Abstract

Background

   This study sought to investigate crucial genes correlated with diabetic
   nephropathy (DN), and their potential functions, which might contribute
   to a better understanding of DN pathogenesis.

Methods

   The microarray dataset [31]GSE1009 was downloaded from Gene Expression
   Omnibus, including 3 diabetic glomeruli samples and 3 healthy glomeruli
   samples. The differentially expressed genes (DEGs) were identified by
   LIMMA package. Their potential functions were then analyzed by the GO
   and KEGG pathway enrichment analyses using the DAVID database.
   Furthermore, miRNAs and transcription factors (TFs) regulating DEGs
   were predicted by the GeneCoDis tool, and miRNA-DEG-TF regulatory
   network was visualized by Cytoscape. Additionally, the expression of
   DEGs was validated using another microarray dataset [32]GSE30528.

Results

   Totally, 14 up-regulated DEGs and 430 down-regulated ones were
   identified. Some DEGs (e.g. MTSS1, CALD1 and ACTN4) were markedly
   relative to cytoskeleton organization. Besides, some other ones were
   correlated with arrhythmogenic right ventricular cardiomyopathy (e.g.
   ACTN4, CTNNA1 and ITGB5), as well as complement and coagulation
   cascades (e.g. C1R and C1S). Furthermore, a series of miRNAs and TFs
   modulating DEGs were identified. The transcription factor LEF1
   regulated the majority of DEGs, such as ITGB5, CALD1 and C1S.
   Hsa-miR-33a modulated 28 genes, such as C1S. Additionally, 143 DEGs
   (one upregulated gene and 142 downregulated genes) were also
   differentially expressed in another dataset [33]GSE30528.

Conclusions

   The genes involved in cytoskeleton organization, cardiomyopathy, as
   well as complement and coagulation cascades may be closely implicated
   in the progression of DN, via the regulation of miRNAs and TFs.

Electronic supplementary material

   The online version of this article (doi:10.1186/s12882-016-0343-2)
   contains supplementary material, which is available to authorized
   users.

   Keywords: Diabetic nephropathy, Differentially expressed gene,
   MicroRNA, Transcription factor, Network

Background

   Diabetic nephropathy (DN) is a complication correlated with both type 1
   and type 2 diabetes and is characterized by glomerulosclerosis due to
   accumulation of extracellular matrix [[34]1]. Despite great attention
   from both clinicians and basic scientists, the morbidity of end-stage
   renal disease and glomerulosclerosis in diabetic patients is increasing
   dramatically [[35]2].

   In the past years, many molecules associated with DN have been
   uncovered. For example, the mammalian target of rapamycin (mTOR)
   complex 1 activation has a key role in podocyte dysfunction in diabetic
   mice [[36]3, [37]4]. Rooney et al. have shown that connective tissue
   growth factor/CCN family protein 2 (CTGF/CCN2) can activate the Wnt
   signaling in mesangial cells through low density lipoprotein
   receptor-related protein 6 (LRP6), which may be implicated in the
   pathogenesis of DN [[38]5]. In diabetic mice, Glo1 (Glyoxalase 1)
   overexpression completely inhibits diabetes-induced increases in
   methylglyoxal modification of glomerular proteins, and promotes the
   development of diabetic kidney disease (DKD) [[39]6]. Furthermore,
   there is evidence that miR-192 enhances collagen expression by
   regulating the E-box repressors Zeb1/2, and locked nucleic
   acid–anti-miR-192 alleviates proteinuria in the diabetic mice [[40]7].
   Based on a gene expression profiling of DN, Hans et al. have shown that
   some genes in glomeruli from patients with DN are down-regulated, such
   as bone morphogenetic protein 2, fibroblast growth factor 1, vascular
   endothelial growth factor, nephrin and insulin-like growth factor
   binding protein 2, suggesting that progression of DN might be due to
   diminished tissue repair ability [[41]8]. However, the pathways which
   the differentially expressed genes (DEGs) participate in and regulators
   that target these genes remain unknown.

   In this study, the microarray dataset [42]GSE1009 deposited by Hans
   [[43]8] was used to identify DEGs between diabetic glomeruli samples
   and healthy controls. Gene Ontology (GO) and pathway enrichment
   analyses were then performed for the up- and down-regulated DEGs.
   Furthermore, microRNAs (miRNAs) and transcription factors (TFs)
   regulating DEGs were predicted, and miRNA-DEG-TF regulatory network was
   constructed. These findings may contribute to a better understanding of
   the nosogenesis of DN.

Methods

Affymetrix microarray data

   The gene expression profile data of [44]GSE1009 [[45]8] were downloaded
   from the public database Gene Expression Omnibus (GEO,
   [46]http://www.ncbi.nlm.nih.gov/geo/), which was based on the platform
   of [HG_U95Av2] Affymetrix Human Genome U95 Version 2 Array
   ([47]GPL8300, Affymetrix Inc., Santa Clara, California, USA). This
   dataset contained 6 glomeruli samples, including 3 samples from 2
   kidneys from patients with diabetes mellitus type 2, and 3 samples from
   2 healthy kidneys.

   Another gene expression dataset [48]GSE30528 [[49]9] in GEO, which
   contains a relatively big sample size and has a high data quality, was
   used for validation. The data in [50]GSE30528 were produced by the
   platform of [HG-U133A_2] Affymetrix Human Genome U133A 2.0 Array
   ([51]GPL571, Affymetrix Inc., Santa Clara, California, USA). A total of
   9 diabetic glomeruli samples from patients with DKD and 13 healthy
   glomeruli samples were included in this dataset.

   CEL files and the probe annotation files were downloaded, and the gene
   expression data of all samples were preprocessed via background
   correction, quantile normalization and probe summarization using the
   Robust Multi-array Average (RMA) algorithm in Affy software package of
   Bioconductor (available at
   [52]http://www.bioconductor.org/packages/release/bioc/html/affy.html)
   [[53]10].

DEGs screening

   Linear Models for Microarray Data (LIMMA) package [[54]11] of
   Bioconductor (available at
   [55]http://www.bioconductor.org/packages/release/bioc/html/limma.html)
   was used to identify genes that were differentially expressed in
   diabetic glomeruli. Only the genes meeting p-value < 0.05 and |log[2]FC
   (fold change)| ≥ 1 were chosen as DEGs.

Enrichment analysis for DEGs

   To explore the functions of DEGs in diabetic glomeruli samples, the
   DAVID (Database for Annotation, Visualization and Integrated Discovery)
   database [[56]12] was used to perform GO and KEGG (Kyoto Encyclopedia
   of Genes and Genomes) pathway enrichment analyses for DEGs. The
   p-value < 0.01 and gene count ≥ 2 were set as the cut-off criteria.

   Furthermore, the category of enriched GO terms and the gene number were
   displayed as a histogram which was constructed by WEGO (Web Gene
   Ontology Annotation Plot) [[57]13] with the cut-off criterion of
   level = 2.

Construction of miRNA-DEG-TF regulatory network

   GeneCoDis (Gene Annotation Co-occurrence Discovery,
   [58]http://genecodis.cnb.csic.es/) [[59]14], which is a grid-based tool
   that integrates biological information from different sources to search
   for biological characteristics (annotations) that frequently co-occur
   in a series of genes and rank them by statistical significance, was
   used to identify miRNAs and TFs regulating DEGs with hypergeometric
   algorithm, and the adjusted p-value < 0.01 was used as the cut-off
   criteria. The regulatory network consisting of DEGs, miRNAs and TFs
   were then visualized by Cytoscape ([60]http://cytoscape.org/) [[61]15].

Validation of the expression level of DEGs

   The DEGs between DN and control samples in the dataset [62]GSE30528
   were identified using the aforementioned methods. The overlapped DEGs
   in both of [63]GSE30528 and [64]GSE1009 were then identified using the
   VennDiagram package. Here, if a gene was differentially expressed in
   both of [65]GSE30528 and [66]GSE1009 with the same expression pattern
   (up-regulated or down-regulated expression), this gene was identified
   as an overlapped DEG.

Results

Identification of DEGs

   Based on the cut-off criteria, a set of 444 genes were identified to be
   differentially expressed in the diabetic glomeruli samples, including
   14 up-regulated and 430 down-regulated ones, compared with the controls
   (Additional file [67]1).

GO and KEGG pathway enrichment analysis of up- and down-regulated DEGs

   A total of 111 GO terms for the down-regulated DEGs were generated.
   Some genes (e.g. MTSS1, CALD1 and ACTN4) were related to cytoskeleton
   organization, such as actin filament-based process, actin cytoskeleton
   and cytoskeletal protein binding; some other genes were distinctly
   enriched in negative regulation of cell proliferation (e.g. AIF1,
   IGFBP7 and COL4A3) (Table [68]1). Using WEGO (level = 2), 25 categories
   of GO terms for down-regulated DEGs were displayed. The majority of
   genes were enriched in biological processes (BP), such as anatomical
   structure formation, biological regulation, death and response to
   stimulus. Moreover, several GO terms in cellular component (CC) were
   obtained, such as cell part and organelle. Furthermore, three GO terms
   in molecular function (MF) were enriched by the down-regulated genes,
   including binding, structural molecule and transporter (Fig. [69]1). In
   addition, three up-regulated DEGs (CD69, LGALS2 and FBP1) were
   significantly enriched in sugar binding.

Table 1.

   The top 5 enriched GO terms with the most low p-value for
   differentially expressed genes
   Category Terms Description Count p-value Genes
   BP GO:0030029 actin filament-based process 23 5.38E-07 MTSS1, ROCK1,
   ACTN4, AIF1, CALD1, ARF6, MYH9, CAPZB, TNNT2, ACTG1…
   GO:0030036 actin cytoskeleton organization 22 7.32E-07 MTSS1, ROCK1,
   ACTN4, AIF1, CALD1, ARPC5, MYH9, CAPZB, TNNT2, ACTG1…
   GO:0008285 negative regulation of cell proliferation 28 1.48E-06 AIF1,
   IGFBP7, PTH1R, ING1, COL4A3, BMP2, CTBP1, GAS1, CD164, TGFBR3…
   GO:0007010 cytoskeleton organization 31 2.17E-06 MTSS1, AIF1, CALD1,
   ARF6, ACTG1, PAK1, PLS3, ACTN4, ROCK1, MYH9…
   GO:0030832 regulation of actin filament length 11 6.01E-06 ACTR3, PFN2,
   CAPZA1, TMSB4X, RDX, ARF6, ARPC5, CAPZB, SPTAN1, DSTN…
   CC GO:0015629 actin cytoskeleton 31 2.60E-11 MTSS1, AIF1, CALD1, MYL9,
   ACTR3, ACTG1, ACTN4, MYO1B, MYH9, CTNNA1…
   GO:0005938 cell cortex 20 1.02E-08 SEPT2, ACTN4, SEPT1, CALD1, ARF6,
   MYH9, SEPT10, ACTG1, FNBP1, CLIC5…
   GO:0005856 cytoskeleton 70 1.28E-07 MTSS1, AIF1, CALD1, MYL9, ACTR3,
   ACTG1, ACTN4, MYO1B, MYH9, CTNNA1…
   GO:0044448 cell cortex part 13 1.78E-06 SEPT2, ACTN4, SEPT1, CALD1,
   PRKCI, MYH9, CAPZB, SEPT10, DSTN, ACTG1…
   GO:0043232 intracellular non-membrane-bounded organelle 104 5.54E-06
   SEPT2, ACTN4, SEPT1, CALD1, PRKCI, MYH9, CAPZB, SEPT10, DSTN, ACTG1…
   MF GO:0008092 cytoskeletal protein binding 40 7.17E-10 MTSS1, AIF1,
   TNNC1, CALD1, EZR, TARDBP, ACTN4, MYO1B, SUN2, MYH9…
   GO:0003779 actin binding 30 5.79E-09 MTSS1, AIF1, TNNC1, CALD1, ACTN4,
   MYH9, PALLD, NEBL, TNNT2, PARVA…
   GO:0005520 insulin-like growth factor binding 6 3.80E-04 CTGF, HTRA1,
   IGFBP7, IGFBP2, CRIM1, CYR61
   GO:0005200 structural constituent of cytoskeleton 9 6.07E-04 TNNT2,
   ACTG1, DMD, VIM, AGRN, ARPC5, CD2AP, ADD3, SPTAN1
   GO:0050839 cell adhesion molecule binding 6 9.18E-04 EZR, PVRL2, NPTN,
   CTNNA1, CD2AP, CTNNB1
   [70]Open in a new tab

   GO Gene Ontology, BP biological process, MF molecular function, CC
   cellular component

Fig. 1.

   Fig. 1
   [71]Open in a new tab

   The histogram of the category of enriched GO (Gene Ontology) terms for
   the down-regulated genes. CC represents cellular component; MF
   represents molecular function; and BP represents biological process.
   WEGO represents the tool Web Gene Ontology Annotation Plot

   According to KEGG enrichment analysis, the down-regulated DEGs were
   significantly enriched in 6 pathways, such as arrhythmogenic right
   ventricular cardiomyopathy (ARVC) (e.g. ACTN4, CTNNA1 and ITGB5),
   regulation of actin cytoskeleton (e.g. ACTN4, MYL9 and ITGB5) and
   complement and coagulation cascades (e.g. C1R and C1S). There were no
   significant pathways for the up-regulated DEGs (Table [72]2).

Table 2.

   The results of pathway enrichment analysis for the down-regulated genes
   Category Pathway Name Count p-value Genes
   KEGG_PATHWAY Arrhythmogenic right ventricular cardiomyopathy (ARVC) 11
   1.41E-04 ACTG1, ATP2A2, ACTN4, DMD, DAG1, CACNB2, ITGB5, ITGA3, CTNNA1,
   CTNNB1…
   ACTN4, ROCK1, ITGB5, RDX, ITGA3, ARPC5, MYL12A, MYH9, MYL9, ACTG1…
   KEGG_PATHWAY Regulation of actin cytoskeleton 19 1.65E-04
   KEGG_PATHWAY Hypertrophic cardiomyopathy (HCM) 9 0.00575 TNNT2, ACTG1,
   ATP2A2, TNNC1, DMD, DAG1, CACNB2, ITGB5, ITGA3
   KEGG_PATHWAY Complement and coagulation cascades 8 0.00641 CD55, F3,
   CD46, CD59, C1R, SERPING1, C1S, F2R
   KEGG_PATHWAY Dilated cardiomyopathy 9 0.00920 TNNT2, ACTG1, ATP2A2,
   TNNC1, DMD, DAG1, CACNB2, ITGB5, ITGA3
   KEGG_PATHWAY Pathogenic Escherichia coli infection 7 0.00956 ACTG1,
   EZR, ROCK1, TUBB2A, YWHAQ, ARPC5, CTNNB1
   [73]Open in a new tab

   KEGG, Kyoto Encyclopedia of Genes and Genomes

Analysis of miRNA-DEG-TF regulatory network

   Totally, 896 regulatory relationships between miRNAs, TFs and DEGs were
   identified to construct the miRNA-DEG-TF regulatory network, including
   24 miRNAs, 44 TFs and 275 down-regulated genes (Fig. [74]2). The
   transcription factor LEF1 regulated most of genes (degree = 79), such
   as ITGB5, CALD1, RTN4 and PAK1. Hsa-miR-33a modulated 28 genes, such as
   C1S and RTN4.

Fig. 2.

   Fig. 2
   [75]Open in a new tab

   The network consisting of differentially expressed genes, microRNAs and
   transcription factors. Rounded nodes represent genes; trigonal nodes
   represent microRNAs; and quadrate nodes represent transcription factors

   Besides, no miRNAs were predicted to regulate the up-regulated genes,
   whereas, two TFs (AML1 and NFKB) were identified to modulate several
   up-regulated genes. Among them, AML1 regulated CD96 and CYP17A1.

Screening of overlapped DEGs in the two datasets

   To validate the expression of the identified DEGs in the dataset
   [76]GSE1009, another dataset [77]GSE30528 was used. A total of 635 DEGs
   were identified in [78]GSE30528. Among them, 143 DEGs, including one
   upregulated gene (TRIM16) and 142 downregulated genes (e.g. MTSS1,
   ACTN4 and ITGB5), were also differentially expressed in the dataset
   [79]GSE1009 (Fig. [80]3), indicating that the expression of the 143
   DEGs in [81]GSE1009 identified above were validated by the dataset
   [82]GSE30528.

Fig. 3.

   Fig. 3
   [83]Open in a new tab

   The Venn diagram showing the overlapped differentially expressed genes
   in the two datasets [84]GSE30528 and [85]GSE1009. “[86]GSE30528-UP”
   represents the upregulated genes in the dataset [87]GSE30528;
   “[88]GSE30528-DOWN” represents the downregulated genes in the dataset
   [89]GSE30528; “[90]GSE1009-UP” represents the upregulated genes in the
   dataset [91]GSE1009; and “[92]GSE1009-DOWN” represents the
   downregulated genes in the dataset [93]GSE1009. Arabic numerals in the
   diagram represent the numbers of the overlapped genes

Discussion

   In the present study, a set of 444 DEGs in the dataset [94]GSE1009 were
   identified from diabetic glomeruli samples, including 14 up-regulated
   ones and 430 down-regulated ones, compared with healthy glomeruli
   samples. Among them, the expression of 143 DEGs (one upregulated gene
   and 142 downregulated genes) were validated by another dataset
   [95]GSE30528. According to the GO functional enrichment analysis, a set
   of DEGs were related to the function of cytoskeleton organization, such
   as MTSS1, ACTN4 and CALD1.

   MTSS1 encodes metastasis suppressor 1, which is also known as MIM
   (missing in metastasis). It is an actin and membrane binding protein
   that was originally identified as a potential tumor metastasis
   suppressor [[96]16]. MTSS1 can induce actin-rich protrusions at the
   plasmalemma and promote disintegration of actin stress fibers [[97]17],
   indicating that it may be crucial in regulating cytoskeletal dynamics.
   Renal tubular cell and podocyte apoptosis is an inevitable event in the
   progression of glomerulosclerosis [[98]18, [99]19], and major
   modifications of the cytoskeleton are involved in the apoptosis
   progress, including dynamic membrane blebbing, nuclear disintegration,
   chromatin condensation and cell fragmentation [[100]20]. Therefore,
   MTSS1 may play a pivotal role in DN, via participation in the
   regulation of cytoskeleton organization. ACTN4 encodes an actinin,
   which participates in cytoskeleton action. Previous studies have been
   reported that mutations in ACTN4 cause focal segmental
   glomerulosclerosis [[101]21–[102]23]. There is evidence that two
   single-nucleotide polymorphisms in ACTN4 are associated with DN in
   women [[103]24]. Moreover, the up-regulation of ACTN4 was observed
   during the proteinuria stage [[104]25]. Thereby, ACTN4 may be also
   important in the progression of DN. CALD1 encodes a caldesmon that
   plays a key role in the regulation of smooth muscle and nonmuscle
   contraction [[105]26]. A previous study has shown that CALD1 is
   over-expressed in fibroblasts from the diabetic patients with
   nephropathy, compared with those from the controls [[106]27]. Conway et
   al. have demonstrated that caldesmon is a candidate susceptibility gene
   for DN [[107]28]. In this study, CALD1 was found to be regulated by
   several TFs, such as LEF1. LEF1 (lymphoid enhancer-binding factor 1) is
   a nuclear transcription factor modulated by Wnt, and it expedites
   epithelial to mesenchymal transition (EMT) when its activity is
   activated by β-catenin [[108]29]. Rooney et al. have reported that
   overexpression of CCN2 during the progression of DN likely results in
   the activation of Wnt signaling and subsequent initiation of TCF/LEF
   transcription [[109]5]. Collectively, CALD1 may play an essential role
   in the development of DN likely through the regulation of LEF1.

   Furthermore, a series of downregulated DEGs were discovered to be
   enriched in the pathways of arrhythmogenic right ventricular
   cardiomyopathy and hypertrophic cardiomyopathy in our study, such as
   ITGB5. Diabetic cardiomyopathy is a frequent event in diabetic patients
   due to hyperglycemia [[110]30]. ITGB5 encodes integrin beta 5
   [[111]31], and it has been found to be differentially expressed in DKD
   glomeruli and tubuli [[112]9]. Furthermore, integrins are the primary
   receptors for intercellular adhesion molecule 1 (ICAM1), which has been
   reported to have a close relationship with DN [[113]32]. Additionally,
   in our study, ITGB5 was also regulated by LEF1. These results suggest
   that ITGB5 may have a significant function in the development of DN,
   via participating in cardiomyopathy pathways.

   Additionally, a set of genes were markedly enriched in complement and
   coagulation cascades, such as C1S and C1R. These two genes both encode
   members of the human complement subcomponent C1, which is involved in
   immune response [[114]33]. Migration of immune cells into the renal is
   a feature of early DN, and it is implicated in the development of this
   complication [[115]34–[116]37]. Previous studies also have found C1S
   and C1R to be differentially expressed in DN [[117]38, [118]39]. In our
   study, C1S and C1R were regulated by LEF1 and hsa-miR-33a. MiR-33a has
   been reported to be implicated in diabetes due to its regulation of
   insulin signaling and fatty acid metabolism [[119]40, [120]41].
   Therefore, C1S and C1R may be crucial in the development of DN.

   However, this study has some limitations. For example, these
   predictions were not validated by experiments. The number of samples
   used for analysis is small. In further studies, more samples will be
   analyzed, and the predictions will be validated by experimental data.

Conclusion

   In conclusion, a total of 14 up-regulated genes and 430 down-regulated
   ones were identified. Some DEGs related to cytoskeleton organization
   (e.g. MTSS1, ACTN4 and CALD1), cardiomyopathy (e.g. ITGB5) and immune
   response (e.g. C1S and C1R), as well as some regulators (e.g. LEF1 and
   hsa-miR-33a) might play pivotal roles in the progression of DN. These
   findings may contribute to our better understanding of DN pathogenesis,
   and provide a theoretical basis for further experimental studies.

Acknowledgement