was significantly increased in patients with diabetes mellitus and urolithiasis compared with patients with diabetes mellitus alone. In addition, skeletal system development (GO:0001501) and skeletal system morphogenesis (GO:0048705) were significantly increased in GHS rats. Our previous studies demonstrated the occurrence of osteochondral differentiation progression in primary renal tubular epithelial cells in GHS rats (He et al., 2015), which may be linked to the pathogenesis of calcium stone development.

By identifying pathway membership of dysregulated mRNAs, we can improve our understanding of underlying disease-related processes. We detected 93 enriched pathways including the MAPK signaling pathway, mineral absorption as well as the TGF-beta signaling pathway. Khan (2013) reported that the p38 MAPK/JNK pathway regulates crystallization modulator production and influences plaque formation as well as calcium oxalate nephrolithiasis. Our previous research also showed that calcium and TGF-β may participate in the pathogenesis of epithelial-mesenchymal transition and lead to stone formation (He et al., 2015).

To better understand the biological processes linked to differentially expressed miRNAs and their predicted target genes, we constructed an interaction network where rno-miR-674-5p, rno-miR-672-5p, rno-miR-138-5p and rno-miR-21-3p were found to be highly connected, which means that they may play crucial roles in the regulatory network. Interestingly, Wang et al. (2011) reported that miR-674-5p was able to stimulate the expression of osteogenic marker genes; miR-138-5p is a risk factor for pancreatic cancer (Yu et al., 2015) and Alzheimer’s disease (Lugli et al., 2015); and miR-21-3p increases resistance to cisplatin in a range of ovarian cell lines (Pink et al., 2015). In addition, rno-miR-484, rno-miR-138-1-3p, rno-miR-201-3p and rno-miR-203a-3p are downregulated in the network, nevertheless, previous studies reported these miRNAs to be tumor-associated (Pizzini et al., 2013; Liu et al., 2015; Merhautova et al., 2015; Murray et al., 2014; Yang et al., 2016; Ye et al., 2015). To our knowledge, there has been very little research on the relationship between these miRNAs and urolithiasis or calcium metabolism. Hu et al. (2014) reported serum and urinary levels of miR-155 were significantly elevated in patients with nephrolithiasis, and miR-155 might influence pathophysiology of nephrolithiasis via regulating inflammatory cytokines expression. However, the expressions of miR-155 were not significantly different between the two groups of our present study. Regarding mRNAs in the constructed network, Sema5a, which belongs to the semaphorin gene family, had the highest degree of connectivity of 13. It has previously been reported that Sema5a increases cell proliferation and metastasis and suppresses tumor formation (Lu et al., 2010; Sadanandam et al., 2012). In addition, Ding et al. (2008) suggested that Sema5a is a risk factor for Parkinson’s disease. Lpin2, which is associated with fatty acid, triacylglycerol, and ketone body metabolism (Chen et al., 2015), also had a high degree of connectivity of 9. Olr1, a crucial regulator of lipid metabolism (Tejedor et al., 2015), had a degree of connectivity of 8. Increasing numbers of studies are reporting a strong correlation between obesity/dyslipidemia and kidney stones (De, Liu & Monga, 2014; Fujimura et al., 2014). However, the mechanism whereby obesity and kidney stone disease are linked is still unknown. Recent research has indicated that obesity may increase reactive oxygen species and oxidative stress, which would influence the interaction between calcium oxalate/calcium phosphate crystals and renal epithelial cells (Khan, 2012).

The NF-kappa B signaling pathway was also significantly enriched in GHS rats, in addition, signal-net analysis has shown that NF-kappa B family might be a core role of our gene regulatory network including the members of NF-kappa B1, NF-kappa B2 and RelA. NF-kappa B is a transcription regulator that is activated by various intra- and extra-cellular stimuli such as cytokines, oxidant-free radicals, and bacterial or viral products (Hoesel & Schmid, 2013). Activated NF-kappa B translocates into the nucleus and stimulates the expression of genes involved in a wide variety of biological functions. Menon et al. (2014) found that the receptor activator of NF-kappa B ligand mediates bone resorption in IH, while Tozawa et al. (2008) reported that oxalate induced OPN expression by activating NF-kappa B in renal tubular cells. But there have been few researches focus on the function of NF-kappa B in the formation of idiopathic hypercalciuria urolithiasis. Furthermore, abnormal fat metabolism would also activate the NF-kappa B signaling pathway (Yang et al., 2015), which might contribute to the increased risk of urolithiasis in obesity populations. However, the molecular mechanism of NF-kappa B in IH desease is need further explore, which may assist in improving the clinical diagnosis and treatment of patients with IH.

Conclusions

Here, we successfully identify rat hypercalciuria-related miRNAs and their target mRNAs. This comprehensive analysis will provide valuable insights for future research, which should aim at confirming the role of these genes in the pathophysiology of hypercalciuria stone desease. More further work needs to be done to get the whole picture right in the disease context. To the best of our knowledge, this is the first study to focus on the role of miRNA-mRNA interactions in hypercalciuria urolithiasis.

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