Vision 2017; 23:395-406
Received 12 February 2017 | Accepted 30 June 2017 | Published 03 July 2017
The first two authors contributed equally to this study.
1Department of Ophthalmology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Ophthalmology, Chongqing Eye Institute, Chongqing, China; 2Cancer Research Institute, Southern Medical University, Guangzhou, China; 3Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Henan Eye Institute, Henan Eye Hospital, Zhengzhou, China
Correspondence to: Bo Lei, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Henan Eye Institute, Henan Eye Hospital, Zhengzhou, China; Phone: 86 37165580903; FAX: 86 37165580903; email: firstname.lastname@example.org
Purpose: Endotoxin-induced uveitis (EIU) is a well-established mouse model for studying human acute inflammatory uveitis. The purpose of this study is to investigate the genome-wide retinal transcriptome profile of EIU.
Methods: The anterior segment of the mice was examined with a slit-lamp, and clinical scores were evaluated simultaneously. The histological changes in the posterior segment of the eyes were evaluated with hematoxylin and eosin (H&E) staining. A high throughput RNA sequencing (RNA-seq) strategy using the Illumina Hiseq 2500 platform was applied to characterize the retinal transcriptome profile from lipopolysaccharide (LPS)-treated and untreated mice. The validation of the differentially expressed genes (DEGs) was analyzed with real-time PCR.
Results: At the 24th hour after challenge, the clinical score of the LPS group was significantly higher (3.83±0.75, mean ± standard deviation [SD]) than that of the control group (0.08±0.20, mean ± SD; p<0.001). The histological evaluation showed a large number of inflammatory cells infiltrated into the vitreous cavity in the LPS group compared with the control group. A total of 478 DEGs were identified with RNA-seq. Among these genes, 406 were upregulated and 72 were downregulated in the LPS group. Gene Ontology (GO) enrichment showed three significantly enriched upregulated terms. Twenty-one upregulated and seven downregulated pathways were remarkably enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Eleven inflammatory response–, complement system–, fibrinolytic system–, and cell stress–related genes were validated to show similar results as the RNA-seq.
Conclusions: We first reported the retinal transcriptome profile of the EIU mouse with RNA-seq. The results indicate that the abnormal changes in the inflammatory response–, complement system–, fibrinolytic system–, and cell stress–related genes occurred concurrently in EIU. These genes may play an important role in the pathogenesis of EIU. This study will lead to a better understanding of the underlying mechanisms and shed light on discovering novel therapeutic targets for ocular inflammation.
Uveitis is a common inflammatory ocular disease that compromises the vision of patients and even causes blindness within the working population worldwide . Several etiologies that could lead to uveitis have been proposed, including trauma, infection, and autoimmune disease . Uveitis can act either as an isolated intraocular inflammation or as a part of systemic autoimmune diseases [3,4].
Currently, the conventional treatment for uveitis includes topical or systemic administration of corticosteroids, non-steroidal anti-inflammatory drugs (NSAIDs), and immunosuppressive agents. Despite these available treatments, some patients do not respond well to these drugs; consequently, the inflammation becomes refractory or chronic. Even if the drugs are highly efficacious to patients, long-term application may bring about numerous adverse effects, including cataract, glaucoma, metabolic disorders, and even liver or kidney failure [5-7]. Therefore, identification of novel and effective therapeutic targets with fewer side effects for the treatment of uveitis is highly desired.
Involving inflammation of the uveal tract, endotoxin-induced uveitis (EIU) is a widely used animal model that shares many clinical and histological characteristics with human acute infectious uveitis. EIU can be induced via a single systemic or local injection of lipopolysaccharide (LPS) . The process of inflammatory response induced by LPS is well understood. LPS stimulation activates Toll-like receptor 4 (TLR4) to initiate the cellular inflammatory pathways, including the transcription factors nuclear factor (NF)-κB and activator protein (AP)-1, and results in the release of cytokines and chemokines and increased expression of adhesion molecules [8-10].
Identification of the abnormally changed genes or molecular pathways may discover potential therapeutic targets, which can be facilitated with high-throughput systems analyses, particularly at the transcriptome levels. The transcriptome is an integrated set of transcripts at a specific stage or under a given physiologic condition . Transcriptome analysis is one of the approaches that aims to identify the genetic variations of a disease and investigate its pathogenesis. Next-generation sequencing (NGS) technology has launched a new era of tremendous potential and applications in genomic and transcriptomic analyses [12,13].
RNA-sequencing with NGS (RNA-seq) provides a comprehensive evaluation and quantification of all subtypes of RNA molecules expressed in a cell or tissue . RNA-seq technology has many advantages compared with earlier technologies. First, it is highly reproducible and has a much greater dynamic range than the microarray analysis. Second, RNA-seq can detect transcripts expressed at low levels . Within the constraints of the coverage depth, RNA-seq can measure the level of any transcripts present in the library and permit the identification of unannotated transcripts, different splice variants, and non-coding RNAs [14,16-19]. Therefore, RNA-seq is expected to reveal a better representation of the transcriptome. Moreover, with a steady reduction in the costs of NGS, RNA-seq is now emerging as a method of choice for comprehensive transcriptome profiling.
Accumulating evidence indicates that genome-wide characterization of the retinal transcriptome is essential to understanding the cell development, physiology, as well as disease of the eye [20-22]. Thus, RNA-seq technology may have the potential to reveal comprehensive and detailed information about the transcriptome changes during a disease. In the present study, we used NGS-based RNA-seq analysis to investigate the retinal transcriptome profile of EIU and control mice. We identified a series of differentially expressed genes. This study illustrates that RNA-seq offers a more complete, more accurate, and faster approach for comparative and comprehensive analysis of retinal transcriptomes in EIU and may open up new avenues for discovering new therapeutic targets for ocular inflammation.
The study was performed according to the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. The protocols were approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. All surgeries were performed under anesthesia, and all efforts were made to minimize animal discomfort and stress.
Female BALB/c mice at the age of 6 to 8 weeks were purchased from Jackson Laboratories (Bar Harbor, ME) and were housed under specific pathogen-free conditions at the Animal Care Service of Chongqing Medical University with a 12 h:12 h light-dark cycle. The mice were randomly divided into two groups: the LPS group and the control group. For the LPs group, to induce EIU, each mouse eye received a single intravitreal injection of 125 ng LPS prepared in 1 μl sterile PBS (140 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4, PH 7.4) as previously described . For the control group, each mouse eye was given an injection of 1 μl sterile PBS.
Inflammation in the anterior chamber was evaluated with a slit-lamp (Shangbang, Chongqing, China) observation at the 24th hour after LPS administration. The mice were euthanized by receiving an intraperitoneal injection with the mixture of 75 mg/kg (ketamine) and 13.6 mg/kg (xylazine). The clinical severity of ocular inflammation was graded by two independent observers according to criteria described previously . The clinical severity of ocular inflammation was graded according to the severity of iris hyperemia, exudate in anterior chamber, hypopyon and synechia by two independent observers according to the criteria described previously.
To evaluate the histological changes in the posterior segment, the eyes of the mice were then enucleated at the 24th hour after LPS injection and fixed in 4% paraformaldehyde (PFA) at 4 °C overnight. Eyeballs were embedded in paraffin. Serial 4 μm sections were cut through the cornea-optic nerve axis and stained with hematoxylin and eosin (H&E). Then the infiltrating cells in the posterior chamber and the retina were examined under a light microscope.
The RNA-sequencing were performed by Novogene (Beijing, China). The library was prepared for sequencing using standard Illumina protocols. Total RNAs were isolated from the retinas of the LPS group and the control group using TRIzol reagent (Invitrogen, Carlsbad, CA) and treated with RNase-free DNase I (New England Biolabs, Ipswich, MA), to remove any contaminating genomic DNA. A total amount of 3 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (New England Biolabs) following the manufacturer’s instruction. mRNA extraction was performed using Dynabeads oligo (dT; Invitrogen Dynal). Double-stranded cDNAs were synthesized using Superscript II reverse transcriptase (Invitrogen) and random hexamer primers. The cDNAs were then fragmented with nebulization, and the standard Illumina protocol was followed thereafter to create the RNA-seq library. The library quality was assessed with an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). The cDNA libraries were sequenced by the Illumina Hiseq 2500 device. For the data analysis, base calls were performed using CASAVA. Reads were aligned to the genome using the split read aligner TopHat (v2.0.12) with the mismatches set at 2 and with the default parameters.
Differentially expressed gene (DEG) analysis of the LPS group and the control group (each group has three biologic replicates per condition) was performed using the DESeq R package (1.10.1). DESeq provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The p values were adjusted using the Benjamini and Hochberg approach for controlling the false discovery rate (FDR). Genes with an adjusted p value of less than 0.05 found by DESeq were assigned as differentially expressed.
Gene Ontology (GO) enrichment analysis of the DEGs was performed by the GOseq R package, in which gene length bias was corrected. GO terms with a corrected p value of less than 0.05 were considered significantly enriched by differential expressed genes. Pathway enrichment was determined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. KEGG is a database resource for understanding high-level functions and utilities of the biologic system, such as the cell, the organism, and the ecosystem, from molecular-level information, especially large-scale molecular data sets generated by genome sequencing and other high-through put experimental technologies (KEGG). We used KOBAS software (v 2.0) to test the statistical enrichment of differential expression genes in the KEGG pathways. Pathways were considered significantly enriched with an adjusted p value of less than 0.05.
Mice were euthanized at the 24th hour after LPS injection, and the retinas were freshly microdissected under a dissecting microscope to separate the neuroretina and the RPE-choroid complex. Total RNA of the neuroretina was then extracted using TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. cDNA was synthesized by using the PrimeScript RT reagent kit (Takara Biotechnology, Dalian, China). Real-time PCR analysis of IRF8 was performed in a volume of 20 μl using SYBR Premix Ex TaqTM II (Takara Biotechnology) with the ABI Prism 7500 system (Applied Biosystems, Foster City, CA). The conditions were 95 °C for 10 min, followed by 40 cycles of 15 s at 95 °C and 60 s at 60 °C. Each reaction was run in duplicate. Data were normalized to the expression of GAPDH. Relative quantification was achieved with the comparative 2-ΔΔCt method as described previously . The sequences of the PCR primer pairs used in this study are shown in Table 1.
Data are shown as mean±standard error of the mean (SEM). Real-time PCR data were analyzed with GraphPad Prism 5 software (GraphPad Software, Inc., San Diego, CA). The statistical significance of differences was determined with the independent-sample t test. A p value of less than 0.05 was considered statistically significant.
Inflammation in the anterior chamber was assessed with a slit-lamp at 24 hours after LPS injection. The clinical scores were evaluated based on the criteria described previously  simultaneously with the slit-lamp. There was significant iris hyperemia and exudation into the anterior chamber, and hypopyon was observed in the LPS group. No inflammation was observed in the control group at the 24th hour. Evaluation of the clinical score showed that at the 24th hour, the clinical score for the mice in the LPS group was statistically significantly higher (3.83±0.75, mean ± SD) than that for the control group (0.08±0.20, mean ± SD; ***p<0.001; Figure 1).
To evaluate the histological changes, eyes were collected at the 24th hour after LPS injection. Histological examination showed that no inflammatory cells had infiltrated into the ciliary body or the vitreous cavity in the control group (Figure 2A,C). However, severe intraocular inflammation was observed in the LPS group as evidenced by a large number of inflammatory cells infiltrated into the vitreous cavity and the ciliary body (Figure 2B,D).
To examine the global gene expression in the LPS-treated tissue and the control retinal tissue, mRNA was extracted from three individual biologic replicates, and RNA-seq was performed. To compare the difference in the global gene expression profiles of the different samples, average linkage hierarchical cluster analysis was performed (Figure 3). The LPS-treated retina samples clearly separated from the control retina samples, suggesting that there is a distinct difference in gene expression between control and LPS-treated retinas.
To investigate the changes in gene expression after LPS administration, computational analysis of the DEGs between the LPS group and the control group was performed. Genes were considered to be differentially expressed with an adjusted p value of less than 0.05. A total of 478 genes exhibited altered expression in the LPS group versus the control group. Among these DEGs, 406 (85%) genes were upregulated in the LPS group (Figure 4, red dots; Appendix 1), and 72 genes (15%) were downregulated (Figure 4, green dots; Appendix 2).
To investigate the GO, including the biological process, molecular function, and cellular location of the differentially expressed genes, GO enrichment analysis was applied to analyze all the DEGs in the LPS group compared with the control group. The top 30 most enriched upregulated and the top 30 most enriched downregulated GO terms are shown in Figure 5. The adjusted p values of the three upregulated GO terms were less than 0.05; thus, these GO terms were considered significantly enriched GO terms, including antigen processing and presentation, immune response, and immune system process (Table 2). These significantly upregulated GO terms were heavily associated with immune-related GO terms, indicating there was an increase in the immune response in the LPS-treated retinas. No significantly downregulated GO terms were enriched (Figure 5).
To identify the pathways involved in EIU, KEGG pathway enrichment analysis were applied. The top 20 upregulated pathways and the top 20 downregulated pathways are shown in Figure 6. A total of 21 significantly enriched upregulated pathways were found, including phagosome, viral myocarditis, staphylococcus aureus infection, herpes simplex infection, HTLV-I infection, allograft rejection, graft-versus-host disease, type I diabetes mellitus, viral carcinogenesis, autoimmune thyroid disease, complement and coagulation cascades, antigen processing and presentation, Epstein-Barr virus infection, cell adhesion molecules (CAMs), measles, osteoclast differentiation, Chagas disease (American trypanosomiasis), legionellosis, pertussis, the p53 signaling pathway, and the tumor necrosis factor (TNF) signaling pathway. Six significantly downregulated pathways were enriched, including platelet activation, nitrogen metabolism, gastric acid secretion, bacterial invasion of epithelial cells, protein digestion and absorption, and glyoxylate and dicarboxylate metabolism (Table 3).
To validate the DEGs from the RNA-seq findings, we prepared new mouse retinas in each group and performed real-time PCR. A total of 11 genes (log2 fold change >2 and adjusted p<0.05) from the significantly enriched GO terms and KEGG pathways were chosen to be validated. The chosen genes are shown in Table 4. The chosen genes that met the criteria were all upregulated in the LPS-treated group according to the RNA-seq results. The validated genes were chosen from the significantly enriched GO terms, including immune response and immune system process. These genes were also involved in 21 significantly upregulated enriched KEGG pathways, including phagosome, viral myocarditis, staphylococcus aureus infection, herpes simplex infection, HTLV-I infection, allograft rejection, graft-versus-host disease, type I diabetes mellitus, viral carcinogenesis, autoimmune thyroid disease, complement and coagulation cascades, antigen processing and presentation, Epstein-Barr virus infection, cell adhesion molecules (CAMs), measles, osteoclast differentiation, Chagas disease (American trypanosomiasis), legionellosis, pertussis, the p53 signaling pathway, and the TNF signaling pathway. No DEGs met the criteria of a log2 fold-change greater than 2 and an adjusted p value of less than 0.05 in the significantly downregulated pathways. The 11 chosen genes can be divided into four groups: inflammatory response–related genes (Ccl5, JAK3, and Sell), complement system–related genes (C1ra and C3), fibrinolytic system–related genes (F13a1 and A2m), and cell stress–related genes (Cdkn1a, Gadd45b, Egr1, and Atf3). We found that the results obtained with real-time PCR were in consistent with those obtained with RNA-seq. All the validated genes were significantly upregulated in the LPS group compared with the control group (*p<0.05, **p<0.01,***p<0.001; Figure 7).
In the present study, we identified a series of differentially expressed genes occurring in the retinal transcriptome of LPS-induced uveitis mice. We also attempted to identify relevant molecular pathways, with the goal of discovering potential therapeutic targets of uveitis. The results indicated that in addition to the inflammatory response, abnormal changes in complement system–, fibrinolytic system–, and cell stress–related genes may be involved in the pathogenesis of EIU. Treatment targeting these genes may assist in discovering novel therapeutic targets and finding candidate drugs.
The top five genes with the greatest fold changes include Ccl5, A2m, Jak3, Egr1, and Atf3. Ccl5 is a key inflammatory mediator of uveitis in animal models  and patients . This mediator attracts mononuclear cells to the inflammatory site , allowing escalation of the inflammatory response in the ocular vasculature . α2-macroglobulin (A2m) is a specific cytokine carrier that binds inflammatory cytokines implicated in LPS-induced inflammation  and has great potential to regulate cytokine homeostasis . Janus kinase 3 (Jak3) plays an important role in the activation of cytokines during immune and inflammation responses . Early growth response 1 (Egr1) is an important transcription factor for regulating the genes involved in inflammation, immunity, and coagulation . Activating transcription factor 3 (Atf3) is a counter-regulatory immune transcription factor induced by Toll-like receptor (TLR) signaling . Existing evidence showed these genes would be increased in response to LPS stimuli [24,33-36]. In parallel with previous studies, the present RNA-seq results showed the mRNA expression of these genes was significantly increased in response to LPS.
In general, the GO and KEGG pathway enrichment analysis of the RNA-seq data revealed that four types of genes were significantly upregulated, including the inflammatory response–related genes (Ccl5, JAK3, and Sell), complement system–related genes (C1ra and C3), fibrinolytic system–related genes (F13a1 and A2m), and cell stress–related genes (Cdkn1a, Gadd45b, Egr1, and Atf3). Consistent with the well-accepted notion, we showed a remarkable increase in the inflammatory response–related genes: (Ccl5, JAK3, and Sell). Notably, we also found some complement system– and fibrinolytic system–related genes were increased in response to LPS, indicating that not only inflammatory response occurs during uveitis, but also the complement and fibrinolytic system may be involved in the pathogenesis of EIU. In addition, previous studies showed that a drug targeted to the fibrinolytic system plays a role in the regulation of coagulation and inflammation and may also regulate the complement systems in infectious gastroduodenal disease and sepsis-induced liver injury in mice [37,38], indicating that the inflammation, complement, and fibrinolytic systems may be closely related to and affect each other in the infectious inflammatory responses.
The present data suggested that abnormal changes in the inflammatory response–, complement system–, and fibrinolytic system–related genes happened concurrently during EIU and may be associated with the pathogenesis of LPS-induced uveitis. Additionally, candidate drugs that target the genes of these systems may provide better therapeutic strategies for treating patients who do not respond well to conventional drugs. However, the functional evidence of these DEGs needs to be proved in future work.
This study has several limitations. First, we analyzed transcriptional changes in the retinas at the 24th hour after LPS injection. The retinal RNA profile presented at this time point could likely not only be reflective of retina cells but also show what is occurring within the infiltrated cells. Samples collected at a single time point may not be enough to reveal the transcriptional changes involved in the development of EIU. Thus, dynamic transcriptome studies of EIU at different time points would provide more comprehensive knowledge. Moreover, the marked influx of blood cells within the posterior segment would also be included in this transcriptome profile. The transcripts derived from these cells cannot be excluded in this study. Therefore, further studies of retina samples treated with perfusion should exclude the influence of these infiltrated cells.
Taken together, the retinal transcriptomic analysis, which relied on RNA-seq, was an effective and comprehensive method for acquiring a global view of gene expression changes in the retina after the exposure to LPS. This study may provide new insights into the molecular mechanisms underlying LPS-induced ocular inflammation and may provide useful information toward discovering novel therapeutic targets for ocular inflammatory diseases.
Appendix 1. The up-regulated DEGs in the LPS group compared with the control group.
Appendix 2. The down-regulated DEGs in the LPS group compared with the control group.
We are grateful for financial support from the National Natural Science Foundation of China grants (81,271,033, 81,470,621), Chongqing Science and Technology Commission (2014pt-sy10002) and National Key Clinical Specialties Construction Program of China. The authors alone are responsible for the content and writing of this article. Author contributions: Y. G. Q. and B. L. conceived the ideas and designed the experiments; Y. G. Q., P. Y., R. L., and X. Y. F. performed all the experiments; Y. G. Q. and P. Y. analyzed all the data; Y. G. Q. wrote the manuscript; B. L. and B. T. H. reviewed and revised the manuscript. All authors read and approved the final version of the manuscript.