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International Journal of Ophthalmology logoLink to International Journal of Ophthalmology
. 2018 Jun 18;11(6):910–917. doi: 10.18240/ijo.2018.06.03

Bioinformatics analysis of microarray data to explore the key genes involved in HSF4 mutation-induced cataract

Rui Tian 1, Yang Xu 2, Wen-Wen Dou 1, Hui Zhang 1
PMCID: PMC6010373  PMID: 29977800

Abstract

AIM

To reveal the mechanisms of heat-shock transcription factor 4 (HSF4) mutation-induced cataract.

METHODS

GSE22362, including 3 HSF4-null lens and 3 wild-type lens, was obtained from Gene Expression Omnibus database. After data preprocessing, the differentially expressed genes (DEGs) were identified using the limma package. Based on Database for Annotation, Visualization and Integrated Discovery (DAVID) tool, functional and pathway enrichment analyses were performed for the DEGs. Followed by protein-protein interaction (PPI) network was constructed using STRING database and Cytoscape software. Furthermore, the validated microRNA (miRNA)-DEG pairs were obtained from miRWalk2.0 database, and then miRNA-DEG regulatory network was visualized by Cytoscape software.

RESULTS

A total of 176 DEGs were identified in HSF4-null lens compared with wild-type lens. In the PPI network, FBJ osteosarcoma oncogene (FOS), early growth response 1 (EGR1) and heme oxygenase (decycling) 1 (HMOX1) had higher degrees and could interact with each other. Besides, mmu-miR-15a-5p and mmu-miR-26a-5p were among the top 10 miRNAs in the miRNA-DEG regulatory network. Additionally, mmu-miR-26a-5p could target EGR1 in the regulatory network.

CONCLUSION

FOS, EGR1, HMOX1, mmu-miR-26a-5p and mmu-miR-15a-5p might function in the pathogenesis of HSF4 mutation-induced cataract.

Keywords: cataract, heat-shock transcription factor 4, differentially expressed genes, protein-protein interaction network, regulatory network

INTRODUCTION

As a clouding of the lens in the eye, cataract may result in blurry vision, faded colors, trouble with bright lights, halos around light, and trouble seeing at night[1]. Cataract-caused poor vision may further lead to an increased risk of falling into depression[2]. The risk factors for cataract include smoking tobacco, diabetes, alcohol, and prolonged exposure to sunlight[3]. Globally, cataract accounts for one-third of visual impairment and half of blindness[4]–[5]. Cataract results in blindness of about 1 to 4 per 100 000 children and 10 to 40 per 100 000 children in the developed countries and the developing countries, respectively[6]. In worldwide, cataract can induce disability in 53.8 million people, and 52.2 million of them are living in poor countries[7]. Thus, it's important to explore the mechanisms of cataract and develop novel therapies.

Previous study reports that heat-shock transcription factor 4 (HSF4) is critical for lens development and its disruption can cause cataract through reducing the expression of lens beaded filament, down-regulating γ-crystallin, and decreasing the post-translational modification of αA-crystallin[8]–[9]. HSF4 pathogenic mutations result in nuclear cataracts via abrogating the induction of expression and DNase activity of DLAD (DNase 2β)[10]. Mou et al[11] deem that vimentin is targeted by HSF4 in lens and plays a role in the aberrant lens development and cataractogenosis induced by HSF4 mutation. Based on a p53-dependent manner, HSF4 mutations may induce cataract through affecting the switch between the proliferation of lens epithelial cell (LEC) and the differentiation of secondary fiber cell[12]. In 2010, He et al[13] deposited GSE22362 and analyzed the differentially expressed genes (DEGs) in HSF4 homozygous lens, finding many genes co-regulated by HSF4 (especially the down-regulated DNase Iiβ, an enzyme for the denucleation of lens fiber cells). However, the action mechanisms of HSF4 mutations in lens development and cataract formation remaining largely unknown.

Using the microarray data deposited by He et al[13], we further analyzed the DEGs between HSF4-null lens and wild-type lens. Subsequently, the functions of the DEGs were predicted using enrichment analysis. Furthermore, the key genes co-regulated by HSF4 in lens were deeply investigated by protein-protein interaction (PPI) network and microRNA (miRNA)-DEG regulatory network analyses. This study might contribute to finding the mechanisms regarding how HSF4 mutations induce cataract formation.

SUBJECTS AND METHODS

Microarray Data

The dataset of GSE22362 was downloaded from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database, which was sequenced on the platform of GPL8321 [Mouse430A_2] Affymetrix Mouse Genome 430A 2.0 Array. The dataset included 3 HSF4-null lens and 3 wild-type lens. Lens of wild-type and day E15.5 transgenic embryos were isolated and then kept in RNA Later (Ambion, Woodlands, TX, USA). He et al[13] deposited GSE22362, and their study was performed according to the approved protocol of the Association for Research in Vision and Ophthalmology and the Albert Einstein College of Medicine Animal Institute Committee.

Data Preprocessing and Differential Expression Analysis

The raw data were preprocessed by the Robust MultiArray Averaging (RMA) method[14] in R package oligo (version 1.38.0, http://bioconductor.org/packages/release/bioc/html/oligo.html), including background correction, normalization and expression calculation. Probes were annotated based on platform annotation files, and the probes without corresponding gene symbols were removed. For the probes mapped to one and the same gene symbol, their average value was obtained as the gene expression value. Using the limma (Linear Models for Microarray Data) package[15] (version3.10.3, http://www.bioconductor.org/packages/2.9/bioc/html/limma.html) in R language, the DEGs between HSF4-null and wild-type lens were identified. Genes with |logFC (fold-change)| >1 and P-value <0.05 were identified as DEGs.

Functional and Pathway Enrichment Analysis

Gene Ontology (GO) is a database developed for describing biological process, molecular function and subcellular location of gene products[16]. Kyoto Encyclopedia of Genes and Genomes (KEGG) database, which is composed by genes and their corresponding functions, can be used for predicting potential functions of gene lists[17]. Using database for annotation, visualization and integrated discovery (DAVID) tool (version 6.8, https://david-d.ncifcrf.gov/)[18], the up-regulated and down-regulated genes were conducted with GO functional and KEGG pathway enrichment analyses, respectively. The terms with P-value <0.05 and count (the number of enriched genes) ≥2 were selected as significant terms.

Construction of Protein-protein Interaction Network

The PPI pairs among the DEGs were predicted by the Search Tool for the Retrieval of Interacting Genes (STRING, version 10, http://www.string-db.org/)[19] database, with PPI score (medium confidence) was set as 0.4. Then, PPI network was constructed using Cytoscape software (version 3.2.0, http://www.cytoscape.org)[20]. Subsequently, degree centrality of the nodes in the PPI network were calculated by the CytoNCA plug-in[21] in Cytoscape software, with parameter was set as without weight. The nodes with higher degrees were identified as the hub proteins[22].

MicroRNA-gene Regulatory Network Analysis

Using miRWalk2.0 database (validated gene-miRNA interaction information retrieval system, http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/)[23], the validated miRNA-DEG pairs were obtained. Afterwards, miRNA-DEG regulatory network was visualized by Cytoscape software[20].

RESULTS

Differential Expression Analysis

The gene expression distribution before and after normalization are shown in Figure 1.

Figure 1. The gene expression distribution before and after normalization.

Figure 1

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Red and white boxes represent HSF4-null and wild-type lens, respectively.

The median values were almost at the same level, indicating that the effect of data preprocessing was good. With |logFC| >1 and P-value <0.05 as the thresholds, a total of 176 DEGs (51 up-regulated and 125 down-regulated genes) were identified in HSF4-null lens compared with wild-type lens. The number of down-regulated genes exceeded that of up-regulated genes. The heatmap of the DEGs is shown in Figure 2.

Figure 2. The heatmap of the DEGs.

Figure 2

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Color changes from green to red indicates that expression values range from low to high.

Functional and Pathway Enrichment Analysis

The top 10 GO terms enriched for the up-regulated genes were listed in Table 1, including cytoskeleton organization (P=2.06E-03), cellular response to fibroblast growth factor stimulus (P=2.41E-03) and cellular response to tumor necrosis factor (P=2.42E-03). Besides, 4 pathways were enriched for the up-regulated genes, including malaria (P=2.39E-04), rheumatoid arthritis (P=1.87E-02), Chagas disease (American trypanosomiasis) (P=2.86E-02) and tumor necrosis factor (TNF) signaling pathway (P=3.18E-02) (Table 1).

Table 1. The terms enriched for the up-regulated genes.

ID Description Count P Genes
GO
 0007010 Cytoskeleton organization 4 2.06E-03 CCL12, CCL2, TAGLN, MICAL2
 0044344 Cellular response to fibroblast growth factor stimulus 3 2.41E-03 HYAL1, CCL2, COL1A1
 0071356 Cellular response to tumor necrosis factor 4 2.42E-03 CCL12, HYAL1, CCL2, COL1A1
 0006935 Chemotaxis 4 2.95E-03 CCL12, CCL2, CMTM3, CYR61
 0030199 Collagen fibril organization 3 4.05E-03 SFRP2, COL1A1, GREM1
 0051591 Response to cAMP 3 6.84E-03 FOS, PYGM, COL1A1
 2000502 Negative regulation of natural killer cell chemotaxis 2 7.28E-03 CCL12, CCL2
 0001666 Response to hypoxia 4 1.13E-02 EGR1, CCL2, PYGM, BNIP3
 0006915 Apoptotic process 6 1.21E-02 DNASE1, SGK1, CHAC1, SFRP2, BNIP3, GREM1
 0048023 Positive regulation of melanin biosynthetic process 2 1.21E-02 TYRP1, PMEL
KEGG
 mmu05144 Malaria 4 2.39E-04 HBA-A1, CCL12, CCL2, HBB-B1
 mmu05323 Rheumatoid arthritis 3 1.87E-02 CCL12, FOS, CCL2
 mmu05142 Chagas disease (American trypanosomiasis) 3 2.86E-02 CCL12, FOS, CCL2
 mmu04668 TNF signaling pathway 3 3.18E-02 CCL12, FOS, CCL2
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ID: Identification; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; TNF: tumor necrosis factor.

Meanwhile, enrichment analysis was also performed for the down-regulated genes. For the down-regulated genes, 10 GO terms including cellular response to cisplatin (P=3.18E-04), negative regulation of inclusion body assembly (P=6.62E-04) and lipid storage (P=1.03E-02) were enriched (Table 2). Moreover, protein processing in endoplasmic reticulum (P=2.00E-02) was the only pathway enriched for the down-regulated genes (Table 2).

Table 2. The terms enriched for the down-regulated genes.

ID Description Count P Genes
GO
 0072719 Cellular response to cisplatin 3 3.18E-04 TIMELESS, HMOX1, SLC31A1
 0090084 Negative regulation of inclusion body assembly 3 6.62E-04 DNAJB2, DNAJB1, DNAJA4
 0019915 Lipid storage 3 1.03E-02 CRY2, DGAT2, BSCL2
 0043029 T cell homeostasis 3 1.34E-02 SPNS2, RAG1, FAS
 0002246 Wound healing involved in inflammatory response 2 1.70E-02 CD44, HMOX1
 0045766 Positive regulation of angiogenesis 4 3.19E-02 HMOX1, HSPB1, CELA1, ANGPT2
 0042752 Regulation of circadian rhythm 3 3.54E-02 CRY2, TIMELESS, MAPK10
 0006457 Protein folding 4 3.67E-02 HSPA4L, DNAJB1, DNAJB4, DNAJA4
 0008630 Intrinsic apoptotic signaling pathway in response to DNA damage 3 3.80E-02 PGAP2, HMOX1, EPHA2
 0014066 Regulation of phosphatidylinositol 3-kinase signaling 2 4.47E-02 1190002N15RIK, CEACAM1
KEGG
 mmu04141 Protein processing in endoplasmic reticulum 5 2.00E-02 MAP3K5, HSPA4L, DNAJB2, DNAJB1, MAPK10
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ID: Identification; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genome.

Protein-protein Interaction Network Analysis

The PPI network constructed for the DEGs included 88 nodes and 121 interactions (Figure 3).

Figure 3. The PPI network constructed for the DEGs.

Figure 3

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Red circles and green squares separately represent up-regulated and down-regulated genes. A larger size indicates a higher degree of the node.

According to the degrees of nodes, the top 20 nodes with higher degrees were identified, including FBJ osteosarcoma oncogene (FOS, degree=16), early growth response 1 (EGR1, degree=10) and heme oxygenase (decycling) 1 (HMOX1, degree=9) (Table 3).

Table 3. The top 20 nodes with higher degrees in the PPI network.

Gene Description Degree
FOS Up 16
HSPA4l Down 13
EGR1 Up 10
HMOX1 Down 9
HSPB1 Down 9
HSF4 Down 8
SERPINB6B Down 8
CCL2 Up 7
FAS Down 7
MAPK10 Down 6
DNAJB1 Down 6
DNAJA4 Down 6
GCG Down 5
ACTN2 Down 5
ANGPT2 Down 5
MAP3K5 Down 4
CYR61 Up 4
CCL12 Up 4
SGK1 Up 4
AHSA2 Down 4
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Especially, FOS, EGR1 and HMOX1 had interactions with each other in the PPI network. Afterwards, pathway enrichment analysis was performed for the top 20 nodes, the enriched pathways mainly included TNF signaling pathway (P=1.37E-06), Chagas disease (American trypanosomiasis) (P=3.64E-05) and influenza A (P=2.62E-04) (Table 4).

Table 4. The pathways enriched for the top 20 nodes in the PPI network.

ID Description Count P Genes
mmu04668 TNF signaling pathway 6 1.37E-06 CCL12, FOS, MAP3K5, CCL2, FAS, MAPK10
mmu05142 Chagas disease (American trypanosomiasis) 5 3.64E-05 CCL12, FOS, CCL2, FAS, MAPK10
mmu05164 Influenza A 5 2.62E-04 CCL12, CCL2, FAS, DNAJB1, MAPK10
mmu05168 Herpes simplex infection 5 5.53E-04 CCL12, FOS, CCL2, FAS, MAPK10
mmu04010 MAPK signaling pathway 5 1.16E-03 FOS, MAP3K5, HSPB1, FAS, MAPK10
mmu04141 Protein processing in endoplasmic reticulum 4 3.80E-03 MAP3K5, HSPA4L, DNAJB1, MAPK10
mmu04621 NOD-like receptor signaling pathway 3 5.11E-03 CCL12, CCL2, MAPK10
mmu05323 Rheumatoid arthritis 3 1.07E-02 CCL12, FOS, CCL2
mmu05146 Amoebiasis 3 2.10E-02 SERPINB6B, HSPB1, ACTN2
mmu05161 Hepatitis B 3 3.17E-02 FOS, FAS, MAPK10
mmu04932 Non-alcoholic fatty liver disease 3 3.63E-02 MAP3K5, FAS, MAPK10
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ID: Identification.

MicroRNA-gene Regulatory Network Analysis

The constructed miRNA-DEG regulatory network had 243 nodes (including 18 up-regulated genes, 44 down-regulated genes and 181 miRNAs) (Figure 4). Among the nodes involved in the regulatory network, the top 10 genes and miRNAs (including mmu-miR-15a-5p and mmu-miR-26a-5p) with higher degrees were listed in Table 5. In addition, mmu-miR-26a-5p could target EGR1 in the regulatory network.

Figure 4. The microRNA-DEG regulatory network.

Figure 4

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Red circles, green squares and blue triangles represent up-regulated genes, down-regulated genes and miRNAs, respectively.

Table 5. The top 10 genes and microRNAs in the regulatory network.

Gene Description Degree miRNA Degree
ABCC9 Down 26 mmu-miR-324-3p 6
HYAL1 Up 21 mmu-miR-344d-3p 5
BPNT1 Down 21 mmu-miR-410-3p 5
CEACAM1 Down 21 mmu-miR-344e-3p 5
CRY2 Down 20 mmu-miR-30e-5p 5
RASSF5 Down 20 mmu-miR-15a-5p 5
IARS2 Down 15 mmu-miR-26a-5p 4
RUNXLT1 Down 12 mmu-miR-9-5p 4
SPPL2A Up 11 mmu-miR-297a-5p 4
DNAJB1 Down 10 mmu-miR-7b-5p 4
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DISCUSSION

This study was aimed to reveal the genes and miRNAs involved in HSF4 mutation-induced cataract, which provided targets for the further experimental researches and the clinical therapy of this disease. In this study, a total of 176 DEGs were identified in HSF4-null lens, including 51 up-regulated and 125 down-regulated genes. FOS, EGR1 and HMOX1 had relatively higher degrees in the PPI network. Besides, the pathways of TNF signaling pathway, Chagas disease (American trypanosomiasis) and influenza A were enriched for the top 20 nodes in the PPI network. Moreover, mmu-miR-15a-5p and mmu-miR-26a-5p also were among the top 10 miRNAs in the miRNA-DEG regulatory network.

FOS protein was temporarily expressed in equatorial LECs following anterior capsule rubbing, indicating that equatorial LECs are transcriptionally activated after the anterior lens surface experience minor mechanical stimuli[24]. FOS, UBC (E2 ubiquitin-conjugating protein UBC), EGR1 and PTGS2 (prostaglandin-endoperoxide synthase 2) expression levels are mediated by HSF4 mutations in cataract and can serve as therapeutic targets for the disease[25]. The expression of FOS and JUN (jun proto-oncogene) are dysregulated in the terminal differentiation process of lens fiber cells[26]. FOS can interact with JUN to constitute the AP-1 (activating protein 1) transcription factor, and AP-1 may act in mediating the behavior of lens cells during lens wound repair[27]. These declared that FOS might function in the pathogenesis of HSF4 mutation-induced cataract.

EGR1 may function in the embryonic fibrotic phenotype in the β1MLR10 (β1 F/F and homozygous for MLR10-Cre) lens, and may also be involved in the pathogenesis of posterior capsular opacification (PCO) and other lens diseases[28]. The mRNA levels of EGR1 in a mammalian retina exhibiting a biphasic response to adversing ocular growth stimuli, indicating that retinal EGR1 may serve as a signal for directing ocular growth in various species[29]. Nakajima et al[30] demonstrate that increased activity of EGR1 may play a role in selenite-induced LEC death. Ma et al[31] find that HMOX1 protects LECs from oxidant stress induced by hydrogen peroxide (H2O2) through decreasing the generation of reactive oxygen species (ROS) and enhancing the activity of antioxidant enzyme, and therefore restraining caspase family-dependent apoptosis. Therefore, EGR1 and HMOX1 might play roles in formation of HSF4 mutation-induced cataract. FOS, EGR1 and HMOX1 could interactions with each other in the PPI network, indicating that FOS, EGR1 and HMOX1 might also act in HSF4 mutation-induced cataract through interacting with other genes.

Hsa-miR-15a-5p, hsa-miR-15a-3p, and hsa-miR-16-1-5p are overexpressed in the LECs of age-related cataract, and may promote the progression of age-related cataract through inhibiting the expression levels of the anti-apoptotic genes myeloid cell leukemia sequence 1 (MCL1) and B-cell CLL/lymphoma 2 (BCL2)[32]. Previous studies report that miR-26b plays an important role in growth and proliferation of LECs in cataract rat[33]–[34]. Downregulation of miR-26b can postpone the progression of oxidative cataract via mediating cell proliferation, and suppressing LEC apoptosis, nuclear factor kappa B (NF-κB) expression and inflammatory factors[35]. mmu-miR-26a-5p could target EGR1 in the miRNA-DEG regulatory network, suggesting that mmu-miR-26a-5p targeting EGR1 and mmu-miR-15a-5p might also be involved in the procession of HSF4 mutation-induced cataract.

In conclusion, a total of 176 DEGs were identified in HSF4-null lens through a series of bioinformatics analyses. Besides, FOS, EGR1, HMOX1, mmu-miR-26a-5p and mmu-miR-15a-5p might play roles in the pathogenesis of HSF4 mutation-induced cataract. However, these results were obtained from bioinformatics analyses and in-depth experimental researches should be done in the future.

Acknowledgments

Foundation: Supported by the Scientific and Technological Developing Scheme of Jilin Province (No.20150414038GH).

Conflicts of Interest: Tian R, None; Xu Y, None; Dou WW, None; Zhang H, None.

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