Proteomic Analysis Reveals Lipid Metabolism Disruption and Key Targets in ARPE-19 Cells after RNF13 Knockdown

Proteomic Analysis Reveals Lipid Metabolism Disruption and Key Targets in ARPE-19 Cells after RNF13 Knockdown | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Proteomic Analysis Reveals Lipid Metabolism Disruption and Key Targets in ARPE-19 Cells after RNF13 Knockdown Liyin Wang, Xin Yu, Chen Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5741175/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Despite RNF13 is dysregulated in retinal degeneration models, its precise role in retinal function is not well understood. Previous studies suggest RNF13 may affect cellular pathways, including lipid metabolism, in the retina. However, the molecular mechanisms underlying its effects remain unclear. This study aims to identify the pathways regulated by RNF13 and key molecular targets involved in retinal degeneration. Methods To investigate the impact of RNF13 on the retina, the ARPE-19 cell line was transduced with lentivirus-RNF13-RNAi to interfere RNF13 expression. Cell viability was assessed via CCK-8 assay. Differentially expressed proteins (DEPs) were identified using tandem mass tag proteomics and further analyzed with KEGG pathway analysis, Gene Ontology (GO) annotation, and protein-protein interaction network analysis. Western blotting, qRT-PCR, and parallel reaction monitoring (PRM) were used to validate and identify RNF13-regulated targets. Oil Red O staining and CCK-8 were employed to assess phenotypic changes induced by siRNA-mediated SCD knockdown. Results A total of 340 DEPs were identified, with 80 downregulated and 260 upregulated after RNF13 knockdown. GO analysis showed that DEPs were enriched in "membrane" components and linked to “cellular processes” and “metabolic processes”, involving “binding” and “catalytic activity.” KEGG pathway analysis revealed significant disruptions in metabolic and PPAR signaling pathways. Western blotting, qRT-PCR, and PRM validation indicated that proteins in PPAR signaling pathway, such as SCD, were potential downstream targets regulated by RNF13. SCD knockdown demonstrated significant reduction in Oil Red O staining area and inhibition of cellular proliferation. Conclusions RNF13 knockdown primarily disrupts lipid metabolism via interference with the PPAR signaling pathway. SCD emerged as a key target among multiple PPAR-related proteins, suggesting its important role in retinal degeneration. Retinal degeneration ARPE-19 RNF13 Proteomics Lipid metabolism PPAR signalling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The really interesting new gene (RING) finger (RNF) protein, which includes the RING motif, functions as an E3 ubiquitin ligase that facilitates the binding of ubiquitin (Ub) to specific target proteins [1]. The RNF protein family has numerous subtypes which serves a pivotal function in maintaining various physiological functions. RNF182, a member of the RNF family, has shown great importance in suppressing non-small cell lung cancer; Bap-induced RNF182 suppression enhances lung cancer tumorigenesis by stimulating AhR and facilitating aberrant methylation [2]. In addition, a previous study unraveled that RNF213 is correlated with moyamoya disease by aggravating inflammatory reactions and destructing tight junction, which disrupts pericyte homeostasis and blood‒brain barrier integrity [3]. As a core member of the RNF family, RNF13 (an E3 ubiquitin-protein ligase) has recently been identified to play a crucial role in eye development and disease. Recently, in our previous studies, through microarray analyses of genome-wide expression patterns, we observed that RNF13 was significantly decreased in the retinas of mice with early-stage retinal photoreceptor degeneration (RPD) following low-intensity LED light-induced damage [4]. Meanwhile, Edvardson S et al. reported that a patient carrying an RNF13 heterozygous mutation (c.935T > C [p.Leu 312Pro] and c.932T > C [p.Leu 311Ser]) presented with blindness, congenital microcephaly and maldevelopment [5]. Nevertheless, the specific effects of RNF13 on retinal tissue are still elusive. Retinal pigment epithelium (RPE) cells contribute to clearing outer segments of photoreceptors to maintain a normal state. Photoreceptors degenerate when RPE function is impaired; therefore, the RPE cells is critical for retinal degeneration [6]. Building on the above, this study focused on the role of RNF13 in RPE cells and successfully established RNF13-knockdown ARPE-19 cell lines to investigate the effects of RNF13 depletion on RPE cell function. The influence of genes on cell functions has been reflected at multiple levels, including the genome, transcriptome, proteome, and even the metabolome. Among these, the proteome plays a crucial role, as proteins are directly involved in carrying out cellular processes, making proteomics an essential tool for understanding the functional mechanisms at the molecular level. With the emergence of mass spectrometry, proteomic methods, represented by tandem mass tag (TMT) labelling, have been extensively applied in the study of protein expression levels, posttranslational modifications, protein-protein interactions, and interactions between proteins and other biomolecules [7]. Therefore, this study mainly used mass spectrometry-based proteomics to dissect the proteomic profile of RPE cells following RNF13 depletion. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were integrated to identify the critical pathways and to characterize the affected cellular functions and processes. Among top 10 enriched terms, the pathway of peroxisome proliferator-activated receptors (PPARs) was chosen for expression validation. As many studies have indicated [8], a lack of RNF13 disrupts lipid metabolism in ARPE-19 cells. Consequently, our findings might provide a comprehensive understanding of the effect of RNF13 as well as new insights into the mechanism of RNF13-mediated protection. 2. Materials and methods 2.1 Cell culture Human ARPE-19 (Hunan Fenghui Biotechnology Co., Ltd.; China) cells, derived from a 19-year-old male donor and authenticated by short tandem repeat (STR) analysis (Supplementary Fig. S1 ), were used in the cell experiments. The cells were cultured in complete DMEM/F-12 medium with 10% fetal bovine serum and maintained at proper temperature in a humidified environment with 5% CO2. 2.2 Establishment of stable RNF13-knockdown ARPE-19 cells The sequences of the shRNAs used in this study: shRNA-NC (Negtive Control), 5’- TTCTCCGAACGTGTCACGT-3’; and shRNA-RNF13, 5’-GAACGGGATTACAACATAGCA-3’. Lentivirus-RNF13-RNAi (MOI of 100) was transduced into ARPE-19 cells at 20% confluence. Puromycin (1 µg/ml) (GeneChem Co., Ltd.; China) was used to select stably transducted cells. The transduction efficiency was assessed by visualizing GFP-labeled fragment under fluorescence microscope (Olympus Co.; Japan). 2.3 Transient transfection of small interfering RNA (siRNA) ARPE-19 cells were seeded in 6-well plates with 1×10 5 cells. The supernatant was replaced with Opti-MEM (Thermo Fisher Scientific; USA) containing si-NC (sequence: 5’-UUCUCCGAACGUGUCACGUTT-3’, 5’-ACGUGACACGUUCGGAGAATT-3’) or si-SCD (sequence: 5’-GAUAUGCUGUGGUGCUUAATT-3’, 5’-UUAAGCACCACAGCAUAUCTT-3’) (after 24 hours. The cells were incubated for the desired transfection duration. 2.4 Cell viability evaluation Stable RNF13-knockdown ARPE-19 cells were cultured in 96-well plates overnight. ARPE-19 cells were transfected with si-NC or si-SCD for 48h in 96-well plates as described above. Subsequently, the medium in each well was changed by 100 µL of 10% CCK-8 reagent in serum-free media (Biosharp Life Science; China) for 1 h at 37°C. The mean ODs were assessed to determine cell viability (Molecular Devices; USA), with background absorbance from blank wells subtracted to minimize artifacts. Cell viability was determined based on mean OD values, with each condition tested in quadruplicate wells and the entire experiment repeated three times independently to ensure reproducibility. 2.5 Proteomic sample preparation and quality test Lentivirus-transfected ARPE-19 cells were disrupted in SDT buffer supplemented with Tris-HCl (100 mM, pH 7.6) and 4% SDS. The protein quantification was performed using the BCA Protein Assay Kit (Beyotime; China). Total protein (20 µg) was mixed with 6× loading buffer, heated to 100°C for 5 minutes, and subsequently separated by a 12.5% SDS‒PAGE gel. Then, the gels were stained with Coomassie blue (R-250) to showed the protein bands (Supplementary Fig. S2). 2.6 TMT labelling The peptide compound (100 µg/sample) was labelled using TMT reagent (Thermo Fisher Scientific; USA). Before TMT labelling, the reagent was reconstituted using anhydrous acetonitrile; the prepared reagent was suitable for the analysis of approximately 100 mg of protein. Forty-one microlitres of TMT labelling reagent was added to each 100 µL sample. A total of six samples were then respectively labelled using six distinct tags. Subsequently, the mixtures were incubated at room temperature for 2 hours. The hydroxylamine (5%, 8µL) was added to each reaction mixture for 15 min to halt trypsin activity. The labelled samples were consolidated into the fresh microcentrifuge tube. Subsequently, 1 mL of the mixed peptides was dried using a vacuum concentrator. 2.7 TMT-labelled peptide fractionation with RP chromatography An Infinity II HPLC system (Agilent 1260) was used for RP chromatography. The prepared peptide mixture was first diluted using buffer A (pH 10.0), which consisted of 10 mM ammonium formate (HCOONH4) and 5% acetonitrile (ACN). Subsequently, the diluted peptide mixture was attached to a specific column, namely, an XBridge Peptide BEH C18 Column 130A. The elution of the peptides was carried out using the gradient elution (1 ml/min). The elution process involved the use of buffer B (pH = 10.0, 85% ACN, 10 mM HCOONH4). The peptides were washed in 0–7% buffer B (5 min), 7–40% buffer B (5 to 40 min), 40–100% buffer B (45–50 min), and 100% buffer B (50–65 min). The elution process was monitored using a UV light trace (214 nm). Fractions were gathered at one-minute intervals from 5 to 50 minutes and subsequently combined to form 10 larger fractions. The vacuum centrifugation was performed at 45°C to remove the solvent and concentrate the peptides in the combined fractions. 2.8 LC–MS/MS analysis The experiment lasted 60 minutes and utilized a Q Exactive mass spectrometer (Thermo Fisher Scientific; USA) in conjunction with an Easy nLC system (Thermo Fisher Scientific; USA). MS data were obtained by dynamically selecting the most abundant precursor ions at a survey scan ratio of 350–1800 m/z for HCD fragmentation. Survey scans were performed at a resolution of 70,000 at m/z 200 with an AGC target of 3e6 and a maximum injection time (maxIT) of 50 ms. MS2 scans were conducted at a resolution of 17,500 for HCD spectra at m/z 200. The ions with a minimum intensity of 2e3 and a charge state ranging from 2 to 6 were chosen for fragmentation. The normalized collision energy was maintained at 30 eV. The dynamic exclusion time for the selected ions was set at 30 seconds. 2.9 Data analysis The MS/MS raw files were tackled using the MASCOT engine (Matrix Science 2.6; UK) and searched against the Protein Database (UniProt). The precursor mass tolerance was set to 10 ppm, and a tolerance of 0.05 Da was applied for MS2 fragment matching. Except TMT labels, the fixed modification applied was carbamidomethyl (C). The variable modifications comprised acetyl (protein N-term) and oxidation (M). Proteins exhibiting a fold change ≥ 1.2 and a p value ≤ 0.05 (determined using Student's t test) were identified as DEPs. 2.10 Bioinformatics analysis A global heatmap was generated using Matplotlib v3.3.3 to visualize the DEPs. The GO annotations [molecular function (MF), cellular component (CC), and biological process (BP)] for the identified proteins were obtained from the Protein Database (NCBI). The KEGG database was utilized for pathway analysis. The software applied in GO and KEGG analysis was SciPy v1.7.1. The Fisher’s exact test was employed to determine significantly enriched pathways by contrasting the count of DEPs with the overall number of proteins associated with each pathway. Finally, protein‒protein interactions (PPIs) were evaluated utilizing the STRING database ( https://string-db.org/ ). 2.11 qRT-PCR analysis TRIzol reagent (Takara; Japan) was utilized to extract the total RNA from the cell samples. Subsequently, reverse transcription was carried out with HiScript III RT SuperMix for qRT-PCR (Vazyme; China). The gene expressions were quantified using kit from Vazyme, China. All primers used for the genes are enumerated in Supplementary Table S1 . 2.12 Protein extraction and validation Cells were disrupted in RIPA buffer mixed with 1% phosphatase inhibitors, 1% phenylmethanesulfonyl fluoride, and 1% protease inhibitor (MedChemExpress; USA). The protein concentration was determined after centrifugation for 15 min at 13,000×g. Quantified protein samples were all heated to 100 ℃ for 5 min, and protein (30 µg) was loaded and run on 4–20% gels (GenScript; USA) and then transferred to polyvinylidene fluoride membranes. Phosphate-buffered saline (PBS) was loaded into the first lane as a negative control to rule out potential antibody-mediated false-positive signals. 5% nonfat dry milk was formulated freshly to blocked the membrane at indoor temperature. After that the membranes were sequentially immersed in primary antibodies and secondary antibodies following standard protocols. Protein bands on membrane were visualized through enhanced chemiluminescence (ECL) reagents (FD8020; Fude Biology; China) and scanned with a Bio-Rad exposure system (Bio-Rad; USA). ImageJ was used to quantify the relative band intensity (optical density) of target proteins. The antibodies utilized for western blotting: anti-GAPDH (10494-1-AP; Proteintech; 1:5000); anti-SCD (db4883; Diagbio; 1:500); anti-FADS2 (ab314317; Abcam; 1:1000); anti-CPT1α (ab220789; Abcam; 1:1000); anti-RNF13 (ab151601; Abcam; 1:500); anti-HMGCS1 (A3916; ABclonal; 1:1000); and anti-rabbit IgG HRP-linked antibody (BL003A; Biosharp Life Science; 1:5000). The parallel reaction monitoring (PRM) procedure for analysing HMGCS1 expression is described in the Appendix Materials. The peptides used were confirmed by TMT-based quantitative proteomic analysis and are presented in Supplementary Table S2. 2.13 Oil red O staining Cell samples were washed with PBS and then stained with Oil Red O working solution (Njjcbio, Co., Ltd.; China) for 15 min at room temperature. After staining, excess dye was removed by washing with PBS for 15 min. Counterstaining was done using Harris hematoxylin for 5 min, with subsequent rinsing in PBS. The samples were finally mounted with a coverslip using a mounting medium and examined under a light microscope. 2.14 Statistical analysis Statistical analysis was conducted using SPSS v19.0 for Windows (IBM, Armonk, USA). The results are presented as the mean ± standard deviation (SD) for at least three biological samples. A t-test was used for pairwise comparisons between the two groups. Statistical significance was defined as p ≤ 0.05. 3. Results 3.1 Effect of RNF13 knockdown on cellular viability of ARPE-19 cells To identify the relationship between RNF13 expression and RPE degeneration for its potential contribution to retinopathy progression, we constructed an in vitro RNF13 knockdown model with lentivirus-mediated RNF13-RNAi. The expression of the reporter gene green fluorescence protein (GFP) was used to determine the transduction efficiency, which was greater than 90% at 72 h post-transduction (Fig. 1 A). The knockdown efficiency of RNF13 was verified in ARPE-19 cells using qRT-PCR and western blotting (Fig. 1 B-D). Additionally, the impact of RNF13 knockdown on ARPE-19 cell proliferation was assessed using a CCK-8 assay. A lower cell density was observed in the RNF13-knockdown cells than in the wild-type ARPE-19 cells (Fig. 1 E). Results demonstrated that lentivirus-mediated RNF13-RNAi efficiently reduced RNF13 expression and significantly impaired the viability of ARPE-19 cells. 3.2 Proteome analysis of RNF13-knockdown ARPE-19 cells To clarify the biological effect of RNF13 on the RPE, we compared the whole-cell proteomes between the vehicle and KD groups using TMT-based quantitative proteomics technology. A total of 6400 proteins were found to be highly dependent proteins, 340 of which were considered DEPs, with changes in expression over than 1.2 times with p values < 0.05. In the KD group vs. the vehicle group, we found 260 upregulated DEPs and 80 downregulated DEPs, as displayed in the volcano plot (Fig. 2 A). The DEPs in each group were examined and visually represented through a hierarchical clustering heatmap (Fig. 2 B). A total of 340 DEPs were annotated to BP, CC, and MF by GO analysis. After that, the GO functional enrichment of the DEPs was speculated using Fisher’s exact test. The top 10 enriched GO terms in each major functional category are manifested in Fig. 3 A. In the BP category, “cellular process”, “biological regulation” and “metabolic process” were found to be enriched with the most dysregulated proteins. In the CC category, “cell part” and “membrane” were found to be enriched with dysregulated proteins. In the MF category, “binding” and “catalytic activity” were enriched with dysregulated proteins. The subcellular localization results exhibited that the upregulated proteins were located mainly in the cytosol (32.69%), nucleus (31.92%) and plasma membrane (11.15%). Moreover, 26.25% of the downregulated proteins were localized to the plasma membrane, 20% to the mitochondria, 18.75% to the cytosol, 18.75% to the extracellular region and 16.25% to other localized areas (Fig. 3 B, C). KEGG analysis objectively classified the pathways enriched in DEPs, with metabolic pathways and the PPAR signaling pathway showing significant enrichment (Fig. 3 D). The PPAR signaling pathway is closely associated with energy metabolism, encompassing both lipid and glucose metabolism [9, 10]. Studies by Sun et al. and Wen et al. have shown that activating this pathway helps reduce diabetic retinopathy and mitigates oxidative stress-induced damage to RPE cells [11, 12]. Considering “metabolic pathways” was too broad to be interpreted in a limited bioinformatics analysis, we focused mainly on the DEPs in the PPAR signalling pathway in subsequent experiments. Five proteins (SCD, CPT1α, FADS2, PLIN2, and HMGGC1) were involved in the PPAR signalling pathway and exhibited the lowest p value among the proteins within the identified pathways (Fig. 3 E). 3.3 Validation of PPAR signalling pathway-associated proteins determined by the TMT According to the proteome analysis results, 5 DEPs were selected and validated via qRT-PCR to prove the reliability of sequencing data: perilipin-2 (PLIN2), stearoyl-CoA desaturase (SCD), carnitine O-palmitoyltransferase 1 (CPT1α), hydroxymethylglutaryl-CoA synthase 1 (HMGCS1) and acyl-CoA 6-desaturase 2 (FADS2) (Fig. 4 A). The qRT-PCR quantification were consistent with the protein expression data for all the genes. A network of physical and functional PPIs was constructed to predict interactions (PPI enrichment p value < 1.0 e-16) among the above five proteins. SCD was predicted to play a critical role in this network (Fig. 4 B). Proteins linked to more than three other proteins in the PPI network were validated by western blotting and PRM (Fig. 4 C-E). The findings aligned with the predicted outcomes, suggesting that RNF13 knockout in RPE notably deregulates the PPAR signaling pathway. 3.4. Imapct of SCD knockdown on cellular viability and lipid metabolism of ARPE-19 cells Based on PPI predictions, this research next evaluated the phenotypic consequences of SCD knockdown in ARPE-19 cells using siRNA. Transfection with 50 nM si- SCD significantly downregulated SCD mRNA expression (Fig. 5 A), which was further confirmed at the protein level by quantitative western blotting analysis (Fig. 5 B–C). To assess the impact of SCD suppression on lipid metabolism, Oil Red O staining was performed to visualize intracellular lipid droplets, which serve as dynamic indicators of neutral lipid storage and turnover. Notably, the si- SCD group exhibited a marked reduction in the area of Oil Red O-positive lipid droplets compared to the si-NC control group (Fig. 5 D–E), suggesting impaired lipid accumulation. Additionally, it was observed decreased cell density in the si- SCD group (Fig. 5 F). Collectively, these data demonstrated that siRNA-mediated SCD knockdown not only efficiently suppresses SCD expression but also disrupts lipid homeostasis and compromises ARPE-19 cell viability. 4. Discussion In recent years, proteomics has impacted research in numerous fields by identifying and selecting DEPs as targets. Since the RNF protein was first identified in 1993 [13], the elucidation of the function of RNF proteins in ophthalmic diseases has been a crucial task, and the application of proteomics offers a vital pathway for exploring the mechanism of action of RNF13. As an E3 ubiquitin ligase, RNF13 was found to be involved in the pathogenesis of multiple diseases, such as cardiac hypertrophy, neurological disease, and so on. Guo et al. established by RNA-seq analysis of RNF13-knockout mice with cardiac hypertrophy that RNF13 exerts cardioprotective effects through activation of the p62-NRF2 antioxidant pathway [14]. Through RNF13 inhibition and overexpression experiments in mammalian cell lines, Arshad et al. established RNF13 as a novel regulatory node connecting ER stress to apoptosis via the IRE1α-TRAF2-JNK signaling axis [15]. Although some studies have noted associations between RNF13 and certain ocular diseases such as uveal melanoma [16], the regulatory mechanisms of RNF13 in ophthalmic pathologies remain unexplored. On the basis of the microarray analyses of genome-wide expression patterns in the retinas of RPD mice in our previous study [4], the roles and mechanisms of the RNF13 were characterized in the light of biological process (BP), cellular component (CC), and molecular function (MF) by GO analysis. These findings suggested that the DEPs were located mainly in cell parts and that their paired binding functions deserve attention. Additionally, in virtue of the GO enrichment analysis, for the BP category, the DEPs showed notable enrichment in cellular processes and biological regulation; for the CC category, the majority of DEPs were associated with cell parts, organelles, membranes and protein-containing complexes; and for the CC category, the DEPs participated in binding and catalytic activity. These results indicated the extensive function of RNF13 in RPE cells. Through KEGG analysis, we determined that RNF13 interference was related to several lipid-related metabolic pathways: ketone body synthesis and degradation, PPAR signalling pathway, fatty acid metabolism, and steroid biosynthesis. The ketone body synthesis and degradation have been shown to be associated with retinal diseases. Beta-hydroxybutyrate (BHB), an activator of hydroxycarboxylic acid receptor 2, has been shown to reduce NOD-like receptor protein 3 inflammasome activity and minimize harm to the retina in diabetic retinopathy (DR) mouse model [17]. Gambhir et al. reported that the ketone body BHB was increased and its receptor GPR109A was overexpressed in the RPE layer of DR mice, potentially functioning as mediators of inflammation [18]. In addition, the PPAR signalling pathway is necessary for activating and controlling the ketogenic transcriptional programme [19], which includes CPT1α, which was found to be differentially expressed based on the proteomic analysis conducted in the current study. Fatty acid metabolism contributes to the following four major processes in some tissues: anabolic processes, catabolism to generate energy division, ferroptosis, and signalling molecule production [20]. Steroids are vital constituents of cell membranes and serve as signalling molecules that play a crucial role in maintaining cellular homeostasis [21]. Fatty acid metabolism and steroid biosynthesis in the RPE retinal are both related to SCD, which is a constituent of the PPAR signalling pathway. Therefore, we speculated that the PPAR signalling pathway might be a critical pathway through which RNF13 deletion induces a change in phenotype. In the future, more validation experiments should be implemented centred around the DEPs of the PPAR pathway. Proteomic analysis unraveled that the proteins enriched in the PPAR pathway were PLIN2, SCD, CPT1α, HMGCS1 and FADS2. qRT-PCR was used to provide additional support for proteomic analysis results: CPT1α and PLIN2 expression was upregulated after RNF13 knockdown, but SCD, HMGCS1 and FADS2 expression was downregulated at the mRNA level. To get deeper insights into the potential interactions of these DEPs, we subsequently analyzed PPI proteomics network using STRING software. SCD was predicted as the central core of network proteins of the PPAR signalling pathway in RNF13-knockdown ARPE-19 cells; these findings were validated and consistent with the proteomics results. SCD, also called delta-9-desaturase, is the membrane-bound enzyme that restricts the reaction rate of biosynthesis of unsaturated fatty acids [22, 23]. SCD expression is significantly associated with the invasion, proliferation, and survival of tumor cells due to lipotoxicity mediated by saturated fatty acids [24]. Given that SCD expression was downregulated in RNF13-knockdown ARPE-19 cells and bioinformatically identified as the central regulator of lipid metabolism, we investigated the phenotypic consequences of SCD knockdown in vitro . The results demonstrated that siRNA-mediated SCD knockdown not only disrupted normal lipid droplet biogenesis but also significantly impaired cellular viability. These findings are consistent with previous report [25]. Collectively, these data partially support the hypothesis that RNF13 regulates both lipid metabolism and proliferative capacity in ARPE-19 cells through SCD-dependent mechanisms. To provide more robust evidence supporting this hypothesis, rescue experiments should be conducted in future studies. Moreover, the validation results of protein level for CPT1α, FADS2 and HMGCS1 were in line with the proteomics results. These findings confirmed the reliability of the TMT-based proteomics analysis. Dysregulated lipid metabolism has been observed in the RPE in age-related macular degeneration pathology [26]. In humans, CPT1α is predominantly expressed in the RPE and facilitates the initial step of β-oxidation by conveying long-chain fatty acids in the cytosol to the matrix of mitochondria [27]. CPT1α overexpression causes an excessive influx of fatty acids into mitochondria, leading to oxidative damage of mitochondrial DNA and mitochondrial dysfunction, which could explain the inhibition of cell viability in the KD group. FADS2 are membrane-bound enzymes functioning to catalyse the constitution process of long-chain polyunsaturated fatty acids, like eicosapentaenoic acid and docosahexaenoic acid [28], the depletion of which has been shown to enhance apoptosis in zebrafish retinas treated with intense light [29]. HMGCS1 is a crucial hepatic gene responsible for cholesterol synthesis and a key enzyme that facilitates fat utilization as an energy source without carbohydrates [30]. It modulates lipid homeostasis by affecting both cellular structure and enzymatic protein mechanisms [31, 32]. Previous studies have indicated that PPARs might regulate and/or influence lipid metabolism through the regulation of HMGCS1 [33]. We speculated that the downregulation of RNF13 gene expression can suppress sterol synthesis by downregulating the expression of HMGCS1. Moreover, 5 peptides related to the HMGCS1 protein were captured by TMT-based proteomics and verified with PRM. Further exploration should be conducted to clarify the HMGCS1 functions as one of the primary regulators.Notably, as a E3 ubiquitin ligase subtype, RNF13 might regulate the abovementioned molecules through the disruption of Ub activity. Therefore, further studies could focus on elucidating whether RNF13 regulates the ubiquitination of these lipid metabolism-related molecules via more in vitro and in vivo experiments. Moreover, whether SCD in the PPAR pathway is the decisive factor involved in ARPE-19 cell viability following RNF13 knockdown could be further investigated through SCD overexpression and interference in subsequent research. 5. Conclusions In summary, the results of this study demonstrated that RNF13 plays a critical role in maintaining ARPE-19 cell biological function. TMT-based proteomics revealed that RNF13 knockdown significantly decreased RPE cell viability and dysregulated lipid metabolism. The PPAR pathway and associated molecules including SCD, may mediate the detrimental effects of RNF13 knock down on RPE cell function. Abbreviations RNF13 Really interesting new gene finger protein 13 RPD Retinal photoreceptor degeneration RPE Retinal pigment epithelium STR Short tandem repeat siRNA Small interfering RNA NC Negtive control TMT Tandem mass tag labelling DEP Differentially expressed protein KEGG Kyoto Encyclopedia of Genes and Genomes GO Gene ontology PPAR Peroxisome proliferator-activated receptor qRT-PCR Real-time quantitative polymerase chain reaction PBS Phosphate-buffered saline KD Knockdown PRM Parallel reaction monitoring PPI Protein-protein interaction MF Molecular function CC Cellular component BP Biological process SCD Stearoyl-coa desaturase CPT1α Carnitine O-palmitoyltransferase 1 FADS2 Acyl-coa 6-desaturase 2 PLIN2 Perilipin-2 HMGGC1 Hydroxymethylglutaryl-coa synthase 1 Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data availability Data will be made available on request. Competing interest The authors declare no competing interests. Funding This research received funding from the National Natural Science Foundation of China (Grant Number 82201194). Authors’ contributions statement C.X. designed the study. L.W. and X.Y. acquired and analyzed the data. X.Y. conducted the additional experiments during manuscript revision. L.W. and C.X. interpreted the data and drafted the initial manuscript. All authors reviewed the manuscript. Acknowledgements Not applicable. References C. Cai, Y.D. Tang, J. Zhai, C. Zheng, The RING finger protein family in health and disease, Signal Transduct Target Ther 7(1) (2022) 300. Y. Liu, L. Ouyang, C. Mao, Y. Chen, N. Liu, L. Chen, Y. Shi, D. Xiao, S. Liu, Y. Tao, Inhibition of RNF182 mediated by Bap promotes non-small cell lung cancer progression, Front Oncol 12 (2022) 1009508. W. Li, X. Niu, Y. Dai, X. Wu, J. Li, W. 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Li, RNF13, a RING finger protein, mediates endoplasmic reticulum stress-induced apoptosis through the inositol-requiring enzyme (IRE1alpha)/c-Jun NH2-terminal kinase pathway, J Biol Chem 288(12) (2013) 8726-8736. C. Ness, K. Katta, O. Garred, T. Kumar, O.K. Olstad, G. Petrovski, M.C. Moe, A. Noer, Integrated differential DNA methylation and gene expression of formalin-fixed paraffin-embedded uveal melanoma specimens identifies genes associated with early metastasis and poor prognosis, Exp Eye Res 203 (2021) 108426. M.C. Trotta, R. Maisto, F. Guida, S. Boccella, L. Luongo, C. Balta, G. D'Amico, H. Herman, A. Hermenean, C. Bucolo, M. D'Amico, The activation of retinal HCA2 receptors by systemic beta-hydroxybutyrate inhibits diabetic retinal damage through reduction of endoplasmic reticulum stress and the NLRP3 inflammasome, PLoS One 14(1) (2019) e0211005. D. Gambhir, S. Ananth, R. Veeranan-Karmegam, S. Elangovan, S. Hester, E. Jennings, S. Offermanns, J.J. Nussbaum, S.B. Smith, M. Thangaraju, V. Ganapathy, P.M. Martin, GPR109A as an anti-inflammatory receptor in retinal pigment epithelial cells and its relevance to diabetic retinopathy, Invest Ophthalmol Vis Sci 53(4) (2012) 2208-17. M. Grabacka, M. Pierzchalska, M. Dean, K. Reiss, Regulation of Ketone Body Metabolism and the Role of PPARα, Int J Mol Sci 17(12) (2016). J. Miska, N.S. Chandel, Targeting fatty acid metabolism in glioblastoma, J Clin Invest 133(1) (2023). P. Melchinger, B.M. Garcia, Mitochondria are midfield players in steroid synthesis, Int J Biochem Cell Biol 160 (2023) 106431. F.S. Falvella, R.M. Pascale, M. Gariboldi, G. Manenti, M.R. De Miglio, M.M. Simile, T.A. Dragani, F. Feo, Stearoyl-CoA desaturase 1 (Scd1) gene overexpression is associated with genetic predisposition to hepatocarcinogenesis in mice and rats, Carcinogenesis 23(11) (2002) 1933-6. W. Samuel, R.K. Kutty, T. Duncan, C. Vijayasarathy, B.C. Kuo, K.M. Chapa, T.M. Redmond, Fenretinide induces ubiquitin-dependent proteasomal degradation of stearoyl-CoA desaturase in human retinal pigment epithelial cells, J Cell Physiol 229(8) (2014) 1028-38. M.L. Watson, M. Coghlan, H.S. Hundal, Modulating serine palmitoyl transferase (SPT) expression and activity unveils a crucial role in lipid-induced insulin resistance in rat skeletal muscle cells, Biochem J 417(3) (2009) 791-801. A. Pariente, Ã. Pérez-Sala, R. Ochoa, R. Peláez, I.M. Larráyoz, Genome-Wide Transcriptomic Analysis Identifies Pathways Regulated by Sterculic Acid in Retinal Pigmented Epithelium Cells, Cells 9(5) (2020). M. Landowski, C. Bowes Rickman, Targeting Lipid Metabolism for the Treatment of Age-Related Macular Degeneration: Insights from Preclinical Mouse Models, J Ocul Pharmacol Ther 38(1) (2022) 3-32. Z.S. Ulhaq, Y. Ogino, W.K.F. Tse, Deciphering the pathogenesis of retinopathy associated with carnitine palmitoyltransferase I deficiency in zebrafish model, Biochem Biophys Res Commun 664 (2023) 100-107. D.M. Merino, D.W. Ma, D.M. Mutch, Genetic variation in lipid desaturases and its impact on the development of human disease, Lipids Health Dis 9 (2010) 63. Y. Ashikawa, Y. Nishimura, S. Okabe, Y. Sato, M. Yuge, T. Tada, H. Miyao, S. Murakami, K. Kawaguchi, S. Sasagawa, Y. Shimada, T. Tanaka, Potential protective function of the sterol regulatory element binding factor 1-fatty acid desaturase 1/2 axis in early-stage age-related macular degeneration, Heliyon 3(3) (2017) e00266. E. Metwally, S.M. Farouk, A.K. Osman, Molecular cloning and cellular expression of the cholesterol synthesizing enzymes during the prenatal development of the optic nerve in the dromedary camel (Camelus Dromedarius), Acta Histochem 121(5) (2019) 584-594. W.L. Miller, A brief history of adrenal research: steroidogenesis - the soul of the adrenal, Mol Cell Endocrinol 371(1-2) (2013) 5-14. T.F. Liu, J.J. Tang, P.S. Li, Y. Shen, J.G. Li, H.H. Miao, B.L. Li, B.L. Song, Ablation of gp78 in liver improves hyperlipidemia and insulin resistance by inhibiting SREBP to decrease lipid biosynthesis, Cell Metab 16(2) (2012) 213-25. M. Kotula-Balak, E. Gorowska-Wojtowicz, A. Milon, P. Pawlicki, W. Tworzydlo, B.J. Płachno, I. Krakowska, A. Hejmej, J.K. Wolski, B. Bilinska, Towards understanding leydigioma: do G protein-coupled estrogen receptor and peroxisome proliferator-activated receptor regulate lipid metabolism and steroidogenesis in Leydig cell tumors?, Protoplasma 257(4) (2020) 1149-1163. Additional Declarations No competing interests reported. Supplementary Files 4.15Supplementarymaterials.docx floatimage1.png Graphical abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5741175","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443908966,"identity":"c3995515-cb9e-46a6-92f0-f30a310635f5","order_by":0,"name":"Liyin Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Liyin","middleName":"","lastName":"Wang","suffix":""},{"id":443908967,"identity":"593f9d85-0c76-4fd8-a7dd-61e9ff609f59","order_by":1,"name":"Xin Yu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yu","suffix":""},{"id":443908968,"identity":"b305c138-36ba-439b-a7c0-4522809fb36d","order_by":2,"name":"Chen Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACZjBiYGBsYD5w4MMP0rSwJR6c2UO8RSDAY3yYg40I5QbHmQ9+Lqi4Y888I+fDYQYeBnl+sQP4tUg2syVLzzjzjJlxRu6GwwUWDIYzZyfg18LPzGPGzNt2mA2sZQYPQ4LBbQJa2Jj5vzHz/jvMwzgj58FhHjYitABtYWPmbTgsAdTCQJwWoF+MpXmOHTZg7HlmAAxkCcJ+MTh/+OFnnprD9obtyY8/fPhhI88vTUALHBg2gCkJIpWDgDwJakfBKBgFo2CEAQBYaT/FAhlLGQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-12-31 10:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5741175/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5741175/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80796939,"identity":"f687a477-043e-453e-a509-108a23280d97","added_by":"auto","created_at":"2025-04-17 07:49:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":291543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeasurement of RNF13 knockdown efficiency cell viability in ARPE-19 cells. \u003c/strong\u003e(A) The transduction of lentivirus-NC-RNAi or lentivirus-RNF13-RNAi into ARPE-19 cells was observed using green fluorescence channel. Scale bar=10 μm. (B) The quantitative result of RNF13 mRNA level. (C-D) Representative western blot images analysis of RNF13 and GAPDH. (E) Assessment of cell viability and death after transduction with lentivirus-RNF13-RNAi by CCK-8 assay. *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5741175/v1/6a93898c83f3b17242d9b6c5.png"},{"id":80796704,"identity":"b8066488-3710-437f-81ce-1f0e6e860839","added_by":"auto","created_at":"2025-04-17 07:41:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the identification of proteins in the NC and KD groups. \u003c/strong\u003e(A) The volcano plot displays the downregulated (blue) or upregulated (red) proteins in the comparison between the NC and KD groups. (B) Heat map of DEPs between the NC and KD groups. The color scale bar is positioned on the right side, with blue representing decreased expression and red indicating increased levels of the identified proteins. For the NC1, NC2 and NC3 groups, 3 replicates were included; for the KD1, KD2 and KD3 groups, 3 replicates were included.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5741175/v1/e662a3b31d2be70186d6a6d7.png"},{"id":80796709,"identity":"bd7bca5d-a303-4fdb-9472-880cbb5c764d","added_by":"auto","created_at":"2025-04-17 07:41:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":321724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnotation and enrichment of DEPs. \u003c/strong\u003e(A) Classification of DEPs (BP, CC, MF). (B) The subcellular distribution of the upregulated proteins. (C) The subcellular distribution of the downregulated proteins. (D) KEGG pathway analysis of the DEPs. (E) Abundance of proteins detected in TMT. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5741175/v1/a9e03d2a540baab4b8206cf6.png"},{"id":80796707,"identity":"29771b27-4e74-471f-8713-685d6023f23f","added_by":"auto","created_at":"2025-04-17 07:41:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":363911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVerification of DEPs in cells between the NC and KD groups.\u003c/strong\u003e (A) Quantitative analysis of SCD, CPT1α, FADS2, PLIN2, and HMGGC1 mRNA expression. (B) PPI network analysis of DEPs in the PPAR signalling pathway. (C-D) Representative western blot images of SCD, CPT1α, FADS2, HMGCS1 and GAPDH. (E) Parallel reaction monitoring results for HMGCS1. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5741175/v1/93c9d36c8595ecb414e040f8.png"},{"id":80796705,"identity":"4763f36c-66ae-4a8d-9726-9ce70871c0c1","added_by":"auto","created_at":"2025-04-17 07:41:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":307704,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of SCD on the Viability and Lipid Metabolism of ARPE-19 Cells.\u003c/strong\u003e (A) Quantitative analysis of SCD mRNA expression in SCD-knockdown cells. (B-C) Representative western blot images of SCD and GAPDH. (D) Representative images of Oil Red O staining in SCD-knockdown cells. The arrow indicates the lipid droplets stained with Oil Red O. Scale bar=50 μm. (E) Quantitative analysis of the Oil Red O staining area. (F) Assessment of cell proliferation viability and death after transfection with si-SCD by CCK-8 assay. * p \u0026lt; 0.05, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5741175/v1/bf89aba983d20111c071c7be.png"},{"id":90964573,"identity":"310c1064-0c05-4ac8-9de5-65065fa4a8d6","added_by":"auto","created_at":"2025-09-10 06:08:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2376658,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5741175/v1/0c804ef9-2d89-42b3-8a77-354648a4925b.pdf"},{"id":80796722,"identity":"8a744a05-71a1-446a-91d8-3589bbc84040","added_by":"auto","created_at":"2025-04-17 07:41:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3150764,"visible":true,"origin":"","legend":"","description":"","filename":"4.15Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5741175/v1/73601754dcc95db3a257021c.docx"},{"id":80796940,"identity":"53844ae8-8896-47c4-9bab-0a70669ae101","added_by":"auto","created_at":"2025-04-17 07:49:39","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":185147,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5741175/v1/07855b6d9b8ac183c44f46d8.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proteomic Analysis Reveals Lipid Metabolism Disruption and Key Targets in ARPE-19 Cells after RNF13 Knockdown","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe really interesting new gene (RING) finger (RNF) protein, which includes the RING motif, functions as an E3 ubiquitin ligase that facilitates the binding of ubiquitin (Ub) to specific target proteins [1]. The RNF protein family has numerous subtypes which serves a pivotal function in maintaining various physiological functions. RNF182, a member of the RNF family, has shown great importance in suppressing non-small cell lung cancer; Bap-induced RNF182 suppression enhances lung cancer tumorigenesis by stimulating AhR and facilitating aberrant methylation [2]. In addition, a previous study unraveled that RNF213 is correlated with moyamoya disease by aggravating inflammatory reactions and destructing tight junction, which disrupts pericyte homeostasis and blood‒brain barrier integrity [3].\u003c/p\u003e \u003cp\u003eAs a core member of the RNF family, RNF13 (an E3 ubiquitin-protein ligase) has recently been identified to play a crucial role in eye development and disease. Recently, in our previous studies, through microarray analyses of genome-wide expression patterns, we observed that RNF13 was significantly decreased in the retinas of mice with early-stage retinal photoreceptor degeneration (RPD) following low-intensity LED light-induced damage [4]. Meanwhile, Edvardson S et al. reported that a patient carrying an RNF13 heterozygous mutation (c.935T\u0026thinsp;\u0026gt;\u0026thinsp;C [p.Leu 312Pro] and c.932T\u0026thinsp;\u0026gt;\u0026thinsp;C [p.Leu 311Ser]) presented with blindness, congenital microcephaly and maldevelopment [5]. Nevertheless, the specific effects of RNF13 on retinal tissue are still elusive. Retinal pigment epithelium (RPE) cells contribute to clearing outer segments of photoreceptors to maintain a normal state. Photoreceptors degenerate when RPE function is impaired; therefore, the RPE cells is critical for retinal degeneration [6]. Building on the above, this study focused on the role of RNF13 in RPE cells and successfully established RNF13-knockdown ARPE-19 cell lines to investigate the effects of RNF13 depletion on RPE cell function.\u003c/p\u003e \u003cp\u003eThe influence of genes on cell functions has been reflected at multiple levels, including the genome, transcriptome, proteome, and even the metabolome. Among these, the proteome plays a crucial role, as proteins are directly involved in carrying out cellular processes, making proteomics an essential tool for understanding the functional mechanisms at the molecular level. With the emergence of mass spectrometry, proteomic methods, represented by tandem mass tag (TMT) labelling, have been extensively applied in the study of protein expression levels, posttranslational modifications, protein-protein interactions, and interactions between proteins and other biomolecules [7]. Therefore, this study mainly used mass spectrometry-based proteomics to dissect the proteomic profile of RPE cells following RNF13 depletion. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were integrated to identify the critical pathways and to characterize the affected cellular functions and processes. Among top 10 enriched terms, the pathway of peroxisome proliferator-activated receptors (PPARs) was chosen for expression validation. As many studies have indicated [8], a lack of RNF13 disrupts lipid metabolism in ARPE-19 cells. Consequently, our findings might provide a comprehensive understanding of the effect of RNF13 as well as new insights into the mechanism of RNF13-mediated protection.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell culture\u003c/h2\u003e \u003cp\u003eHuman ARPE-19 (Hunan Fenghui Biotechnology Co., Ltd.; China) cells, derived from a 19-year-old male donor and authenticated by short tandem repeat (STR) analysis (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), were used in the cell experiments. The cells were cultured in complete DMEM/F-12 medium with 10% fetal bovine serum and maintained at proper temperature in a humidified environment with 5% CO2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Establishment of stable RNF13-knockdown ARPE-19 cells\u003c/h2\u003e \u003cp\u003eThe sequences of the shRNAs used in this study: shRNA-NC (Negtive Control), 5\u0026rsquo;- TTCTCCGAACGTGTCACGT-3\u0026rsquo;; and shRNA-RNF13, 5\u0026rsquo;-GAACGGGATTACAACATAGCA-3\u0026rsquo;. Lentivirus-RNF13-RNAi (MOI of 100) was transduced into ARPE-19 cells at 20% confluence. Puromycin (1 \u0026micro;g/ml) (GeneChem Co., Ltd.; China) was used to select stably transducted cells. The transduction efficiency was assessed by visualizing GFP-labeled fragment under fluorescence microscope (Olympus Co.; Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Transient transfection of small interfering RNA (siRNA)\u003c/h2\u003e \u003cp\u003eARPE-19 cells were seeded in 6-well plates with 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells. The supernatant was replaced with Opti-MEM (Thermo Fisher Scientific; USA) containing si-NC (sequence: 5\u0026rsquo;-UUCUCCGAACGUGUCACGUTT-3\u0026rsquo;, 5\u0026rsquo;-ACGUGACACGUUCGGAGAATT-3\u0026rsquo;) or si-SCD (sequence: 5\u0026rsquo;-GAUAUGCUGUGGUGCUUAATT-3\u0026rsquo;, 5\u0026rsquo;-UUAAGCACCACAGCAUAUCTT-3\u0026rsquo;) (after 24 hours. The cells were incubated for the desired transfection duration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Cell viability evaluation\u003c/h2\u003e \u003cp\u003eStable RNF13-knockdown ARPE-19 cells were cultured in 96-well plates overnight. ARPE-19 cells were transfected with si-NC or si-SCD for 48h in 96-well plates as described above. Subsequently, the medium in each well was changed by 100 \u0026micro;L of 10% CCK-8 reagent in serum-free media (Biosharp Life Science; China) for 1 h at 37\u0026deg;C. The mean ODs were assessed to determine cell viability (Molecular Devices; USA), with background absorbance from blank wells subtracted to minimize artifacts. Cell viability was determined based on mean OD values, with each condition tested in quadruplicate wells and the entire experiment repeated three times independently to ensure reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Proteomic sample preparation and quality test\u003c/h2\u003e \u003cp\u003eLentivirus-transfected ARPE-19 cells were disrupted in SDT buffer supplemented with Tris-HCl (100 mM, pH 7.6) and 4% SDS. The protein quantification was performed using the BCA Protein Assay Kit (Beyotime; China).\u003c/p\u003e \u003cp\u003eTotal protein (20 \u0026micro;g) was mixed with 6\u0026times; loading buffer, heated to 100\u0026deg;C for 5 minutes, and subsequently separated by a 12.5% SDS‒PAGE gel. Then, the gels were stained with Coomassie blue (R-250) to showed the protein bands (Supplementary Fig. S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 TMT labelling\u003c/h2\u003e \u003cp\u003eThe peptide compound (100 \u0026micro;g/sample) was labelled using TMT reagent (Thermo Fisher Scientific; USA). Before TMT labelling, the reagent was reconstituted using anhydrous acetonitrile; the prepared reagent was suitable for the analysis of approximately 100 mg of protein. Forty-one microlitres of TMT labelling reagent was added to each 100 \u0026micro;L sample. A total of six samples were then respectively labelled using six distinct tags. Subsequently, the mixtures were incubated at room temperature for 2 hours. The hydroxylamine (5%, 8\u0026micro;L) was added to each reaction mixture for 15 min to halt trypsin activity. The labelled samples were consolidated into the fresh microcentrifuge tube. Subsequently, 1 mL of the mixed peptides was dried using a vacuum concentrator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 TMT-labelled peptide fractionation with RP chromatography\u003c/h2\u003e \u003cp\u003eAn Infinity II HPLC system (Agilent 1260) was used for RP chromatography. The prepared peptide mixture was first diluted using buffer A (pH 10.0), which consisted of 10 mM ammonium formate (HCOONH4) and 5% acetonitrile (ACN). Subsequently, the diluted peptide mixture was attached to a specific column, namely, an XBridge Peptide BEH C18 Column 130A. The elution of the peptides was carried out using the gradient elution (1 ml/min). The elution process involved the use of buffer B (pH\u0026thinsp;=\u0026thinsp;10.0, 85% ACN, 10 mM HCOONH4). The peptides were washed in 0\u0026ndash;7% buffer B (5 min), 7\u0026ndash;40% buffer B (5 to 40 min), 40\u0026ndash;100% buffer B (45\u0026ndash;50 min), and 100% buffer B (50\u0026ndash;65 min).\u003c/p\u003e \u003cp\u003eThe elution process was monitored using a UV light trace (214 nm). Fractions were gathered at one-minute intervals from 5 to 50 minutes and subsequently combined to form 10 larger fractions. The vacuum centrifugation was performed at 45\u0026deg;C to remove the solvent and concentrate the peptides in the combined fractions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 LC\u0026ndash;MS/MS analysis\u003c/h2\u003e \u003cp\u003eThe experiment lasted 60 minutes and utilized a Q Exactive mass spectrometer (Thermo Fisher Scientific; USA) in conjunction with an Easy nLC system (Thermo Fisher Scientific; USA). MS data were obtained by dynamically selecting the most abundant precursor ions at a survey scan ratio of 350\u0026ndash;1800 m/z for HCD fragmentation. Survey scans were performed at a resolution of 70,000 at m/z 200 with an AGC target of 3e6 and a maximum injection time (maxIT) of 50 ms. MS2 scans were conducted at a resolution of 17,500 for HCD spectra at m/z 200. The ions with a minimum intensity of 2e3 and a charge state ranging from 2 to 6 were chosen for fragmentation. The normalized collision energy was maintained at 30 eV. The dynamic exclusion time for the selected ions was set at 30 seconds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Data analysis\u003c/h2\u003e \u003cp\u003eThe MS/MS raw files were tackled using the MASCOT engine (Matrix Science 2.6; UK) and searched against the Protein Database (UniProt). The precursor mass tolerance was set to 10 ppm, and a tolerance of 0.05 Da was applied for MS2 fragment matching. Except TMT labels, the fixed modification applied was carbamidomethyl (C). The variable modifications comprised acetyl (protein N-term) and oxidation (M). Proteins exhibiting a fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.2 and a \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026le;\u0026thinsp;0.05 (determined using Student's t test) were identified as DEPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Bioinformatics analysis\u003c/h2\u003e \u003cp\u003eA global heatmap was generated using Matplotlib v3.3.3 to visualize the DEPs. The GO annotations [molecular function (MF), cellular component (CC), and biological process (BP)] for the identified proteins were obtained from the Protein Database (NCBI). The KEGG database was utilized for pathway analysis. The software applied in GO and KEGG analysis was SciPy v1.7.1. The Fisher\u0026rsquo;s exact test was employed to determine significantly enriched pathways by contrasting the count of DEPs with the overall number of proteins associated with each pathway. Finally, protein‒protein interactions (PPIs) were evaluated utilizing the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 qRT-PCR analysis\u003c/h2\u003e \u003cp\u003eTRIzol reagent (Takara; Japan) was utilized to extract the total RNA from the cell samples. Subsequently, reverse transcription was carried out with HiScript III RT SuperMix for qRT-PCR (Vazyme; China). The gene expressions were quantified using kit from Vazyme, China. All primers used for the genes are enumerated in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Protein extraction and validation\u003c/h2\u003e \u003cp\u003eCells were disrupted in RIPA buffer mixed with 1% phosphatase inhibitors, 1% phenylmethanesulfonyl fluoride, and 1% protease inhibitor (MedChemExpress; USA). The protein concentration was determined after centrifugation for 15 min at 13,000\u0026times;g. Quantified protein samples were all heated to 100 ℃ for 5 min, and protein (30 \u0026micro;g) was loaded and run on 4\u0026ndash;20% gels (GenScript; USA) and then transferred to polyvinylidene fluoride membranes. Phosphate-buffered saline (PBS) was loaded into the first lane as a negative control to rule out potential antibody-mediated false-positive signals. 5% nonfat dry milk was formulated freshly to blocked the membrane at indoor temperature. After that the membranes were sequentially immersed in primary antibodies and secondary antibodies following standard protocols. Protein bands on membrane were visualized through enhanced chemiluminescence (ECL) reagents (FD8020; Fude Biology; China) and scanned with a Bio-Rad exposure system (Bio-Rad; USA). ImageJ was used to quantify the relative band intensity (optical density) of target proteins.\u003c/p\u003e \u003cp\u003eThe antibodies utilized for western blotting: anti-GAPDH (10494-1-AP; Proteintech; 1:5000); anti-SCD (db4883; Diagbio; 1:500); anti-FADS2 (ab314317; Abcam; 1:1000); anti-CPT1α (ab220789; Abcam; 1:1000); anti-RNF13 (ab151601; Abcam; 1:500); anti-HMGCS1 (A3916; ABclonal; 1:1000); and anti-rabbit IgG HRP-linked antibody (BL003A; Biosharp Life Science; 1:5000).\u003c/p\u003e \u003cp\u003eThe parallel reaction monitoring (PRM) procedure for analysing HMGCS1 expression is described in the Appendix Materials. The peptides used were confirmed by TMT-based quantitative proteomic analysis and are presented in Supplementary Table S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Oil red O staining\u003c/h2\u003e \u003cp\u003eCell samples were washed with PBS and then stained with Oil Red O working solution (Njjcbio, Co., Ltd.; China) for 15 min at room temperature. After staining, excess dye was removed by washing with PBS for 15 min. Counterstaining was done using Harris hematoxylin for 5 min, with subsequent rinsing in PBS. The samples were finally mounted with a coverslip using a mounting medium and examined under a light microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS v19.0 for Windows (IBM, Armonk, USA). The results are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for at least three biological samples. A t-test was used for pairwise comparisons between the two groups. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effect of RNF13 knockdown on cellular viability of ARPE-19 cells\u003c/h2\u003e \u003cp\u003eTo identify the relationship between RNF13 expression and RPE degeneration for its potential contribution to retinopathy progression, we constructed an \u003cem\u003ein vitro\u003c/em\u003e RNF13 knockdown model with lentivirus-mediated RNF13-RNAi. The expression of the reporter gene green fluorescence protein (GFP) was used to determine the transduction efficiency, which was greater than 90% at 72 h post-transduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The knockdown efficiency of RNF13 was verified in ARPE-19 cells using qRT-PCR and western blotting (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-D). Additionally, the impact of RNF13 knockdown on ARPE-19 cell proliferation was assessed using a CCK-8 assay. A lower cell density was observed in the RNF13-knockdown cells than in the wild-type ARPE-19 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Results demonstrated that lentivirus-mediated RNF13-RNAi efficiently reduced RNF13 expression and significantly impaired the viability of ARPE-19 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Proteome analysis of RNF13-knockdown ARPE-19 cells\u003c/h2\u003e \u003cp\u003eTo clarify the biological effect of RNF13 on the RPE, we compared the whole-cell proteomes between the vehicle and KD groups using TMT-based quantitative proteomics technology. A total of 6400 proteins were found to be highly dependent proteins, 340 of which were considered DEPs, with changes in expression over than 1.2 times with \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In the KD group vs. the vehicle group, we found 260 upregulated DEPs and 80 downregulated DEPs, as displayed in the volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The DEPs in each group were examined and visually represented through a hierarchical clustering heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eA total of 340 DEPs were annotated to BP, CC, and MF by GO analysis. After that, the GO functional enrichment of the DEPs was speculated using Fisher\u0026rsquo;s exact test. The top 10 enriched GO terms in each major functional category are manifested in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. In the BP category, \u0026ldquo;cellular process\u0026rdquo;, \u0026ldquo;biological regulation\u0026rdquo; and \u0026ldquo;metabolic process\u0026rdquo; were found to be enriched with the most dysregulated proteins. In the CC category, \u0026ldquo;cell part\u0026rdquo; and \u0026ldquo;membrane\u0026rdquo; were found to be enriched with dysregulated proteins. In the MF category, \u0026ldquo;binding\u0026rdquo; and \u0026ldquo;catalytic activity\u0026rdquo; were enriched with dysregulated proteins. The subcellular localization results exhibited that the upregulated proteins were located mainly in the cytosol (32.69%), nucleus (31.92%) and plasma membrane (11.15%). Moreover, 26.25% of the downregulated proteins were localized to the plasma membrane, 20% to the mitochondria, 18.75% to the cytosol, 18.75% to the extracellular region and 16.25% to other localized areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C).\u003c/p\u003e \u003cp\u003eKEGG analysis objectively classified the pathways enriched in DEPs, with metabolic pathways and the PPAR signaling pathway showing significant enrichment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The PPAR signaling pathway is closely associated with energy metabolism, encompassing both lipid and glucose metabolism [9, 10]. Studies by Sun et al. and Wen et al. have shown that activating this pathway helps reduce diabetic retinopathy and mitigates oxidative stress-induced damage to RPE cells [11, 12]. Considering \u0026ldquo;metabolic pathways\u0026rdquo; was too broad to be interpreted in a limited bioinformatics analysis, we focused mainly on the DEPs in the PPAR signalling pathway in subsequent experiments. Five proteins (SCD, CPT1α, FADS2, PLIN2, and HMGGC1) were involved in the PPAR signalling pathway and exhibited the lowest \u003cem\u003ep\u003c/em\u003e value among the proteins within the identified pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation of PPAR signalling pathway-associated proteins determined by the TMT\u003c/h2\u003e \u003cp\u003eAccording to the proteome analysis results, 5 DEPs were selected and validated via qRT-PCR to prove the reliability of sequencing data: perilipin-2 (PLIN2), stearoyl-CoA desaturase (SCD), carnitine O-palmitoyltransferase 1 (CPT1α), hydroxymethylglutaryl-CoA synthase 1 (HMGCS1) and acyl-CoA 6-desaturase 2 (FADS2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The qRT-PCR quantification were consistent with the protein expression data for all the genes. A network of physical and functional PPIs was constructed to predict interactions (PPI enrichment \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;1.0 e-16) among the above five proteins. SCD was predicted to play a critical role in this network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Proteins linked to more than three other proteins in the PPI network were validated by western blotting and PRM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). The findings aligned with the predicted outcomes, suggesting that RNF13 knockout in RPE notably deregulates the PPAR signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Imapct of SCD knockdown on cellular viability and lipid metabolism of ARPE-19 cells\u003c/h2\u003e \u003cp\u003eBased on PPI predictions, this research next evaluated the phenotypic consequences of SCD knockdown in ARPE-19 cells using siRNA. Transfection with 50 nM si-\u003cem\u003eSCD\u003c/em\u003e significantly downregulated SCD mRNA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), which was further confirmed at the protein level by quantitative western blotting analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u0026ndash;C). To assess the impact of SCD suppression on lipid metabolism, Oil Red O staining was performed to visualize intracellular lipid droplets, which serve as dynamic indicators of neutral lipid storage and turnover. Notably, the si-\u003cem\u003eSCD\u003c/em\u003e group exhibited a marked reduction in the area of Oil Red O-positive lipid droplets compared to the si-NC control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026ndash;E), suggesting impaired lipid accumulation. Additionally, it was observed decreased cell density in the si-\u003cem\u003eSCD\u003c/em\u003e group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Collectively, these data demonstrated that siRNA-mediated SCD knockdown not only efficiently suppresses \u003cem\u003eSCD\u003c/em\u003e expression but also disrupts lipid homeostasis and compromises ARPE-19 cell viability.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn recent years, proteomics has impacted research in numerous fields by identifying and selecting DEPs as targets. Since the RNF protein was first identified in 1993 [13], the elucidation of the function of RNF proteins in ophthalmic diseases has been a crucial task, and the application of proteomics offers a vital pathway for exploring the mechanism of action of RNF13.\u003c/p\u003e \u003cp\u003eAs an E3 ubiquitin ligase, RNF13 was found to be involved in the pathogenesis of multiple diseases, such as cardiac hypertrophy, neurological disease, and so on. Guo et al. established by RNA-seq analysis of RNF13-knockout mice with cardiac hypertrophy that RNF13 exerts cardioprotective effects through activation of the p62-NRF2 antioxidant pathway [14]. Through RNF13 inhibition and overexpression experiments in mammalian cell lines, Arshad et al. established RNF13 as a novel regulatory node connecting ER stress to apoptosis via the IRE1α-TRAF2-JNK signaling axis [15]. Although some studies have noted associations between RNF13 and certain ocular diseases such as uveal melanoma [16], the regulatory mechanisms of RNF13 in ophthalmic pathologies remain unexplored. On the basis of the microarray analyses of genome-wide expression patterns in the retinas of RPD mice in our previous study [4], the roles and mechanisms of the RNF13 were characterized in the light of biological process (BP), cellular component (CC), and molecular function (MF) by GO analysis. These findings suggested that the DEPs were located mainly in cell parts and that their paired binding functions deserve attention. Additionally, in virtue of the GO enrichment analysis, for the BP category, the DEPs showed notable enrichment in cellular processes and biological regulation; for the CC category, the majority of DEPs were associated with cell parts, organelles, membranes and protein-containing complexes; and for the CC category, the DEPs participated in binding and catalytic activity. These results indicated the extensive function of RNF13 in RPE cells.\u003c/p\u003e \u003cp\u003eThrough KEGG analysis, we determined that RNF13 interference was related to several lipid-related metabolic pathways: ketone body synthesis and degradation, PPAR signalling pathway, fatty acid metabolism, and steroid biosynthesis. The ketone body synthesis and degradation have been shown to be associated with retinal diseases. Beta-hydroxybutyrate (BHB), an activator of hydroxycarboxylic acid receptor 2, has been shown to reduce NOD-like receptor protein 3 inflammasome activity and minimize harm to the retina in diabetic retinopathy (DR) mouse model [17]. Gambhir et al. reported that the ketone body BHB was increased and its receptor GPR109A was overexpressed in the RPE layer of DR mice, potentially functioning as mediators of inflammation [18]. In addition, the PPAR signalling pathway is necessary for activating and controlling the ketogenic transcriptional programme [19], which includes CPT1α, which was found to be differentially expressed based on the proteomic analysis conducted in the current study. Fatty acid metabolism contributes to the following four major processes in some tissues: anabolic processes, catabolism to generate energy division, ferroptosis, and signalling molecule production [20]. Steroids are vital constituents of cell membranes and serve as signalling molecules that play a crucial role in maintaining cellular homeostasis [21]. Fatty acid metabolism and steroid biosynthesis in the RPE retinal are both related to SCD, which is a constituent of the PPAR signalling pathway. Therefore, we speculated that the PPAR signalling pathway might be a critical pathway through which RNF13 deletion induces a change in phenotype. In the future, more validation experiments should be implemented centred around the DEPs of the PPAR pathway.\u003c/p\u003e \u003cp\u003eProteomic analysis unraveled that the proteins enriched in the PPAR pathway were PLIN2, SCD, CPT1α, HMGCS1 and FADS2. qRT-PCR was used to provide additional support for proteomic analysis results: CPT1α and PLIN2 expression was upregulated after RNF13 knockdown, but SCD, HMGCS1 and FADS2 expression was downregulated at the mRNA level. To get deeper insights into the potential interactions of these DEPs, we subsequently analyzed PPI proteomics network using STRING software. SCD was predicted as the central core of network proteins of the PPAR signalling pathway in RNF13-knockdown ARPE-19 cells; these findings were validated and consistent with the proteomics results. SCD, also called delta-9-desaturase, is the membrane-bound enzyme that restricts the reaction rate of biosynthesis of unsaturated fatty acids [22, 23]. SCD expression is significantly associated with the invasion, proliferation, and survival of tumor cells due to lipotoxicity mediated by saturated fatty acids [24]. Given that SCD expression was downregulated in RNF13-knockdown ARPE-19 cells and bioinformatically identified as the central regulator of lipid metabolism, we investigated the phenotypic consequences of SCD knockdown \u003cem\u003ein vitro\u003c/em\u003e. The results demonstrated that siRNA-mediated SCD knockdown not only disrupted normal lipid droplet biogenesis but also significantly impaired cellular viability. These findings are consistent with previous report [25]. Collectively, these data partially support the hypothesis that RNF13 regulates both lipid metabolism and proliferative capacity in ARPE-19 cells through SCD-dependent mechanisms. To provide more robust evidence supporting this hypothesis, rescue experiments should be conducted in future studies.\u003c/p\u003e \u003cp\u003eMoreover, the validation results of protein level for CPT1α, FADS2 and HMGCS1 were in line with the proteomics results. These findings confirmed the reliability of the TMT-based proteomics analysis. Dysregulated lipid metabolism has been observed in the RPE in age-related macular degeneration pathology [26]. In humans, CPT1α is predominantly expressed in the RPE and facilitates the initial step of β-oxidation by conveying long-chain fatty acids in the cytosol to the matrix of mitochondria [27]. CPT1α overexpression causes an excessive influx of fatty acids into mitochondria, leading to oxidative damage of mitochondrial DNA and mitochondrial dysfunction, which could explain the inhibition of cell viability in the KD group. FADS2 are membrane-bound enzymes functioning to catalyse the constitution process of long-chain polyunsaturated fatty acids, like eicosapentaenoic acid and docosahexaenoic acid [28], the depletion of which has been shown to enhance apoptosis in zebrafish retinas treated with intense light [29]. HMGCS1 is a crucial hepatic gene responsible for cholesterol synthesis and a key enzyme that facilitates fat utilization as an energy source without carbohydrates [30]. It modulates lipid homeostasis by affecting both cellular structure and enzymatic protein mechanisms [31, 32]. Previous studies have indicated that PPARs might regulate and/or influence lipid metabolism through the regulation of HMGCS1 [33]. We speculated that the downregulation of RNF13 gene expression can suppress sterol synthesis by downregulating the expression of HMGCS1. Moreover, 5 peptides related to the HMGCS1 protein were captured by TMT-based proteomics and verified with PRM. Further exploration should be conducted to clarify the HMGCS1 functions as one of the primary regulators.Notably, as a E3 ubiquitin ligase subtype, RNF13 might regulate the abovementioned molecules through the disruption of Ub activity. Therefore, further studies could focus on elucidating whether RNF13 regulates the ubiquitination of these lipid metabolism-related molecules via more \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments. Moreover, whether SCD in the PPAR pathway is the decisive factor involved in ARPE-19 cell viability following RNF13 knockdown could be further investigated through SCD overexpression and interference in subsequent research.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, the results of this study demonstrated that RNF13 plays a critical role in maintaining ARPE-19 cell biological function. TMT-based proteomics revealed that RNF13 knockdown significantly decreased RPE cell viability and dysregulated lipid metabolism. The PPAR pathway and associated molecules including SCD, may mediate the detrimental effects of RNF13 knock down on RPE cell function.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eRNF13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eReally interesting new gene finger protein 13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eRPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eRetinal photoreceptor degeneration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eRPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eRetinal pigment epithelium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eShort tandem repeat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003esiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eSmall interfering RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eNegtive control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eTMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eTandem mass tag labelling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eDEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eDifferentially expressed protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eGene ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003ePPAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003ePeroxisome proliferator-activated receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eqRT-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eReal-time quantitative polymerase chain reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003ePBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003ePhosphate-buffered saline\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eKnockdown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003ePRM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eParallel reaction monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eProtein-protein interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eMolecular function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eCellular component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eBiological process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eStearoyl-coa desaturase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eCPT1\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eCarnitine O-palmitoyltransferase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eFADS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eAcyl-coa 6-desaturase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003ePLIN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003ePerilipin-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eHMGGC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 497px;\"\u003e\n \u003cp\u003eHydroxymethylglutaryl-coa synthase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received funding from the National Natural Science Foundation of China (Grant Number 82201194).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.X. designed the study. L.W. and X.Y. acquired and analyzed the data. X.Y. conducted the additional experiments during manuscript revision. L.W. and C.X. interpreted the data and drafted the initial manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC. Cai, Y.D. Tang, J. Zhai, C. Zheng, The RING finger protein family in health and disease, Signal Transduct Target Ther 7(1) (2022) 300.\u003c/li\u003e\n\u003cli\u003eY. Liu, L. Ouyang, C. Mao, Y. Chen, N. Liu, L. Chen, Y. Shi, D. Xiao, S. Liu, Y. Tao, Inhibition of RNF182 mediated by Bap promotes non-small cell lung cancer progression, Front Oncol 12 (2022) 1009508.\u003c/li\u003e\n\u003cli\u003eW. Li, X. Niu, Y. Dai, X. Wu, J. Li, W. Sheng, Rnf-213 Knockout Induces Pericyte Reduction and Blood-Brain Barrier Impairment in Mouse, Mol Neurobiol 60(11) (2023) 6188-6200.\u003c/li\u003e\n\u003cli\u003eC. Xie, H. Zhu, S. Chen, Y. Wen, L. Jin, L. Zhang, J. Tong, Y. Shen, Chronic retinal injury induced by white LED light with different correlated color temperatures as determined by microarray analyses of genome-wide expression patterns in mice, J Photochem Photobiol B 210 (2020) 111977.\u003c/li\u003e\n\u003cli\u003eS. Edvardson, C.M. Nicolae, G.J. Noh, J.E. Burton, G. Punzi, A. Shaag, J. Bischetsrieder, A. De Grassi, C.L. Pierri, O. Elpeleg, G.L. Moldovan, Heterozygous RNF13 Gain-of-Function Variants Are Associated with Congenital Microcephaly, Epileptic Encephalopathy, Blindness, and Failure to Thrive, Am J Hum Genet 104(1) (2019) 179-185.\u003c/li\u003e\n\u003cli\u003eE.E. Brown, A.J. DeWeerd, C.J. Ildefonso, A.S. Lewin, J.D. Ash, Mitochondrial oxidative stress in the retinal pigment epithelium (RPE) led to metabolic dysfunction in both the RPE and retinal photoreceptors, Redox Biol 24 (2019) 101201.\u003c/li\u003e\n\u003cli\u003eD. van Hoof, J. Krijgsveld, C. Mummery, Proteomic analysis of stem cell differentiation and early development, Cold Spring Harb Perspect Biol 4(3) (2012).\u003c/li\u003e\n\u003cli\u003eZ. Lin, P. Yang, Y. Hu, H. Xu, J. Duan, F. He, K. Dou, L. Wang, RING finger protein 13 protects against nonalcoholic steatohepatitis by targeting STING-relayed signaling pathways, Nat Commun 14(1) (2023) 6635.\u003c/li\u003e\n\u003cli\u003eY. Li, Y. Pan, X. Zhao, S. Wu, F. Li, Y. Wang, B. Liu, Y. Zhang, X. Gao, Y. Wang, H. Zhou, Peroxisome proliferator-activated receptors: A key link between lipid metabolism and cancer progression, Clin Nutr 43(2) (2024) 332-345.\u003c/li\u003e\n\u003cli\u003eW. Lai, L. Yu, Y. Deng, PPAR\u0026Icirc;\u0026sup3; alleviates preeclampsia development by regulating lipid metabolism and ferroptosis, Commun Biol 7(1) (2024) 429.\u003c/li\u003e\n\u003cli\u003eC. Wen, X. Yu, J. Zhu, J. Zeng, X. Kuang, Y. Zhang, S. Tang, Q. Zhang, J. Yan, H. Shen, Gastrodin ameliorates oxidative stress-induced RPE damage by facilitating autophagy and phagocytosis through PPAR\u0026Icirc;\u0026plusmn;-TFEB/CD36 signal pathway, Free Radic Biol Med 224 (2024) 103-116.\u003c/li\u003e\n\u003cli\u003eH.H. Sun, X.L. Chai, H.L. Li, J.Y. Tian, K.X. Jiang, X.Z. Song, X.R. Wang, Y.S. Fang, Q. Ji, H. Liu, G.M. Hao, W. Wang, J. Han, Fufang Xueshuantong alleviates diabetic retinopathy by activating the PPAR signalling pathway and complement and coagulation cascades, J Ethnopharmacol 265 (2021) 113324.\u003c/li\u003e\n\u003cli\u003eP.S. Freemont, The RING finger. A novel protein sequence motif related to the zinc finger, Ann N Y Acad Sci 684 (1993) 174-92.\u003c/li\u003e\n\u003cli\u003eS. Guo, B.B. Zhang, L. Gao, X.Y. Yu, J.H. Shen, F. Yang, W.C. Zhang, Y.G. Jin, G. Li, Y.G. Wang, Z.Y. Han, Y. Liu, RNF13 protects against pathological cardiac hypertrophy through p62-NRF2 pathway, Free Radic Biol Med 209(Pt 2) (2023) 252-264.\u003c/li\u003e\n\u003cli\u003eM. Arshad, Z. Ye, X. Gu, C.K. Wong, Y. Liu, D. Li, L. Zhou, Y. Zhang, W.P. Bay, V.C. Yu, P. Li, RNF13, a RING finger protein, mediates endoplasmic reticulum stress-induced apoptosis through the inositol-requiring enzyme (IRE1alpha)/c-Jun NH2-terminal kinase pathway, J Biol Chem 288(12) (2013) 8726-8736.\u003c/li\u003e\n\u003cli\u003eC. Ness, K. Katta, O. Garred, T. Kumar, O.K. Olstad, G. Petrovski, M.C. Moe, A. Noer, Integrated differential DNA methylation and gene expression of formalin-fixed paraffin-embedded uveal melanoma specimens identifies genes associated with early metastasis and poor prognosis, Exp Eye Res 203 (2021) 108426.\u003c/li\u003e\n\u003cli\u003eM.C. Trotta, R. Maisto, F. Guida, S. Boccella, L. Luongo, C. Balta, G. D\u0026apos;Amico, H. Herman, A. Hermenean, C. Bucolo, M. D\u0026apos;Amico, The activation of retinal HCA2 receptors by systemic beta-hydroxybutyrate inhibits diabetic retinal damage through reduction of endoplasmic reticulum stress and the NLRP3 inflammasome, PLoS One 14(1) (2019) e0211005.\u003c/li\u003e\n\u003cli\u003eD. Gambhir, S. Ananth, R. Veeranan-Karmegam, S. Elangovan, S. Hester, E. Jennings, S. Offermanns, J.J. Nussbaum, S.B. Smith, M. Thangaraju, V. Ganapathy, P.M. Martin, GPR109A as an anti-inflammatory receptor in retinal pigment epithelial cells and its relevance to diabetic retinopathy, Invest Ophthalmol Vis Sci 53(4) (2012) 2208-17.\u003c/li\u003e\n\u003cli\u003eM. Grabacka, M. Pierzchalska, M. Dean, K. Reiss, Regulation of Ketone Body Metabolism and the Role of PPAR\u0026Icirc;\u0026plusmn;, Int J Mol Sci 17(12) (2016).\u003c/li\u003e\n\u003cli\u003eJ. Miska, N.S. Chandel, Targeting fatty acid metabolism in glioblastoma, J Clin Invest 133(1) (2023).\u003c/li\u003e\n\u003cli\u003eP. Melchinger, B.M. Garcia, Mitochondria are midfield players in steroid synthesis, Int J Biochem Cell Biol 160 (2023) 106431.\u003c/li\u003e\n\u003cli\u003eF.S. Falvella, R.M. Pascale, M. Gariboldi, G. Manenti, M.R. De Miglio, M.M. Simile, T.A. Dragani, F. Feo, Stearoyl-CoA desaturase 1 (Scd1) gene overexpression is associated with genetic predisposition to hepatocarcinogenesis in mice and rats, Carcinogenesis 23(11) (2002) 1933-6.\u003c/li\u003e\n\u003cli\u003eW. Samuel, R.K. Kutty, T. Duncan, C. Vijayasarathy, B.C. Kuo, K.M. Chapa, T.M. Redmond, Fenretinide induces ubiquitin-dependent proteasomal degradation of stearoyl-CoA desaturase in human retinal pigment epithelial cells, J Cell Physiol 229(8) (2014) 1028-38.\u003c/li\u003e\n\u003cli\u003eM.L. Watson, M. Coghlan, H.S. Hundal, Modulating serine palmitoyl transferase (SPT) expression and activity unveils a crucial role in lipid-induced insulin resistance in rat skeletal muscle cells, Biochem J 417(3) (2009) 791-801.\u003c/li\u003e\n\u003cli\u003eA. Pariente, \u0026Atilde;. P\u0026Atilde;\u0026copy;rez-Sala, R. Ochoa, R. Pel\u0026Atilde;\u0026iexcl;ez, I.M. Larr\u0026Atilde;\u0026iexcl;yoz, Genome-Wide Transcriptomic Analysis Identifies Pathways Regulated by Sterculic Acid in Retinal Pigmented Epithelium Cells, Cells 9(5) (2020).\u003c/li\u003e\n\u003cli\u003eM. Landowski, C. Bowes Rickman, Targeting Lipid Metabolism for the Treatment of Age-Related Macular Degeneration: Insights from Preclinical Mouse Models, J Ocul Pharmacol Ther 38(1) (2022) 3-32.\u003c/li\u003e\n\u003cli\u003eZ.S. Ulhaq, Y. Ogino, W.K.F. Tse, Deciphering the pathogenesis of retinopathy associated with carnitine palmitoyltransferase I deficiency in zebrafish model, Biochem Biophys Res Commun 664 (2023) 100-107.\u003c/li\u003e\n\u003cli\u003eD.M. Merino, D.W. Ma, D.M. Mutch, Genetic variation in lipid desaturases and its impact on the development of human disease, Lipids Health Dis 9 (2010) 63.\u003c/li\u003e\n\u003cli\u003eY. Ashikawa, Y. Nishimura, S. Okabe, Y. Sato, M. Yuge, T. Tada, H. Miyao, S. Murakami, K. Kawaguchi, S. Sasagawa, Y. Shimada, T. Tanaka, Potential protective function of the sterol regulatory element binding factor 1-fatty acid desaturase 1/2 axis in early-stage age-related macular degeneration, Heliyon 3(3) (2017) e00266.\u003c/li\u003e\n\u003cli\u003eE. Metwally, S.M. Farouk, A.K. Osman, Molecular cloning and cellular expression of the cholesterol synthesizing enzymes during the prenatal development of the optic nerve in the dromedary camel (Camelus Dromedarius), Acta Histochem 121(5) (2019) 584-594.\u003c/li\u003e\n\u003cli\u003eW.L. Miller, A brief history of adrenal research: steroidogenesis - the soul of the adrenal, Mol Cell Endocrinol 371(1-2) (2013) 5-14.\u003c/li\u003e\n\u003cli\u003eT.F. Liu, J.J. Tang, P.S. Li, Y. Shen, J.G. Li, H.H. Miao, B.L. Li, B.L. Song, Ablation of gp78 in liver improves hyperlipidemia and insulin resistance by inhibiting SREBP to decrease lipid biosynthesis, Cell Metab 16(2) (2012) 213-25.\u003c/li\u003e\n\u003cli\u003eM. Kotula-Balak, E. Gorowska-Wojtowicz, A. Milon, P. Pawlicki, W. Tworzydlo, B.J. Płachno, I. Krakowska, A. Hejmej, J.K. Wolski, B. Bilinska, Towards understanding leydigioma: do G protein-coupled estrogen receptor and peroxisome proliferator-activated receptor regulate lipid metabolism and steroidogenesis in Leydig cell tumors?, Protoplasma 257(4) (2020) 1149-1163.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Retinal degeneration, ARPE-19, RNF13, Proteomics, Lipid metabolism, PPAR signalling pathway","lastPublishedDoi":"10.21203/rs.3.rs-5741175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5741175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite RNF13 is dysregulated in retinal degeneration models, its precise role in retinal function is not well understood. Previous studies suggest RNF13 may affect cellular pathways, including lipid metabolism, in the retina. However, the molecular mechanisms underlying its effects remain unclear. This study aims to identify the pathways regulated by RNF13 and key molecular targets involved in retinal degeneration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the impact of RNF13 on the retina, the ARPE-19 cell line was transduced with lentivirus-RNF13-RNAi to interfere RNF13 expression. Cell viability was assessed via CCK-8 assay. Differentially expressed proteins (DEPs) were identified using tandem mass tag proteomics and further analyzed with KEGG pathway analysis, Gene Ontology (GO) annotation, and protein-protein interaction network analysis. Western blotting, qRT-PCR, and parallel reaction monitoring (PRM) were used to validate and identify RNF13-regulated targets. Oil Red O staining and CCK-8 were employed to assess phenotypic changes induced by siRNA-mediated SCD knockdown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 340 DEPs were identified, with 80 downregulated and 260 upregulated after RNF13 knockdown. GO analysis showed that DEPs were enriched in \"membrane\" components and linked to “cellular processes” and “metabolic processes”, involving “binding” and “catalytic activity.” KEGG pathway analysis revealed significant disruptions in metabolic and PPAR signaling pathways. Western blotting, qRT-PCR, and PRM validation indicated that proteins in PPAR signaling pathway, such as SCD, were potential downstream targets regulated by RNF13. SCD knockdown demonstrated significant reduction in Oil Red O staining area and inhibition of cellular proliferation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNF13 knockdown primarily disrupts lipid metabolism via interference with the PPAR signaling pathway. SCD emerged as a key target among multiple PPAR-related proteins, suggesting its important role in retinal degeneration.\u003c/p\u003e","manuscriptTitle":"Proteomic Analysis Reveals Lipid Metabolism Disruption and Key Targets in ARPE-19 Cells after RNF13 Knockdown","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 07:41:34","doi":"10.21203/rs.3.rs-5741175/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f3e0ca8-a208-429e-b7b0-c03a99407a63","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T06:08:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 07:41:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5741175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5741175","identity":"rs-5741175","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}