With the development of life sciences and informatics, bioinformatics is developing as an interdisciplinary subject. Its main application is the relationship between genes and proteins and their expression. With the help of genomics, proteomics, transcriptomics, and metabolomics, researchers introduce bioinformatics research methods into fundus disease research. A series of gratifying research results have been achieved including the screening of genetic susceptibility genes, the screening of diagnostic markers, and the exploration of pathogenesis. Genomics has the characteristics of high efficiency and accuracy. It has been used to detect new mutation sites in retinoblastoma and retinal pigment degeneration research, which helps to further improve the pathogenesis of retinal genetic diseases. Transcriptomics, proteomics, and metabolomics have high throughput characteristics. They are used to analyze changes in the expression profiles of RNA, proteins, and metabolites in intraocular fluid or isolated cells in disease states, which help to screen biomarkers and further elucidate the pathogenesis. With the advancement of technology, bioinformatics will provide new ideas for the study of ocular fundus diseases.
Objective To study the effects of connective tissue growth factor (CTGF) on retinal Müller cells based on transcriptome analysis of RNA-seq technology.MethodsRetinal Müller cells were divided into the control group and the CTGF treatment group which was continuously cultured with 10 ng/ml of CTGF for 24 h. The influence of CTGF on the migration of Müller cells were tested by scratching experiments. The RNA transcriptome analysis was applied to complete transcriptome sequencing, differentially expressed genes and functional enrichment analysis of the two groups of cells. HiSeq sequencing technology was used to sequence the whole transcriptome of the two groups of cells to obtain biological big data, and analyze the differentially expressed miRNAs on this basis. The functions and signal pathways of differential miRNAs were analyzed through gene annotation (GO) functional significance enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway significant enrichment analysis. Based on transcriptome data, genes with differential expression multiples in the top ten between the two groups were screened out, and the expression of bone morphogenetic protein 4 (BMP4) gene was verified by real time fluorescence quantification PCR (qRT-PCR), immunofluorescence and Western blot.ResultsAfter CTGF stimulation of Müller cells, cell viability and mobility which compared with the control group were significantly increased, with statistically significant differences (t=3.453, P＜0.05). The differential gene expression profile of CTGF induced Müller cells was obtained by RNA transcriptome analysis. Comparing the sequencing results of the two groups, it was found that 325 differentially expressed genes included 152 up-regulated genes and 173 down-regulated genes. The results of GO functional significance enrichment analysis showed that the functions of differential miRNA were mainly divided into three categories: biological processes, cellular components, and molecular functions. These differentially expressed genes were involved in signaling between nervous systems, adhesion between cells, and the interaction between cytokines and their receptors. These differentially expressed genes were involved in different metabolic pathways and biological processes such as tissue inflammation and fibrosis. BMP4 gene was seected for verification through immunofluorescence, qRT-PCR and western blot. The results showed that the expression of BMP4 was significantly higher than that in the control group, and the difference was statistically significant (t=39.490, 10.110, 5.470; P=0.004, 0.001, 0.006).ConclusionCTGF promotes cell proliferation and migration by up-regulating the expression of BMP4 in Müller cells, leading to tissue fibrosis and inducing inflammation.
ObjectiveTo analyze the early changes of gene expression levels and signaling pathways in 661W cell line under hypoxic conditions and to find potential functional target genes.MethodsThe cultured mouse 661W cells were divided into hypoxia treatment group and normoxia control group. Cells in the hypoxia treatment group were cultured in a three-gas incubator with volume fraction of 1% and 5% CO2 at 37 ℃. Cells in the normoxia control group were cultured in an incubator at 37 ℃ with volume fraction of 5% CO2. High-throughput sequencing technology was used to sequence the transcriptome of 661W cell treated with hypoxia and normoxia for 4 hours to screen for differentially expressed genes (DEG). Clustering heat map analysis, gene ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction network (PPI) analysis were performed. The reverse transcription-polymerase chain reaction (RT-PCR) was used to verify the accuracy of the sequencing results.ResultsA total of 506 differentially expressed genes were screened, including 459 up-regulated genes and 47 down-regulated genes. GO functional enrichment analysis showed that the main biological processes of DEG were the cell's response to hypoxia, glycolysis, negative regulation of cell proliferation and apoptosis. hypoxia inducible factor (HIF)-1α pathway, glycolysis, Forkhead box O (FoxO) pathway, Insulin signaling pathway and Adenosine 5'-monophosphate-activated protein kinase (AMPK) pathway were involved in the above process. PPI analysis results showed that hub genes related to hypoxia were Aldoa, Aldoc, Gpi1, Hk2, Hk1, Pfkl, Pfkp, Vhl, Fbxo10 and Fbxo27. The RT-PCR results showed that the relative expression levels of 15 DEG mRNA in the hypoxic treatment group were higher than that of the normoxic control group, and the difference was statistically significant (P＜0.05). The mRNA expression levels of N-myc downstream-regulated gene-1 (Ndrg1), Mt1, and vascular endothelial growth factor A (VEGFA) were time-dependent on hypoxia.ConclusionsUnder hypoxia, DEG is mainly related to glucose metabolism, cell response to hypoxia, regulation of proliferation and apoptosis. HIF-1α pathway, glycolysis, FoxO pathway and AMPK pathway are involved in the early changes of 661W cells under hypoxia. Aldoa, Aldoc, Gpi1, Hk2, Hk1, Pfkl, Pfkp, Vhl, Fbxo10, Fbxo27 may play key roles in the response of 661W cells to hypoxia. Ndrg1, Mt1 and VEGFA could be potential functional target genes for the study of ischemia and hypoxia-related fundus diseases.
ObjectiveTo screening differentially expressed genes (DEGs) in proliferative diabetic retinopathy (DR) patients to provide new biological therapeutic targets for proliferative DR (PDR) therapy. MethodsA basic research. A total of 3 PDR patients (group PDR) and 3 non-diabetic patients (control group) were enrolled in the study in Tianjin Medical University Eye Hospital in October 2020. In addition, 40 cases of PDR and non-diabetic patients were selected and divided into PDR validation group and control validation group. Peripheral blood validation test was performed in PDR validation group and control validation group; RNA sequencing was performed in PDR group and control group. Transcriptomics (RNAseq) sequencing technology was used to screen DEG in PDR group and control group. The selected DEGs were analyzed by gene ontology (GO) function enrichment analysis, signal pathway enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction network (PPI). The gene expression database was used to find the high-throughput data related to PDR, and multi queue comparison analysis was carried out. The target genes of differentially expressed miRNAs were predicted through targetscan platform, so as to clearly screen the correlation between DEG and PDR. Reverse transcription polymerase chain reaction and Western blot were used to verify the expression of DEG mRNA and protein related to PDR. The relative expression of PDR related DEG mRNA and protein between PDR validation group and control validation group were compared by paired t-test. ResultsA total of 1 337 DEGs were screened by RNAseq sequencing in the peripheral blood of patients with PDR, of which 419 genes were up-regulated and 918 down-regulated. Among them, direct inhibitor of apoptosis protein-binding protein with low isoelectric point (DIABLO), zinc finger and BTB domain containing 10 (ZBTB10), polo-like kinases 3 (PLK3), regulatory subunit 1 (PIK3R1) and B cell translocation gene 3 (BTG3) were differentially expressed in PDR patients. The function of GO was enriched from the analysis of molecular function, biological process and cellular composition. The results showed that DIABLO, ZBTB10, PLK3, PIK3R1, BTG3 were involved in the pathological process related to PDR. KEGG enrichment analysis showed that glucose metabolic pathways such as extracellular matrix receptors, cytokine regulatory pathway, p53 signal pathway and galactose metabolism may be involved in the process of differential genes. The analysis of PPI protein interaction network showed that the larger the DEG-associated protein node, the greater the number of associated nodes. Among them, DIABLO, ZBTB10, PLK3, PIK3R1 and BTG3 played significant roles in the formation of the action network. By comparing and analyzing the existing high-throughput data related to diabetic retinopathy in Gene Expression Omnibus database and predicting by Targetscan platform, it was found that some significant differences in miRNA reported in aqueous humor, vitreous fluid and plasma of DR patients can be regulated by the differential genes found in this study. Compared with the control verification group, the relative expressions of DIABLO, ZBTB10, PLK3, PIK3R1 mRNA and protein in peripheral blood of the PDR verification group were up-regulated, and the relative expression of BTG3 mRNA and protein was down-regulated. ConclusionDIABLO, ZBTB10, PLK3, PIK3R1 and BTG3 are DEGs in patients with PDR, and they can participate in the disease process by regulating the biological processes of cell proliferation, fibrosis and oxidative stress.
Uveal melanoma (UM) is an aggressive and lethal tumor in the eye. The complexity and heterogeneity of UM and its microenvironment leads to a lack of strategies for early prevention and treatment of metastases. Single-cell sequencing technologies provide critical insights into deciphering the complexity of intratumor heterogeneity and the microenvironment by enabling genomic, transcriptomic, and epigenetic analysis at the single-cell level. With the help of bioinformatics analysis combined with artificial intelligence algorithms, molecular indicator systems related to prognosis as well as therapeutic targets can be found, which can provide a basis for guiding the selection of clinical treatment plans. However, the single-cell sequencing technology also has certain limitations, such as high sample requirements, expensive and time-consuming sequencing. It is believed that with the improvement of science and technology and the update of analytical methods, these shortcomings can be gradually solved, and this rare tumor will eventually be overcome in the future, and the goal of long-term survival of UM patients will be achieved.
ObjectiveTo analyze the change of differential genes and signaling pathways in high glucose induced BV2 cells, and to explore the mechanism of transgelin-2 (TAGLN2) regulating cellular inflammatory response and metabolic process. MethodsAn experimental study. The cultured BV2 cells were divided into mannitol treatment (Man) group, glucose treatment (Glu) group, overexpression control Glu treatment (Con) group, overexpression TAGLN2 Glu treatment group, silence control Glu treatment (shCon Glu) group, and silence TAGLN2 Glu treatment (shTAGLN2 Glu) group. Cells in the Man group were cultured in modified Eagle high glucose medium (DMEM) containing 25 mmol/L mannitol and 25 mmol/L glucose, cells in other groups (Glu group, Con Glu group, TAGLN2 Glu group, shCon Glu group and shTAGLN2 Glu group) were cultured in DMEM medium containing 50 mmol/L glucose. After 24 hours of cells culture, transcriptome sequencing of cells in each group were performed using high-throughput sequencing technology, and significantly differentially expressed genes (DEG) were screened. |log2 (fold change)|≥1 and P≤0.05 were adopted as criteria to screen for DEG. Gene Ontology (GO) function enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction network analysis were performed. Real-time polymerase chain reaction (RT-PCR) was used to detect the relative expression level of DEG mRNA. The data between groups were compared by independent sample t-test. ResultsWhen compared with Man group, a total of 517 differentially expressed genes were screened in Glu group, which including 277 up-regulated genes and 240 down-regulated genes. KEGG pathway enrichment analysis showed that the up-regulated genes were significantly enriched in immune system processes such as nuclear factor (NF)-κB signal pathway, Jak-signal transducers and activators of transcription (STAT) signal pathway, while down-regulated genes were significantly enriched in glycosaminoglycan degradation and glyceride metabolic pathway. Compared with Con Glu group, a total of 480 DEG were screened in TAGLN2 Glu group, among which 147 up-regulated and 333 down-regulated genes were detected. Up-regulated genes were significantly enriched in the metabolic processes of fatty acid, glyceride and pyruvate, while down-regulated genes were significantly enriched in immune system processes such as NF-κB signal pathway, Jak-STAT signal pathway and tumor necrosis factor (TNF) signal pathway. Compared with shCon Glu group, a total of 582 DEG were screened in shTAGLN2 Glu group, among which 423 up-regulated and 159 down-regulated genes were detected. Up-regulated DEG were significantly enriched in immune system processes such as TNF signal pathway and chemokine signal pathway, while down-regulated DEG were significantly enriched in pattern recognition receptor signal pathway. RT-PCR results showed that the relative expression levels of DEG mRNA Card11 (t=13.530), Icos (t=3.482), Chst3 (t=6.949), Kynu (t=5.399), interleukin (IL)-1β (t=2.960), TNF-α (t=5.800), IL-6(t=3.130), interferon-γ (t=7.690) and IL-17 (t=6.530) in the TAGLN2 Glu treatment group were decreased significantly compared with Con Glu group, and the difference was statistically significant. ConclusionTAGLN2 can inhibit glucose induced microglia inflammation by NF-κB and Jak-STAT signaling pathways, Card11, Icos, Chst3 and Kynu play an important role in the anti-inflammatory process of TAGLN2.