Integrated Transcriptomic and Secretome Proteomic Analysis of Hyperglycemia-Stimulated Endothelial Cells and Screening of Target Compounds

Summary

This protocol establishes an integrated multi-omics pipeline (transcriptome and proteome) combined with network pharmacological screening to identify molecular drivers and therapeutic targets for endothelial dysfunction in diabetic complications.

Abstract

Endothelial dysfunction is a key driver of diabetic kidney disease (DKD), but its systemic molecular mechanisms remain incompletely decoded. We hypothesized that integrated multi-omics analysis could map hyperglycemia-induced endothelial damage and identify reusable therapeutics. A reusable computational pipeline was applied to integrate transcriptomic/secretome profiles from hyperglycemic endothelial cells and diabetic kidneys. This identified 534 commonly upregulated genes/proteins. Functional enrichment revealed activation of extracellular matrix remodeling, intercellular communication, and inflammation pathways. Cross-database validation refined 278 high-confidence mediators, and protein-protein interaction network analysis pinpointed ten hub genes. Using network pharmacology, we screened an approved drug library, identifying several candidate compounds (e.g., bruceantin, idelalisib) that potentially target this network. Furthermore, transcription factor regulation and exemplary molecular docking simulations (e.g., idelalisib with CTCF/BRD4) provided mechanistic hypotheses for experimental validation. In conclusion, this study establishes a reusable multi-omics framework that delineates endothelial pathogenic mechanisms in DKD and nominates repurposable drug candidates, offering a strategic approach for mechanistic and therapeutic discovery.

Introduction

Diabetes mellitus, as a global health challenge, affected 529 million people worldwide in 2021, with projections indicating it will impact 1.31 billion people by 2050¹. Beyond glycemic control, long-term complications are the primary source of morbidity, mortality, and reduced quality of life. These include microvascular complications (e.g., diabetic kidney disease (DKD), retinopathy, neuropathy) and macrovascular complications (e.g., accelerated atherosclerosis). Critically, DKD affects 30%-40% of diabetic patients, representing the leading cause of end-stage renal disease (ESRD) worldwide². The profound burden of these complications underscores an urgent unmet need for novel therapies targeting their underlying mechanisms.

Endothelial cells (ECs), forming the inner lining of all blood vessels, are pivotal gatekeepers of vascular health. Hyperglycemia-induced endothelial dysfunction acts as a central driver of diabetic vasculopathy and organ damage^3,4. High glucose levels trigger a cascade of maladaptive responses within ECs, encompassing dysregulated nitric oxide (NO) bioavailability, increased endothelin-1 (ET-1) secretion, upregulated adhesion molecule expression (e.g., VCAM-1, ICAM-1), heightened oxidative stress through excessive reactive oxygen species (ROS) generation (e.g., via NADPH oxidases), and persistent low-grade inflammation^3,4. Collectively, these alterations drive impaired vasodilation, increased vascular permeability, leukocyte adhesion and infiltration, pro-thrombotic states, and ultimately, vascular remodeling⁵. Within the renal microvasculature, glomerular endothelial damage disrupts the filtration barrier, promotes albuminuria, and contributes directly to extracellular matrix (ECM) accumulation, glomerulosclerosis, and tubulointerstitial fibrosis^5,6. The subsequent activation of resident renal cells, coupled with the influx of inflammatory cells, establishes a vicious cycle amplifying renal injury^5,7. Crucially, clinical studies demonstrate that markers of systemic endothelial dysfunction correlate strongly with the onset and progression of albuminuria and declining glomerular filtration rate (GFR) in diabetic patients^6,7,8, underscoring its direct relevance to human disease outcomes. Despite its recognized centrality, a comprehensive, systems-level understanding of the precise transcriptional and secretory reprogramming induced by chronic hyperglycemia in ECs-especially alterations driving ECM remodeling, key to renal fibrosis-remains incompletely characterized, limiting the identification of novel, mechanistic therapeutic targets for DKD and other diabetic vascular complications^7,8.

The profound molecular complexity underpinning endothelial dysfunction in diabetes presents a formidable challenge to traditional single-target therapeutic approaches. This is where computational biology and bioinformatics emerge as indispensable tools, enabling the integration and mining of diverse, high-dimensional omics datasets to prioritize causal hubs within pathological networks and identify multi-targeting therapeutic strategies⁹. Critically, conventional single-modality approaches (e.g., transcriptome/proteome-only or pharmacological screening) often miss critical interplay between molecular layers (e.g., gene expression, protein dynamics, drug response), failing to capture synergistic drivers or off-target effects^10,11. The integrated multi-omics pipeline overcomes these limitations by concurrently profiling transcriptomic, secretomic, and pharmacologic data within the same system, yielding a holistic disease mechanism view. Network pharmacology, ligand/target prediction algorithms, and in silico molecular docking allow for the systematic exploration of approved drug libraries, accelerating the repositioning or discovery of compounds capable of counteracting complex disease signatures³. This paradigm shift holds particular promise for identifying therapies targeting endothelial dysregulation and its devastating microvascular consequences, such as DKD, offering the potential for improved efficacy and reduced side effect profiles. Integration of transcriptomic/secretomic analyses of hyperglycemia-exposed endothelial cells identified 534 commonly upregulated molecules. Functional enrichment implicated extracellular matrix remodeling, oxidative stress, and inflammation in early DKD pathogenesis. Cross-database analysis prioritized 278 high-confidence targets, refined via PPI networks of hub genes. Network pharmacology screening of approved drugs predicted 85 candidate compounds (including brefeldin-A, bruceantin, paracetamol, and idelalisib) targeting these hubs. In silico transcription factor prediction and docking explored mechanisms. This work delineates endothelial drivers of DKD, establishes a computational discovery pipeline, and provides therapeutic candidates for validation. Ultimately, this pipeline is anticipated to promote therapeutic discovery programs for diabetic complications.

Protocol

The animal experiments were conducted with the approval of the Committee on Animal Research and Ethics of the Hebei Yiling Medical Research Institute (approval number: N2024082). Check the Table of Materials for the experimental materials and instruments used in this study. Personnel must strictly wear appropriate personal protective equipment (PPE) when handling hazardous reagents such as TRIzol (skin/eye irritant, contains phenol) and protease inhibitors (potential respiratory/skin sensitizers). Segregate biological/liquid waste in autoclavable bags, and hazardous chemical waste in designated containers for institutional hazardous disposal. Decontaminate consumables before disposal.

Diabetic nephropathy mice modeling

The male db/db and db/m mice (12 weeks old) were maintained under specific pathogen-free (SPF) conditions at 22 ± 2 °C and 56% ± 5% humidity with 12 h light/dark cycles and ad libitum access to food/water. After a 1 week acclimatization period, the mice were randomly assigned numerical identifiers and allocated to experimental groups. The db/db mice were fed a high-protein diet for 6 weeks to establish DKD. Successful modeling was confirmed by a significantly elevated urinary albumin-to-creatinine ratio (UACR) versus db/m controls, with UACR >30 mg/g serving as the primary functional biomarker for early DKD¹².

High-glucose endothelial cell modeling

Human Renal Glomerular Endothelial Cells (HRGECs) were prepared under standardized conditions. HRGECs were cultured in either normal glucose (NG, 5.5 mM D-glucose) or high glucose (HG, 30 mM D-glucose) medium and maintained in a humidified incubator at 37 °C with 5% CO₂. Cells were either exposed uninterrupted to HG or grown in control conditions for 24 h prior to downstream analyses. Osmotic control (HM, containing 5.5 mM D-glucose with 24.5 mM mannitol) should be included. All conditions should meet strict cell fitness criteria: viability remained ≥85%, apoptosis levels stayed below 15%, ROS increases in HG compared to HM did not exceed 20%, and LDH release remained under 10%, validating cellular integrity prior to downstream analyses.

Conditioned media collection

Conditioned media (CM) were collected using a standardized protocol for secretome analysis¹³ with modifications. After 24 h of high glucose (30 mM D-glucose), normal glucose (5.5 mM D-glucose), or osmotic control (5.5 mM D-glucose + 24.5 mM mannitol) stimulation, HRGECs were washed with sterile phosphate-buffered saline (PBS) to remove residual serum proteins. Cells were replenished with serum-free, phenol red-free endothelial basal medium and incubated for 12 h at 37 °C with 5% CO₂ to accumulate secreted factors.

CM was harvested using pre-chilled centrifugation tubes. Sequentially, sample processing was done as described below.

Primary clarification: CM was transferred to RNase/DNase-free conical tubes and centrifuged at 3,000 x g for 10 min at 4 °C. The pelleted debris was discarded.

Protease inhibition: Within 2 min of primary clarification, EDTA-free protease inhibitor cocktail was added to a 1x final concentration (1:50) containing 1x phosphatase inhibitors.

Concentration: Supernatant was immediately loaded into pre-rinsed PBS centrifugal filters (3 kDa MWCO) and centrifuged at 4,000 x g for 90 min at 4 °C to achieve a 10x concentration.

Secondary clarification: Concentrate was transferred to pre-cooled microcentrifuge tubes and centrifuged at 12,000 x g for 15 min at 4 °C. Supernatant was collected, avoiding pelleted aggregates.

Storage: Aliquots of 50 µL volumes were made in sterile low-protein-binding cryovials. The vials were flash-frozen in liquid nitrogen within 30 min of harvest completion. Vials were stored at -80 °C (no freeze-thaw cycles).

CM batches underwent endotoxin testing (<0.25 EU/mL). Serum-free incubation protocol showed no stress induction (validated by HSP70/CHOP immunoblots). CM protein yield normalized to viable cell count/DNA content at harvest. Processing rigor maintained: sterile technique throughout; ≤30 min harvest-to-freeze window; rotor temperature equilibration confirmed.

CCK-8 assay of conditioned media-treated renal tubular cells

To evaluate the functional impact of the endothelial secretome on renal tubular cell viability, a CCK-8 assay was performed on the Human renal proximal tubular epithelial cell line HK-2 treated with CM derived from human renal glomerular endothelial cells (HRGECs), in accordance with established protocols. HK-2 cells were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS). For the assay, cells were seeded into 96-well plates at a density of 5 x 10³ cells per well. Serum-starvation of cells was done for 12 h in medium containing 0.5% FBS immediately prior to CM treatment.

CM was applied with equal total normalized protein mass. Frozen CM aliquots were thawed at 37 °C in a dry bath (<5 min) and clarified by centrifugation at 10,000 x g for 5 min at 4 °C. HK-2 cells (100 µL/well) were treated with HG-CM, NG-CM, or HM-CM, normalized to matched protein/DNA content. A total of 3 biological replicates per condition (with 3 technical replicates each) were prepared. Samples were incubated at 37 °C with 5% CO₂ for 24 h. Cell viability was assessed using the Cell Counting Kit-8. To do this, 10 µL of CCK-8 solution was added to each well and incubated for 1-4 h at 37 °C. Absorbance was measured at 450 nm using a microplate reader. Viability was calculated as:

Viability (%) = [(A_sample - A_blank) / (A_baseline_control - A_blank)] x 100

Oxidative stress detection

Cellular malondialdehyde (MDA) levels were quantified, one of the main end products of membrane lipid peroxidation, via the TBA assay. Cell lysates (1 x 10⁷ cells) were clarified by centrifuging at 10,000 x g for 15 min, and then 0.02 mL supernatant was aliquoted into sample tubes alongside 0.02 mL of MDA standards. To the aliquot, 0.02 mL of clarificant and 0.6 mL of acid reagent were added. The samples/standards were treated with 0.2 mL of the chromogenic agent (control tubes: 0.2 mL of 50% acetic acid was added). After sealing, mixing, and incubating at 100 °C for 40 min, the samples were cooled and then centrifuged at 9569 x g for 10 min. The supernatant was transferred to microplates at 250 µL for OD₅₃₂ measurement.

Transcriptomic profiling

Total RNA was isolated using TRIzol reagent following the manufacturer's protocol. RNA quantity and purity were assessed with a spectrophotometer and fragment analyzer, respectively. Only high-quality RNA samples were used with RNA Integrity Number (RIN) > 7.0 for sequencing library construction.

Polyadenylated (poly(A)+) mRNA was enriched by two rounds of oligo(dT) magnetic bead-based selection. The purified mRNA was fragmented to 200-300 nucleotides using a magnesium-based fragmentation kit at 94 °C for 5-7 min. First-strand cDNA synthesis was done with reverse transcriptase, followed by second-strand cDNA synthesis using E. coli DNA polymerase I and RNase H in the presence of dUTP (to enable strand specificity). Subsequent steps included end repair, A-tailing, adapter ligation, and size selection using magnetic beads. Prior to PCR amplification, the dUTP-incorporated second strand was digested with UDG enzyme.

A strand-specific library was constructed with an average insert size of 400 ± 50 bp using PCR under the following conditions: initial denaturation at 98 °C for 1 min; 14 cycles of denaturation at 98 °C for 10 s, annealing at 60 °C for 30 s, and extension at 72 °C for 30 s; and a final extension at 72 °C for 5 min. Paired-end 150 bp sequencing was done on an Illumina platform, with a sequencing depth of ≥40 million reads per sample (Q30 > 85%).

Bioinformatic analysis of transcriptomic data

Quality check of the raw reads was done using FastQC (v0.11.8), and alignment to the GRCh38.p13 reference genome with HISAT2 (v2.2.1) was done. Transcript quantification and per-sample transcript assembly were performed using StringTie (v2.1.6) with default parameters. All assembled transcriptomes were merged from individual samples to reconstruct a comprehensive transcriptome using gffcompare software (v0.12.6; gffcompare is a standalone tool, not part of StringTie, and its version is corrected to the common stable release). Differential expression analysis was done with DESeq2 (v1.38.3) using thresholds of |log2 fold change (FC)| > 1 and false discovery rate (FDR) < 0.05. Batch effects were corrected via the variancePartition package.

Secretome proteomic analysis

CM was collected from HRGECs cultured under NG, HG, or HM control conditions using a previously described method¹³ with modifications for secretome analysis. Cells were washed with PBS after stimulation and incubated in serum-free medium for 12 h. CM was collected and centrifuged at 3,000 x g for 10 min (4 °C).

Equal amounts of peptide from each sample were mixed and then diluted with solvent A (5% ACN, pH 9.8) and injected into the column. Fractionation of the peptide mixture was done using a 3.5 µm 4.6 x 150 300Extend- C18 column on the Binary Rapid Separation System. Gradient elution was performed at a flow rate of 0.3 mL/min: 5% to 21% solvent B (97% ACN, pH 9.8) in 38 min, 21.5% to 40% solvent B in 20 min, 40% to 90% solvent B in 2 min, 90% solvent B for 3 min, and 5% solvent B equilibrated for 10 min. Elution peaks were monitored at 214 nm, and fractions were collected every minute. Fractions were combined according to chromatograms of the elution peaks. A total of 10 fractions were collected, which were then freeze-dried.

Mobile phases A (100% water, 0.1% formic acid) and B (80% acetonitrile) were prepared, and the dried peptide samples were reconstituted with 0.1% formic acid and centrifuged at 20,000 x g for 10 min. Separation was done using a UHPLC liquid phase system. The samples were fed onto an ES906 HPLC column (150 mm) with an 8-min gradient at 2.5 µL/min, and separated with the following effective gradient: 0-4 min, 4% mobile phase B linearly ramping up to 25%; 4-6.9 min, mobile phase B linearly ramping up from 25% to 35%; 6.9-7.3 min, mobile phase B linearly ramping up from 35% to 99%; 7.3-8.0 min, maintained mobile phase B at 99%. The liquid-phase separated peptides were transferred to a mass spectrometer for DIA mode acquisition. The main parameters were set as follows: normalized collision energy 25%, default charge state 2, resolution 240,000, scanning every 0.6 s, 380-980 m/z scanning range, mass spectrometry AGC 500%. Fragmentation ion scans were recorded with a maximum scan time of 3 ms and utilized 300 2-Th scanning windows, ranging from 380 to 980 m/z.

The DIA-NN (https://www.nature.com/articles/s41592-019-0638-x, version 1.9.1) was used to analyze the DIA data with a library-free method. MS/MS data were searched against protein sequences, which were downloaded from Uniprot database with the following settings: enzyme: Trypsin/P; maximum missed cleavages: 2; fixed modification: carbamidomethyl (C); variable modifications: oxidation (M) and acetyl (protein N-term); precursor mass tolerance: 20 ppm; fragment mass tolerance: 0.05Da. Results were filtered by 1% FDR, and only those protein groups were used that passed this filter criterion in downstream analysis.

Omics functional enrichment analysis (GO, KEGG, and GSEA)

Functional enrichment analyses were conducted on filtered sets of differentially expressed genes (transcriptomics) and proteins (secretome proteomics), following established bioinformatics pipelines. Identifiers were mapped using Org.Hs.eg.db. GO enrichment (biological processes, molecular functions, cellular components) was executed via clusterProfiler. Benjamini-Hochberg adjusted p-value < 0.05, and semantic similarity reduction (cutoff=0.7) was applied to condense terms. KEGG pathway analysis was performed using KEGG REST API (hsa, 2023 release). Hypergeometric testing was utilized with Yekutieli FDR correction. Pathway topology visualization was done with Pathview. GSEA was applied using a rank-based approach with signal-to-noise gene ranking. Test enrichment against MSigDB collections (HALLMARK, C2, C5; v2023.2) and curated diabetic endothelial signatures was done. Significance was assessed after 1000 phenotype permutations (FDR < 25%).

Protein-protein interaction network analysis

To identify core molecular regulators within the pathogenic candidate pool, PPI network construction and topological analysis were performed using an integrated computational workflow. The 278 candidate genes were submitted to the STRING database (v12.0; Homo sapiens; evidence sources included experimental, co-expression, and curated databases; an interaction confidence (combined score) threshold >0.7 for high-confidence edges; and no additional interactor inflation. The results were imported into Cytoscape (v3.9.1), and network topology analysis was performed using MCODE (v1.5.2) and CytoHubba (v0.4.1) plugins. Subsequently, the network was visually optimized according to the degree of connection of nodes, so that nodes with high connectivity are darker in color, larger in size, and more prominent in label.

Development of a diabetic kidney disease target gene set

The criteria for co-upregulated mRNAs/proteins were defined as follows: transcriptome (FDR < 0.05 and |log2FC| > 1) and secretome (FDR < 0.01 and |log2FC| > 1.5). Protein and gene names were normalized using UniProt and converted to a unified format (Gene Symbol). A target gene set was constructed for diabetic kidney disease by integrating disease-associated genes from multiple public databases, including GeneCards (Version 5.25, query term: Diabetic Nephropathies, accessed date July 2025), the Comparative Toxicogenomics Database (CTD; https://ctdbase.org/, query term: Diabetic Nephropathies, accessed date July 2025), and Open Targets Platform (https://platform.opentargets.org/, Version 0.13.8, query term: Diabetic Nephropathies, accessed date July 2025)^13,14,15. This was designated as the comprehensive diabetic kidney disease reference set. The intersection between co-upregulated mRNAs/proteins and this gene set was identified for downstream analyses.

CCL2 protein detection

To quantify CCL2, a 96-well plate was coated with capture antibody (100 µL/well) and incubated at 4 °C overnight. The next day, the plate was washed with 2x with buffer and blocked with ELISA diluent (200 µL/well) for 1 h at RT. A 7-point standard curve was prepared by 2-fold serial dilutions of the S1 standard, then the samples (100 µL/well) were added and incubated for 2 h at 400 rpm. After washing 5x, detection antibody (100 µL/well) was added and incubated for 1 h at 400 rpm, followed by enzyme solution (100 µL/well) addition and incubation for 30 min at 400 rpm. The samples were developed with TMB substrate (15-30 min, RT). The reaction stopped with acid (100 µL/well), and the absorbance was read at 450 nm (620 nm reference optional). Key steps include stringent washing, timed incubations, and standardized dilutions to ensure reproducibility.

Drug repositioning

Therapeutic compounds were predicted using the following platform: LINCS L1000 Characteristic Direction Signatures Search Engine (L1000CDS2; https://maayanlab.cloud/L1000CDS2). This platform employs the MODZ method and characteristic direction analysis to identify small molecules or compound combinations capable of reversing or mimicking input gene expression signatures¹⁶. Candidate drugs were identified as those that reverse the dysregulation of differentially expressed renal genes using this platform. The compounds were ranked based on their reversal scores in renal cell lines by select kidney in the tissue column, and the top-ranked candidates were subjected to molecular docking to evaluate their binding affinities to the key protein targets.

Co-regulatory transcription factor screen

The hTFtarget database was queried for co-regulatory TFsbinding to the gene set. hTFtarget database was analyzed to identify human transcription factor (TF)-target gene relationships by computationally analyzing large-scale ChIP-Seq datasets across diverse cells, tissues, and conditions. The integrative pipeline combined ChIP-Seq peak signals with epigenetic context to pinpoint TF binding in promoters/enhancers, explicitly accounting for spatiotemporal regulatory dynamics. As shown by Zhang et al., it served as a multidimensional platform enabling exploration of TF targets or gene regulators, visualization of ChIP-Seq data, investigation of TF cooperativity, and prediction of binding sites¹⁷.

Computational pipeline and analysis for small molecule-protein docking

AutoDock Vina 1.2.0¹⁸ was used to conduct molecular docking analysis of binding interactions between active ingredients and the transcription factors BRD4, CTCF, EP300, and SPI1. The crystal structures of BRD4 (PDB ID: 7REK), CTCF (PDB ID: 5K5I), EP300 (PDB ID: 5NU5), and SPI1 (PDB ID: 8E3K) were obtained from the Protein Data Bank (https://www.rcsb.org/)^19,20. Small-molecule structures in SDF format were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/²¹, converted to MOL2 format using Open Babel 2.4.1, and energy-minimized with PyRx 0.8. Protein preprocessing was done by removing water molecules, adding hydrogen atoms, calculating Gasteiger charges, and converting them to PDBQT format (required for AutoDock Vina). A semi-flexible docking strategy was employed, defining the docking grid position and dimensions based on the binding sites of co-crystallized ligands in each protein. MA9-086 serves as the positive-control ligand for BRD4, and XDM-CBP for EP300. For CTCF and SPI1 (lacking known positive ligands), potential binding pockets were identified via structural analysis using the Proteins Plus online platform (https://proteins.plus/). Docking grid parameters were set as follows (coordinates and dimensions in Å along the x, y, and z axes): BRD4 center at (-11.907, -8.617, -3.973) with a box size of (18.387, 18.387, 18.387); CTCF center at (15.165, 4.854, 6.388) and box size (17.25, 20.25, 9.75); EP300 center at (39.781, 5.326, 9.998) and box size (16.516, 16.516, 16.516); SPI1 center at (3.155, -3.853, 0.668) and box size (17.25, 25.5, 10.5). During docking, the energy range was set to 3, the exhaustiveness parameter to 8, and the grid spacing to 0.375 Å. Binding stability was assessed from binding energy, where lower values indicate more stable conformations and higher interaction probability, with a threshold of <-5 kcal/mol defined as strong binding activity^22,23. Finally, interaction modes between compounds and receptors were identified using PyMOL.

Statistical analysis

Data is presented as mean ± SEM. Statistical tests were selected based on normality (Shapiro-Wilk) and variance homogeneity (Levene's test). Unpaired t-tests (two groups) or one-way ANOVA with LSD post hoc tests (≥3 groups) were used for between-group comparisons. A p < 0.05 was defined as a significance threshold. Graphs were generated with GraphPad Prism 9.0.

Results

Demonstration of the pathogenic impact of the hyperglycemic endothelial secretome on renal tubules

This study commenced with the validation of the pathogenic effect of the hyperglycemic endothelial secretome. Conditioned medium from hyperglycemic human glomerular endothelial cells (HG-CM) significantly impaired the viability of human proximal tubule epithelial cells (HK-2) compared to the control groups.

Discussion

This study establishes hyperglycemia-induced endothelial secretome dysfunction as a critical driver of diabetic kidney injury through integrated multi-omics and computational approaches. Demonstration that secretome-mediated cytotoxicity toward renal tubules coincides with systematic dysregulation of 534 endothelial-derived factors converging on matrix remodeling, oxidative stress, and inflammation-core pathways in DKD pathogenesis. Identification of 278 high-confidence mediators, including mechanistically validated hubs...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CCL2 ELISA kit	Thermo Fisher Scientific	#88-7399-88
CCR2 inhibitor	Merck Millipore	#227016
Cell Viability Assay Kit	Seven Biotechnology	CCK-8, SC119-01
DNA Sequencer	Illumina	NovaSeq 6000
High-Performance Liquid Chromatography System	Thermo Fisher Scientific	UltiMate 3000 Binary RSLC
HRGECs Culture medium	Shanghai Zhong Qiao Xin Zhou Biotechnology Co., Ltd.	1001
Human Renal Glomerular Endothelial Cells (HRGECs)	Shanghai Zhongqiao Xinzhou Biotechnology	PRI-H-00038
Human Renal Tubular Epithelial Cell Complete Medium	Procell	CM-H193
Human Renal Tubular Epithelial Cells (HK2)	Procell	CP-H193
Male db/db and db/m mice (4-week-old)	Hangzhou Ziyuan Biotech Co., Ltd.	SCXK 20190004
Mass Spectrometer	Thermo Fisher Scientific	Astral
Microplate Absorbance Reader	Molecular Devices	SpectraMax iD5
Protease Inhibitor Cocktail (EDTA-free)	Roche	cOmplete, #5056489001
RNA Extraction Reagent	Thermo Fisher Scientific	TRIzol, #15596026
RNA Quality Analyzer	Agilent Technologies	5300 Fragment Analyzer, M5311AA
RNA Quantification Instrument	Thermo Fisher Scientific	Qubit 3.0, Q33216
ROS detection kit	Thermo Fisher Scientific	#EEA019
Ultrafiltration Centrifugal Devices (3 kDa MWCO)	Merck Millipore	Amicon Ultra-4