Elucidating TREM2's Role in Proliferative Diabetic Retinopathy: A Transcriptomic Approach

Elucidating TREM2's Role in Proliferative Diabetic Retinopathy: A Transcriptomic Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elucidating TREM2's Role in Proliferative Diabetic Retinopathy: A Transcriptomic Approach Qi Liu, Ya-Ni Wu, Wan-Zhao Yi, Shuo-Shuo Gu, Ling-Xiao Xia, Jian-Ying Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4477575/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Proliferative diabetic retinopathy (PDR) is a leading cause of vision loss in diabetic patients. This study aims to identify novel biomarkers for PDR progression using next-generation sequencing (NGS) transcriptome analysis. Methods We conducted weighted gene co-expression network analysis (WGCNA) on RNA-seq data from 43 post-mortem donor retinas to identify key gene modules associated with diabetic retinopathy (DR) stages. Differential gene expression analysis was performed on transcriptomes from PDR patients and healthy controls. Protein expression levels in retinal tissues from a streptozotocin (STZ)-induced diabetic mouse model were validated using immunofluorescence and Western blot analyses. Results WGCNA identified the "MEyellow" module, comprising 231 genes, as significantly associated with PDR. Intersection analysis with differentially expressed genes revealed 29 key genes common to both datasets. Gene ontology (GO) analysis highlighted the biological significance of these genes, particularly TREM2. Immunofluorescence and Western blot analyses confirmed the upregulation of TREM2 and the microglial marker IBA-1 in retinal tissues from STZ-induced diabetic mice, corroborating its critical role. Conclusions TREM2 is significantly implicated in the pathogenesis of PDR, underscoring its potential as a therapeutic target to mitigate disease progression. proliferative diabetic retinopathy WGCNA TREM2 RNA-seq microglia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Diabetic retinopathy, a common complication of diabetes, is the leading cause of blindness among working-age adults [ 1 , 2 ]. Its pathogenesis involves hyperglycemia, oxidative stress, inflammation, and angiogenesis, resulting in concurrent vascular and neural abnormalities that cause microaneurysms, hemorrhages, hard exudates, capillary loss, retinal vascular leakage, diabetic macular edema (DME), and neovascularization [ 3 , 4 ]. Diabetic retinopathy is classified into two main categories, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR), based on visible clinical features such as microaneurysms, hemorrhages, intraretinal microvascular abnormalities (IRMA), venous caliber changes, and preretinal neovascularization [ 5 ]. DME, an additional categorization in DR, is the primary cause of vision loss in diabetic retinopathy patients and can occur across all severity levels [ 5 ]. Therefore, the discovery of novel biomarkers associated with the occurrence and progression of DR may provide new and better potential strategies for the clinical diagnosis and treatment of DR and DME patients. In this study, we conducted weighted gene co-expression network analysis (WGCNA) on human retinal RNA-seq data obtained from 43 post-mortem donors. By categorizing the data based on the different stages of diabetic retinopathy (DR), we identified a key gene module that exhibits a strong correlation with the PDR stage. Further analysis was performed on an additional transcriptome profile, comparing proliferative fibrovascular membranes and retinal tissues between PDR patients and normal controls. Differentially expressed genes from this analysis were intersected with the previously identified key module genes, resulting in the identification of 29 genes highly correlated with the PDR stage. Subsequent gene ontology (GO) analysis of these 29 genes elucidated their biological significance, notably highlighting the pivotal role of TREM2, which is strongly associated with microglial activation in the retina. This finding was further corroborated by the significant upregulation of TREM2 protein expression in STZ-induced diabetic mouse retinas. These findings underscore the critical role of TREM2 in the pathogenesis of PDR, offering promising insights for the development of targeted therapeutic strategies to mitigate the progression of diabetic retinopathy. Materials and methods Data sources The bulk RNA-seq raw data utilized in this investigation was sourced from the Gene Expression Omnibus (GEO) database, under accession numbers GSE160306 and GSE102485. The GSE160306 dataset comprises post-mortem human retinal samples from 43 donors, stratified into normal controls (n = 12) and various stages of diabetic retinopathy, including diabetics without diagnosis of DR (n = 12), NPDR (n = 11), NPDR with DME (n = 6), and PDR with DME (n = 2). The samples were collected from both macular and peripheral retinal tissues [ 6 ]. The GSE102485 dataset includes retinal samples from three normal controls (n = 3) and patients with PDR, comprising two retinal samples (n = 2) and seven samples of whole proliferative fibrovascular membranes (n = 7), excluding tissues only from the surrounding part, hemal arch, nasal side, and optic disc [ 7 ]. The raw data was processed in R (version 4.2.1) using the FPKM method to calculate gene expression levels [ 8 ]. Detailed clinical profiles of the donors, covering gender, age, diabetes duration, medication history, and disease classification, are provided in Supplementary Table 1. Weighted gene co-expression network analysis (WGCNA) To investigate the relationship between gene co-expression and different stages of diabetic retinopathy, we conducted a weighted gene co-expression network analysis (WGCNA) using the R package WGCNA [ 9 , 10 ]. The FPKM values of the top 5000 genes with the highest median absolute deviation (MAD) were used as input data for the analysis. Briefly, the soft thresholding power was determined using the pickSoftThreshold function. The resulting adjacency matrix was transformed into a topological overlap matrix (TOM) [ 11 ]. Hierarchical clustering was then performed to identify gene modules based on the TOM. To discern the heterogeneity among different samples, principal component analysis (PCA) was employed, aiming to uncover the primary axes of variation within the gene expression dataset [ 12 ]. Module eigengenes, representing the first principal component of each module, were calculated to summarize the gene expression profiles within each module [ 13 ]. Correlations between module eigengenes and traits were examined to determine module-trait associations. Differential gene expression analysis To discern the differentially expressed genes (DEGs) of proliferative diabetic retinopathy (PDR) patients and normal controls, we performed differential gene expression analysis using the raw count data from normal controls and PDR samples available in the GEO dataset GSE102485. The analysis was executed utilizing the DESeq2 package in R (version 4.2.1), which normalizes the gene expression data for library size and RNA composition effects [ 14 , 15 ]. DEGs were identified based on stringent criteria: an absolute log2 fold change (log2FC) greater than 5 and an adjusted p-value (Padj) less than 0.05, ensuring the significance and magnitude of expression differences. Following the identification of DEGs, we visualized the results using a volcano plot, highlighting the genes that met our criteria for significant differential expression. Gene ontology analysis To elucidate the biological context of pivotal genes unearthed in our analysis, we employed the clusterProfiler package for Gene Ontology (GO) analysis. This methodological approach allowed for the systematic classification of the identified genes according to the GO framework, encompassing biological processes (BP), cellular components (CC), and molecular functions (MF) [ 16 , 17 ]. Subsequent to the GO categorization, visualization of the analysis results was achieved through the construction of a cnetplot. This graphical representation was specifically tailored to highlight the top 10 biological processes, thereby pinpointing the most significant BPs alongside the central hub genes within these processes. Animal In compliance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and with approval from the Institutional Animal Care and Use Committee (IACUC) at Jinan University, male C57BL/6J mice aged 6–8 weeks were utilized. Streptozotocin (STZ) was injected intraperitoneally at a dose of 50 mg/kg for five consecutive days following a 6-hour fasting period. One week post-injection, diabetes was confirmed by fasting blood glucose levels exceeding 16.7 mmol/L [ 18 – 20 ]. 40-week-old littermate diabetic mice (n = 7) and their age-matched littermate non-diabetic controls (n = 7) were anesthetized with isoflurane and euthanized by cervical dislocation. Retinal tissues were then harvested for further analysis. Immunofluorescence and western blot analysis of retinal tissues Retinal cryosections were blocked with 5% bovine serum albumin (BSA) and incubated overnight at 4°C with primary antibodies TREM-2 (sc-373828, Santa Cruz Biotechnology, CA, USA) and IBA-1 (#DF6442, Affinity Biosciences, OH, USA). Following primary antibody incubation, sections were treated with Alexa Fluor-labeled secondary antibodies (A-11001, A-11005, Invitrogen, CA, USA) and counterstained with DAPI. Slides were imaged using a fluorescence microscope. For Western blot analysis, retinal tissues were homogenized in RIPA buffer containing protease and phosphatase inhibitors. Protein concentrations were determined using the BCA assay, and 20 µg of protein was separated by SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% BSA and incubated overnight at 4°C with primary antibodies. After washing, membranes were incubated with HRP-conjugated secondary antibodies (#7076, Cell Signaling Technology, MA, USA) and visualized using chemiluminescence with an Amersham Imager 600. Statistical analysis Statistical analysis was conducted using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). Data are expressed as mean ± SEM. Statistical significance was assessed using the unpaired Student's t-test, with a p-value of < 0.05 considered significant. Results Identification of a key gene module associated with PDR in GSE160306 We performed a transcriptional analysis of the macula and retinal periphery using publicly available dataset of post-mortem human retinas. The study included 43 donors with different stages of DR and normal controls, resulting in a total of 76 bulk RNA-seq profiles from the macula and retinal periphery. This included 38 samples from the macula and 38 samples from the retinal periphery. The sample groups were classified based on the severity of DR progression and the sample collection site, including Control Macular (MacuCon, n = 10), Diabetic Macular (MacuDB, n = 10), NPDR Macular (MacuNPDR, n = 9), NPDR with diabetic macular edema (DME) Macular (MacuNPDRdme, n = 6), PDR with DME Macular (MacuPDRdme, n = 3), Control Periphery (PeriCon, n = 10), Diabetic Periphery (PeriDB, n = 10), NPDR Periphery (PeriNPDR, n = 10), NPDR with DME Periphery (PeriNPDRdme, n = 6), and PDR with DME Periphery (PeriPDRdme, n = 2) (Fig. 1 A). We performed WGCNA to identify gene co-expression modules associated with DR in bulk RNA-seq profiles obtained from macular and retinal periphery tissue samples. Prior to the analysis, we assessed the quality of both the samples and gene expression data. Hierarchical clustering based on gene expression profiles was used to assess overall sample similarity, while principal component analysis (PCA) was used to visualize sample distribution in a low-dimensional space (Figs. 1 B and 1 C). The samples from macular and retinal periphery tissue were seen to cluster separately in both the clustering tree and PCA plot, indicating tissue-specific gene expression patterns. These results suggest a certain degree of heterogeneity in the gene expression profiles between macular and retinal periphery tissue samples. To construct a scale-free co-expression network, we determined the soft threshold power as 3 and employed the dynamic tree cut algorithm to cluster genes into seven co-expression modules, each represented by a distinct color (Fig. 2 A). The gene modules contained the following number of genes: blue (594), brown (554), green (101), grey (1012), red (79), turquoise (2429), and yellow (231), as detailed in Supplementary Table 2. We subsequently generated correlation heatmaps to investigate the relationships between these modules and disease statuses in both macular and retinal periphery samples (Figs. 2 B and 2 C). Our analysis unveiled a significant correlation between the "MEyellow" module and the "PDR with DME" trait in both macular (correlation coefficient = 0.46, p-value = 0.003) and retinal periphery (correlation coefficient = 0.74, p-value < 0.001) samples. Remarkably, the "MEyellow" module also displayed a substantial correlation with the "NPDR with DME" trait in macular samples (correlation coefficient = 0.43, p-value = 0.008). Additionally, the "MEyellow" module consistently exhibited an inverse correlation with the "Control" trait, with the lowest correlation coefficients observed in macular samples (correlation coefficient = -0.32, p-value = 0.05) and retinal periphery samples (correlation coefficient = -0.3, p-value = 0.07). Comprising 231 genes, the "MEyellow" module presents a complex network of potential biological interactions and functions crucial for understanding the molecular mechanisms underlying the progression of PDR. Transcriptomic profiling reveals key upregulated genes in PDR Our analysis of transcriptomic changes in PDR utilized another data from the GSE102485 dataset, incorporating retinal samples from individuals diagnosed with PDR as well as normal controls. This comparative study identified a significant set of 531 upregulated genes and 66 downregulated genes in PDR samples, clearly distinguishing them from the normal control group (Fig. 3 A). The "MEyellow" module from the GSE160306 dataset, which encompasses 231 genes, was analyzed for its intersection with the differentially expressed genes from the GSE102485 dataset. A Venn diagram illustrates the convergence between the two datasets, revealing 29 key genes as common factors. These key genes include S100A9, GPNMB, TREM2, APOBEC3C, LYZ, SRGN, CD163, CD74, COL1A2, FCGBP, S100A8, LAPTM5, C1R, TIMP1, ANXA1, IGFBP7, FZD7, C1QA, OSMR, TRBC2, CD93, C1QB, CD14, IER5L, OXTR, SLC11A1, C1QC, IFITM1, and RHOD (Fig. 3 B). Heatmaps were employed to showcase the differential expression of those key genes across diverse samples. Our analysis, leveraging genes from GSE102485, elucidates the contrasting gene expression between normal retinal tissues and those altered by PDR. These genes are presented within the heatmap, accompanied by their respective log2 fold changes and adjusted p-values, affirming their differential expression (Fig. 3 C). Similarly, we observed a significant upregulation of these key genes in the "PDR with DME" samples compared to others from GSE160306 (Fig. 4 ). Notably, in macular samples, both "NPDR with DME" and "PDR with DME" showed substantial upregulation. This alignment with the trends observed in the correlation heatmaps of the "MEyellow" module furnishes additional proof of their relevance in the context of PDR (Figs. 2 B and 2 C). Gene Ontology enrichment analysis identifies key biological processes in PDR The Gene Ontology (GO) enrichment analysis was conducted to elucidate the biological functions and pathways that are significantly related to the 29 key genes identified in PDR. This analysis systematically organized the enriched terms into the three principal GO categories: Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF). The most salient GO terms within these categories were ascertained based on their corrected P-value significance (Padj), and the top ten terms were selected for each category. These terms were then depicted in a bubble plot, sorted by gene ratio, to demonstrate the fraction of these key genes implicated in each biological term, thereby providing a comprehensive view of their functional relevance in PDR. In the category of Biological Processes, terms related to immune response, such as 'adaptive immune response' and 'humoral immune response', were notably enriched, suggesting an active involvement of immune mechanisms in PDR pathogenesis. Additionally, processes related to 'complement activation', 'astrocyte development' and 'B cell mediated immunity' were highlighted, reflecting the complex interplay between inflammation and cellular response in the retinal environment. Within the Cellular Components category, significant enrichment was seen in terms associated with the extracellular matrix, including 'collagen-containing extracellular matrix' and 'secretory granule lumen', indicating alterations in the structural and supportive frameworks of the retina in PDR. The Molecular Functions category revealed a significant representation of terms related to protein binding, including 'peptide binding', 'calcium-dependent protein binding', 'amyloid-beta binding' and 'growth factor binding' implying disruptions in molecular signaling and interactions (Fig. 5 ). Gene network analysis in biological processes associated with PDR Our network analysis delineates the pivotal intersection of the top 10 biological processes mediated by the genes TREM2, C1QA, C1QB, C1QC, and C1R in the context of PDR (Fig. 6 ). This graphical representation maps the nexus where these genes converge on key biological processes, underscoring their collective significance in the disease's progression. TREM2, along with the complement components C1QA, C1QB, C1QC, and the receptor C1R, emerges as a focal point within the network, interfacing with several critical biological processes. Validation of TREM2 protein expression in diabetic mouse model To validate TREM2 protein expression, Western blotting and immunofluorescence analyses were conducted on retinal samples from 40-week-old littermate diabetic mice and age-matched littermate non-diabetic controls. Western blot analysis revealed a significant increase in TREM2 protein levels in the STZ-induced diabetic group (STZ) compared to the control group (CON), indicating an upregulation of TREM2 protein in diabetic retinas (Fig. 7 A). Immunofluorescence staining of retinal cryosections further supported these findings. The intensity of TREM-2 (red) and the microglial marker IBA-1 (green) was markedly increased in the STZ group compared to the CON group. This colocalization suggests a strong association between TREM2 expression and microglial activation in diabetic retinas (Fig. 7 B). Quantitative analysis confirmed significant elevations in the fluorescence intensities of both TREM-2 and IBA-1 in the STZ group, highlighting the role of TREM2 in microglial activation during diabetic retinopathy. These results collectively indicate that TREM2 is upregulated in microglia in the diabetic retina, contributing to the pathophysiology of diabetic retinopathy. Discussion Recent advances in genomic, transcriptomic, and epigenomic data have paved the way for the development of systems-level approaches to quantitatively analyze molecular interactions in specific cells or tissues. Next-generation sequencing (NGS) techniques have rapidly gained widespread applicability in the field of medicine, including genetic diagnosis, disease network analysis, drug discovery, and pharmacogenomics [ 21 ]. RNA sequencing (RNA-seq) has become a prominent technique in transcriptome analysis due to its ability to provide comprehensive and quantitative information on gene expression, as well as identify novel non-coding RNA species such as microRNA and long non-coding RNA [ 22 ]. Hu et al. employed single-cell RNA sequencing to analyze surgically excised fibrovascular membranes (FVMs) from patients with PDR, where they identified a GPNMB + microglial subpopulation with profibrotic and fibrogenic characteristics [ 23 ]. Shan et al. reported that circHIPK3, a circular noncoding RNA, is significantly upregulated in both diabetic retinas and retinal endothelial cells in response to stressors associated with diabetes mellitus. Through acting as a miR-30a-3p sponge, circHIPK3 inhibits miR-30a-3p activity and increases the expression of VEGF-C, FZD4, and WNT2, which results in enhanced endothelial proliferation and vascular dysfunction [ 24 ]. By leveraging NGS-based transcriptome analysis, researchers have been able to identify critical genes and cell types that contribute to disease progression, and have also developed potential biomarkers for early detection and diagnosis. Our study underscored the significance of TREM2 and complement components C1QA, C1QB, C1QC, and the receptor C1R within the gene network, highlighting an immunological component in PDR pathogenesis. The enrichment of biological processes related to 'adaptive immune response' and 'humoral immune response' indicates immune mechanisms' active involvement in PDR, suggesting it is both a microvascular and immunopathological condition [ 25 – 27 ]. Additionally, the association of these genes with 'complement activation' suggests a role for innate immunity, while connections to 'astrocyte development' and 'B cell mediated immunity' illustrate the complex interplay between immune activities and retinal cell function [ 28 ]. Our study demonstrated significant upregulation of TREM2 at the transcriptional level in human retinal samples from PDR patients and fibrovascular membrane (FVM) samples. TREM2 emerged as a central node within the network, interfacing with critical biological processes. Furthermore, we validated the upregulation of retinal TREM2 protein expression in a 40-week-old diabetic mouse model, along with the microglial marker IBA-1. These findings highlight TREM2’s role in microglial activation and its potential contribution to PDR pathogenesis. The Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), a member of the immunoglobulin superfamily and a type I membrane receptor, plays a pivotal role in modulating the immune functions of myeloid cells [ 29 ]. Recent studies have identified TREM2 as crucial in neurodegenerative disorders [ 30 ]. TREM2 deficiency leads to impaired microglial responses to amyloid plaques, underscoring its importance in Alzheimer's disease [ 31 ]. Furthermore, TREM2/DAP12 signaling regulates microglial phagocytic activity and inflammatory responses, influencing the progression of multiple sclerosis and other neurodegenerative conditions [ 32 ]. In retinopathy, TREM2’s role in microglial activation is particularly relevant. Our findings align with previous research showing that TREM2 is upregulated in activated microglia, contributing to inflammatory responses in retinal diseases [ 33 ]. TREM2 signaling facilitates microglial migration to sites of retinal degeneration, inducing galectin-3 expression, observed in both mouse models of retinal degeneration and human AMD subjects [ 34 ]. Research into microglial transcriptional phenotype regulation, particularly through the TREM2-APOE pathway, shows promise in alleviating Alzheimer's-induced retinal vasculopathy. Modulating this pathway could reduce neurodegenerative microglia populations, protect the inner blood-retina barrier, and decrease vascular amyloidosis, offering a novel approach for treating retinal inflammation and vascular damage [ 35 ]. TREM2’s role extends beyond neurodegeneration to the tumor microenvironment, where it regulates macrophage function, emphasizing its significance across various pathological conditions [ 36 ]. Moreover, TREM2 modulates the classical complement pathway by interacting with C1q, the pathway's initial trigger [ 37 , 38 ]. In aging models, TREM2 knockout mice showed lower transcription levels of microglial and oxidative stress markers, suggesting a regulatory role of TREM2 on microglial density and age-related neuronal loss [ 39 ]. iPSC-derived microglia with the TREM2 R47H/+ mutation, associated with increased AD risk, exhibited a proinflammatory gene expression signature and upregulation of complement cascade components, impairing microglial functions like movement and synaptic uptake [ 40 ]. The intricate interplay between innate and adaptive immunity plays a pivotal role in the pathophysiology of PDR. In the setting of neurodegenerative diseases like Alzheimer's, which share common pathological features with PDR, microglia (innate immune cells of brain and retina) exhibit a functional shift in response to disease markers. This shift includes interactions with the adaptive immune system, demonstrating a complex dialogue between innate and adaptive mechanisms that could similarly influence PDR pathogenesis [ 41 , 42 ]. The presence of lipopolysaccharide-binding protein and soluble CD14 in the vitreous fluid of PDR patients underscores the role of innate immune mechanisms in the disease's pathogenesis. These markers are associated with the innate immune response triggered by inflammatory injury characteristic of PDR, providing insights into the inflammatory processes at play [ 26 ]. Neuropilin-1's role in mediating myeloid cell chemoattraction and influencing retinal neuroimmune crosstalk offers a novel understanding of immune cell migration and function in PDR [ 43 ]. Understanding these mechanisms further could pave the way for targeted therapies that address the immune component of PDR's pathology. Our research utilized WGCNA and differential gene expression analysis on bulk RNA-seq data to explore PDR, revealing significant insights despite limitations posed by scarce human PDR retina samples and small sample sizes at different DR stages. The lack of extensive retinal samples restricted our ability to perform detailed transcriptomic analysis across varying stages and cell types of PDR. Future multi-omics and experimental studies are crucial for a deeper understanding of the cellular mechanisms and gene signatures involved in PDR. Conclusions Our study underscores the significant role of TREM2 in the pathogenesis of proliferative diabetic retinopathy. The upregulation of TREM2 in both human retinal samples and a diabetic mouse model highlights its involvement in microglial activation and inflammation. These findings suggest that TREM2, along with complement system components, contributes to the complex immunopathology of PDR. The insights gained from our investigation into the molecular and immune pathways implicated in PDR pave the way for innovative approaches in therapy and diagnostics. Declarations Code availability The analysis scripts are available upon request. Funding This study is supported by the Guangzhou Municipal Science and Technology Project (202206010153) (H.P.), National Natural Science Foundation of China (81970775, 81770897) (H.P.), Natural Science Foundation of Guangdong Province, China (2021A1515010721, 2018A0303130185) (Y.C.), Medical Joint Fund of Jinan University (MF220212) (J.M.) and College Students Innovation and Entrepreneurship Training Program of Jinan University (20230957) (Y.L.). Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval All procedures were conducted in compliance with the Association for Research in Vision and Ophthalmology (ARVO) Statement for the Use of Animals in Ophthalmic and Vision Research. Ethical approval was granted by the Institutional Animal Care and Use Committee (IACUC) at Jinan University (IACUC-20220822-32). Author Contribution QL participated in study design, data acquisition, statistical analysis, and manuscript drafting. YW contributed to data acquisition. WY and LX supervised the study and provided advice on drafting the manuscript. SG, JC, and TL assisted with data collection and analysis. YL and YC provided advice regarding protocol development and assisted with study design. JM and HP read and approved the final manuscript. Data Availability All data are available upon request. References Cheung N, Mitchell P, Wong TY (2010) Diabetic retinopathy. 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Diabetes 71:762-773. https://doi.org/10.2337/db21-0551 Shan K, Liu C, Liu BH, Chen X, Dong R, Liu X, Zhang YY, Liu B, Zhang SJ, Wang JJ, Zhang SH, Wu JH, Zhao C, Yan B (2017) Circular Noncoding RNA HIPK3 Mediates Retinal Vascular Dysfunction in Diabetes Mellitus. Circulation 136:1629-1642. https://doi.org/10.1161/circulationaha.117.029004 Zou C, Han C, Zhao M, Yu J, Bai L, Yao Y, Gao S, Cao H, Zheng Z (2018) Change of ranibizumab-induced human vitreous protein profile in patients with proliferative diabetic retinopathy based on proteomics analysis. Clinical proteomics 15:12. https://doi.org/10.1186/s12014-018-9187-z Hernández C, Ortega F, García-Ramírez M, Villarroel M, Casado J, García-Pascual L, Fernández-Real JM, Simó R (2010) Lipopolysaccharide-binding protein and soluble CD14 in the vitreous fluid of patients with proliferative diabetic retinopathy. Retina (Philadelphia, Pa) 30:345-352. https://doi.org/10.1097/iae.0b013e3181b7738b Zou C, Zhao M, Yu J, Zhu D, Wang Y, She X, Hu Y, Zheng Z (2018) Difference in the Vitreal Protein Profiles of Patients with Proliferative Diabetic Retinopathy with and without Intravitreal Conbercept Injection. Journal of ophthalmology 2018:7397610. https://doi.org/10.1155/2018/7397610 Paisley CE, Kay JN (2021) Seeing stars: Development and function of retinal astrocytes. Developmental biology 478:144-154. https://doi.org/10.1016/j.ydbio.2021.07.007 Peshoff MM, Gupta P, Oberai S, Trivedi R, Katayama H, Chakrapani P, Dang M, Migliozzi S, Gumin J, Kadri DB, Lin JK, Milam NK, Maynard ME, Vaillant BD, Parker-Kerrigan B, Lang FF, Huse JT, Iavarone A, Wang L, Clise-Dwyer K, Bhat KP (2024) Triggering receptor expressed on myeloid cells 2 (TREM2) regulates phagocytosis in glioblastoma. 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Immunity 56:1794-1808.e1798. https://doi.org/10.1016/j.immuni.2023.06.016 Monroe KM, Lewcock JW (2023) Cleaning crew: Soluble TREM2 mops up complement. Immunity 56:1701-1703. https://doi.org/10.1016/j.immuni.2023.07.012 Linnartz-Gerlach B, Bodea LG, Klaus C, Ginolhac A, Halder R, Sinkkonen L, Walter J, Colonna M, Neumann H (2019) TREM2 triggers microglial density and age-related neuronal loss. Glia 67:539-550. https://doi.org/10.1002/glia.23563 Penney J, Ralvenius WT, Loon A, Cerit O, Dileep V, Milo B, Pao PC, Woolf H, Tsai LH (2024) iPSC-derived microglia carrying the TREM2 R47H/+ mutation are proinflammatory and promote synapse loss. Glia 72:452-469. https://doi.org/10.1002/glia.24485 Chen X, Holtzman DM (2022) Emerging roles of innate and adaptive immunity in Alzheimer's disease. Immunity 55:2236-2254. https://doi.org/10.1016/j.immuni.2022.10.016 Mills SA, Jobling AI, Dixon MA, Bui BV, Vessey KA, Phipps JA, Greferath U, Venables G, Wong VHY, Wong CHY, He Z, Hui F, Young JC, Tonc J, Ivanova E, Sagdullaev BT, Fletcher EL (2021) Fractalkine-induced microglial vasoregulation occurs within the retina and is altered early in diabetic retinopathy. Proceedings of the National Academy of Sciences of the United States of America 118. https://doi.org/10.1073/pnas.2112561118 Dejda A, Mawambo G, Cerani A, Miloudi K, Shao Z, Daudelin JF, Boulet S, Oubaha M, Beaudoin F, Akla N, Henriques S, Menard C, Stahl A, Delisle JS, Rezende FA, Labrecque N, Sapieha P (2014) Neuropilin-1 mediates myeloid cell chemoattraction and influences retinal neuroimmune crosstalk. The Journal of clinical investigation 124:4807-4822. https://doi.org/10.1172/jci76492 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4477575","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310663370,"identity":"63481958-343b-4f9e-95b4-1f71d09b5669","order_by":0,"name":"Qi Liu","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Liu","suffix":""},{"id":310663373,"identity":"33111e97-1738-40a1-86b3-0b1adf1315c1","order_by":1,"name":"Ya-Ni Wu","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ya-Ni","middleName":"","lastName":"Wu","suffix":""},{"id":310663375,"identity":"ea75477a-7205-403b-8c81-991c18e7fa40","order_by":2,"name":"Wan-Zhao Yi","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Wan-Zhao","middleName":"","lastName":"Yi","suffix":""},{"id":310663377,"identity":"ae0ab4bc-3a6e-4a4b-9ded-234cad5ec90b","order_by":3,"name":"Shuo-Shuo Gu","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Shuo-Shuo","middleName":"","lastName":"Gu","suffix":""},{"id":310663379,"identity":"8c54ff3b-109f-4c11-8316-483336ce6a37","order_by":4,"name":"Ling-Xiao Xia","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ling-Xiao","middleName":"","lastName":"Xia","suffix":""},{"id":310663381,"identity":"c611ae17-e53c-469f-8a05-3beee81c3fbb","order_by":5,"name":"Jian-Ying Chen","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Jian-Ying","middleName":"","lastName":"Chen","suffix":""},{"id":310663383,"identity":"7badf343-41d9-46ae-88e0-94a3c7468855","order_by":6,"name":"Ting-Ting Liu","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ting-Ting","middleName":"","lastName":"Liu","suffix":""},{"id":310663385,"identity":"4d6ef7ff-bbcb-4a53-a0f8-2fa2ca265e81","order_by":7,"name":"Ying-Hui Lu","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ying-Hui","middleName":"","lastName":"Lu","suffix":""},{"id":310663386,"identity":"93d3c093-f79c-46d8-a006-cdf7b9fc4db3","order_by":8,"name":"Yu-Hong Cui","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Hong","middleName":"","lastName":"Cui","suffix":""},{"id":310663387,"identity":"2acfec61-12a5-4f53-a399-8fffc60eb6de","order_by":9,"name":"Jing Meng","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Meng","suffix":""},{"id":310663388,"identity":"efbf5952-28cf-4f55-ac07-36c0bbba4177","order_by":10,"name":"Hong-Wei Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYJACZgjF2PggoaKGNC3NBg/OHCNJCwOb5MMWZrwqwcCc/ewB5oKKO3Ybjje3VSQ2sDHwt3cn4NVi2ZOXwDzjzLPkDWcOtt1I3CHDIHHm7Aa8WgwO5Bgw87YdTja7kQjUcoaNwUAil4CW82+gWu4/bCtIbGMmQssNiC12ZjcY2xiI0mI5A2gLz5nDCfZnEpslEs4c4yHoF3N+oC08FYftJduPP/z4o6JGjr+9l4DDGBjYfwDpxAaoAA9e5VAtYGBPUOUoGAWjYBSMXAAAcTxLTHAvYx0AAAAASUVORK5CYII=","orcid":"","institution":"Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Hong-Wei","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2024-05-25 16:37:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4477575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4477575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58169926,"identity":"955517e3-dede-46b7-bd4f-36abf9eea279","added_by":"auto","created_at":"2024-06-12 03:35:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4411473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional analysis of macula and retinal periphery in GSE160306: Sample classification, Clustering, and PCA analysis.\u003c/strong\u003e (A) Retinal sample classification based on the severity of DR progression and the sample collection site. (B) Hierarchical clustering of gene expression profiles for assessing overall sample similarity. (C) Principal component analysis (PCA) plot illustrating the distribution of samples in a low-dimensional space, highlighting the heterogeneity between macular and retinal periphery tissue samples. MacuCon: Control Macular, MacuDB: Diabetic Macular, MacuNPDR: NPDR Macular, MacuNPDRdme: NPDR with DME Macular, MacuPDRdme: PDR with DME Macular, PeriCon: Control Periphery, PeriDB: Diabetic Periphery, PeriNPDR: NPDR Periphery, PeriNPDRdme: NPDR with DME Periphery, PeriPDRdme: PDR with DME Periphery.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/1d00a756983c35bc4e2e963f.jpg"},{"id":58169922,"identity":"faaed3b2-9542-4554-8ca4-e4d087ef865e","added_by":"auto","created_at":"2024-06-12 03:35:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4354688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-expression modules and correlation analysis in DR using WGCNA.\u003c/strong\u003e (A) Scale-free co-expression network constructed using a soft threshold power of 3 and dynamic tree cut algorithm to cluster genes into 7 co-expression modules, represented by different colors. The correlation heatmaps of macular samples (B) and retinal periphery samples (C) illustrate the associations between module eigengenes and traits. Each value in the heatmap represents the correlation coefficient and p-value between a specific module eigengene and a trait.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/8dc94144e4ffc691fdba5e69.jpg"},{"id":58169932,"identity":"5933eb4d-b192-43b8-bdb9-3465a9f7ab0f","added_by":"auto","created_at":"2024-06-12 03:35:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4835038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic insights into PDR: Differential expression and gene intersection analysis.\u003c/strong\u003e (A) Volcano plot showing differential gene expression between normal and PDR-affected retinal tissues from the GSE102485 dataset. The plot identifies 531 significantly upregulated genes and 66 significantly downregulated genes in the PDR condition. (B) Venn diagram illustrating the overlap between differentially expressed genes in PDR from the GSE102485 dataset and the 231 genes in the \"MEyellow\" module from the GSE160306 dataset, identifying 29 genes common to both datasets as potential key genes in PDR pathogenesis. (C) Heatmaps displaying the expression levels of the overlapping genes across multiple samples from GSE102485. The genes, listed with their log2 fold changes and adjusted p-values, show distinct expression patterns when comparing normal and PDR retinal tissues, underscoring their relevance to PDR.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/c60838baaba270ffc92e904b.jpg"},{"id":58171159,"identity":"3b02aaef-a21e-44b1-9a1e-f901e7e823e0","added_by":"auto","created_at":"2024-06-12 03:43:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3638646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of the differential expression level of key genes in the \"MEyellow\" module across macular and retinal periphery samples in GSE160306.\u003c/strong\u003e A notable upregulation of these key genes was observed in \"PDR with DME\" samples in the retinal periphery. In macular samples, both \"NPDR with DME\" and \"PDR with DME\" samples exhibited substantial upregulation of these key genes.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/de6f715597ee012d97c510bb.jpg"},{"id":58169928,"identity":"eb641d1c-357a-4436-a64e-c21e24288b3f","added_by":"auto","created_at":"2024-06-12 03:35:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3586114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology (GO) enrichment analysis of key genes in PDR.\u003c/strong\u003e Bubble plot depicting GOenrichment analysis for the 29 PDR-associated key genes across Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF) categories. Enrichment significance is indicated by color intensity based on the adjusted p-value (Padj), and the gene ratio is represented by bubble size, revealing the proportion of genes involved in each GO term.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/3c1f766313619aa554cde64e.jpg"},{"id":58169933,"identity":"03405195-a106-4061-8855-2e431b19a1c3","added_by":"auto","created_at":"2024-06-12 03:35:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2566911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntersecting biological processes in PDR: A gene network analysis. \u003c/strong\u003eNetwork diagram showcasing the interconnection of key genes TREM2, C1QA, C1QB, C1QC, and C1R with the top 10 biological processes implicated in PDR.\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/4a548b40b15dae8a5ce6cc89.jpg"},{"id":58169929,"identity":"c968d28f-5d49-4d44-8f1c-c7fc44cc6550","added_by":"auto","created_at":"2024-06-12 03:35:23","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3368962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased TREM-2 protein levels in STZ-induced diabetic mice. \u003c/strong\u003e(A) Western blot analysis showing TREM-2 protein levels in retinal tissues from control (CON) and streptozotocin (STZ)-induced diabetic mice. The bar graph quantifies relative TREM-2/β-Actin levels (mean ± SEM; n = 3/group; *p \u0026lt; 0.05). (B) Immunofluorescence staining of retinal sections from CON and STZ mice. Sections are stained for IBA-1 (green), TREM-2 (red), and DAPI (blue). Fluorescence intensity quantification shows significant increases in IBA-1 and TREM-2 in the STZ group (mean ± SEM; n = 4/group; *p \u0026lt; 0.05, ***p \u0026lt; 0.001). Scale bar: 100 μm. GCL (ganglion cell layer), INL (inner nuclear layer), ONL (outer nuclear layer).\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/875b5858ab090e6a5a230773.jpg"},{"id":60686299,"identity":"5940d397-f704-479d-99d8-09c38541482e","added_by":"auto","created_at":"2024-07-19 13:54:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27415510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/e69e7ecd-159d-43bc-8bcd-cd70a2ab4566.pdf"},{"id":58169931,"identity":"6fc38726-575b-45b1-99ad-276ee57006de","added_by":"auto","created_at":"2024-06-12 03:35:23","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17715,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/29f42d77c43ddc3a7d3a8c57.xlsx"},{"id":58169927,"identity":"7df861a3-cded-430d-bbec-a441bdba7b64","added_by":"auto","created_at":"2024-06-12 03:35:22","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":162602,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4477575/v1/2a414eed67154b05b55ad654.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating TREM2's Role in Proliferative Diabetic Retinopathy: A Transcriptomic Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic retinopathy, a common complication of diabetes, is the leading cause of blindness among working-age adults [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Its pathogenesis involves hyperglycemia, oxidative stress, inflammation, and angiogenesis, resulting in concurrent vascular and neural abnormalities that cause microaneurysms, hemorrhages, hard exudates, capillary loss, retinal vascular leakage, diabetic macular edema (DME), and neovascularization [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Diabetic retinopathy is classified into two main categories, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR), based on visible clinical features such as microaneurysms, hemorrhages, intraretinal microvascular abnormalities (IRMA), venous caliber changes, and preretinal neovascularization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. DME, an additional categorization in DR, is the primary cause of vision loss in diabetic retinopathy patients and can occur across all severity levels [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, the discovery of novel biomarkers associated with the occurrence and progression of DR may provide new and better potential strategies for the clinical diagnosis and treatment of DR and DME patients.\u003c/p\u003e \u003cp\u003eIn this study, we conducted weighted gene co-expression network analysis (WGCNA) on human retinal RNA-seq data obtained from 43 post-mortem donors. By categorizing the data based on the different stages of diabetic retinopathy (DR), we identified a key gene module that exhibits a strong correlation with the PDR stage. Further analysis was performed on an additional transcriptome profile, comparing proliferative fibrovascular membranes and retinal tissues between PDR patients and normal controls. Differentially expressed genes from this analysis were intersected with the previously identified key module genes, resulting in the identification of 29 genes highly correlated with the PDR stage. Subsequent gene ontology (GO) analysis of these 29 genes elucidated their biological significance, notably highlighting the pivotal role of TREM2, which is strongly associated with microglial activation in the retina. This finding was further corroborated by the significant upregulation of TREM2 protein expression in STZ-induced diabetic mouse retinas. These findings underscore the critical role of TREM2 in the pathogenesis of PDR, offering promising insights for the development of targeted therapeutic strategies to mitigate the progression of diabetic retinopathy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe bulk RNA-seq raw data utilized in this investigation was sourced from the Gene Expression Omnibus (GEO) database, under accession numbers GSE160306 and GSE102485. The GSE160306 dataset comprises post-mortem human retinal samples from 43 donors, stratified into normal controls (n\u0026thinsp;=\u0026thinsp;12) and various stages of diabetic retinopathy, including diabetics without diagnosis of DR (n\u0026thinsp;=\u0026thinsp;12), NPDR (n\u0026thinsp;=\u0026thinsp;11), NPDR with DME (n\u0026thinsp;=\u0026thinsp;6), and PDR with DME (n\u0026thinsp;=\u0026thinsp;2). The samples were collected from both macular and peripheral retinal tissues [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The GSE102485 dataset includes retinal samples from three normal controls (n\u0026thinsp;=\u0026thinsp;3) and patients with PDR, comprising two retinal samples (n\u0026thinsp;=\u0026thinsp;2) and seven samples of whole proliferative fibrovascular membranes (n\u0026thinsp;=\u0026thinsp;7), excluding tissues only from the surrounding part, hemal arch, nasal side, and optic disc [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The raw data was processed in R (version 4.2.1) using the FPKM method to calculate gene expression levels [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Detailed clinical profiles of the donors, covering gender, age, diabetes duration, medication history, and disease classification, are provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eWeighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eTo investigate the relationship between gene co-expression and different stages of diabetic retinopathy, we conducted a weighted gene co-expression network analysis (WGCNA) using the R package WGCNA [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The FPKM values of the top 5000 genes with the highest median absolute deviation (MAD) were used as input data for the analysis. Briefly, the soft thresholding power was determined using the pickSoftThreshold function. The resulting adjacency matrix was transformed into a topological overlap matrix (TOM) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hierarchical clustering was then performed to identify gene modules based on the TOM. To discern the heterogeneity among different samples, principal component analysis (PCA) was employed, aiming to uncover the primary axes of variation within the gene expression dataset [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Module eigengenes, representing the first principal component of each module, were calculated to summarize the gene expression profiles within each module [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Correlations between module eigengenes and traits were examined to determine module-trait associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e \u003cp\u003eTo discern the differentially expressed genes (DEGs) of proliferative diabetic retinopathy (PDR) patients and normal controls, we performed differential gene expression analysis using the raw count data from normal controls and PDR samples available in the GEO dataset GSE102485. The analysis was executed utilizing the DESeq2 package in R (version 4.2.1), which normalizes the gene expression data for library size and RNA composition effects [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. DEGs were identified based on stringent criteria: an absolute log2 fold change (log2FC) greater than 5 and an adjusted p-value (Padj) less than 0.05, ensuring the significance and magnitude of expression differences. Following the identification of DEGs, we visualized the results using a volcano plot, highlighting the genes that met our criteria for significant differential expression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology analysis\u003c/h2\u003e \u003cp\u003eTo elucidate the biological context of pivotal genes unearthed in our analysis, we employed the clusterProfiler package for Gene Ontology (GO) analysis. This methodological approach allowed for the systematic classification of the identified genes according to the GO framework, encompassing biological processes (BP), cellular components (CC), and molecular functions (MF) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Subsequent to the GO categorization, visualization of the analysis results was achieved through the construction of a cnetplot. This graphical representation was specifically tailored to highlight the top 10 biological processes, thereby pinpointing the most significant BPs alongside the central hub genes within these processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnimal\u003c/h2\u003e \u003cp\u003eIn compliance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and with approval from the Institutional Animal Care and Use Committee (IACUC) at Jinan University, male C57BL/6J mice aged 6\u0026ndash;8 weeks were utilized. Streptozotocin (STZ) was injected intraperitoneally at a dose of 50 mg/kg for five consecutive days following a 6-hour fasting period. One week post-injection, diabetes was confirmed by fasting blood glucose levels exceeding 16.7 mmol/L [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 40-week-old littermate diabetic mice (n\u0026thinsp;=\u0026thinsp;7) and their age-matched littermate non-diabetic controls (n\u0026thinsp;=\u0026thinsp;7) were anesthetized with isoflurane and euthanized by cervical dislocation. Retinal tissues were then harvested for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence and western blot analysis of retinal tissues\u003c/h2\u003e \u003cp\u003eRetinal cryosections were blocked with 5% bovine serum albumin (BSA) and incubated overnight at 4\u0026deg;C with primary antibodies TREM-2 (sc-373828, Santa Cruz Biotechnology, CA, USA) and IBA-1 (#DF6442, Affinity Biosciences, OH, USA). Following primary antibody incubation, sections were treated with Alexa Fluor-labeled secondary antibodies (A-11001, A-11005, Invitrogen, CA, USA) and counterstained with DAPI. Slides were imaged using a fluorescence microscope. For Western blot analysis, retinal tissues were homogenized in RIPA buffer containing protease and phosphatase inhibitors. Protein concentrations were determined using the BCA assay, and 20 \u0026micro;g of protein was separated by SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% BSA and incubated overnight at 4\u0026deg;C with primary antibodies. After washing, membranes were incubated with HRP-conjugated secondary antibodies (#7076, Cell Signaling Technology, MA, USA) and visualized using chemiluminescence with an Amersham Imager 600.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). Data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Statistical significance was assessed using the unpaired Student's t-test, with a p-value of \u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of a key gene module associated with PDR in GSE160306\u003c/h2\u003e \u003cp\u003eWe performed a transcriptional analysis of the macula and retinal periphery using publicly available dataset of post-mortem human retinas. The study included 43 donors with different stages of DR and normal controls, resulting in a total of 76 bulk RNA-seq profiles from the macula and retinal periphery. This included 38 samples from the macula and 38 samples from the retinal periphery. The sample groups were classified based on the severity of DR progression and the sample collection site, including Control Macular (MacuCon, n\u0026thinsp;=\u0026thinsp;10), Diabetic Macular (MacuDB, n\u0026thinsp;=\u0026thinsp;10), NPDR Macular (MacuNPDR, n\u0026thinsp;=\u0026thinsp;9), NPDR with diabetic macular edema (DME) Macular (MacuNPDRdme, n\u0026thinsp;=\u0026thinsp;6), PDR with DME Macular (MacuPDRdme, n\u0026thinsp;=\u0026thinsp;3), Control Periphery (PeriCon, n\u0026thinsp;=\u0026thinsp;10), Diabetic Periphery (PeriDB, n\u0026thinsp;=\u0026thinsp;10), NPDR Periphery (PeriNPDR, n\u0026thinsp;=\u0026thinsp;10), NPDR with DME Periphery (PeriNPDRdme, n\u0026thinsp;=\u0026thinsp;6), and PDR with DME Periphery (PeriPDRdme, n\u0026thinsp;=\u0026thinsp;2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe performed WGCNA to identify gene co-expression modules associated with DR in bulk RNA-seq profiles obtained from macular and retinal periphery tissue samples. Prior to the analysis, we assessed the quality of both the samples and gene expression data. Hierarchical clustering based on gene expression profiles was used to assess overall sample similarity, while principal component analysis (PCA) was used to visualize sample distribution in a low-dimensional space (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The samples from macular and retinal periphery tissue were seen to cluster separately in both the clustering tree and PCA plot, indicating tissue-specific gene expression patterns. These results suggest a certain degree of heterogeneity in the gene expression profiles between macular and retinal periphery tissue samples.\u003c/p\u003e \u003cp\u003eTo construct a scale-free co-expression network, we determined the soft threshold power as 3 and employed the dynamic tree cut algorithm to cluster genes into seven co-expression modules, each represented by a distinct color (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The gene modules contained the following number of genes: blue (594), brown (554), green (101), grey (1012), red (79), turquoise (2429), and yellow (231), as detailed in Supplementary Table\u0026nbsp;2. We subsequently generated correlation heatmaps to investigate the relationships between these modules and disease statuses in both macular and retinal periphery samples (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Our analysis unveiled a significant correlation between the \"MEyellow\" module and the \"PDR with DME\" trait in both macular (correlation coefficient\u0026thinsp;=\u0026thinsp;0.46, p-value\u0026thinsp;=\u0026thinsp;0.003) and retinal periphery (correlation coefficient\u0026thinsp;=\u0026thinsp;0.74, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) samples. Remarkably, the \"MEyellow\" module also displayed a substantial correlation with the \"NPDR with DME\" trait in macular samples (correlation coefficient\u0026thinsp;=\u0026thinsp;0.43, p-value\u0026thinsp;=\u0026thinsp;0.008). Additionally, the \"MEyellow\" module consistently exhibited an inverse correlation with the \"Control\" trait, with the lowest correlation coefficients observed in macular samples (correlation coefficient = -0.32, p-value\u0026thinsp;=\u0026thinsp;0.05) and retinal periphery samples (correlation coefficient = -0.3, p-value\u0026thinsp;=\u0026thinsp;0.07). Comprising 231 genes, the \"MEyellow\" module presents a complex network of potential biological interactions and functions crucial for understanding the molecular mechanisms underlying the progression of PDR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic profiling reveals key upregulated genes in PDR\u003c/h2\u003e \u003cp\u003eOur analysis of transcriptomic changes in PDR utilized another data from the GSE102485 dataset, incorporating retinal samples from individuals diagnosed with PDR as well as normal controls. This comparative study identified a significant set of 531 upregulated genes and 66 downregulated genes in PDR samples, clearly distinguishing them from the normal control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The \"MEyellow\" module from the GSE160306 dataset, which encompasses 231 genes, was analyzed for its intersection with the differentially expressed genes from the GSE102485 dataset. A Venn diagram illustrates the convergence between the two datasets, revealing 29 key genes as common factors. These key genes include S100A9, GPNMB, TREM2, APOBEC3C, LYZ, SRGN, CD163, CD74, COL1A2, FCGBP, S100A8, LAPTM5, C1R, TIMP1, ANXA1, IGFBP7, FZD7, C1QA, OSMR, TRBC2, CD93, C1QB, CD14, IER5L, OXTR, SLC11A1, C1QC, IFITM1, and RHOD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeatmaps were employed to showcase the differential expression of those key genes across diverse samples. Our analysis, leveraging genes from GSE102485, elucidates the contrasting gene expression between normal retinal tissues and those altered by PDR. These genes are presented within the heatmap, accompanied by their respective log2 fold changes and adjusted p-values, affirming their differential expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Similarly, we observed a significant upregulation of these key genes in the \"PDR with DME\" samples compared to others from GSE160306 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, in macular samples, both \"NPDR with DME\" and \"PDR with DME\" showed substantial upregulation. This alignment with the trends observed in the correlation heatmaps of the \"MEyellow\" module furnishes additional proof of their relevance in the context of PDR (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGene Ontology enrichment analysis identifies key biological processes in PDR\u003c/h2\u003e \u003cp\u003eThe Gene Ontology (GO) enrichment analysis was conducted to elucidate the biological functions and pathways that are significantly related to the 29 key genes identified in PDR. This analysis systematically organized the enriched terms into the three principal GO categories: Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF). The most salient GO terms within these categories were ascertained based on their corrected P-value significance (Padj), and the top ten terms were selected for each category. These terms were then depicted in a bubble plot, sorted by gene ratio, to demonstrate the fraction of these key genes implicated in each biological term, thereby providing a comprehensive view of their functional relevance in PDR. In the category of Biological Processes, terms related to immune response, such as 'adaptive immune response' and 'humoral immune response', were notably enriched, suggesting an active involvement of immune mechanisms in PDR pathogenesis. Additionally, processes related to 'complement activation', 'astrocyte development' and 'B cell mediated immunity' were highlighted, reflecting the complex interplay between inflammation and cellular response in the retinal environment. Within the Cellular Components category, significant enrichment was seen in terms associated with the extracellular matrix, including 'collagen-containing extracellular matrix' and 'secretory granule lumen', indicating alterations in the structural and supportive frameworks of the retina in PDR. The Molecular Functions category revealed a significant representation of terms related to protein binding, including 'peptide binding', 'calcium-dependent protein binding', 'amyloid-beta binding' and 'growth factor binding' implying disruptions in molecular signaling and interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGene network analysis in biological processes associated with PDR\u003c/h2\u003e \u003cp\u003eOur network analysis delineates the pivotal intersection of the top 10 biological processes mediated by the genes TREM2, C1QA, C1QB, C1QC, and C1R in the context of PDR (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This graphical representation maps the nexus where these genes converge on key biological processes, underscoring their collective significance in the disease's progression. TREM2, along with the complement components C1QA, C1QB, C1QC, and the receptor C1R, emerges as a focal point within the network, interfacing with several critical biological processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation of TREM2 protein expression in diabetic mouse model\u003c/h2\u003e \u003cp\u003eTo validate TREM2 protein expression, Western blotting and immunofluorescence analyses were conducted on retinal samples from 40-week-old littermate diabetic mice and age-matched littermate non-diabetic controls. Western blot analysis revealed a significant increase in TREM2 protein levels in the STZ-induced diabetic group (STZ) compared to the control group (CON), indicating an upregulation of TREM2 protein in diabetic retinas (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Immunofluorescence staining of retinal cryosections further supported these findings. The intensity of TREM-2 (red) and the microglial marker IBA-1 (green) was markedly increased in the STZ group compared to the CON group. This colocalization suggests a strong association between TREM2 expression and microglial activation in diabetic retinas (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Quantitative analysis confirmed significant elevations in the fluorescence intensities of both TREM-2 and IBA-1 in the STZ group, highlighting the role of TREM2 in microglial activation during diabetic retinopathy. These results collectively indicate that TREM2 is upregulated in microglia in the diabetic retina, contributing to the pathophysiology of diabetic retinopathy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecent advances in genomic, transcriptomic, and epigenomic data have paved the way for the development of systems-level approaches to quantitatively analyze molecular interactions in specific cells or tissues. Next-generation sequencing (NGS) techniques have rapidly gained widespread applicability in the field of medicine, including genetic diagnosis, disease network analysis, drug discovery, and pharmacogenomics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. RNA sequencing (RNA-seq) has become a prominent technique in transcriptome analysis due to its ability to provide comprehensive and quantitative information on gene expression, as well as identify novel non-coding RNA species such as microRNA and long non-coding RNA [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Hu et al. employed single-cell RNA sequencing to analyze surgically excised fibrovascular membranes (FVMs) from patients with PDR, where they identified a GPNMB\u0026thinsp;+\u0026thinsp;microglial subpopulation with profibrotic and fibrogenic characteristics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Shan et al. reported that circHIPK3, a circular noncoding RNA, is significantly upregulated in both diabetic retinas and retinal endothelial cells in response to stressors associated with diabetes mellitus. Through acting as a miR-30a-3p sponge, circHIPK3 inhibits miR-30a-3p activity and increases the expression of VEGF-C, FZD4, and WNT2, which results in enhanced endothelial proliferation and vascular dysfunction [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By leveraging NGS-based transcriptome analysis, researchers have been able to identify critical genes and cell types that contribute to disease progression, and have also developed potential biomarkers for early detection and diagnosis.\u003c/p\u003e \u003cp\u003eOur study underscored the significance of TREM2 and complement components C1QA, C1QB, C1QC, and the receptor C1R within the gene network, highlighting an immunological component in PDR pathogenesis. The enrichment of biological processes related to 'adaptive immune response' and 'humoral immune response' indicates immune mechanisms' active involvement in PDR, suggesting it is both a microvascular and immunopathological condition [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, the association of these genes with 'complement activation' suggests a role for innate immunity, while connections to 'astrocyte development' and 'B cell mediated immunity' illustrate the complex interplay between immune activities and retinal cell function [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study demonstrated significant upregulation of TREM2 at the transcriptional level in human retinal samples from PDR patients and fibrovascular membrane (FVM) samples. TREM2 emerged as a central node within the network, interfacing with critical biological processes. Furthermore, we validated the upregulation of retinal TREM2 protein expression in a 40-week-old diabetic mouse model, along with the microglial marker IBA-1. These findings highlight TREM2\u0026rsquo;s role in microglial activation and its potential contribution to PDR pathogenesis.\u003c/p\u003e \u003cp\u003eThe Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), a member of the immunoglobulin superfamily and a type I membrane receptor, plays a pivotal role in modulating the immune functions of myeloid cells [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Recent studies have identified TREM2 as crucial in neurodegenerative disorders [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. TREM2 deficiency leads to impaired microglial responses to amyloid plaques, underscoring its importance in Alzheimer's disease [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, TREM2/DAP12 signaling regulates microglial phagocytic activity and inflammatory responses, influencing the progression of multiple sclerosis and other neurodegenerative conditions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn retinopathy, TREM2\u0026rsquo;s role in microglial activation is particularly relevant. Our findings align with previous research showing that TREM2 is upregulated in activated microglia, contributing to inflammatory responses in retinal diseases [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. TREM2 signaling facilitates microglial migration to sites of retinal degeneration, inducing galectin-3 expression, observed in both mouse models of retinal degeneration and human AMD subjects [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Research into microglial transcriptional phenotype regulation, particularly through the TREM2-APOE pathway, shows promise in alleviating Alzheimer's-induced retinal vasculopathy. Modulating this pathway could reduce neurodegenerative microglia populations, protect the inner blood-retina barrier, and decrease vascular amyloidosis, offering a novel approach for treating retinal inflammation and vascular damage [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTREM2\u0026rsquo;s role extends beyond neurodegeneration to the tumor microenvironment, where it regulates macrophage function, emphasizing its significance across various pathological conditions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, TREM2 modulates the classical complement pathway by interacting with C1q, the pathway's initial trigger [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In aging models, TREM2 knockout mice showed lower transcription levels of microglial and oxidative stress markers, suggesting a regulatory role of TREM2 on microglial density and age-related neuronal loss [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. iPSC-derived microglia with the TREM2 R47H/+ mutation, associated with increased AD risk, exhibited a proinflammatory gene expression signature and upregulation of complement cascade components, impairing microglial functions like movement and synaptic uptake [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe intricate interplay between innate and adaptive immunity plays a pivotal role in the pathophysiology of PDR. In the setting of neurodegenerative diseases like Alzheimer's, which share common pathological features with PDR, microglia (innate immune cells of brain and retina) exhibit a functional shift in response to disease markers. This shift includes interactions with the adaptive immune system, demonstrating a complex dialogue between innate and adaptive mechanisms that could similarly influence PDR pathogenesis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The presence of lipopolysaccharide-binding protein and soluble CD14 in the vitreous fluid of PDR patients underscores the role of innate immune mechanisms in the disease's pathogenesis. These markers are associated with the innate immune response triggered by inflammatory injury characteristic of PDR, providing insights into the inflammatory processes at play [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Neuropilin-1's role in mediating myeloid cell chemoattraction and influencing retinal neuroimmune crosstalk offers a novel understanding of immune cell migration and function in PDR [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Understanding these mechanisms further could pave the way for targeted therapies that address the immune component of PDR's pathology.\u003c/p\u003e \u003cp\u003eOur research utilized WGCNA and differential gene expression analysis on bulk RNA-seq data to explore PDR, revealing significant insights despite limitations posed by scarce human PDR retina samples and small sample sizes at different DR stages. The lack of extensive retinal samples restricted our ability to perform detailed transcriptomic analysis across varying stages and cell types of PDR. Future multi-omics and experimental studies are crucial for a deeper understanding of the cellular mechanisms and gene signatures involved in PDR.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study underscores the significant role of TREM2 in the pathogenesis of proliferative diabetic retinopathy. The upregulation of TREM2 in both human retinal samples and a diabetic mouse model highlights its involvement in microglial activation and inflammation. These findings suggest that TREM2, along with complement system components, contributes to the complex immunopathology of PDR. The insights gained from our investigation into the molecular and immune pathways implicated in PDR pave the way for innovative approaches in therapy and diagnostics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis scripts are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the Guangzhou Municipal Science and Technology Project (202206010153) (H.P.), National Natural Science Foundation of China (81970775, 81770897) (H.P.), Natural Science Foundation of Guangdong Province, China (2021A1515010721, 2018A0303130185) (Y.C.), Medical Joint Fund of Jinan University (MF220212) (J.M.) and College Students Innovation and Entrepreneurship Training Program of Jinan University (20230957) (Y.L.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eAll procedures were conducted in compliance with the Association for Research in Vision and Ophthalmology (ARVO) Statement for the Use of Animals in Ophthalmic and Vision Research. Ethical approval was granted by the Institutional Animal Care and Use Committee (IACUC) at Jinan University (IACUC-20220822-32).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQL participated in study design, data acquisition, statistical analysis, and manuscript drafting. YW contributed to data acquisition. WY and LX supervised the study and provided advice on drafting the manuscript. SG, JC, and TL assisted with data collection and analysis. YL and YC provided advice regarding protocol development and assisted with study design. JM and HP read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCheung N, Mitchell P, Wong TY (2010) Diabetic retinopathy. 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The Journal of clinical investigation 124:4807-4822. https://doi.org/10.1172/jci76492\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"proliferative diabetic retinopathy, WGCNA, TREM2, RNA-seq, microglia","lastPublishedDoi":"10.21203/rs.3.rs-4477575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4477575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eProliferative diabetic retinopathy (PDR) is a leading cause of vision loss in diabetic patients. This study aims to identify novel biomarkers for PDR progression using next-generation sequencing (NGS) transcriptome analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe conducted weighted gene co-expression network analysis (WGCNA) on RNA-seq data from 43 post-mortem donor retinas to identify key gene modules associated with diabetic retinopathy (DR) stages. Differential gene expression analysis was performed on transcriptomes from PDR patients and healthy controls. Protein expression levels in retinal tissues from a streptozotocin (STZ)-induced diabetic mouse model were validated using immunofluorescence and Western blot analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eWGCNA identified the \"MEyellow\" module, comprising 231 genes, as significantly associated with PDR. Intersection analysis with differentially expressed genes revealed 29 key genes common to both datasets. Gene ontology (GO) analysis highlighted the biological significance of these genes, particularly TREM2. Immunofluorescence and Western blot analyses confirmed the upregulation of TREM2 and the microglial marker IBA-1 in retinal tissues from STZ-induced diabetic mice, corroborating its critical role.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e TREM2 is significantly implicated in the pathogenesis of PDR, underscoring its potential as a therapeutic target to mitigate disease progression.\u003c/p\u003e","manuscriptTitle":"Elucidating TREM2's Role in Proliferative Diabetic Retinopathy: A Transcriptomic Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 03:35:17","doi":"10.21203/rs.3.rs-4477575/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9ec7514-41c1-418e-b197-dd2fc9d82b68","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-21T15:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 03:35:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4477575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4477575","identity":"rs-4477575","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}