Combined proteomics and metabolomics analyses revealed molecular signatures associated with proliferative diabetic retinopathy

Combined proteomics and metabolomics analyses revealed molecular signatures associated with proliferative diabetic retinopathy | 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 Article Combined proteomics and metabolomics analyses revealed molecular signatures associated with proliferative diabetic retinopathy Yixin Cui, Lingna Rao, Dongli Li, Lu Shen, Qirui Li, Liang Peng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7345162/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Purpose Diabetic retinopathy (DR) is a microvascular complication of diabetes with its exact underlying mechanisms have not been fully elucidated. This study aimed to investigate the effects of key proteins and metabolites on the development of DR. Methods Undiluted vitreous fluid samples were collected from eight patients with proliferative diabetic retinopathy (PDR) and six non-diabetic idiopathic macular hole (iMH) controls. Integration of TMT-tagged quantitative proteomics and untargeted metabolomics analyses was combined with bioinformatics approaches (PCA, differential expression, PPI network, OPLS-DA, pathway enrichment). Key results were validated by ELISA and immunohistochemistry. Results Seven key proteins with six key metabolites were identified to be significantly dysregulated in the PDR. In the vitreous body and retinal nerve fiber layer of the DR group, CD5L expression was upregulated, while CLU was downregulated with SERPINF1 (PEDF). These molecules were co-enriched in pathways such as the “complement and coagulation cascade” and “prion disease,” suggesting a common mechanism of abnormal vascular permeability, inflammatory response, and microthrombosis. Disturbances in creatine metabolism suggested AMPK-related energy dysregulation, and the interaction between CD5L and microglia emphasized its neuroinflammatory regulatory function. Conclusions This study revealing biomarkers and therapeutic targets, which provide new ideas for diagnosis and precise intervention. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research diabetic retinopathy proteomics metabolomics multi-omics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Diabetic retinopathy (DR), a major microvascular complication of diabetes, is the leading cause of preventable blindness in working-age adults globally 1 . With the continuous rise in the global prevalence of diabetes, the disease burden of DR is showing unprecedented growth. According to the latest statistics from the International Diabetes Federation (IDF), there are approximately 589 million diabetic patients worldwide, and this number is projected to surge to 853 million by 2050. [Available from: https://idf.org ]. This epidemiological trend directly leads to a sharp increase in the number of DR patients, with the latest model projections showing that the global number of DR patients will reach 160 million by 2045, of which approximately 10% will progress to vision-threatening proliferative diabetic retinopathy (PDR) or diabetic macular edema (DME) 2 . In terms of pathophysiological mechanisms, the development and progression of DR involve complex multifactorial interactions. Chronic hyperglycemia leads to retinal microvascular endothelial cell damage, pericyte loss, and blood-retinal barrier disruption through mechanisms such as polyol pathway activation, accumulation of advanced glycation end products (AGEs), protein kinase C (PKC) pathway activation, and oxidative stress responses 3 .Recent studies have further revealed that neuroinflammatory responses and neurovascular unit dysfunction play critical roles in the early stages of DR, manifested by microglial activation, reactive changes in glial cells, and downregulation of neuroprotective factors (e.g. Pigment Epithelium-Derived Factor (PEDF)) 4 .These pathological changes collectively contribute to the characteristic formation of microaneurysms, retinal ischemia-hypoxia, and ultimately pathological neovascularization in DR. The current clinical treatment strategies mainly include intravitreal injection of anti-vascular endothelial growth factor (VEGF), intravitreal injection of dexamethasone implant, retinal laser photocoagulation, and vitrectomy. 5 , 6 . Although these treatments can delay disease progression to some extent, there are still many limitations: some patients respond poorly to anti-VEGF or dexamethasone implant; laser treatment may lead to peripheral visual field defects and nyctalopia; and surgical intervention is only applicable to end-stage cases and cannot reverse existing retinal damage 7 . More notably, existing treatment modalities primarily target advanced vascular pathologies, while lacking effective interventions for early neurodegenerative changes and metabolic abnormalities, which highlights the urgent need to explore novel mechanisms of DR pathogenesis and develop early intervention strategies. Recent advances in multi-omics technologies have revolutionized the study of disease mechanisms, particularly in deciphering complex pathological processes such as DR Metabolomics can uncover metabolic alterations in DR by reflecting perturbations in functional metabolic networks 8 , whereas proteomics can identify differentially expressed proteins (DEPs) in DR, providing direct evidence of disease-related pathways 9 . Compared with single-omics approaches, the integration of proteomic and metabolomic data enables the reconstruction of protein–metabolite interaction networks and the identification of key cross-omics driving pathways and regulatory nodes, thereby laying the groundwork for precision medicine 10 , 11 . In this study, we performed a combined proteomic and metabolomic analysis of vitreous samples from DR patients to investigate the potential roles and mechanisms of proteins and metabolites in the pathogenesis of DR. By providing a multidimensional perspective on the pathophysiological changes in DR, our research aims to offer new insights into the molecular mechanisms underlying DR and to establish a theoretical basis for the development of targeted interventional strategies. 2. Results 2.1 81 DEPs were identified in DR, involving CLU, SERPINF1, CD5L. The Principal Component Analysis (PCA) revealed a clear distinction between the control and DR group samples, with a pronounced clustering of samples within each group (Fig. 1 a). This indicated an essentially uniform expression pattern within the groups, reflecting statistically reproducible samples. Furthermore, a total of 81 DEPs were identified between the DR and control groups, with 41 up-regulated and 40 down-regulated in the DR group (Fig. 1 b-c). Subsequently, enrichment analyses were conducted to provide preliminary insights into the signaling pathways implicated by the DEPs. Functional enrichment analysis with ClueGO revealed that the DEPs were predominantly enriched in pathways related to the “positive regulation of substrate adhesion-dependent cell spreading”, “complement activation”, and “immunoglobulin complex, circulating” (Fig. 1 d). Additionally, the DEPs were significantly enriched in 332 Gene Ontology (GO) entries with 235 biological processes (BPs), 42 cellular components (CCs) and 55 molecular functions (MFs). Among the identified BPs, those associated with candidate genes exhibited a prominent role in “humoral immune response” and “activation of immune response” ( Table S1 ). Meanwhile, the CCs were primarily concentrated within “blood microparticle” and “collagen-containing extracellular matrix”, while the MFs were mainly enriched in “glycosaminoglycan binding” and “enzyme inhibitor activity” (Fig. 1 e). The results indicated that DEPs likely played a significant role in immune response mechanisms and the makeup of the extracellular matrix. Moreover, a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the candidate genes revealed the enrichment of 5 pathways, including “complement and coagulation cascades”, “lysosome”, “pertussis”, “staphylococcus aureus infection” and “Cholesterol metabolism”. (Fig. 1 f). 2.2 Top 10 DEPs revealed distinct subcellular distributions. 52 proteins out of 81 DEPs were found to interact in PPI network interactions, such as CLU, FGG and HP (Fig. 2 a). The subsequent analysis identified 25 DEPs with betweenness centrality exceeding 10, which were then selected as candidate proteins for further investigation. Among the candidate proteins, the top 10 with the highest betweenness centrality included CLU, CD5L, HP, C5, C4BPA, JCHAIN, PRSS1, PRNP, SERPINF1, and APLP2. Further, subcellular localization analyses were conducted to gain insight into the distribution of the aforementioned 10 candidate proteins within the cells. CLU was ubiquitously distributed within the cell, encompassing locations such as the nucleus, cytoplasm, and microsome. CD5L was observed to localize to both the secreted and cytoplasm. Meanwhile, HP, C5, C4BPA, and JCHAIN were all positioned within the secreted compartment. PRSS1 was distributed in the secreted and extracellular space, while PRNP was located on the cell membrane, lipid-anchor, GPI-anchor, and Golgi apparatus. SERPINF1 was found distributed in both the secreted and melanosome compartments, and APLP2 was distributed across the cell membrane and within the nucleus (Fig. 2 ). 2.3 26 differently expressed metabolites (DEMs) were enriched, with most belonging to fatty acyls and carboxylic acids and derivatives. In the Orthogonal partial least squares-discriminant analysis (OPLS-DA), both the positive and negative ion modes, a distinct differentiation in the relative contents of metabolites was observed between the DR and control groups (Fig. 3 a-b), indicating a fundamental similarity in the expression patterns within each group. Afterwards, the DEMs were identified between the DR and control groups. In the positive ion mode, a total of 9 DEMs1 were identified between the DR and control groups, all of which exhibited differential down-regulation in the DR group (Fig. 3 c). Conversely, in the negative ion mode, a total of 17 DEMs2 were discerned, with only 4 of these metabolites displaying up-regulation in the DR group, relative to the control (Fig. 3 d). Subsequently, a total of 26 DEMs were achieved through the amalgamation of the DEMs1 and DEMs2. Further analysis was conducted on the classification and annotation of DEMs. In the HMDB annotation process, all 26 DEGs were successfully annotated, with the majority (27%) being classified as fatty acyls. The second largest category was that of carboxylic acids and derivatives, which constituted 23% of the total (Fig. 3 e). Furthermore, a total of 10 DEMs were annotated and categorized into five distinct categories in the KEGG annotations: carbohydrates, lipids, organic acids, peptides, as well as vitamins and cofactors (Fig. 3 f). Of particular note was the lipids category, which encompassed 4 distinct lipid metabolites: fatty acids, prenol lipids, fatty acyls, and polyketides. Notably, the strongest correlation was observed between hexadec-7-enoic acid and 9E-heptadecenoic acid (cor = 0.991, P < 0.0001) (Fig. 3 g). This indicated that the metabolic pathways of these two metabolites were closely related, and could be involved in intimately connected biological functions. Further, the DEMs were found to be significantly enriched in the “DNA damage reversal” and “metabolism of carbohydrates” (Fig. 3 h). The findings indicated that DEMs could be associated with genomic stability, as well as in energy generation and storage. 2.4 The correlation of key DEPs and DEMs indicating CLU, SERPINF1, CD5L and 2-Butyne-1,4-diol in PDR. Proteomics and metabolomics association analysis identified 6 key metabolites: Aminooxyacetic acid, Creatine, Methyl ethaneperoxoate, N6-Methyladenosine, Orsellinic acid, and 2-Butyne-1,4-diol, alongside 7 key proteins: Clusterin (CLU), CD5 antigen-like protein (CD5L), C5, serpin family F member 1 (SERPINF1), APLP2, PSAP, and RBP3 (Fig. 4 a, Table 1 ). Remarkably, 2-butyn-1,4-diol demonstrated strong positive correlations with SERPINF1 (cor = 0.873), APLP2 (cor = 0.829), PSAP (cor = 0.855), and RBP3 (cor = 0.846), while exhibiting notable negative correlations with CD5L (cor = -0.895) and C5 (cor = -0.807). In contrast, CLU was positively correlated with all key metabolites, except 2-Butyne-1,4-diol (Fig. 4 b).CLU is positively correlated with Aminooxyacetic acid, Creatine, Methyl ethaneperoxoate, N6-Methyladenosine, and Orsellinic acid. SERPINF1 is positively correlated with 2-Butyne-1,4-diol. CD5L is negatively correlated with 2-Butyne-1,4-diol. Furthermore, the DR group exhibited down-regulation of all 6 key metabolites in expression (Fig. 4 c, Table S2 ), while key proteins C5 and CD5L were upregulated (Fig. 4 d, Table S3 ). Table 1 Key proteins and key metabolisms DEM DEP cor P Aminooxyacetic acid CLU 0.81098901 0.00043221 Creatine CLU 0.81978022 0.04282204 Methyl ethaneperoxoate CLU 0.81538462 0.00037913 N6-Methyladenosine CLU 0.81538462 0.00037913 Orsellinic acid CLU 0.87252747 0.00004680 2-Butyne-1,4-diol CD5L -0.8945055 0.00001580 2-Butyne-1,4-diol C5 -0.8065934 0.00049111 2-Butyne-1,4-diol SERPINF1 0.87252747 0.00004681 2-Butyne-1,4-diol APLP2 0.82857143 0.00025053 2-Butyne-1,4-diol PSAP 0.85494505 0.00009766 2-Butyne-1,4-diol RBP3 0.84615385 0.00013626 2.5 The DEPs and DEMs were confirmed in both clinical and experimental DR. Vitreous humor samples from PDR patients (DR group) and iMH patients (Control group) were assayed by ELISA for CD5L, CLU, and SERPINF1. As shown in Fig. 6 , CD5L levels were significantly elevated in the DR group compared with controls (P < 0.05), whereas CLU and SERPINF1 concentrations were significantly reduced in the DR group (P < 0.05). Paraffin-embedded retinal sections were prepared from streptozotocin-induced diabetic SD rats (DR group) and age-matched non-diabetic controls (Control group) and stained for CD5L, CLU, and SERPINF1. Whole-retina analysis revealed that, compared with controls, DR rats exhibited a significant increase in CD5L immunoreactivity (P < 0.01), while both CLU and SERPINF1 staining were significantly reduced (P < 0.01 for each). In the retinal nerve fiber layer, cell-specific quantification likewise showed elevated CD5L expression in DR animals (P < 0.05) and decreased CLU and SERPINF1 levels compared with controls (both P < 0.001).(Fig. 6 ). 2.6 Prediction of transcription factors (TFs) and modification sites in key proteins The TFs for key proteins were predicted, with 16, 1, 4, 18, 8, 11, and 2 TFs identified as potential regulators for CLU, CD5L, C5, SERPINF1, APLP2, PSAP, and RBP3, respectively (Fig. 7 a). Among them, SERPINF1 predicted the most TFs, including FOXA2, JUND, EBF1. The predictions of modification sites for key proteins indicated that APLP2, RBP3, and SERPINF1 had strong literature support for phosphorylation (Fig. 7 b-d), while CLU and PSAP exhibited significant support for acetylation (Fig. 7 e-f). C5 was noteworthy for having the most substantial literature support for phosphorylation, acetylation, ubiquitination, and other modifications (Fig. 7 g). CD5L was also found to undergo modifications at other sites (Fig. 7 h). 2.7 Drug prediction and molecular docking of key proteins A total of 10 potential drugs were predicted for the genes corresponding to the targeted key proteins (Table 2 ). Among them, RBP3 and vitamin A palmitate exhibited the highest interaction score. In light of the inadequate binding affinity between RBP3 and vitamin A palmitate, a molecular docking was conducted to illustrate the interaction between CD5L and fenoprofen. The CD5L and fenoprofen molecules were showed to dock in Fig. 8 a-b. Concurrently, the results of the molecular docking demonstrated that the intermolecular binding energies between CD5L and fenoprofen were determined to be -6.6 kcal/mol (Table 3 ). A molecular free energy of ≤ -1.2 kcal/mol indicated a robust binding affinity between the ligand and the receptor. Therefore, this finding indicated a robust binding affinity between CD5L and fenoprofen. Table 2 Potential drugs predicted from key proteins Proteins Drugs Interaction score RBP3 VITAMIN A PALMITATE 8.751279 CD5L FENOPROFEN 3.500511 C5 POZELIMAB 3.500511 C5 VILOBELIMAB 3.500511 CD5L KETOPROFEN 2.500365 C5 ECULIZUMAB 1.591142 RBP3 VITAMINA 1.458546 CD5L IBUPROFEN, SODIUM SALT 0.448784 C5 RAVULIZUMAB 0.388946 C5 ZILUCOPLAN 0.388946 Table 3 Drug docking binding energy biomarkers Drugs binding energy CD5L fenoprofen -6.6 kcal/mol 3. Discussion DR is a progressive and vision-threatening microvascular complication of diabetes, marked by retinal capillary dysfunction and irreversible structural damage. Its pathogenesis involves a complex interplay of hyperglycemia-induced metabolic derangements, endothelial injury, inflammation, oxidative stress, and neurodegeneration of the retina 12 – 14 . Chronic hyperglycemia and hypertension compromise capillary integrity, leading to increased vascular permeability, ischemia, and pathological neovascularization, ultimately resulting in permanent vision loss. By integrating quantitative proteomics and untargeted metabolomics, we identified seven proteins and six metabolites closely associated with DR progression. Correlation analyses and joint pathway enrichment further revealed their convergent roles in key molecular networks, and findings were validated by ELISA and immunohistochemistry, thus uncovering novel mechanistic clues for DR. Among the candidate proteins, CD5L is a multifunctional immune-modulator secreted by macrophages and belongs to the scavenger receptor cysteine‐rich family. CD5L regulates apoptosis, pattern recognition, inflammation, autophagy, cell polarization, and lipid metabolism 15 . It modulates macrophage polarization during inflammatory responses 16 and, at higher concentrations, inhibits HepG2 proliferation, suggesting antitumor activity 17 Although little is known about CD5L in DR, recent work implicates it in adaptive resistance to anti‐angiogenic therapy, possibly by shaping the vascular or inflammatory microenvironment 18 . Consistent with this, we observed significantly elevated CD5L in the vitreous of PDR patients and marked upregulation in the nerve‐fiber layer of DR rat retinas. Under normal conditions, retinal microglia reside in the inner plexiform, outer plexiform, and ganglion‐cell layers 19 , 20 ; however, hyperglycemia activates microglia and draws them toward the nerve‐fiber layer, mirroring the focal CD5L overexpression we detected 21 , 22 .These studies collectively demonstrate that CD5L may play a dual role in DR progression, simultaneously involving in both immune modulation and microvascular dysfunction. CLU is a secreted glycoprotein chaperone protein found in intra- and extracellular fluids (e.g., blood, semen) 23 – 25 that inhibits complement activation, regulates immune cell function, maintains tolerance, scavenges immune complexes and exerts antioxidant effects 26 . Serum CLU is elevated in obese and type 2 diabetic patients and correlates with adipose insulin resistance, signaling its potential as a biomarker for diabetes 27 . In contrast, in DR, hyperglycemia-induced oxidative and inflammatory stress inhibits CLU expression, weakening retinal defenses against reactive oxygen species and disrupting vascular homeostasis maintained by vascular endothelial growth factor-driven pathological angiogenesis 28 , 29 . Our proteomic and immunohistochemical analyses confirmed that CLU levels were significantly reduced in the vitreous of PDR patients as well as in the retinas of DR rats. The tissue-specific dichotomy of CLU (systemic elevation vs. retinal depletion) suggests its dual role as a metabolic regulator and local retinal protector. SERPINF1 is a member of the serine protease inhibitor (Serpin) superfamily and encodes a protein called PEDF, which is an endogenous Wnt signaling inhibitor that is highly expressed in the vitreous fluid is highly expressed, antagonizes Wnt signaling and stabilizes retinal vasculature 30 , 31 . In DR, reduced levels of PEDF expression disrupt vascular homeostasis, resulting in increased pathologic angiogenesis and inflammatory responses 30 . In this study, significant downregulation of SERPINF1 was detected in vitreous fluid of PDR patients and retinal tissues of DR rats, verifying its key inhibitory role in the pathological process of DR. The consistent downregulation of SERPINF1/PEDF in both clinical and preclinical DR models underscores its dual potential as a therapeutic target and biomarker for disease severity. Restoring PEDF levels (e.g., via gene therapy or recombinant protein delivery) could concurrently suppress pathological angiogenesis and neuroinflammation, offering a multi-mechanistic therapeutic strategy for DR.Creatine, a nitrogenous organic acid endogenously synthesized in vertebrates, serves as a critical energy buffer for high metabolic-demand tissues such as neurons and vascular endothelial cells 32 In DR, hyperglycemia-induced oxidative stress and chronic inflammation disrupt creatine-mediated energy homeostasis, leading to mitochondrial dysfunction and exacerbated ROS production—key drivers of retinal microvascular damage 33 . In addition, creatine metabolism is associated with the AMPK (AMP-activated protein kinase) pathway, and dysregulation of creatine metabolism may impair AMPK signaling, further impairing vascular function in DR 34 . Therefore, creatine abnormalities may be closely related to the pathogenesis of DR, and it may be one of the biomarkers for early diagnosis of DR in the future. Analysis of protein-metabolite co-enrichment pathways showed significant enrichment of the “complement and coagulation cascades”, “prion diseases”, “Pertussis” and “staphylococcus aureus infection” pathways. Pertussis” and ‘staphylococcus aureus infection’ pathways were significantly enriched. The Complement and Coagulation Cascades pathway plays a central role in innate immunity and hemostasis. It plays a central role in regulating vascular permeability and microthrombosis through inflammatory mediator release, pathogen clearance, fibrin deposition and platelet activation 35 . In DR, hyperglycemia can activate the complement system, leading to vascular endothelial cell damage and blood-retinal barrier disruption, promoting retinal ischemia and neovascularization, and an abnormally activated coagulation cascade may exacerbate microvascular thrombosis, which has been associated with vascular leakage and fibrosis in DR 36 , 37 . Common aspects of these pathways in disease include activation of the coagulation cascade and the complement system promoting increased vascular permeability and fibrosis in both DR and infectious diseases 35 , 37 . It also allows the complement system, Toll-like receptors and NF-κB pathway 35 , 36 to cross-regulate the inflammatory response in infectious and metabolic diseases, which together promote DR. In this study, the changes of several key proteins (CD5L, CLU, SERPINF1) and metabolites (AOAA, creatine) in DR were verified by joint proteomics-metabolomics analysis as well as in vitro experiments, which revealed their potential roles in the pathogenesis of DR, laying the foundation for further elucidation of the molecular mechanisms of DR and development of new diagnostic markers and therapeutic strategies. 4. Materials and Methods 4.1. Patients and study design This study adhered to the tenets of the Declaration of Helsinki and was approved by the Ethics Committee of Kunming Medical University (Approval No. kmmu20220686). All subjects provided signed consent forms prior to study participation. Between March 2025 to May 2025, fourteen patients who underwent pars plana vitrectomy at the Department of Ophthalmology, First Affiliated Hospital of Kunming Medical University, were prospectively recruited. Vitreous samples were divided into two groups: the disease group comprised eight eyes from patients with PDR, and the control group comprised six eyes from nondiabetic patients with idiopathic macular hole (iMH). Inclusion criteria for the PDR group were: (1) age ≥ 18 years; (2) diagnosis of type 2 diabetes mellitus (T2DM) with PDR requiring vitrectomy for non-clearing vitreous hemorrhage or tractional detachment of retina, DRSS score of 65–85. Inclusion criteria for the control group were: (1) age ≥ 18 years; (2) idiopathic full-thickness macular hole requiring surgical intervention; (3) no history of diabetes (fasting blood glucose < 6.1 mmol/L and HbA1c < 6.0%); and (4) absence of any clinical signs of diabetic retinopathy on fundus examination. Exclusion criteria for both groups included: (1) vitreous hemorrhage from non-diabetic causes; (2) active ocular inflammation or infection (e.g., blepharitis, dacryocystitis, conjunctivitis, keratitis, uveitis, chorioretinitis, retinitis); (3) other ocular diseases affecting the posterior segment (e.g., glaucoma, age-related macular degeneration, retinal vasculitis, rhegmatogenous retinal detachment, high myopia); (4) prior intraocular interventions, including laser photocoagulation, intravitreal anti-VEGF or dexamethasone injections, or vitreoretinal surgery; and (5) uncontrolled or untreated systemic conditions such as hypertension, autoimmune or inflammatory disorders, systemic infections, or hematologic diseases. Undiluted vitreous humor (1.0–1.5 mL) was aspirated at the outset of standard 25-gauge pars plana vitrectomy, before infusion onset, using the vitrector connected to a sterile syringe under low vacuum (< 300 mmHg). Upon collection, samples were placed in ice-cooled microcentrifuge tubes and spun at 3,000 × g (10 min, 4°C) to pellet cellular debris. The clarified supernatant was then divided into aliquots and frozen at − 80°C for later proteom 4.2 Proteomics and Metabolomics Data Pre-processing 4.2.1 TMT-based Proteomics Total protein was extracted in Tris–HCl buffer, and concentration was determined with a microplate reader. Protein integrity was confirmed by SDS–PAGE. For digestion, 150 µg of protein was incubated with sequencing-grade trypsin (Promega, V5280-100 µg) at a 50:1 protein-to-enzyme ratio for 14 h at 37 ℃. Peptides were labeled using the TMT-10plex reagent kit according to the manufacturer’s instructions. The TMT-labeled peptide samples were combined in equal amounts, mixed with mobile phase A (5% ACN, pH 9.8), and injected into an UltiMate™ 3000 HPLC system (Thermo Scientific) for fractionation. Separation was performed on an Agilent ZORBAX 300Extend-C18 column (4.6 × 150 mm, 3.5 µm) with a linear gradient. The eluted fractions were lyophilized and redissolved in 0.1% FA before nanoLC-MS/MS analysis using an EASY-nLC™ 1200 system connected to an Orbitrap Exploris™ 480 mass spectrometer (Thermo Fisher Scientific) in DDA mode. MS parameters included: 2.2 kV spray voltage; MS^1 scans at 120,000 resolution (350–1,500 m/z); 300% AGC target; HCD-based MS^2 at 32% collision energy (45,000 resolution, 110 m/z start); dynamic exclusion of 60 s; precursor charge states 2+–6+; 200% AGC target; 120 ms max injection time. Data were analyzed using MaxQuant (v 2.1.4.0) with Trypsin/P digestion, carbamidomethyl (C) as a fixed modification, and oxidation (M) plus N-terminal acetylation as variable modifications. Database searches employed the UniProt reference proteome, with 1% FDR thresholds for peptides/proteins and exclusion of contaminants/reverse matches. 4.2.2 Untargeted Lipid Metabolomics Each vitreous sample (20 µL) was combined with lipid extraction buffer, incubated for 10 min at room temperature, and kept at − 20°C overnight. After centrifugation (4,000 × g, 20 min), the clarified supernatant was collected for metabolite profiling. Chromatographic separation was performed on an ACQUITY UPLC system (Waters) equipped with a Kinetex C18 column (100 × 2.1 mm, 100 Å; Phenomenex) maintained at 55°C (flow rate: 0.3 mL/min). The mobile phases included: A) ACN/water (60:40, 0.1% FA) and B) isopropanol/ACN (90:10, 0.1% FA). Metabolite detection was conducted using a Q-Exactive mass spectrometer (Thermo Scientific) in dual-polarity mode. Full-scan MS (70–1,050 m/z) was acquired at 70,000 resolution (AGC: 3 × 10^6; max IT: 100 ms), followed by dd-MS/MS (top 3) at 17,500 resolution (AGC: 1 × 10^5; max IT: 80 ms). Data were converted to mzXML and analyzed via XCMS/metaX in R, with features aligned by retention time and m/z. 4.3 Principal component analysis (PCA) and differential expression analysis To ascertain whether the reproducibility of proteomic sequencing samples was by statistical standards, PCA was conducted on both sample sets utilizing the procmp function from the stats package (v 4.2.2) ( https://www.r-project.org/ ). Moreover, the objective was to identify proteins that exhibited differential expression in the control and DR groups, and DEPs in both groups were identified using the limma package (v 3.54.0) 38 (|log2FoldChange (FC)| >0.5, P < 0.05). Furthermore, the volcano plot and heatmap of DEPs were plotted utilizing the ggplot2 package (v 3.4.1) 39 and pheatmap package (v 1.0.12) 40 , respectively. The top 10 most significantly up- and down-regulated proteins were labelled in the volcano plot (sorted by log2FC value), and the heatmap illustrated their expression profiles. 4.4 Functional analysis of DEPs The biological functions of the DEPs were subjected to further analysis. Initially, the molecular function of DEPs was conducted utilising the ClueGO plugin for the Cytoscape software (v 3.9.1) 41 (P < 0.05). Subsequently, the biological functions of the DEPs were elucidated through the utilization of the clusterProfiler package (v 4.7.1.003) 42 , which facilitated the performance of GO (P < 0.05) and KEGG enrichment analysis (adj. P < 0.05) on the DEPs. Of which, GO contained 3 parts: BP, (CC, and MF. The GO entries were ordered in descending order of P-values, and displayed the top 3 pathways with the most significant enrichment in BP, CC, and MF, respectively. 4.5 Identification and subcellular localisation analysis of candidate proteins To investigate the interactions among DEPs, the search tool that retrieves interaction genes (STRING) database ( https://www.string-db.org ) was employed to generate a protein-protein interaction (PPI) network. The DEPs were imported into the STRING database for the construction of a PPI network (interaction score ≥ 0.4), and the resulting data were subsequently visualized using Cytoscape software (v 3.9.1). Afterwards, the betweenness centrality of DEPs was calculated utilizing the CytoNCA plugin within Cytoscape software (v 3.9.1). Proteins with betweenness centrality values exceeding 10 were selected as candidate proteins. Eventually, subcellular localization analyses of the top 10 candidate proteins, ranked by betweenness centrality, were conducted utilizing the unified protein (UniProt) database ( https://www.uniprot.org/ ) to investigate their distributions within subcellular structures. 4.6 OPLS-DA The OPLS-DA model was performed to analyze the metabolite profile from disparate sample groups. The variable importance in projection (VIP) values for each metabolite of the control and DR groups from the metabolomic data were calculated using the mixOmics package (v 6.22.0) 43 in both positive and negative ion modes for the OPLS-DA models, respectively. A differential expression analysis of metabolome sequencing data, encompassing total samples, was conducted using the limma package (v 3.54.0) in both positive and negative ion modes to ascertain P-values for metabolites between the DR and control groups. Afterwards, incorporating VIP values from the OPLS-DA model, metabolites exhibiting VIP > 1.0 and P < 0.05 were designated as significantly distinct. The process was performed independently for positive and negative ion modes to obtain DEMs1 and DEMs2, which were visualized via volcano plots. Subsequently, DEMs1 and DEMs2 were combined to obtain DEMs after removing duplicates. 4.7 Functional analysis of DEMs The classification and annotation of DEMs were subjected to further investigation. Initially, the mass-to-charge ratio (m/z) values of the DEMs were imported into the human metabolome database (HMDB) ( http://www.hmdb.ca/ ) to identify the respective metabolite classes. Following this, the DEMs were mapped to the KEGG database ( https://www.genome.jp/kegg/ ) to elucidate their classification. Further explore potential associations between DEMs, based on all samples from metabolome sequencing, a Spearman correlation analysis (|correlation coefficient (cor) | >0.8 and P < 0.05) was conducted on the DEMs using the corrplot package (v 0.92) 44 . To elucidate the pathways associated with DEMs, a KEGG metabolic pathway analysis (P < 0.05) was conducted on DEMs via the MetaboAnalyst platform ( https://www.metaboanalyst.ca/ ). The top 20 metabolic pathways of significance were presented as bubble plot. 4.8 Proteomics and metabolomics association analysis To identify key proteins and key metabolites, all samples from proteomic and metabolomic sequencing data were integrated, and Spearman correlation coefficients were computed between candidate proteins and DEMs utilizing the corrplot package (v 0.92). Furthermore, a heatmap of the correlation between candidate proteins and DEMs was plotted using the ComplexHeatmap package (v 2.14.0) 45 . Candidate proteins and DEMs with |cor| >0.8 and P < 0.05 were considered to be strongly correlated, consequently identified as key proteins and key metabolites. Subsequently, the interaction network between key proteins and key metabolites was visualized using Cytoscape software (v 3.9.1) to elucidate their interconnections. The expression of key metabolites and key proteins in both the control and DR groups was further illustrated utilizing the ggplot2 package (v 3.4.1). Thereafter, the KEGG metabolic pathway analysis (adj. P < 0.05) of the key proteins and key metabolites was conducted using the MetaboAnalyst platform, which revealed their co-enriched biological functions. 4.9 TFs prediction and protein post-translational modifications analysis of key proteins The chromatin immunoprecipitation sequencing (ChIP-seq) data from the chromatin enrichment analysis database 3 (ChEA3) ( https://maayanlab.cloud/chea3/ ) was utilized to predict TFs that potentially target these key proteins, thereby facilitating a deeper understanding of the regulatory mechanisms governing these key proteins. Besides, the post-translational modifications analysis of key proteins was conducted, which revealed intricate mechanisms underlying their functional regulation through chemical modifications, such as phosphorylation and acetylation, that occur after translation. The potential modification sites on key proteins were retrieved from the PhosphoSitePlus database ( https://www.phosphosite.org/homeAction.action ) upon inputting their names, thereby facilitating insights into protein function and regulatory mechanisms. 4.10 Potential drug prediction and molecular The “approved” drugs with the potential to target genes corresponding to key proteins were identified through the drug-gene interaction database (DGIdb) ( https://www.dgidb.org/ ). The drug exhibiting the highest gene interaction score corresponding to the key protein was selected as a candidate drug. Subsequently, molecular docking of candidate drugs and key proteins was performed. The candidate drugs were imported into the Public Chemistry Database (PubChem) ( https://pubchem.ncbi.nlm.nih.gov/ ) to obtain their 3D structures. Following this, the key proteins were subjected to molecular docking with the drug candidate utilizing the CB-Dock2 online tools ( https://pubchem.ncbi.nlm.nih.gov/ ), and the resulting free binding energies were evaluated. Eventually, the results of the molecular docking were visualised using PyMOL software (v 3.0.3) 46 . 4.11 Statistical analysis Statistical analyses were performed in R (version 4.2.2), with group comparisons assessed by the Wilcoxon test (significance threshold: P < 0.05). 4.12 Molecular Experiments 4.12.1 Quantification of CD5L, CLU, and SERPINF1 in Vitreous Humor by Enzyme linked immunosorbent assay (ELISA) Vitreous samples (150 µL) collected from PDR patients and controls (see Section 4.1.1) were assayed for CD5L, CLU, and SERPINF1 using commercially available ELISA kits (CD5L: LY0561-HA; CLU: LY1452-A; SERPINF1: LY0563-HA; all from Enzyme-Linked, Jiangsu, China). Samples and standards (150 µL each) were added to precoated 96-well plates (50 µL per well), mixed gently, and sealed with plate film. Plates were incubated at 37°C for 30 min. After removing the film, wells were emptied, patted dry, and washed five times with 200 µL wash buffer (30 s per wash). Next, 50 µL of horseradish peroxidase–conjugated detection antibody was added to each well, plates were resealed and incubated at 37 ℃ for 30 min, then washed once. For color development, 50 µL each of substrate solutions A and B were added sequentially, mixed by gentle tapping, and incubated in the dark at 37 ℃ for 10 min. The reaction was stopped with 50 µL stop solution, and optical density at 450 nm was measured within 15 min using a BioTek ELx800 microplate reader. Data were analyzed by one-way ANOVA with post hoc t-test comparisons in GraphPad Prism. 4.12.2 Immunohistochemical Analysis of CD5L, CLU, and SERPINF1 in Retinal Sections 4.12.2.1 Animal Model and Tissue Harvest Fifteen male Sprague–Dawley rats (6–8 weeks old) were obtained from Henan Skebes Biotechnology Co., Ltd. (SCXK [Yu] 2020-0005; SYXK [Dian] K2020-0006). Diabetic retinopathy was induced in the experimental group by intraperitoneal injection of streptozotocin (STZ; 45 mg/kg; Solarbio, S8050) for five consecutive days. Controls received equal volumes of citrate buffer (Solarbio, C1013). Blood glucose was measured on days 2 and 7 post-final injections and weekly thereafter; rats with sustained glucose > 16.7 mmol/L were considered diabetic. Four weeks after model induction, rats were euthanized by cervical dislocation. Eyeballs were enucleated, embedded in OCT compound, and frozen. Retinal blocks were sectioned at 5 µm on a cryostat, mounted on cationic slides, air-dried in 20% ethanol, then floated on a 47 ℃ water bath, and finally baked at 64 ℃ until the tissue adhered firmly. 4.12.2.2 Paraffin Embedding and Sectioning Retinal tissues were fixed in 4% paraformaldehyde for 24–48 h, washed in PBS, and dehydrated through graded ethanol (75%, 4 h; 85%, 2 h; 90%, 2 h; 95%, 60 min; 100% I, 30 min; 100% II, 30 min). Samples were cleared in xylene (I, 8 min; II, 8 min), then infiltrated in molten paraffin (I, 60 min; II, 60 min; III, 60 min). Tissue blocks were embedded in paraffin molds and allowed to solidify. Paraffin blocks were trimmed and sectioned at 3 µm on a rotary microtome (Leica RM2135), floated on 20% ethanol, transferred to a 47 ℃ water bath, and mounted on cationic slides. Slides were dried in a 64 ℃ oven until paraffin melted and tissue adhered. 4.12.2.3 Immunohistochemistry Slides were baked at 64 ℃ for 1 h, deparaffinized in xylene (I, 10 min; II, 10 min), rehydrated through graded alcohols (100% I, 5 min; 100% II, 5 min; 95%, 5 min; 80%, 3 min; 70%, 2 min), and rinsed in PBS (3×5 min). Antigen retrieval was performed in citrate buffer under pressure for 3 min, followed by cooling and PBS washes (3×5 min). Endogenous peroxidase was quenched with 3% H₂O₂ for 20 min at room temperature, then blocked with 5% bovine serum albumin at 37 ℃ for 30 min. Primary antibodies—anti-CD5L (1:50; Bioss, bs-2487R), anti-CLU (1:50; Bioss, bs-1354R), and anti-SERPINF1 (1:50; Bioss, bs-20784R)—were applied overnight at 4 ℃. Slides were warmed to 37 ℃ for 30 min the next day, washed in PBS (3×5 min), and incubated with signal enhancer (100 µL) at 37 ℃ for 20 min. After PBS washes, slides were incubated with polymerized goat anti-mouse/rabbit IgG–HRP (1:200; PV-9000; Zhongshan Golden Bridge, Beijing) at 37 ℃ for 20 min, followed by PBS washes. DAB substrate (ZLI-9019; Zhongshan Golden Bridge) was applied until the optimal color was developed, then rinsed in PBS. Counterstaining was performed with hematoxylin for 5 min, differentiated briefly, rinsed in running water for 15 min, dehydrated through graded alcohols and xylene, and mounted with neutral resin. Whole-slide images were acquired on an SQS-12P scanner (Johnson & Johnson, Shenzhen). Quantification of positive staining was performed in ImageJ Pro-Plus, and statistical analyses were conducted in GraphPad Prism. 5. Conclusions In this study, seven key proteins (CLU, CD5L, C5, SERPINF1, APLP2, PSAP, RBP3) and six key metabolites (Aminooxyacetic acid, Creatine, Methyl ethaneperoxoate, N6- Methyladenosine, Orsellinic acid, 2-Butyne-1,4-diol), revealing their potential role in DR. The present study also confirmed that the expression of CLU, CD5L, and SERPINF1 protein levels in the vitreous humor of patients with DR was significantly higher than that of controls, and a significant increase in the expression of the above three proteins in the nerve fiber layer was confirmed in ocular pathology histological sections of rats with DR. However, this study has several limitations: the clinical vitreous sample size was limited, and rodent models have inherent deficiencies in simulating human DR progression. Future research should focus on advancing the following directions: (1) validating the biomarker value of these candidate molecules in larger-scale, stratified longitudinal cohorts across different DR stages; (2) employing conditional gene knockout models to elucidate the causal role of key proteins (e.g., CD5L) in microglia-vascular interactions; and (3) exploring combination therapy strategies targeting both metabolic pathways (e.g., AMPK activators) and immune pathways (e.g., C5 inhibitors) in preclinical trials. These translational research steps will facilitate the translation of mechanistic discoveries into clinical applications, addressing current unmet clinical needs in DR treatment. Declarations Additional information : Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research was funded by the Applied Basic Research Foundation of the Department of Science and Technology of Yunnan Province, Yunnan, China, grant number 202201AY070001-036; National Natural Science Foundation Project, grant number 82260207; The Ocular Trauma Innovation Team of the First Affiliated Hospital of Kunming Medical University, Yunnan, grant number 202405AS350013. Author Contribution Conceptualization, YX.C. and LN.R.; methodology, YX.C.; software, YX.C..; validation, DL.L., L.S. and QR.L.; formal analysis, YX.C.; investigation, L.P. and T.L.; resources, QC.S.; data curation, XR.Z.,L.S. and BY,Z.; writing—original draft preparation, YX.C.; writing—review and editing, YX.C.; visualization, LN.R.; supervision, L.Y.; project administration, L.Y.; funding acquisition, L.Y. Acknowledgments: We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. 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1","display":"","copyAsset":false,"role":"figure","size":2729902,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination and enrichment analysis of DEPs. (a) PCA plot shows sample distribution (yellow: Control; blue: DR), with closer dots indicating similar expression patterns. (b) Volcano plot: y-axis is -log10(p-value), x-axis is log2FC. Red/blue dots represent up/downregulated proteins; gray dots indicate non-significant proteins. Top 10 most significant proteins are labeled. (c) Heatmap: upper section displays expression density of upregulated proteins; lower section shows top 10 up/downregulated proteins (by log2FC) across samples (blue: Control; red: DR). (d) Molecular function enrichment: nodes represent pathways, connections indicate shared gene counts. (e) GO enrichment (concentric circles): outer layer shows GO IDs (red: biological process; blue: cellular component; green: molecular function); middle layers depict significance (color/length) and up/downregulated DEP counts; innermost layer shows RichFactor (block size). (f) KEGG circle plot: left half lists DEPs (color intensity = logFC); right half shows enriched pathways.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/450640a2010f163857f6db7d.png"},{"id":92532158,"identity":"d7333a35-9720-47a2-9a8e-cea0ef575324","added_by":"auto","created_at":"2025-09-30 16:47:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27172463,"visible":true,"origin":"","legend":"\u003cp\u003eAcquisition of candidate proteins and probing their distribution in subcellular structures. (a) PPI network of DEPs. Color shades represent degree centrality from high to low, nodes represent proteins, and edges represent interactions between proteins. (b) Subcellular localization map for 10 DEPs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/66c4394037704ce1a9270f4e.png"},{"id":92532167,"identity":"d18d0bd8-6ccf-4d4e-bff8-d2e8feff42ad","added_by":"auto","created_at":"2025-09-30 16:47:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54137890,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional exploration of DEMs. (a) OPLS-DA scores of DR Group vs control group in positive ion mode. (b) OPLS-DA scores of DR Group vs control group in negative ion mode. Yellow: Control groups; Blue: D R groups. Each point represented a sample, and the closer the relative distance between samples, the closer the expression patterns. (c) Volcano map of differential metabolites in the DR group and control group in positive ion mode. (d) Volcano map of differential metabolites in the DR group and control group in negative ion mode. The red dots represented upregulated metabolites, while the blue dots represented downregulated metabolites; Gray dots represented metabolites with no significant differences. (e) HMBD Notes of DEMs. (f) KEGG compound classification of DEMs. (g) Analysis of the correlation between DEMs. Red represented positive correlation, the stronger the positive correlation, the redder the color, and blue represented negative correlation, the stronger the negative correlation, the bluer the color. (h) Analysis of the KEGG metabolic pathway in DEMs. The size of the dots represented the number of metabolites or proteins enriched in the pathway, with larger nodes indicating more enrichment. The higher the significance, the redder the color.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/a701b78ac816911408aeca77.png"},{"id":92529558,"identity":"f2846ca2-725b-43ec-9615-a57429dca32f","added_by":"auto","created_at":"2025-09-30 16:31:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4518591,"visible":true,"origin":"","legend":"\u003cp\u003eAcquisition and correlation analysis of key proteins and key metabolites. (a) Correlation heat maps of DEPs -DEMs. Red represented positive correlation, the stronger the positive correlation, the redder the color, and blue represented negative correlation, the stronger the negative correlation, the bluer the color. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ***: p \u0026lt; 0.001, ****: p \u0026lt; 0.0001. (b) Key protein-key metabolite interaction network. The red nodes represented key metabolites, the orange nodes represented key proteins, the edges represented interactions between key metabolites and key proteins, the red line represented positive correlation, and the blue line represented negative correlation. (c) Multiples of differences between key metabolites. (d) Multiples of differences between key proteins. (e) KEGG pathway analysis of co-enrichment of key proteins and key metabolites. The size of the dots represents the number of metabolites or proteins enriched in the pathway, with larger nodes indicating more enrichment.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/523a1ba6ca46d5c15aebec70.png"},{"id":92532154,"identity":"879fcdb5-3371-4f48-b126-02b9def58e85","added_by":"auto","created_at":"2025-09-30 16:47:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":313816,"visible":true,"origin":"","legend":"\u003cp\u003eELISA quantification of CD5L (a), CLU (b), and SERPINF1 (c) protein levels in the vitreous humor of DR and Control patients. *: p \u0026lt; 0.05, ****: p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/d6bbda827793c50273ba0b47.png"},{"id":92532156,"identity":"71327ae6-8000-4f27-acd1-f216e10ae0c8","added_by":"auto","created_at":"2025-09-30 16:47:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11878656,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical analysis of CD5L, CLU, and SERPINF1 expression. Immunohistochemical analysis of CD5L, CLU, and SERPINF1 expression in whole-retinal and retinal nerve fiber layer from DR and control SD rats. Images were acquired at 40× magnification, full-field view; scale bar, 25 μm. Nuclei are counterstained blue, and positive staining for CD5L, CLU, and SERPINF1 appears brown. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ***: p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/63a5ac46625d0030e0766237.png"},{"id":92529563,"identity":"3818d8fa-3e58-4186-b8d4-dd2f1c92bb83","added_by":"auto","created_at":"2025-09-30 16:31:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1427860,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of Transcription factors (TFs) and modification sites in key proteins. (a) Transcription factor prediction of key proteins. (b) Prediction results of PTM sites of key protein APLP2. (c) Prediction results of PTM sites of key protein RBP3. (d) Prediction results of PTM sites of key protein SERPINF1. (e) Prediction results of acetylation of key protein CLU. (f) Prediction results of acetylation of key protein PSAP. (g) Prediction results of phosphorylation, acetylation, ubiquitination of key protein C5. (h) Prediction results of key protein CD5L.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/4fe35c9aee7c420a01b80eee.png"},{"id":92529605,"identity":"ea094f06-d838-4850-bb88-94c3ec130477","added_by":"auto","created_at":"2025-09-30 16:31:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":43691021,"visible":true,"origin":"","legend":"\u003cp\u003eDrug prediction and molecular docking of key proteins. (a) Molecular docking diagram of key protein RBP3 and drug VITAMIN A PALMITATE. (b) Molecular docking diagram of key protein RBP3 and drug VITAMIN A PALMITATE. The purple is the key protein, the iridescent is the drug, and the yellow dotted line is the hydrogen bond between the two, as well as the corresponding bond length and residue.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/fa20f98e2b7b03228a1ae6c1.png"},{"id":92529555,"identity":"4acc29f8-cb69-44f1-b044-d125ae84f1f5","added_by":"auto","created_at":"2025-09-30 16:31:39","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48170,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/4491d3df517fcc016b090983.xlsx"},{"id":92531172,"identity":"005fc711-316d-496d-90dc-b3b4aadf31d6","added_by":"auto","created_at":"2025-09-30 16:39:39","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9699,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/93fe2f5bca222b1dfeb4bd7a.xlsx"},{"id":92529548,"identity":"752a63d7-25bd-4e4f-87ad-3caea34d253f","added_by":"auto","created_at":"2025-09-30 16:31:39","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9700,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7345162/v1/78686d9b9ca1bc3765f741df.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combined proteomics and metabolomics analyses revealed molecular signatures associated with proliferative diabetic retinopathy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetic retinopathy (DR), a major microvascular complication of diabetes, is the leading cause of preventable blindness in working-age adults globally\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. With the continuous rise in the global prevalence of diabetes, the disease burden of DR is showing unprecedented growth. According to the latest statistics from the International Diabetes Federation (IDF), there are approximately 589\u0026nbsp;million diabetic patients worldwide, and this number is projected to surge to 853\u0026nbsp;million by 2050. [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://idf.org\u003c/span\u003e\u003cspan address=\"https://idf.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. This epidemiological trend directly leads to a sharp increase in the number of DR patients, with the latest model projections showing that the global number of DR patients will reach 160\u0026nbsp;million by 2045, of which approximately 10% will progress to vision-threatening proliferative diabetic retinopathy (PDR) or diabetic macular edema (DME)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn terms of pathophysiological mechanisms, the development and progression of DR involve complex multifactorial interactions. Chronic hyperglycemia leads to retinal microvascular endothelial cell damage, pericyte loss, and blood-retinal barrier disruption through mechanisms such as polyol pathway activation, accumulation of advanced glycation end products (AGEs), protein kinase C (PKC) pathway activation, and oxidative stress responses\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.Recent studies have further revealed that neuroinflammatory responses and neurovascular unit dysfunction play critical roles in the early stages of DR, manifested by microglial activation, reactive changes in glial cells, and downregulation of neuroprotective factors (e.g. Pigment Epithelium-Derived Factor (PEDF))\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.These pathological changes collectively contribute to the characteristic formation of microaneurysms, retinal ischemia-hypoxia, and ultimately pathological neovascularization in DR.\u003c/p\u003e\u003cp\u003eThe current clinical treatment strategies mainly include intravitreal injection of anti-vascular endothelial growth factor (VEGF), intravitreal injection of dexamethasone implant, retinal laser photocoagulation, and vitrectomy.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Although these treatments can delay disease progression to some extent, there are still many limitations: some patients respond poorly to anti-VEGF or dexamethasone implant; laser treatment may lead to peripheral visual field defects and nyctalopia; and surgical intervention is only applicable to end-stage cases and cannot reverse existing retinal damage\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. More notably, existing treatment modalities primarily target advanced vascular pathologies, while lacking effective interventions for early neurodegenerative changes and metabolic abnormalities, which highlights the urgent need to explore novel mechanisms of DR pathogenesis and develop early intervention strategies.\u003c/p\u003e\u003cp\u003eRecent advances in multi-omics technologies have revolutionized the study of disease mechanisms, particularly in deciphering complex pathological processes such as DR Metabolomics can uncover metabolic alterations in DR by reflecting perturbations in functional metabolic networks\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, whereas proteomics can identify differentially expressed proteins (DEPs) in DR, providing direct evidence of disease-related pathways\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Compared with single-omics approaches, the integration of proteomic and metabolomic data enables the reconstruction of protein\u0026ndash;metabolite interaction networks and the identification of key cross-omics driving pathways and regulatory nodes, thereby laying the groundwork for precision medicine\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we performed a combined proteomic and metabolomic analysis of vitreous samples from DR patients to investigate the potential roles and mechanisms of proteins and metabolites in the pathogenesis of DR. By providing a multidimensional perspective on the pathophysiological changes in DR, our research aims to offer new insights into the molecular mechanisms underlying DR and to establish a theoretical basis for the development of targeted interventional strategies.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 81 DEPs were identified in DR, involving CLU, SERPINF1, CD5L.\u003c/h2\u003e\u003cp\u003eThe Principal Component Analysis (PCA) revealed a clear distinction between the control and DR group samples, with a pronounced clustering of samples within each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). This indicated an essentially uniform expression pattern within the groups, reflecting statistically reproducible samples. Furthermore, a total of 81 DEPs were identified between the DR and control groups, with 41 up-regulated and 40 down-regulated in the DR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-c). Subsequently, enrichment analyses were conducted to provide preliminary insights into the signaling pathways implicated by the DEPs. Functional enrichment analysis with ClueGO revealed that the DEPs were predominantly enriched in pathways related to the \u0026ldquo;positive regulation of substrate adhesion-dependent cell spreading\u0026rdquo;, \u0026ldquo;complement activation\u0026rdquo;, and \u0026ldquo;immunoglobulin complex, circulating\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Additionally, the DEPs were significantly enriched in 332 Gene Ontology (GO) entries with 235 biological processes (BPs), 42 cellular components (CCs) and 55 molecular functions (MFs). Among the identified BPs, those associated with candidate genes exhibited a prominent role in \u0026ldquo;humoral immune response\u0026rdquo; and \u0026ldquo;activation of immune response\u0026rdquo; (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Meanwhile, the CCs were primarily concentrated within \u0026ldquo;blood microparticle\u0026rdquo; and \u0026ldquo;collagen-containing extracellular matrix\u0026rdquo;, while the MFs were mainly enriched in \u0026ldquo;glycosaminoglycan binding\u0026rdquo; and \u0026ldquo;enzyme inhibitor activity\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The results indicated that DEPs likely played a significant role in immune response mechanisms and the makeup of the extracellular matrix. Moreover, a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the candidate genes revealed the enrichment of 5 pathways, including \u0026ldquo;complement and coagulation cascades\u0026rdquo;, \u0026ldquo;lysosome\u0026rdquo;, \u0026ldquo;pertussis\u0026rdquo;, \u0026ldquo;staphylococcus aureus infection\u0026rdquo; and \u0026ldquo;Cholesterol metabolism\u0026rdquo;. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Top 10 DEPs revealed distinct subcellular distributions.\u003c/h2\u003e\u003cp\u003e52 proteins out of 81 DEPs were found to interact in PPI network interactions, such as CLU, FGG and HP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The subsequent analysis identified 25 DEPs with betweenness centrality exceeding 10, which were then selected as candidate proteins for further investigation. Among the candidate proteins, the top 10 with the highest betweenness centrality included CLU, CD5L, HP, C5, C4BPA, JCHAIN, PRSS1, PRNP, SERPINF1, and APLP2. Further, subcellular localization analyses were conducted to gain insight into the distribution of the aforementioned 10 candidate proteins within the cells. CLU was ubiquitously distributed within the cell, encompassing locations such as the nucleus, cytoplasm, and microsome. CD5L was observed to localize to both the secreted and cytoplasm. Meanwhile, HP, C5, C4BPA, and JCHAIN were all positioned within the secreted compartment. PRSS1 was distributed in the secreted and extracellular space, while PRNP was located on the cell membrane, lipid-anchor, GPI-anchor, and Golgi apparatus. SERPINF1 was found distributed in both the secreted and melanosome compartments, and APLP2 was distributed across the cell membrane and within the nucleus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e2.3 26 differently expressed metabolites (DEMs) were enriched, with most belonging to fatty acyls and carboxylic acids and derivatives.\u003c/p\u003e\u003cp\u003eIn the Orthogonal partial least squares-discriminant analysis (OPLS-DA), both the positive and negative ion modes, a distinct differentiation in the relative contents of metabolites was observed between the DR and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b), indicating a fundamental similarity in the expression patterns within each group. Afterwards, the DEMs were identified between the DR and control groups. In the positive ion mode, a total of 9 DEMs1 were identified between the DR and control groups, all of which exhibited differential down-regulation in the DR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Conversely, in the negative ion mode, a total of 17 DEMs2 were discerned, with only 4 of these metabolites displaying up-regulation in the DR group, relative to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Subsequently, a total of 26 DEMs were achieved through the amalgamation of the DEMs1 and DEMs2. Further analysis was conducted on the classification and annotation of DEMs. In the HMDB annotation process, all 26 DEGs were successfully annotated, with the majority (27%) being classified as fatty acyls. The second largest category was that of carboxylic acids and derivatives, which constituted 23% of the total (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Furthermore, a total of 10 DEMs were annotated and categorized into five distinct categories in the KEGG annotations: carbohydrates, lipids, organic acids, peptides, as well as vitamins and cofactors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Of particular note was the lipids category, which encompassed 4 distinct lipid metabolites: fatty acids, prenol lipids, fatty acyls, and polyketides. Notably, the strongest correlation was observed between hexadec-7-enoic acid and 9E-heptadecenoic acid (cor\u0026thinsp;=\u0026thinsp;0.991, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg). This indicated that the metabolic pathways of these two metabolites were closely related, and could be involved in intimately connected biological functions. Further, the DEMs were found to be significantly enriched in the \u0026ldquo;DNA damage reversal\u0026rdquo; and \u0026ldquo;metabolism of carbohydrates\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). The findings indicated that DEMs could be associated with genomic stability, as well as in energy generation and storage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 The correlation of key DEPs and DEMs indicating CLU, SERPINF1, CD5L and 2-Butyne-1,4-diol in PDR.\u003c/h2\u003e\u003cp\u003eProteomics and metabolomics association analysis identified 6 key metabolites: Aminooxyacetic acid, Creatine, Methyl ethaneperoxoate, N6-Methyladenosine, Orsellinic acid, and 2-Butyne-1,4-diol, alongside 7 key proteins: Clusterin (CLU), CD5 antigen-like protein (CD5L), C5, serpin family F member 1 (SERPINF1), APLP2, PSAP, and RBP3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Remarkably, 2-butyn-1,4-diol demonstrated strong positive correlations with SERPINF1 (cor\u0026thinsp;=\u0026thinsp;0.873), APLP2 (cor\u0026thinsp;=\u0026thinsp;0.829), PSAP (cor\u0026thinsp;=\u0026thinsp;0.855), and RBP3 (cor\u0026thinsp;=\u0026thinsp;0.846), while exhibiting notable negative correlations with CD5L (cor = -0.895) and C5 (cor = -0.807). In contrast, CLU was positively correlated with all key metabolites, except 2-Butyne-1,4-diol (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).CLU is positively correlated with Aminooxyacetic acid, Creatine, Methyl ethaneperoxoate, N6-Methyladenosine, and Orsellinic acid. SERPINF1 is positively correlated with 2-Butyne-1,4-diol. CD5L is negatively correlated with 2-Butyne-1,4-diol. Furthermore, the DR group exhibited down-regulation of all 6 key metabolites in expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e), while key proteins C5 and CD5L were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, \u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKey proteins and key metabolisms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDEP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAminooxyacetic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCLU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81098901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00043221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCLU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81978022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04282204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethyl ethaneperoxoate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCLU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81538462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00037913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN6-Methyladenosine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCLU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81538462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00037913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrsellinic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCLU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.87252747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00004680\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Butyne-1,4-diol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCD5L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.8945055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00001580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Butyne-1,4-diol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.8065934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00049111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Butyne-1,4-diol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSERPINF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.87252747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00004681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Butyne-1,4-diol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPLP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.82857143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00025053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Butyne-1,4-diol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePSAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85494505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00009766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Butyne-1,4-diol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRBP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84615385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00013626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 The DEPs and DEMs were confirmed in both clinical and experimental DR.\u003c/h2\u003e\u003cp\u003eVitreous humor samples from PDR patients (DR group) and iMH patients (Control group) were assayed by ELISA for CD5L, CLU, and SERPINF1. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, CD5L levels were significantly elevated in the DR group compared with controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas CLU and SERPINF1 concentrations were significantly reduced in the DR group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eParaffin-embedded retinal sections were prepared from streptozotocin-induced diabetic SD rats (DR group) and age-matched non-diabetic controls (Control group) and stained for CD5L, CLU, and SERPINF1. Whole-retina analysis revealed that, compared with controls, DR rats exhibited a significant increase in CD5L immunoreactivity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while both CLU and SERPINF1 staining were significantly reduced (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for each). In the retinal nerve fiber layer, cell-specific quantification likewise showed elevated CD5L expression in DR animals (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and decreased CLU and SERPINF1 levels compared with controls (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Prediction of transcription factors (TFs) and modification sites in key proteins\u003c/h2\u003e\u003cp\u003eThe TFs for key proteins were predicted, with 16, 1, 4, 18, 8, 11, and 2 TFs identified as potential regulators for CLU, CD5L, C5, SERPINF1, APLP2, PSAP, and RBP3, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Among them, SERPINF1 predicted the most TFs, including FOXA2, JUND, EBF1. The predictions of modification sites for key proteins indicated that APLP2, RBP3, and SERPINF1 had strong literature support for phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb-d), while CLU and PSAP exhibited significant support for acetylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee-f). C5 was noteworthy for having the most substantial literature support for phosphorylation, acetylation, ubiquitination, and other modifications (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg). CD5L was also found to undergo modifications at other sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eh).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Drug prediction and molecular docking of key proteins\u003c/h2\u003e\u003cp\u003eA total of 10 potential drugs were predicted for the genes corresponding to the targeted key proteins (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among them, RBP3 and vitamin A palmitate exhibited the highest interaction score. In light of the inadequate binding affinity between RBP3 and vitamin A palmitate, a molecular docking was conducted to illustrate the interaction between CD5L and fenoprofen. The CD5L and fenoprofen molecules were showed to dock in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-b. Concurrently, the results of the molecular docking demonstrated that the intermolecular binding energies between CD5L and fenoprofen were determined to be -6.6 kcal/mol (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A molecular free energy of \u0026le; -1.2 kcal/mol indicated a robust binding affinity between the ligand and the receptor. Therefore, this finding indicated a robust binding affinity between CD5L and fenoprofen.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePotential drugs predicted from key proteins\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProteins\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrugs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInteraction score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVITAMIN A PALMITATE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.751279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD5L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFENOPROFEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.500511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePOZELIMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.500511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVILOBELIMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.500511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD5L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKETOPROFEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.500365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECULIZUMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.591142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVITAMINA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.458546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD5L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIBUPROFEN, SODIUM\u003c/p\u003e\u003cp\u003eSALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.448784\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRAVULIZUMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.388946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZILUCOPLAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.388946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDrug docking binding energy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebiomarkers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrugs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ebinding energy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD5L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efenoprofen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.6 kcal/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eDR is a progressive and vision-threatening microvascular complication of diabetes, marked by retinal capillary dysfunction and irreversible structural damage. Its pathogenesis involves a complex interplay of hyperglycemia-induced metabolic derangements, endothelial injury, inflammation, oxidative stress, and neurodegeneration of the retina\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Chronic hyperglycemia and hypertension compromise capillary integrity, leading to increased vascular permeability, ischemia, and pathological neovascularization, ultimately resulting in permanent vision loss. By integrating quantitative proteomics and untargeted metabolomics, we identified seven proteins and six metabolites closely associated with DR progression. Correlation analyses and joint pathway enrichment further revealed their convergent roles in key molecular networks, and findings were validated by ELISA and immunohistochemistry, thus uncovering novel mechanistic clues for DR.\u003c/p\u003e\u003cp\u003eAmong the candidate proteins, CD5L is a multifunctional immune-modulator secreted by macrophages and belongs to the scavenger receptor cysteine‐rich family. CD5L regulates apoptosis, pattern recognition, inflammation, autophagy, cell polarization, and lipid metabolism\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It modulates macrophage polarization during inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and, at higher concentrations, inhibits HepG2 proliferation, suggesting antitumor activity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Although little is known about CD5L in DR, recent work implicates it in adaptive resistance to anti‐angiogenic therapy, possibly by shaping the vascular or inflammatory microenvironment\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Consistent with this, we observed significantly elevated CD5L in the vitreous of PDR patients and marked upregulation in the nerve‐fiber layer of DR rat retinas. Under normal conditions, retinal microglia reside in the inner plexiform, outer plexiform, and ganglion‐cell layers\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e; however, hyperglycemia activates microglia and draws them toward the nerve‐fiber layer, mirroring the focal CD5L overexpression we detected\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.These studies collectively demonstrate that CD5L may play a dual role in DR progression, simultaneously involving in both immune modulation and microvascular dysfunction.\u003c/p\u003e\u003cp\u003eCLU is a secreted glycoprotein chaperone protein found in intra- and extracellular fluids (e.g., blood, semen)\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e that inhibits complement activation, regulates immune cell function, maintains tolerance, scavenges immune complexes and exerts antioxidant effects\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Serum CLU is elevated in obese and type 2 diabetic patients and correlates with adipose insulin resistance, signaling its potential as a biomarker for diabetes\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In contrast, in DR, hyperglycemia-induced oxidative and inflammatory stress inhibits CLU expression, weakening retinal defenses against reactive oxygen species and disrupting vascular homeostasis maintained by vascular endothelial growth factor-driven pathological angiogenesis\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Our proteomic and immunohistochemical analyses confirmed that CLU levels were significantly reduced in the vitreous of PDR patients as well as in the retinas of DR rats. The tissue-specific dichotomy of CLU (systemic elevation vs. retinal depletion) suggests its dual role as a metabolic regulator and local retinal protector.\u003c/p\u003e\u003cp\u003eSERPINF1 is a member of the serine protease inhibitor (Serpin) superfamily and encodes a protein called PEDF, which is an endogenous Wnt signaling inhibitor that is highly expressed in the vitreous fluid is highly expressed, antagonizes Wnt signaling and stabilizes retinal vasculature\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In DR, reduced levels of PEDF expression disrupt vascular homeostasis, resulting in increased pathologic angiogenesis and inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In this study, significant downregulation of SERPINF1 was detected in vitreous fluid of PDR patients and retinal tissues of DR rats, verifying its key inhibitory role in the pathological process of DR. The consistent downregulation of SERPINF1/PEDF in both clinical and preclinical DR models underscores its dual potential as a therapeutic target and biomarker for disease severity. Restoring PEDF levels (e.g., via gene therapy or recombinant protein delivery) could concurrently suppress pathological angiogenesis and neuroinflammation, offering a multi-mechanistic therapeutic strategy for DR.Creatine, a nitrogenous organic acid endogenously synthesized in vertebrates, serves as a critical energy buffer for high metabolic-demand tissues such as neurons and vascular endothelial cells\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e In DR, hyperglycemia-induced oxidative stress and chronic inflammation disrupt creatine-mediated energy homeostasis, leading to mitochondrial dysfunction and exacerbated ROS production\u0026mdash;key drivers of retinal microvascular damage\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In addition, creatine metabolism is associated with the AMPK (AMP-activated protein kinase) pathway, and dysregulation of creatine metabolism may impair AMPK signaling, further impairing vascular function in DR\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Therefore, creatine abnormalities may be closely related to the pathogenesis of DR, and it may be one of the biomarkers for early diagnosis of DR in the future.\u003c/p\u003e\u003cp\u003eAnalysis of protein-metabolite co-enrichment pathways showed significant enrichment of the \u0026ldquo;complement and coagulation cascades\u0026rdquo;, \u0026ldquo;prion diseases\u0026rdquo;, \u0026ldquo;Pertussis\u0026rdquo; and \u0026ldquo;staphylococcus aureus infection\u0026rdquo; pathways. Pertussis\u0026rdquo; and \u0026lsquo;staphylococcus aureus infection\u0026rsquo; pathways were significantly enriched. The Complement and Coagulation Cascades pathway plays a central role in innate immunity and hemostasis. It plays a central role in regulating vascular permeability and microthrombosis through inflammatory mediator release, pathogen clearance, fibrin deposition and platelet activation \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In DR, hyperglycemia can activate the complement system, leading to vascular endothelial cell damage and blood-retinal barrier disruption, promoting retinal ischemia and neovascularization, and an abnormally activated coagulation cascade may exacerbate microvascular thrombosis, which has been associated with vascular leakage and fibrosis in DR\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCommon aspects of these pathways in disease include activation of the coagulation cascade and the complement system promoting increased vascular permeability and fibrosis in both DR and infectious diseases\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. It also allows the complement system, Toll-like receptors and NF-κB pathway\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e to cross-regulate the inflammatory response in infectious and metabolic diseases, which together promote DR.\u003c/p\u003e\u003cp\u003eIn this study, the changes of several key proteins (CD5L, CLU, SERPINF1) and metabolites (AOAA, creatine) in DR were verified by joint proteomics-metabolomics analysis as well as in vitro experiments, which revealed their potential roles in the pathogenesis of DR, laying the foundation for further elucidation of the molecular mechanisms of DR and development of new diagnostic markers and therapeutic strategies.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Patients and study design\u003c/h2\u003e\u003cp\u003e This study adhered to the tenets of the Declaration of Helsinki and was approved by the Ethics Committee of Kunming Medical University (Approval No. kmmu20220686). All subjects provided signed consent forms prior to study participation.\u003c/p\u003e\u003cp\u003eBetween March 2025 to May 2025, fourteen patients who underwent pars plana vitrectomy at the Department of Ophthalmology, First Affiliated Hospital of Kunming Medical University, were prospectively recruited. Vitreous samples were divided into two groups: the disease group comprised eight eyes from patients with PDR, and the control group comprised six eyes from nondiabetic patients with idiopathic macular hole (iMH).\u003c/p\u003e\u003cp\u003eInclusion criteria for the PDR group were: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) diagnosis of type 2 diabetes mellitus (T2DM) with PDR requiring vitrectomy for non-clearing vitreous hemorrhage or tractional detachment of retina, DRSS score of 65\u0026ndash;85. Inclusion criteria for the control group were: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) idiopathic full-thickness macular hole requiring surgical intervention; (3) no history of diabetes (fasting blood glucose\u0026thinsp;\u0026lt;\u0026thinsp;6.1 mmol/L and HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;6.0%); and (4) absence of any clinical signs of diabetic retinopathy on fundus examination.\u003c/p\u003e\u003cp\u003eExclusion criteria for both groups included: (1) vitreous hemorrhage from non-diabetic causes; (2) active ocular inflammation or infection (e.g., blepharitis, dacryocystitis, conjunctivitis, keratitis, uveitis, chorioretinitis, retinitis); (3) other ocular diseases affecting the posterior segment (e.g., glaucoma, age-related macular degeneration, retinal vasculitis, rhegmatogenous retinal detachment, high myopia); (4) prior intraocular interventions, including laser photocoagulation, intravitreal anti-VEGF or dexamethasone injections, or vitreoretinal surgery; and (5) uncontrolled or untreated systemic conditions such as hypertension, autoimmune or inflammatory disorders, systemic infections, or hematologic diseases.\u003c/p\u003e\u003cp\u003eUndiluted vitreous humor (1.0\u0026ndash;1.5 mL) was aspirated at the outset of standard 25-gauge pars plana vitrectomy, before infusion onset, using the vitrector connected to a sterile syringe under low vacuum (\u0026lt;\u0026thinsp;300 mmHg). Upon collection, samples were placed in ice-cooled microcentrifuge tubes and spun at 3,000 \u0026times; g (10 min, 4\u0026deg;C) to pellet cellular debris. The clarified supernatant was then divided into aliquots and frozen at \u0026minus;\u0026thinsp;80\u0026deg;C for later proteom\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Proteomics and Metabolomics Data Pre-processing\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 TMT-based Proteomics\u003c/h2\u003e\u003cp\u003eTotal protein was extracted in Tris\u0026ndash;HCl buffer, and concentration was determined with a microplate reader. Protein integrity was confirmed by SDS\u0026ndash;PAGE. For digestion, 150 \u0026micro;g of protein was incubated with sequencing-grade trypsin (Promega, V5280-100 \u0026micro;g) at a 50:1 protein-to-enzyme ratio for 14 h at 37 ℃. Peptides were labeled using the TMT-10plex reagent kit according to the manufacturer\u0026rsquo;s instructions. The TMT-labeled peptide samples were combined in equal amounts, mixed with mobile phase A (5% ACN, pH 9.8), and injected into an UltiMate\u0026trade; 3000 HPLC system (Thermo Scientific) for fractionation. Separation was performed on an Agilent ZORBAX 300Extend-C18 column (4.6 \u0026times; 150 mm, 3.5 \u0026micro;m) with a linear gradient. The eluted fractions were lyophilized and redissolved in 0.1% FA before nanoLC-MS/MS analysis using an EASY-nLC\u0026trade; 1200 system connected to an Orbitrap Exploris\u0026trade; 480 mass spectrometer (Thermo Fisher Scientific) in DDA mode. MS parameters included: 2.2 kV spray voltage; MS^1 scans at 120,000 resolution (350\u0026ndash;1,500 m/z); 300% AGC target; HCD-based MS^2 at 32% collision energy (45,000 resolution, 110 m/z start); dynamic exclusion of 60 s; precursor charge states 2+\u0026ndash;6+; 200% AGC target; 120 ms max injection time. Data were analyzed using MaxQuant (v 2.1.4.0) with Trypsin/P digestion, carbamidomethyl (C) as a fixed modification, and oxidation (M) plus N-terminal acetylation as variable modifications. Database searches employed the UniProt reference proteome, with 1% FDR thresholds for peptides/proteins and exclusion of contaminants/reverse matches.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Untargeted Lipid Metabolomics\u003c/h2\u003e\u003cp\u003eEach vitreous sample (20 \u0026micro;L) was combined with lipid extraction buffer, incubated for 10 min at room temperature, and kept at \u0026minus;\u0026thinsp;20\u0026deg;C overnight. After centrifugation (4,000 \u0026times; g, 20 min), the clarified supernatant was collected for metabolite profiling. Chromatographic separation was performed on an ACQUITY UPLC system (Waters) equipped with a Kinetex C18 column (100 \u0026times; 2.1 mm, 100 \u0026Aring;; Phenomenex) maintained at 55\u0026deg;C (flow rate: 0.3 mL/min). The mobile phases included: A) ACN/water (60:40, 0.1% FA) and B) isopropanol/ACN (90:10, 0.1% FA). Metabolite detection was conducted using a Q-Exactive mass spectrometer (Thermo Scientific) in dual-polarity mode. Full-scan MS (70\u0026ndash;1,050 m/z) was acquired at 70,000 resolution (AGC: 3 \u0026times; 10^6; max IT: 100 ms), followed by dd-MS/MS (top 3) at 17,500 resolution (AGC: 1 \u0026times; 10^5; max IT: 80 ms). Data were converted to mzXML and analyzed via XCMS/metaX in R, with features aligned by retention time and m/z.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Principal component analysis (PCA) and differential expression analysis\u003c/h2\u003e\u003cp\u003eTo ascertain whether the reproducibility of proteomic sequencing samples was by statistical standards, PCA was conducted on both sample sets utilizing the procmp function from the stats package (v 4.2.2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Moreover, the objective was to identify proteins that exhibited differential expression in the control and DR groups, and DEPs in both groups were identified using the limma package (v 3.54.0)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e (|log2FoldChange (FC)| \u0026gt;0.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the volcano plot and heatmap of DEPs were plotted utilizing the ggplot2 package (v 3.4.1)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and pheatmap package (v 1.0.12)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, respectively. The top 10 most significantly up- and down-regulated proteins were labelled in the volcano plot (sorted by log2FC value), and the heatmap illustrated their expression profiles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Functional analysis of DEPs\u003c/h2\u003e\u003cp\u003eThe biological functions of the DEPs were subjected to further analysis. Initially, the molecular function of DEPs was conducted utilising the ClueGO plugin for the Cytoscape software (v 3.9.1)\u003csup\u003e41\u003c/sup\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, the biological functions of the DEPs were elucidated through the utilization of the clusterProfiler package (v 4.7.1.003)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, which facilitated the performance of GO (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and KEGG enrichment analysis (adj. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on the DEPs. Of which, GO contained 3 parts: BP, (CC, and MF. The GO entries were ordered in descending order of P-values, and displayed the top 3 pathways with the most significant enrichment in BP, CC, and MF, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Identification and subcellular localisation analysis of candidate proteins\u003c/h2\u003e\u003cp\u003eTo investigate the interactions among DEPs, the search tool that retrieves interaction genes (STRING) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.string-db.org\u003c/span\u003e\u003cspan address=\"https://www.string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to generate a protein-protein interaction (PPI) network. The DEPs were imported into the STRING database for the construction of a PPI network (interaction score\u0026thinsp;\u0026ge;\u0026thinsp;0.4), and the resulting data were subsequently visualized using Cytoscape software (v 3.9.1). Afterwards, the betweenness centrality of DEPs was calculated utilizing the CytoNCA plugin within Cytoscape software (v 3.9.1). Proteins with betweenness centrality values exceeding 10 were selected as candidate proteins. Eventually, subcellular localization analyses of the top 10 candidate proteins, ranked by betweenness centrality, were conducted utilizing the unified protein (UniProt) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to investigate their distributions within subcellular structures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.6 OPLS-DA\u003c/h2\u003e\u003cp\u003eThe OPLS-DA model was performed to analyze the metabolite profile from disparate sample groups. The variable importance in projection (VIP) values for each metabolite of the control and DR groups from the metabolomic data were calculated using the mixOmics package (v 6.22.0)\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e in both positive and negative ion modes for the OPLS-DA models, respectively. A differential expression analysis of metabolome sequencing data, encompassing total samples, was conducted using the limma package (v 3.54.0) in both positive and negative ion modes to ascertain P-values for metabolites between the DR and control groups. Afterwards, incorporating VIP values from the OPLS-DA model, metabolites exhibiting VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were designated as significantly distinct. The process was performed independently for positive and negative ion modes to obtain DEMs1 and DEMs2, which were visualized via volcano plots. Subsequently, DEMs1 and DEMs2 were combined to obtain DEMs after removing duplicates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.7 Functional analysis of DEMs\u003c/h2\u003e\u003cp\u003eThe classification and annotation of DEMs were subjected to further investigation. Initially, the mass-to-charge ratio (m/z) values of the DEMs were imported into the human metabolome database (HMDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca/\u003c/span\u003e\u003cspan address=\"http://www.hmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify the respective metabolite classes. Following this, the DEMs were mapped to the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to elucidate their classification. Further explore potential associations between DEMs, based on all samples from metabolome sequencing, a Spearman correlation analysis (|correlation coefficient (cor) | \u0026gt;0.8 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was conducted on the DEMs using the corrplot package (v 0.92)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. To elucidate the pathways associated with DEMs, a KEGG metabolic pathway analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was conducted on DEMs via the MetaboAnalyst platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The top 20 metabolic pathways of significance were presented as bubble plot.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.8 Proteomics and metabolomics association analysis\u003c/h2\u003e\u003cp\u003eTo identify key proteins and key metabolites, all samples from proteomic and metabolomic sequencing data were integrated, and Spearman correlation coefficients were computed between candidate proteins and DEMs utilizing the corrplot package (v 0.92). Furthermore, a heatmap of the correlation between candidate proteins and DEMs was plotted using the ComplexHeatmap package (v 2.14.0)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Candidate proteins and DEMs with |cor| \u0026gt;0.8 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be strongly correlated, consequently identified as key proteins and key metabolites. Subsequently, the interaction network between key proteins and key metabolites was visualized using Cytoscape software (v 3.9.1) to elucidate their interconnections. The expression of key metabolites and key proteins in both the control and DR groups was further illustrated utilizing the ggplot2 package (v 3.4.1). Thereafter, the KEGG metabolic pathway analysis (adj. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of the key proteins and key metabolites was conducted using the MetaboAnalyst platform, which revealed their co-enriched biological functions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.9 TFs prediction and protein post-translational modifications analysis of key proteins\u003c/h2\u003e\u003cp\u003eThe chromatin immunoprecipitation sequencing (ChIP-seq) data from the chromatin enrichment analysis database 3 (ChEA3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/chea3/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/chea3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to predict TFs that potentially target these key proteins, thereby facilitating a deeper understanding of the regulatory mechanisms governing these key proteins. Besides, the post-translational modifications analysis of key proteins was conducted, which revealed intricate mechanisms underlying their functional regulation through chemical modifications, such as phosphorylation and acetylation, that occur after translation. The potential modification sites on key proteins were retrieved from the PhosphoSitePlus database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.phosphosite.org/homeAction.action\u003c/span\u003e\u003cspan address=\"https://www.phosphosite.org/homeAction.action\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) upon inputting their names, thereby facilitating insights into protein function and regulatory mechanisms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.10 Potential drug prediction and molecular\u003c/h2\u003e\u003cp\u003eThe \u0026ldquo;approved\u0026rdquo; drugs with the potential to target genes corresponding to key proteins were identified through the drug-gene interaction database (DGIdb) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dgidb.org/\u003c/span\u003e\u003cspan address=\"https://www.dgidb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The drug exhibiting the highest gene interaction score corresponding to the key protein was selected as a candidate drug. Subsequently, molecular docking of candidate drugs and key proteins was performed. The candidate drugs were imported into the Public Chemistry Database (PubChem) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain their 3D structures. Following this, the key proteins were subjected to molecular docking with the drug candidate utilizing the CB-Dock2 online tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the resulting free binding energies were evaluated. Eventually, the results of the molecular docking were visualised using PyMOL software (v 3.0.3)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.11 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed in R (version 4.2.2), with group comparisons assessed by the Wilcoxon test (significance threshold: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.12 Molecular Experiments\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.12.1 Quantification of CD5L, CLU, and SERPINF1 in Vitreous Humor by Enzyme linked immunosorbent assay (ELISA)\u003c/h2\u003e\u003cp\u003eVitreous samples (150 \u0026micro;L) collected from PDR patients and controls (see Section 4.1.1) were assayed for CD5L, CLU, and SERPINF1 using commercially available ELISA kits (CD5L: LY0561-HA; CLU: LY1452-A; SERPINF1: LY0563-HA; all from Enzyme-Linked, Jiangsu, China). Samples and standards (150 \u0026micro;L each) were added to precoated 96-well plates (50 \u0026micro;L per well), mixed gently, and sealed with plate film. Plates were incubated at 37\u0026deg;C for 30 min. After removing the film, wells were emptied, patted dry, and washed five times with 200 \u0026micro;L wash buffer (30 s per wash). Next, 50 \u0026micro;L of horseradish peroxidase\u0026ndash;conjugated detection antibody was added to each well, plates were resealed and incubated at 37 ℃ for 30 min, then washed once. For color development, 50 \u0026micro;L each of substrate solutions A and B were added sequentially, mixed by gentle tapping, and incubated in the dark at 37 ℃ for 10 min. The reaction was stopped with 50 \u0026micro;L stop solution, and optical density at 450 nm was measured within 15 min using a BioTek ELx800 microplate reader. Data were analyzed by one-way ANOVA with post hoc t-test comparisons in GraphPad Prism.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.12.2 Immunohistochemical Analysis of CD5L, CLU, and SERPINF1 in Retinal Sections\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.12.2.1 Animal Model and Tissue Harvest\u003c/h2\u003e\u003cp\u003eFifteen male Sprague\u0026ndash;Dawley rats (6\u0026ndash;8 weeks old) were obtained from Henan Skebes Biotechnology Co., Ltd. (SCXK [Yu] 2020-0005; SYXK [Dian] K2020-0006). Diabetic retinopathy was induced in the experimental group by intraperitoneal injection of streptozotocin (STZ; 45 mg/kg; Solarbio, S8050) for five consecutive days. Controls received equal volumes of citrate buffer (Solarbio, C1013). Blood glucose was measured on days 2 and 7 post-final injections and weekly thereafter; rats with sustained glucose\u0026thinsp;\u0026gt;\u0026thinsp;16.7 mmol/L were considered diabetic. Four weeks after model induction, rats were euthanized by cervical dislocation. Eyeballs were enucleated, embedded in OCT compound, and frozen. Retinal blocks were sectioned at 5 \u0026micro;m on a cryostat, mounted on cationic slides, air-dried in 20% ethanol, then floated on a 47 ℃ water bath, and finally baked at 64 ℃ until the tissue adhered firmly.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.12.2.2 Paraffin Embedding and Sectioning\u003c/h2\u003e\u003cp\u003eRetinal tissues were fixed in 4% paraformaldehyde for 24\u0026ndash;48 h, washed in PBS, and dehydrated through graded ethanol (75%, 4 h; 85%, 2 h; 90%, 2 h; 95%, 60 min; 100% I, 30 min; 100% II, 30 min). Samples were cleared in xylene (I, 8 min; II, 8 min), then infiltrated in molten paraffin (I, 60 min; II, 60 min; III, 60 min). Tissue blocks were embedded in paraffin molds and allowed to solidify. Paraffin blocks were trimmed and sectioned at 3 \u0026micro;m on a rotary microtome (Leica RM2135), floated on 20% ethanol, transferred to a 47 ℃ water bath, and mounted on cationic slides. Slides were dried in a 64 ℃ oven until paraffin melted and tissue adhered.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.12.2.3 Immunohistochemistry\u003c/h2\u003e\u003cp\u003eSlides were baked at 64 ℃ for 1 h, deparaffinized in xylene (I, 10 min; II, 10 min), rehydrated through graded alcohols (100% I, 5 min; 100% II, 5 min; 95%, 5 min; 80%, 3 min; 70%, 2 min), and rinsed in PBS (3\u0026times;5 min). Antigen retrieval was performed in citrate buffer under pressure for 3 min, followed by cooling and PBS washes (3\u0026times;5 min). Endogenous peroxidase was quenched with 3% H₂O₂ for 20 min at room temperature, then blocked with 5% bovine serum albumin at 37 ℃ for 30 min. Primary antibodies\u0026mdash;anti-CD5L (1:50; Bioss, bs-2487R), anti-CLU (1:50; Bioss, bs-1354R), and anti-SERPINF1 (1:50; Bioss, bs-20784R)\u0026mdash;were applied overnight at 4 ℃. Slides were warmed to 37 ℃ for 30 min the next day, washed in PBS (3\u0026times;5 min), and incubated with signal enhancer (100 \u0026micro;L) at 37 ℃ for 20 min. After PBS washes, slides were incubated with polymerized goat anti-mouse/rabbit IgG\u0026ndash;HRP (1:200; PV-9000; Zhongshan Golden Bridge, Beijing) at 37 ℃ for 20 min, followed by PBS washes. DAB substrate (ZLI-9019; Zhongshan Golden Bridge) was applied until the optimal color was developed, then rinsed in PBS. Counterstaining was performed with hematoxylin for 5 min, differentiated briefly, rinsed in running water for 15 min, dehydrated through graded alcohols and xylene, and mounted with neutral resin. Whole-slide images were acquired on an SQS-12P scanner (Johnson \u0026amp; Johnson, Shenzhen). Quantification of positive staining was performed in ImageJ Pro-Plus, and statistical analyses were conducted in GraphPad Prism.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this study, seven key proteins (CLU, CD5L, C5, SERPINF1, APLP2, PSAP, RBP3) and six key metabolites (Aminooxyacetic acid, Creatine, Methyl ethaneperoxoate, N6- Methyladenosine, Orsellinic acid, 2-Butyne-1,4-diol), revealing their potential role in DR. The present study also confirmed that the expression of CLU, CD5L, and SERPINF1 protein levels in the vitreous humor of patients with DR was significantly higher than that of controls, and a significant increase in the expression of the above three proteins in the nerve fiber layer was confirmed in ocular pathology histological sections of rats with DR. However, this study has several limitations: the clinical vitreous sample size was limited, and rodent models have inherent deficiencies in simulating human DR progression. Future research should focus on advancing the following directions: (1) validating the biomarker value of these candidate molecules in larger-scale, stratified longitudinal cohorts across different DR stages; (2) employing conditional gene knockout models to elucidate the causal role of key proteins (e.g., CD5L) in microglia-vascular interactions; and (3) exploring combination therapy strategies targeting both metabolic pathways (e.g., AMPK activators) and immune pathways (e.g., C5 inhibitors) in preclinical trials. These translational research steps will facilitate the translation of mechanistic discoveries into clinical applications, addressing current unmet clinical needs in DR treatment.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cb\u003eAdditional information\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was funded by the Applied Basic Research Foundation of the Department of Science and Technology of Yunnan Province, Yunnan, China, grant number 202201AY070001-036; National Natural Science Foundation Project, grant number 82260207; The Ocular Trauma Innovation Team of the First Affiliated Hospital of Kunming Medical University, Yunnan, grant number 202405AS350013.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, YX.C. and LN.R.; methodology, YX.C.; software, YX.C..; validation, DL.L., L.S. and QR.L.; formal analysis, YX.C.; investigation, L.P. and T.L.; resources, QC.S.; data curation, XR.Z.,L.S. and BY,Z.; writing\u0026mdash;original draft preparation, YX.C.; writing\u0026mdash;review and editing, YX.C.; visualization, LN.R.; supervision, L.Y.; project administration, L.Y.; funding acquisition, L.Y.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data presented in this study are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSachdeva, M. 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Computational analysis of phytocompounds in Centella asiatica for its antifibrotic and drug-likeness properties - Herb to drug study. \u003cem\u003eHeliyon\u003c/em\u003e 10, e33762 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.heliyon.2024.e33762\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.heliyon.2024.e33762\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"diabetic retinopathy, proteomics, metabolomics, multi-omics analysis","lastPublishedDoi":"10.21203/rs.3.rs-7345162/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7345162/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eDiabetic retinopathy (DR) is a microvascular complication of diabetes with its exact underlying mechanisms have not been fully elucidated. This study aimed to investigate the effects of key proteins and metabolites on the development of DR.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUndiluted vitreous fluid samples were collected from eight patients with proliferative diabetic retinopathy (PDR) and six non-diabetic idiopathic macular hole (iMH) controls. Integration of TMT-tagged quantitative proteomics and untargeted metabolomics analyses was combined with bioinformatics approaches (PCA, differential expression, PPI network, OPLS-DA, pathway enrichment). Key results were validated by ELISA and immunohistochemistry.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSeven key proteins with six key metabolites were identified to be significantly dysregulated in the PDR. In the vitreous body and retinal nerve fiber layer of the DR group, CD5L expression was upregulated, while CLU was downregulated with SERPINF1 (PEDF). These molecules were co-enriched in pathways such as the \u0026ldquo;complement and coagulation cascade\u0026rdquo; and \u0026ldquo;prion disease,\u0026rdquo; suggesting a common mechanism of abnormal vascular permeability, inflammatory response, and microthrombosis. Disturbances in creatine metabolism suggested AMPK-related energy dysregulation, and the interaction between CD5L and microglia emphasized its neuroinflammatory regulatory function.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study revealing biomarkers and therapeutic targets, which provide new ideas for diagnosis and precise intervention.\u003c/p\u003e","manuscriptTitle":"Combined proteomics and metabolomics analyses revealed molecular signatures associated with proliferative diabetic retinopathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 16:31:34","doi":"10.21203/rs.3.rs-7345162/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-05T20:52:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T21:34:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95418859407366701671604792540181358400","date":"2025-10-25T17:04:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T08:09:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-08T11:27:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213664258740602732081550151066823681224","date":"2025-09-22T11:40:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335680896382812379897910346674942849226","date":"2025-09-19T06:53:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T19:20:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T14:24:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-28T08:53:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T06:54:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-14T06:49:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9bce872b-3b40-43d6-b70c-79f9eeb180c7","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55462064,"name":"Health sciences/Biomarkers"},{"id":55462065,"name":"Health sciences/Diseases"},{"id":55462066,"name":"Health sciences/Endocrinology"},{"id":55462067,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-02-23T16:08:03+00:00","versionOfRecord":{"articleIdentity":"rs-7345162","link":"https://doi.org/10.1038/s41598-026-40551-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-18 15:57:34","publishedOnDateReadable":"February 18th, 2026"},"versionCreatedAt":"2025-09-30 16:31:34","video":"","vorDoi":"10.1038/s41598-026-40551-1","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40551-1","workflowStages":[]},"version":"v1","identity":"rs-7345162","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7345162","identity":"rs-7345162","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}