Open Access

Identification of key genes for diabetic kidney disease using biological informatics methods

  • Authors:
    • Fuzhe Ma
    • Tao Sun
    • Meiyan Wu
    • Wanning Wang
    • Zhonggao Xu
  • View Affiliations

  • Published online on: September 29, 2017     https://doi.org/10.3892/mmr.2017.7666
  • Pages:7931-7938
  • Copyright: © Ma et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Diabetic kidney disease (DKD) is a common complication of diabetes, which is characterized by albuminuria, impaired glomerular filtration rate or a combination of the two. The aim of the present study was to identify the potential key genes involved in DKD progression and to subsequently investigate the underlying mechanism involved in DKD development. The array data of GSE30528 including 9 DKD and 13 control samples was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in DKD glomerular and tubular kidney biopsy tissues were compared with normal tissues, and were analyzed using the limma package. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for DEGs using the GO Function software in Bioconductor. The protein‑protein interaction (PPI) network was then constructed using Cytoscape software. A total of 426 genes (115 up‑ and 311 downregulated) were differentially expressed between the DKD and normal tissue samples. The PPI network was constructed with 184 nodes and 335 edges. Vascular endothelial growth factor A (VEGFA), α‑actinin‑4 (ACTN4), proto‑oncogene, Src family tyrosine kinase (FYN), collagen, type 1, α2 (COL1A2) and insulin‑like growth factor 1 (IGF1) were hub proteins. Major histocompatibility complex, class II, DP α1 (HLA‑DPA1) was the common gene enriched in the rheumatoid arthritis and systemic lupus erythematosus pathways, and the immune response was a GO term enriched in module A. VEGFA, ACTN4, FYN, COL1A2, IGF1 and HLA‑DPA1 may be potential key genes associated with the progression of DKD, and immune mechanisms may serve a part in DKD development.

Introduction

Diabetic kidney disease (DKD) is a common complication of diabetes, which is characterized by albuminuria, an impaired glomerular filtration rate (GFR) or a combination of the two (1,2). DKD accounts for ~50% of all cases of end-stage renal disease (ESRD) in the United States and the US ESRD program is a large medical expense/economic burden and costs a great amount of money to run. However, a number of genes closely associated with DKD development have yet to be identified despite many years of intensive study (3). Therefore, the identification of genes associated with DKD development is urgently required, as well as the subsequent elucidation of its molecular mechanism.

Some advancements have been made in the elucidation of the pathological mechanism involved in the development of DKD. The loss of podocytes is an early feature of DKD (4). The levels of almost all podocyte-specific genes including genes for congenital nephrotic syndrome of the finish type (NPHS1), glomerular podocin (NPHS2), the Wilm's tumor gene (WT1) and vascular endothelial growth factor (VEGF) are all severely reduced in DKD (3). Some other studies have also demonstrated that NPHS1 (5,6), NPHS2 (7), bone morphogenetic protein 7 (8), WT1 (4) and VEGF (9,10) are decreased in DKD. In addition, tubulointerstitial fibrosis is a prominent feature of progressive DKD and is likely to be one of the final common pathways leading to the development of ESRD, with patients subsequently requiring dialysis or transplantation (11,12). A previous study revealed that using angiotensin-converting-enzyme-inhibitors and angiotensin II receptor antagonists in patients with diabetes mellitus can respectively improve mortality and delay the progression of DKD (13). In addition, a human genetic study highlighted that the complement system potentially serves a role in low-grade inflammation and the development of DKD (14). Therefore, the aim of the present study was to identify the key genes associated with the development of DKD and elucidate its underlying mechanism.

In the present study, the microarray data of GSE30528 was downloaded the from Gene Expression Omnibus database (GEO; www.ncbi.nlm.nih.gov/geo/). The gene expression profiles in DKD were analyzed and functional analysis was performed for differentially expressed genes (DEGs) in DKD glomerular and tubular kidney biopsy tissues in comparison with normal tissues. In addition, the protein-protein interaction (PPI) network was also constructed. These results were used to discover the key genes associated with DKD development and to clarify the underlying mechanism.

Materials and methods

Affymetrix microarray data

The array data for GSE30528 was downloaded from the GEO database, which was first recorded by Woroniecka et al (3) and was based on the GPL571 platform (Affymetrix Human Genome U133A 2.0 Array; Affymetrix, Inc.; Thermo Fisher Scientific, Inc., Waltham, MA, USA). A total of 44 samples were used to develop the original array data, and of these 9 DKD [age, 64±13.56 years; 5 females, 4 males; body mass index (BMI), 32.74±7.9 kg/m2] and 13 healthy, disease-free control samples (age, 51.38±12.01 years; 5 females, 8 males; BMI, 29.59±9.08 kg/m2) were selected for analysis in the present study.

Data processing and DEG analysis

The raw expression data was preprocessed using the robust multiarray average algorithm (15) and the Affy package in Bioconductor (bioconductor.org/packages/release/bioc/html/affy.html); the expression levels of the probes were then obtained. If several probes mapped to one gene symbol, then the mean value was set as the final expression value of this gene. The DEGs in DKD glomerular and tubular kidney biopsy tissues where then compared with normal tissues using the limma package (16). |logFC| >1 and P<0.05 were considered as the cutoff criterion.

Gene Ontology (GO) and pathway enrichment analysis

GO is used for the unification of biology, collecting defined, structured and controlled vocabulary for gene annotation, which mainly includes the following 3 categories: Molecular function, biological process and cellular component (17). The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database for the classification of relevant gene sets into their respective pathways (18).

In order to analyze the DEGs on a functional level, GO annotation and KEGG pathway enrichment analyses were performed for DEGs using GO Function version 1.24.0 (19) software in Bioconductor version 3.5 (www.bioconductor.org/packages/release/bioc/html/GOFunction.html), and gene annotation information was obtained from the org. Hs. eg. db and GO. db package. P<0.05 and gene counts >2 were set as the cut off values.

PPI network analysis

The Search Tool for the Retrieval of Interacting Genes (STRING) database provides the experimental and predicted interaction information of proteins (20). Protein pair interactions in STRING were presented with a combined score. The DEGs were mapped into PPIs and a combined score of >0.7 was identified as the cutoff standard for the important protein pairs. The PPI network was constructed using Cytoscape software version 2.8.2 (www.cytoscape.org/) (21).

Module analysis

ClusterONE version 1.0 (www.paccanarolab.org/cluster-one/) in the Cytoscape software package was used to analyze the PPI network modules with a minimum size of 3 and a minimum density of 0.5. Modules with P<0.01 were set as significant clustering modules.

Results

Data processing and DEGs analysis

As shown in Fig. 1, a total of 426 genes were differentially expressed in DKD samples when compared with normal samples, amongst which 115 genes were upregulated and 311 were downregulated.

GO and pathway enrichment analysis

GO and KEGG pathway analyses were performed for upregulated and downregulated DEGs. The top 5 GO terms are shown in Table I. The overrepresented GO terms of upregulated DEGs were primarily associated with extracellular region, antigen binding, extracellular space, the defense response, the immune response and peptidase regulator activity (Table IA). The downregulated DEGs were mainly involved in cardiovascular system development, circulatory system development, actin cytoskeleton, cell junction, cytoskeletal protein binding and integrin binding (Table IB).

Table I.

Gene Ontology analysis for differentially expressed genes.

Table I.

Gene Ontology analysis for differentially expressed genes.

A, Upregulated

TermDescriptionCounts (n) P-valuea
GO-BP terms
  GO:0006952Defense response  44<0.0005
  GO:0006955Immune response  41 2.22×10−16
  GO:0002376Immune system process  50 3.00×10−15
  GO:0001775Cell activation  31 1.37×10−14
  GO:0045321Leukocyte activation  26 1.76×10−13
GO-CC terms
  GO:0005576Extracellular region  63 2.06×10−12
  GO:0005615Extracellular space  32 4.37×10−12
  GO:0031982Vesicle  53 9.90×10−11
  GO:0031988Membrane-bounded vesicle  52 1.13×10−10
  GO:0044421Extracellular region part  53 3.50×10−10
GO-MF terms
  GO:0003823Antigen binding     8 2.97×10−07
  GO:0061134Peptidase regulator activity  10 6.67×10−07
  GO:0005539Glycosaminoglycan binding     9 2.84×10−07
  GO:0004866Endopeptidase inhibitor activity     8
8092×10−06
  GO:0061135Endopeptidase regulator activity     8 1.11×10−05

B, Downregulated

TermDescriptionCounts (n) P-valuea

GO-BP terms
  GO:0072358Cardiovascular system development  50 3.77×10−15
  GO:0072359Circulatory system development  50 3.77×10−15
  GO:0009653Anatomical structure morphogenesis  91 4.66×10−15
  GO:0048731System development121 3.40×10−14
  GO:0032502Developmental process147 5.66×10−14
GO-CC terms
  GO:0015629Actin cytoskeleton  31 1.79×10−12
  GO:0030054Cell junction  47 6.04×10−10
  GO:0070161Anchoring junction  28 1.82×10−09
  GO:0005912Adherens junction  27 3.39×10−09
  GO:0044421Extracellular region part  98 3.00×10−08
GO-MF terms
  GO:0008092Cytoskeletal protein binding  40 9.40×10−11
  GO:0005178Integrin binding  12 1.23×10−07
  GO:0032403Protein complex binding  35 4.24×10−07
  GO:0050839Cell adhesion molecule binding  14 8.04×10−07
  GO:0003779Actin binding  21 1.40×10−06

a P<0.00001 vs. normal matched tissues. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; Term, the identification number of GO; Description, the name of the GO term; Counts, the number of genes enriched in the GO term.

The upregulated DEGs were mainly enriched in 17 KEGG pathways, including primary immunodeficiency, extracellular matrix-receptor interactions, rheumatoid arthritis and systemic lupus erythematosus (Table IIA). In addition, major histocompatibility complex, class II, DP α1 (HLA-DPA1) was the common gene in the rheumatoid arthritis and systemic lupus erythematosus pathways. The downregulated DEGs were mainly enriched in 16 KEGG pathways, such as tight junction and adherens junction.

Table II.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the top 10 DEGs.

Table II.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the top 10 DEGs.

A, Upregulated

TermDescriptionCounts (n)P-value
5150Staphylococcus aureus infection  5 1.89×10−04c
5020Prion diseases  4 3.58×10−04c
5340Primary immunodeficiency  4 3.58×10−04c
4512Extracellular matrix-receptor interaction  5 1.42×10−03b
5323Rheumatoid arthritis  5 1.92×10−03b
5142Chagas disease (American trypanosomiasis)  5 3.45×10−03b
4610Complement and coagulation cascades  4 4.61×10−03b
4974Protein digestion and absorption  4 8.12×10−03b
5322Systemic lupus erythematosus  5 1.06×10−02a
4640Hematopoietic cell lineage  4 1.08×10−02a

B, Downregulated

TermDescriptionCounts (n)P-value

4520Adherens junction  8 3.85×10−05d
4510Focal adhesion13 4.51×10−05d
4810Regulation of actin cytoskeleton13 8.65×10−05d
4530Tight junction  9 5.06×10−04c
5410Hypertrophic cardiomyopathy  7 6.17×10−04c
5414Dilated cardiomyopathy  7 1.00×10−03b
5412Arrhythmogenic right ventricular cardiomyopathy  6 1.87×10−03b
4610Complement and coagulation cascades  5 7.26×10−03b
4360Axon guidance  7 7.65×10−03b
5200Pathways in cancer12 1.21×10−02a

a P<0.05

b P<0.01

c P<0.001

d P<0.0001 vs. normal matched tissues. KEGG, Kyoto Encyclopedia of Genes and Genomes; Term, the identification number of the KEGG pathway; Description, the name of the KEGG pathway; Counts, the number of genes enriched in the KEGG pathway.

PPI network analysis

Based on the STRING database, a total of 335 protein pairs with a combined score of >0.7 were obtained. As presented in Fig. 2, the PPI network was constructed with 335 edges and 184 nodes. The nodes of VEGFA (degree score, 19), α-actinin-4 (ACTN4; degree score, 17), proto-oncogene, Src family tyrosine kinase (FYN; degree score, 17), collagen, type 1, α2 (COL1A2; degree score, 15) and insulin-like growth factor 1 (IGF1; degree score, 15) were hub proteins in the network.

Modules analysis

Two significant clustering modules were obtained using ClusterONE software (Fig. 3). A total of 8 and 6 nodes were enriched in modules A and B, respectively. As shown in Table III, nodes in module A (density, 1.0; quality, 0.800; P=1.606×10−4) were mainly enriched in GO: G-protein coupled receptor protein signaling pathway, cell surface receptor linked signal transduction, the immune response, the chemokine signaling pathway and the cytokine-cytokine receptor interaction pathway. Nodes in module B (density, 1.0; quality, 0.789; P=0.001) were mainly enriched in GO: regulation of ATPase activity, regulation of system process and regulation of hydrolase activity.

Table III.

Functional enrichment analysis of protein-protein interaction network clustering modules.

Table III.

Functional enrichment analysis of protein-protein interaction network clustering modules.

TermDescriptionCounts (n)P-value
Module A
  GO_BP:0007186G-protein coupled receptor protein signaling pathway8 2.67×10−08a
  GO_BP:0007166Cell surface receptor linked signal transduction8 9.06×10−07a
  GO_BP:0006955Immune response5 2.08×10−04b
  KEGG_ hsa04062Chemokine signaling pathway3 1.25×10−02c
  KEGG_ hsa04060Cytokine-cytokine receptor interaction3 2.38×10−02c
Module B
  GO_BP:0043462Regulation of ATPase activity3 1.31×10−05d
  GO_BP:0044057Regulation of system process3 4.97×10−03e
  GO_BP:0051336Regulation of hydrolase activity3 5.89×10−03f
  KEGG_ hsa04260Cardiac muscle contraction2 2.32×10−04b
  KEGG_ hsa05410Hypertrophic cardiomyopathy3 2.76×10−04b
  KEGG_ hsa05414Dilated cardiomyopathy2 3.24×10−04b

a P<0.00001

b P<0.0005

c P<0.05

d P<0.0001

e P<0.005

f P<0.01vs. normal matched tissues. GO, Gene Ontology; KEGG, KEGG, Kyoto Encyclopedia of Genes and Genomes; Term, the identification number of GO-Biological Process or KEGG pathway; Description, the name of the GO- Biological Process or KEGG pathway; Counts, the number of genes enriched in GO- Biological Process or KEGG pathway.

Discussion

In the present study, using the gene expression patterns downloaded from the GEO database, 426 DEGs in DKD glomerular and tubular kidney biopsy tissues were obtained and compared with matched normal tissues, identifying 115 upregulated genes and 311 downregulated DEGs. The results demonstrated that HLA-DPA1 was the common gene enriched in the rheumatoid arthritis and systemic lupus erythematosus pathways, and the immune response was a significant GO term enriched in module A. In addition, VEGFA, ACTN4, FYN, COL1A2 and IGF1 had higher degrees and were established as hub nodes in the PPI network; they may therefore contribute to the progression of DKD.

A previous study suggested that cells in the immune system may be involved in the progression of DKD (22). Immune cells take part in vascular injury under DKD-associated conditions (23). Other previous studies have also indicated that the immune system is associated with DKD development (2426). Primary immunodeficiency (27), rheumatoid arthritis (28) and systemic lupus erythematosus (29) are associated with the immune system. In the present study, primary immunodeficiency, rheumatoid arthritis and systemic lupus erythematosus were 3 significantly enriched pathways, and the immune response was a GO term enriched in module A. Thus, the results of the present study are in agreement with previous findings, and therefore indicate that immune mechanisms may serve a role in DKD development.

The work of Woroniecka et al (3) suggested that HLA-DPA1 was a differentially expressed transcript in the tubulointerstitium of patients with DKD when compared with normal samples. Previous studies revealed that HLA-DPA1, which is the closest centromeric gene expressed to HLA-DOα, may contribute to the differences in the associated risks of diabetes (30,31), including DKD, which is a complication of diabetes. In the present study, HLA-DPA1 was the common gene enriched in the rheumatoid arthritis and systemic lupus erythematosus pathways. Therefore, these results are in line with previous findings and suggest that HLA-DPA1 may contribute to DKD development.

In addition, VEGFA, ACTN4, FYN, COL1A2 and IGF1 were identified as hub proteins in the PPI network. VEGFA is an important angiogenic growth factor that regulates endothelial cells' permeability and vasculogenesis (32). It is also important for the differentiation, proliferation, survival and migration of endothelial cells within the glomerulus (33). Previous studies have suggested that VEGFA may serve a significant role in retaining glomerular endothelial cell function as a reduction in VEGFA levels induced abnormal remodeling of glomerular capillaries (34,35). VEGF may also serve a role in the pathogenesis of DKD (36) and the dysregulation of VEGFA may serve a pathogenic role in inducing glomerular injury (37). In DKD, VEGFA has reduced mRNA expression and may be a potential factor that can lead to the development of DKD by inducing microvascular rarefaction and tubular atrophy (9). In addition, neoangiogenesis, which is caused by overexpression of VEGFA, may stimulate the development of DKD and therefore blocking VEGFA or its signaling may ameliorate DKD (38). These findings indicate that VEGFA may serve a role in DKD progression.

ACTNs are actin-binding proteins that are critical in cell adhesion and in the organization of the cytoskeleton (39). Increasing evidence has revealed that in diabetes, there are cytoskeletal changes in podocytes. For instance, advanced glycosylation end products and high glucose can decrease the expression of ACTN4 (40), and a reduced expression of ACTN4 may lead to proteinuria (a symptom of DKD) (41). In addition, FYN is a tyrosine-specific phospho-transferase that belongs to the Src family of tyrosine protein kinases (42). FYN phosphorylation is transiently stimulated by high glucose levels (43). Src/FYN kinase inhibitors disrupt signaling molecules in the VEGF signal transduction pathway (44), and as mentioned above, VEGF may be associated with DKD; thus, FYN may in turn be involved in DKD. In addition, the accumulation of extracellular matrix proteins such as COL1A2 is a key feature of DKD (45). A previous report demonstrated that some key microRNAs (miR) act as effectors of transforming growth factor (TGF)-β and the actions of high glucose in DKD (46). In mesangial cells and the kidney, experimental diabetes was associated with the increased expression of COL1A2, and miR-192 was increased by TGF-β treatment (45). IGF1 as a growth factor receptor has been associated with type 1 DKD (47). In addition, IGF-1 has the capacity to mediate the histological changes characteristic of DKD (48). These previous studies all indicate that these proteins are associated with DKD. Therefore, the results of the present study are in agreement with these findings and provide further evidence that VEGFA, ACTN4, FYN, COL1A2 and IGF1 may serve important roles in DKD development directly or indirectly.

In conclusion, the results of the present study indicated that in addition to VEGFA, ACTN4, FYN, COL1A2, IGF1 and HLA-DPA1, immune mechanisms may also serve an important role in the development of DKD. These genes may serve as target genes for the treatment of DKD in future clinical practice. However, this conclusion has no experimental verification; therefore, further evaluation of the potential applications in clinical practice is required.

Acknowledgements

The present study was supported by the National Natural Science Foundation of China (grant nos. 81070578 and 81270809).

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December 2017
Volume 16 Issue 6

Print ISSN: 1791-2997
Online ISSN:1791-3004

2016 Impact Factor: 1.692
Ranked #19/128 Medicine Research and Experimental
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APA
Ma, F., Sun, T., Wu, M., Wang, W., & Xu, Z. (2017). Identification of key genes for diabetic kidney disease using biological informatics methods. Molecular Medicine Reports, 16, 7931-7938. https://doi.org/10.3892/mmr.2017.7666
MLA
Ma, F., Sun, T., Wu, M., Wang, W., Xu, Z."Identification of key genes for diabetic kidney disease using biological informatics methods". Molecular Medicine Reports 16.6 (2017): 7931-7938.
Chicago
Ma, F., Sun, T., Wu, M., Wang, W., Xu, Z."Identification of key genes for diabetic kidney disease using biological informatics methods". Molecular Medicine Reports 16, no. 6 (2017): 7931-7938. https://doi.org/10.3892/mmr.2017.7666