Abstract Objective Renal ischemia-reperfusion (I/R) injury triggers significant oxidative stress and inflammation, leading to tubular epithelial cell (TEC) damage. This study investigates the protective role of Desflurane (DFE) in renal I/R by modulating the ITGB1/CD9 signaling pathway and mitigating oxidative damage. Methods Single-cell RNA sequencing (scRNA-seq) and transcriptome analysis identified ITGB1 as a key regulatory gene in TECs during renal I/R. The effects of DFE on ITGB1/CD9 expression were evaluated through in vitro experiments using RT-qPCR, Western blot, and TUNEL assays. A mouse model of renal I/R was employed to assess renal function and oxidative stress markers under DFE treatment. Results DFE reduced ITGB1 and CD9 expression, resulting in decreased TEC apoptosis and enhanced proliferation. In vivo, DFE-treated mice exhibited improved renal function, with significantly lower serum creatinine and blood urea nitrogen (BUN) levels. Additionally, DFE reduced oxidative stress, as indicated by decreased malondialdehyde (MDA) and myeloperoxidase (MPO) activity, alongside increased superoxide dismutase (SOD) activity. Conclusion DFE confers renal protection against I/R by modulating the ITGB1/CD9 axis and reducing oxidative stress, offering a promising therapeutic strategy for mitigating kidney damage. Keywords: Single-cell RNA sequencing, Renal ischemia-reperfusion injury, Desflurane, ITGB1, Glomerular epithelial cells 1. Introduction Ischemia-reperfusion (I/R) injury is a pathological process that may occur in various clinical conditions such as kidney transplantation, kidney surgery, and acute kidney injury, posing a serious threat to a patient's health and life [[33][1], [34][2], [35][3], [36][4]]. The pathogenesis of renal I/R is complex, involving oxidative stress, inflammatory response, generation of free radicals, cell apoptosis pathways, autophagy, and damage to endothelial cells and renal TECs in a series of pathophysiological processes. Among these, the interplay of oxidative stress, inflammatory response, and cell apoptosis pathways, especially their crucial roles in causing TEC damage, is essential for understanding the full picture of renal I/R [[37]5]. Particularly, glomerular epithelial cells play a key role in this process, being highly sensitive to I/R, where damage and death of TECs directly lead to the loss of renal unit function [[38]6,[39]7]. Thus, addressing the damage to TECs in renal I/R is crucial. In the context of renal I/R, studies have suggested that ITGB1 regulates the inflammatory response by binding to miR-328-3p. Inhibiting the expression of circITGB1 can alleviate inflammation induced by hypoxia-reoxygenation treatment. It has also been found that the transcription factor GATA1 can bind to the promoter region of ITGB1, thereby promoting the generation of circITGB1. Overall, this study uncovers the regulatory role of GATA1 on circITGB1 expression and how circITGB1 exacerbates hypoxia-reoxygenation-induced inflammation through the miR-328-3p/PIM1 pathway, providing a new perspective for understanding and treating I/R [[40]8]. Desflurane (DFE) is a commonly used inhalation anesthetic in clinical practice and is known for its safety and effectiveness [[41]9]. In recent years, increasing evidence suggests that besides its anesthetic effects, DFE may also protect organs such as the heart, brain, and lungs from I/R [[42]10,[43]11]. However, it remains unclear whether DFE can protect against renal I/R and the mechanisms underlying its protective effects [[44]12]. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for in-depth gene expression analysis at the single-cell level, unraveling the molecular mechanisms of disease etiology, pathology, and progression [[45]13,[46]14]. Through meticulous gene expression analysis in individual cells, scRNA-seq can reveal subtle differences between cells, which is crucial for understanding the complexity of kidney diseases, particularly renal I/R. In renal I/R, the sensitivity of TECs to damage and their role in the recovery process post-injury has made them a focal point of research. scRNA-seq can help identify key signaling pathways and molecular regulators activated during I/R, potentially leading to the discovery of new therapeutic targets associated with specific gene sets related to cell death, survival, or repair processes. By utilizing scRNA-seq, we can uncover gene expression changes in TECs during renal I/R at a single-cell resolution, not only allowing the identification of known cell subpopulations responding to injury but, more importantly, enabling the discovery of new, under-recognized cell subpopulations or cell states, holding significant value for understanding their roles and potential therapeutic targets in the disease process [[47]15]. Given the aforementioned background, in this study, we plan to investigate how DFE influences renal I/R by regulating ITGB1 through a combination of scRNA-seq and transcriptome analysis. We hypothesize that DFE can protect TECs from I/R, further alleviating kidney I/R damage. We aim to explore its potential mechanisms of action to provide new strategies for treating renal I/R. 2. Materials and methods 2.1. Data acquisition The human kidney tissue scRNA-seq dataset [48]GSE197266 related to renal I/R was obtained from the gene expression omnibus (GEO), consisting of two control group samples, [49]GSM5911966 and [50]GSM5911967, and two renal I/R samples, [51]GSM911972 and [52]GSM5911973. The data was analyzed using the “Seurat” package in the R software. Data quality control was conducted based on the criteria of nFeature_RNA between 200 and 5000 percent.mt less than 20, selecting the top 2000 genes with highly variable expression. Transcriptional RNA sequencing data related to mouse renal I/R was downloaded from GEO, with sample information of the chip dataset shown in [53]Table S1. As these data were obtained from public databases, no ethical approval or informed consent was required. 2.2. Clinical sample collection This study utilized renal I/R tissues and control groups from kidney transplant patients who underwent a biopsy at our hospital between January 2020 and January 2022. The cohort included 5 patients with acute kidney injury and 5 stable transplant patients. Prior to participation in the study, all subjects signed informed consent forms, with ages ranging from 43 to 65 years and an average age of 54 years. The obtained tissues were divided into two parts, with one portion immediately preserved in liquid nitrogen and the other fixed in 10 % formaldehyde for paraffin embedding. The diagnosis of acute kidney injury was based on histopathology. This study obtained approval from the clinical ethics committee of our hospital and complied with the Helsinki Declaration. 2.3. t-SNE clustering analysis To reduce the dimensionality of the scRNA-Seq dataset, we performed principal component analysis (PCA) using the top 2000 highly variable genes in terms of variance. The Elbowplot function in the Seurat package was employed for downstream analysis on the first 20 principal components (PCs). Major cell subgroups were identified using the FindClusters function provided by Seurat with default settings (res = 0.8). Subsequently, non-linear dimensionality reduction of the scRNA-Seq sequencing data was carried out using the t-SNE algorithm. The Seurat package was utilized to identify marker genes for individual cell subgroups. Finally, annotation of the cells was performed using specific marker genes of known cell lineages and the online resource CellMarker. 2.4. RNA extraction and sequencing Three samples were collected for each group of kidney tissues, and total RNA was isolated using Trizol reagent (15596026, Invitrogen, Car, Cal, USA). The concentration and purity of RNA samples were determined using the Nanodrop2000 spectrophotometer (1011U, nanodrop, USA). Total RNA samples meeting the following criteria were used for subsequent experiments: RNA Integrity Number (RIN) ≥ 7.0 and 28S:18S ratio ≥1.5. Sequencing libraries were generated and sequenced by CapitalBio Technology (Beijing, China). A total of 5 μg RNA was used for each sample. Briefly, Ribo-Zero™ Magnetic Kit (MRZE706, Epicentre Technologies, Madison, Wisconsin, USA) depleted ribosomal RNA (rRNA) from total RNA. The NEB Next Ultra RNA Library Prep Kit (#E7775, NEB, USA) was utilized for Illumina-based library construction and sequencing. Subsequently, RNA fragments were size-selected to approximately 300 base pairs (bp) in length using NEB Next First Strand Synthesis Reaction Buffer (5 × ). First-strand cDNA was synthesized using reverse transcriptase primers and random primers, followed by second-strand cDNA synthesis in the presence of dUTP Mix (10 × ) Second Strand Synthesis Reaction Buffer. End repair of cDNA fragments was performed, including the addition of polyA tails and ligation of sequencing adapters. After ligating Illumina sequencing adapters, the second strand of cDNA was digested using USER Enzyme (#M5508, NEB, USA) to construct strand-specific libraries. Library DNA was amplified, purified, and enriched through PCR. Subsequently, the libraries were assessed using Agilent 2100 and quantified using the KAPA Library Quantification Kit (KK4844, KAPA Biosystems). Finally, paired-end sequencing was performed on the NextSeq CN500 (Illumina) sequencer. 2.5. Quality control of sequencing data and reference genome mapping FastQC software v0.11.8 assessed the quality of paired-end reads in the raw sequencing data. Raw data was pre-processed with Cutadapt software 1.18: removing Illumina sequencing adapters and poly(A) tail sequences. Reads with over 5 % N content were filtered using a perl script. Reads with a quality score above 20 for 70 % of the base were extracted using FASTX Toolkit software 0.0.13. BBMap software was employed to repair paired-end sequences. Subsequently, the filtered high-quality reads were mapped to the mouse reference genome using hisat2 software (0.7.12). 2.6. Differential gene analysis The “limma” package in R software was used to perform differential gene expression analysis on the high and low expression groups of ITGB1 in TECs, with the selection criteria of │logFC│ > 0.5 and P-value <0.05 for differentially expressed genes. The differential analysis results were visualized using the “ggplot2” package in R to generate relevant volcano plots. Additionally, the “VennDiagram” package in R was employed to conduct an intersection analysis between the obtained differentially expressed genes and target genes, creating Venn diagrams for the intersecting genes. 2.7. Enrichment analysis of GO and KEGG pathways Using the “clusterProfiler” package, “org.Mm.eg.db” package, “enrichplot” package, and “ggplot2” package in R language, GO and KEGG enrichment analysis was conducted on the identified DEGs. The enrichment results of three categories in Gene Ontology (GO): biological process (BP), cellular component (CC), and molecular function (MF), as well as the KEGG pathway enrichment analysis results, were visualized. 2.8. Construction of PPI and prediction of target genes The STRING database contained experimental data, results obtained through text mining of PubMed abstracts, and integrated data from other databases. Additionally, interactions between proteins were predicted using bioinformatics methods within the database. The STRING database ([54]http://www.string-db.org/) was used to explore protein-protein interactions (PPI). Specifically, protein interactions among 16 candidate factors associated with ferroptosis (limited to mice species, confidence set at 0.4) were predicted. Furthermore, the GeneMANIA online analysis tool was used to predict genes that physically interacted with ITGB1 and related pathways. 2.9. Meta-analysis validation Meta-analysis was conducted using the “meta” package in R software. The combined effect was evaluated using the MD and the 95 % CI. Heterogeneity among independent studies was assessed using the Q-test, p-value, and I^2 statistic. If no significant heterogeneity was observed (P > 0.05, I^2 < 50 %), studies were considered statistically homogeneous, allowing for result combination using a fixed-effects model. Conversely, in cases of significant heterogeneity (P < 0.05, I^2 > 50 %), a random-effects model was employed to account for variability due to sampling errors. 2.10. Construction of a mouse renal I/R model This study utilized a mouse model to investigate renal I/R injury. Six-week-old C57BL/6 N mice, sourced from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China), were housed under controlled conditions (23 ± 1 °C, 12-h light-dark cycle, 60–65 % humidity) with ad libitum access to food and water. Prior to the experiment, the mice underwent one week of acclimatization. The experimental protocol and animal use were approved by the institutional animal ethics committee. The mice were randomly assigned to 7 groups (n = 8 per group): 1. Sham group (sham surgery) 2. Sham + DFE group (sham surgery + DFE pretreatment) 3. I/R group (I/R) 4. I/R + DFE group (I/R + DFE pretreatment) 5. I/R + DFE + PBS + IgG group (I/R + DFE pretreatment + injection of PBS + IgG) 6. I/R + DFE + r-ITGB1+IgG group (I/R + DFE pretreatment + injection of r-ITGB1 recombinant protein + IgG) 7. I/R + DFE + r-ITGB1+CD9-mAb group (I/R + DFE pretreatment + injection of r-ITGB1 recombinant protein + CD9 antibody). To ensure the reliability of the experimental results, each experimental group required a minimum of 8 mice, and each experiment was repeated three times. For the I/R group, mice underwent right nephrectomy and were anesthetized by intraperitoneal injection of pentobarbital sodium (80 mg/kg, Sigma-Aldrich; Merck KGaA, Darmstadt, Germany) followed by the kidney I/R experiment. A single-kidney model was used to avoid influencing normal kidney function. The procedure involved exposing the renal pedicle through a flank incision, removing the right kidney, and clamping the left renal pedicle non-invasively for 60 min with an S-shaped vascular clamp. After clamp removal, kidney color change was observed. Restoration of blood flow to the renal pedicle, indicated by the color change from deep purple to red, confirmed the successful establishment of the I/R model to ensure blood flow before suturing the incision. The Sham group underwent the same surgical procedures without renal artery occlusion. For the DFE group, pentobarbital sodium injection was stopped 3 min before kidney reperfusion, and DFE was inhaled within 15 min, maintaining an end-expiratory concentration of 1.0 minimum alveolar concentration (MAC). After natural awakening, the mice were placed in clean and temperature-regulated metabolic cages with standard feed and ad libitum water, and their urine was collected for 48 h. All animals were placed on a 37 °C heating pad throughout the procedure. After 48 h of reperfusion, euthanasia was performed, blood samples were collected via cardiac puncture (8 samples per group, 1.3 mL per sample), and tissue samples were obtained. Intervention targeting ITGB1 and CD9 expression was achieved by intravenous injection of r-ITGB1 recombinant protein (FY–P518590, Fei Yue Bio, Wuhan) and its control PBS, CD9 antibody (MA5-31980, Thermo Fisher) and its control IgG (Abcam, ab6721, UK). Injections were administered twice at a 24-h interval, with a dose of 20 μg per injection for r-ITGB1 recombinant protein and 300 μg per injection for CD9 antibody. 2.11. Construction of ITGB1 gene, CD9 knockout mouse models To achieve precise knockout of the mouse ITGB1 gene, we employed the CRISPR/Cas9 system. Specific sgRNAs targeting the mouse ITGB1 gene were designed using the online tool CRISPOR and obtained synthetically. These sgRNAs were co-transfected with Cas9 protein (from Streptococcus pyogenes, Sigma-Aldrich, USA) into mouse embryonic stem cells using Lipofectamine 3000 (Invitrogen, USA) as the transfection reagent. Following transfection, successful ITGB1 gene knockout and CD9 cell clones were selected through PCR and sequencing analysis for mouse embryo injection to establish the ITGB1 gene knockout mouse model. To investigate the potential protective mechanisms of DFE under ITGB1 regulation in renal I/R, the following experimental groups were designed: Wild-type control group (no I/R treatment, no DFE exposure), Wild-type I/R group (only undergoing I/R operation), Wild-type I/R + DFE group (exposed to DFE after I/R), ITGB1 knockout control group (no I/R operation, no DFE exposure), ITGB1 knockout I/R group (undergoing I/R operation, no DFE exposure), ITGB1 knockout I/R + DFE group (exposed to DFE after I/R). Each group consisted of 10 mice. DFE exposure in the treatment group was achieved through inhalation to simulate a clinical anesthesia environment. All mice received DFE treatment based on dose and timing determined from preliminary experiments both before and after the I/R surgery. Subsequently, renal tissue sections, serum creatinine (SCr), BUN measurements, and comprehensive pathological assessments were conducted. Following an in-depth exploration of ITGB1's regulatory mechanisms, equal attention was also directed towards CD9, a key molecule. CD9, as a significant cell surface protein, plays crucial roles in cell signaling and cell-cell interactions, suggesting its impact on I/R may differ fundamentally from that of ITGB1. This experiment aimed to investigate the role of CD9 in DFE-mediated renal I/R. The experiment comprised six groups: Wild-type control group, Wild-type I/R group, Wild-type I/R + DFE group, CD9 knockout control group, CD9 knockout I/R group, and CD9 knockout I/R + DFE group, each with 10 mice. For groups receiving DFE treatment, mice were exposed via inhalation to ensure a clinical anesthesia-like state before and after the I/R operation. DFE doses and timing were based on previous optimization experiments. Upon completion of treatment, biochemical indicators of renal function (SCr and BUN) were measured in mice, along with a detailed pathological examination of kidney tissues to evaluate the effects of I/R and DFE treatment on kidney damage. 2.12. Detection of SCr and BUN Mouse Renal I/R Model: Following 24 h of reperfusion, euthanasia was performed on mice through overdosing with an anesthetic injection. Blood samples (1.3 mL each, 8 samples per group) were collected via cardiac puncture post-euthanasia for analysis of SCr and BUN. Commercial detection kits (CB10746-Mu, Shanghai Keb Biotech Company, Shanghai, China) were utilized for measuring serum SCr and urea levels. 2.13. ELISA kit detection of inflammation-related factors Levels of inflammatory cytokines were determined using ELISA kits (EF3RBX10, BMS603-2, BMS607-3, Thermofisher, USA). Tissue samples were homogenized on ice for 30 min, centrifuged at 11,000 rpm for 15 min (4 °C), and then the supernatant was collected for analysis. Renal serum samples were diluted to a certain concentration, and levels of tumor necrosis factor TNF-α, interleukin IL-6, and IL-1β were detected following the manufacturer's instructions. The absorbance at 450 nm was measured using an ELISA reader to calculate the levels of inflammation-related factors. For data reliability, each sample's ELISA detection was repeated three times, and the entire experiment was replicated in three independent batches. 2.14. Detection of oxidative stress-related markers Commercial kits were used as per the manufacturer's instructions to detect the levels of lipid peroxidation malondialdehyde (MDA, CB10205-Mu), myeloperoxidase (MPO, CB10641-Mu), glutathione (GSH, CB10323-Mu), superoxide dismutase (SOD, CB10221-Mu), and catalase (CAT, CB10338-Mu). The kits were purchased from Shanghai Keb Biotech Company. Each oxidative stress marker was measured three times for each sample, and the experiment was repeated three times to ensure consistency in the results. 2.15. HE staining To visualize the histological damage in mouse renal tissues, kidney tissues from each group were collected, fixed in 10 % neutral formalin, embedded in paraffin, and sectioned using xylene. Following sectioning, staining was conducted with hematoxylin and eosin (Biyuntian, China, C0105S) and rinsing with distilled water. Subsequently, the sections were immersed in 95 % ethanol, stained with eosin, dehydrated with graded ethanol, and cleared in xylene. Finally, the sections were air-dried and observed under an optical microscope. An Olympus Corporation optical microscope located in Tokyo, Japan was employed for the assessment of the ATN score. Tissue sections from each experimental group consisted of three independent samples, and each sample underwent the staining process three times to ensure staining consistency. Based on the evaluation criteria, a score was assigned according to the percentage of damaged renal tubules in the sample, with higher scores indicating more severe damage (maximum score of 4): 0 indicating normal kidneys, 1 representing minimal necrosis (<5 %), 2 indicating mild necrosis (5–25 %), 3 indicating moderate necrosis (25–75 %), and 4 indicating severe necrosis (>75 %). 2.16. Immunohistochemistry After embedding and sectioning the mouse renal tissues, the sections were baked at 60 °C for 20 min. Subsequently, the sections were sequentially placed in xylene solution and soaked for 15 min; the xylene was changed, and then they were immersed in absolute alcohol for 5 min, followed by changing to fresh absolute alcohol for another 5 min. Then, the sections were hydrated in 95 % and 70 % alcohol for 10 min each. Each section was treated with 3 % H[2]O[2] for 10 min to block endogenous peroxidase activity. Citrate buffer was added, and the sections were microwaved for 3 min. Antigen retrieval solution was applied, followed by incubation at room temperature for 10 min and washing with PBS three times. Subsequently, the sections were incubated with normal goat serum blocking solution (E510009, Shanghai Shenggong Bioengineering Co., Ltd.) at room temperature for 20 min. Primary antibodies Cytokeratin-18 (MA5-12104, ThermoFisher, 1:500), ITGB1 (PA5-78028, ThermoFisher, 1:100), and CD9 (MA5-31980, ThermoFisher, 1:50) were added dropwise, and the sections were incubated overnight at 4 °C. The next day, the sections were washed with PBS three times, incubated with secondary goat anti-rabbit IgG (ab6721, 1:500, Abcam, UK) for 30 min. After washing with PBS again, a DAB chromogenic reagent kit (DAB-M, Sigma, USA) was used by adding chromogen A, B, and C to the specimens and incubating for 6 min. Subsequently, counterstaining with hematoxylin for 30 s, followed by dehydration in 70 %, 80 %, 90 %, 95 % ethanol, and absolute ethanol for 2 min each. Finally, the sections were immersed twice in xylene for 5 min each and sealed with neutral resin. The sections were observed using an upright microscope (BX63, Olympus, Japan). An image analysis system (Aperio Scanscope System, Vista, CA) was used to measure the positive area of protein expression. The calculation of cell area was the product of the total protein positive area and the corresponding tissue weight. To ensure reliable results, three independent samples were used for each experimental group, and each sample underwent at least three immunohistochemistry experiments. 2.17. RT-qPCR Total RNA was extracted using Trizol (16096020, Thermo Fisher Scientific, USA). The concentration and purity of RNA were determined using a NanoDrop One/OneC microvolume nucleic acid protein concentration meter from Thermo Scientific, with the criterion A260/A280 = 2.0 and concentration greater than 5 μg/μL cDNA synthesis was performed using the cDNA first-strand synthesis kit (D7168L) from Shanghai Beyotime (Shanghai, China). According to the instructions provided by Nanjing Vazyme Biotech (Jiangsu, China), RT-qPCR experiments were conducted. A mixture of 2 μL cDNA template, 0.2 μL each of upstream and downstream primers, and 10 μL RT-qPCR Mix was prepared. RNAase-free water was added to make the total volume 20 μL. PCR amplification was carried out in a Bio-Rad real-time quantitative PCR instrument CFX96, with reaction conditions as follows: pre-denaturation at 95 °C for 30 s, 40 cycles of denaturation at 95 °C for 10 s, annealing at 60 °C for 30 s, and extension at 72 °C for 30 s. The melt curve ranged from 65 °C to 95 °C. The primer sequences were designed and provided by Shanghai Shenggong Bioengineering Co., Ltd. (Shanghai, China) and can be seen in [55]Table S2. The expression ratio of the target gene in the experimental group compared to the control group was indicated by calculating 2^−ΔΔCt with GAPDH as the reference gene. The formula is as follows: ΔΔCt = ΔCt experimental group-ΔCt control group, where ΔCt = Ct(target gene)-Ct(reference gene). The RT-qPCR reaction was repeated three times for each sample to ensure data accuracy. The experiment was conducted in three independent batches. 2.18. Translation of the description for Co-immunoprecipitation (Co-IP) and western blot procedures For the Co-IP experiment, HEK-293T cells (1 × 10⁷) were used. Cell lysates were prepared using RIPA buffer to lyse the total cells. Antibodies were incubated with A/G magnetic beads at room temperature for 1.5 h, followed by washing three times with PBS. The cell lysates were then incubated with the antibody-bead complexes overnight at 4 °C. On the following day, the complexes were washed three times with PBS, denatured with SDS, and proteins were extracted. Total protein from each cell group was extracted using a protein extraction kit (Bestbio, BB3101, Shanghai, China), and the protein concentration of each sample was determined using a BCA assay kit (Biyuntian, P0012S, Shanghai, China) and adjusted with deionized water. Subsequently, a 10 % SDS-PAGE gel (Biyuntian, P0012A, Shanghai, China) was prepared. 50 μg of protein sample was added to each sample, and electrophoresis was carried out at a constant voltage of 80V and 120V for 2 h. The proteins were transferred onto a PVDF membrane (Millipore, ISEQ00010, USA) using a wet transfer system with a constant current of 250 mA for 90 min. The PVDF membrane was blocked in TBST buffer containing 5 % skim milk powder for 2 h. After discarding the blocking solution, the membrane was washed once with TBST and then incubated overnight at 4 °C with primary antibodies (human): GAPDH (Abcam, ab8245), ITGB1 (ThermoFisher, PA5-78028, 1:5000), CD9 (ThermoFisher, MA5-31980, 1:500), Cleaved-Caspase 3 (ThermoFisher, PA5-114687, 1:500), Caspase 3 (ThermoFisher, PA5-77887, 1:500). The PVDF membrane was washed 3 times with TBST for 10 min each. Subsequently, horseradish peroxidase-conjugated goat anti-rabbit IgG (1:2000, Abcam, ab6721, UK) or goat anti-mouse IgG (1:2000, Abcam, ab6789, UK) was added dropwise and incubated at room temperature for 1 h. After washing 3 times with PBST for 10 min each, the membrane was subjected to ECL reaction solution (Millipore, KLS0100, USA) for visualization, followed by exposure and imaging. Western blot analysis for each protein sample was conducted three times, and the experiment was repeated in three independent batches to validate result consistency. 2.19. Cell culture and treatment The human renal TEC line HK-2 (CRL-2190) was obtained from the American Type Culture Collection (ATCC). Cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) (ThermoFisher, 11965092) containing 100 U/mL penicillin, 100 μg/mL streptomycin, and 10 % fetal bovine serum (Gibco, USA, 16140071) in a humidified atmosphere with 5 % CO[2] at 37 °C. Construction of the Cell I/R Model: Cells were cultured in a serum-free medium for 24 h, then transferred to a hypoxic incubator containing 1 % O[2], 5 % CO[2], and 94 % N[2] for another 24 h. Following hypoxic treatment, the cells were returned to DMEM and incubated in a normoxic incubator (21 % O[2], 5 % CO[2], 74 % N[2]) for 24 h. For DFE cell treatment: HK-2 cells were pre-treated with 1.0 MAC of DFE in a normal culture medium for 30 min before collecting cells for subsequent experiments. The cell experimental groups included: Control group, H/R group (hypoxia-reoxygenation treatment), H/R + DFE group (hypoxia-reoxygenation + DFE treatment), H/R + DFE + oe-NC group (hypoxia-reoxygenation + DFE treatment + overexpression negative control sequence), H/R + DFE + oe-ITGB1 group (hypoxia-reoxygenation + DFE treatment + overexpression ITGB1), H/R + DFE + oe-NC + sh-NC group (hypoxia-reoxygenation + DFE treatment + overexpression negative control sequence + silencing negative control sequence), H/R + DFE + oe-ITGB1+sh-NC group (hypoxia-reoxygenation + DFE treatment + overexpression ITGB1+silencing negative control sequence), H/R + DFE + oe-ITGB1+sh-CD9 group (hypoxia-reoxygenation + DFE treatment + overexpression ITGB1+silencing CD9). Each cell experiment under different treatment conditions was repeated in three independent experiments. 2.20. Cell transfection The method of lentiviral transfection is based on the construction of corresponding cell lines, including the human kidney-2 cell line overexpressing ITGB1 (oe-ITGB1) and the control cell line (oe-NC). Lentiviral vectors carrying a single luciferase reporter gene plasmid and helper plasmids were co-transfected into 293T cells (CL-0469, Procell), followed by validation, amplification, and purification to obtain packaged lentivirus. For lentivirus-mediated cell transfection, 5 × 10^5 cells were seeded into a 6-well plate. When the cell confluence reached 70–90 %, an appropriate amount of packaged lentivirus (MOI = 10, working titer approximately 5 × 10^6 TU/mL) and 5 μg/mL polybrene (Merck, TR-1003, USA) were added to the culture medium for transfection. After 4 h of transfection, an equal amount of fresh medium was added to dilute polybrene, and 24 h post-transfection, the medium was replaced with fresh medium. After 48 h, transfection efficiency was observed using the luciferase reporter gene, and selection was performed using 60 μg/mL G418 (Sangon Biotech, A100339, Shanghai, China) to obtain stably transfected cell lines. The specific method of lentiviral infection was as follows: HK-2 cells in the logarithmic growth phase were prepared in a 6-well plate at a concentration of 5 × 10^4 cells/mL, 2 mL per well, incubated overnight at 37 °C. Subsequently, each well was infected with lentivirus at a final concentration of 1 × 10^8 TU/mL for gene silencing or overexpression. After 48 h of infection, infection efficiency was determined by RT-qPCR, and the well-performing cells were selected for further experiments. The silencing sequences are detailed in [56]Table S3, and the stability of transfection efficiency and subsequent experimental results were ensured by repeating the transfection process in three independent experiments. 2.21. TUNEL assay for cell apoptosis Cell apoptosis was detected using the Click-iT™ Plus TUNEL Assay Kit (Thermo, [57]C10618, USA) following the manufacturer's instructions. The specific steps included washing the cells three times with PBS, fixing the cells with 4 % formaldehyde for 30 min, permeabilizing with 0.5 % Triton X-100 for 10 min, and incubating the cells with the TUNEL probe for 4 h at room temperature. After staining the cell nuclei with DAPI for 3 min at room temperature, fluorescence microscopy using a BX51 inverted microscope (Olympus Corporation, Tokyo, Japan) was used for visualization. Cells with green fluorescent nuclei were considered positive. The total and apoptotic cells were counted in four randomly selected fields. The experiment was repeated thrice, and the average was calculated. Each sample's TUNEL assay was performed at least three times, and the entire experiment was carried out in three independent experimental batches. 2.22. Scratch assay On the bottom of a 6-well plate, lines were evenly drawn at 0.5–1 cm intervals using a ruler and marker pen, ensuring that each well crossed at least five lines. Approximately 5 × 10^5 cells per well were added to each well of the 6-well plate. The cells were incubated overnight in a medium containing 10 % fetal bovine serum. Using a sterile 10 μL pipette tip, scratches were made perpendicular to the back lines and incubated in serum-free medium for 0 and 24 h. The distance of the scratch was measured and recorded using an optical microscope (Leica, DM500), and images were captured using an inverted microscope (Olympus, IX53) to observe cell migration for each group. To ensure the accuracy of migration assessment, the scratch assay was repeated three times for each experimental condition, and the entire experiment was conducted in three independent experimental batches. 2.23. Assessment of cell viability using MTT method Cells were seeded in a 96-well plate at a density of 5 × 10^3 cells per well and cultured for 24 and 48 h for cell viability assessment. Following incubation in a culture medium containing 10 % fetal bovine serum for 24 and 48 h, cells were washed twice with 200 μL PBS. Cell viability was determined using the MTT Cell Viability Assay Kit (CT01, Sigma-Aldrich, USA) according to the manufacturer's instructions. Absorbance was measured at 490 nm using a microplate reader, with five samples set for each group. The formula for calculating cell viability was: Cell Viability (%) = (Sample-A0)/(Control-A0) × 100 %, where Sample, Control, and A0 represent the absorbance values at 490 nm of the sample, control, and blank wells, respectively. Each experimental group had three parallel wells, and the experiment was repeated three times. 2.24. Statistical software and data analysis methods This study utilized R software (version 4.2.1) and was compiled using the integrated development environment RStudio (version 4.2.1). For file processing, Perl language (version 5.30.0) was employed. Prior to data analysis with R software and Perl language, quality control was conducted on the raw data, including data cleaning (removal of obvious errors or inconsistencies) and exploratory data analysis (EDA) to identify and handle outliers. Perl scripts were used to format datasets, such as encoding conversion and marking missing values, ensuring efficiency and accuracy during the data preparation phase. For non-normally distributed continuous data, non-parametric statistical methods were applied. The Mann-Whitney U test was used to compare differences between the two groups, and the Kruskal-Wallis H test was employed for comparisons among multiple groups. These analyses were performed in R using the Wilcox.test and Kruskal.test functions. Additionally, for repeated measures data that did not meet the normality assumption, the Friedman test was utilized. Missing data were addressed using multiple imputation (MI) or other suitable methods to mitigate biases introduced by missing values. In R, the mice package was used for MI, which generates multiple complete datasets while preserving the inherent variability of the original data. After analyzing each imputed dataset individually, results were aggregated to obtain final estimates and statistical inferences. Data visualization and analysis tools included Cytoscape (version 3.7.2) and GraphPad Prism statistical software (version 9.0). Continuous data were represented as mean ± standard deviation (SD). Differences between the two groups were compared using an independent samples t-test, while one-way ANOVA was employed for comparisons among multiple groups. For data collected at different time points, two-way ANOVA was performed, followed by post hoc Bonferroni tests. A p-value of less than 0.05 was considered statistically significant. 3. Results 3.1. Protective effect and mechanism study of DFE pretreatment on mouse renal I/R The inhaled anesthetic DFE is widely used in clinical anesthesia. Research reports have indicated that DFE plays a crucial role in the process of reperfusion injury in some organs sensitive to I/R damage, such as the lungs, heart, and brain [[58][16], [59][17], [60][18]]. While some progress has been made in studying DFE in renal I/R, its mechanisms have not been completely elucidated. To investigate the impact of DFE on kidney I/R, we established a mouse renal I/R model and evaluated the main features of renal I/R. At 24 h post-reperfusion, blood samples were collected using the cardiac puncture method, and the levels of BUN and Scr in mouse kidney serum were measured using commercial assay kits. Results indicated a significant increase in BUN and Scr levels in the I/R group compared to the Sham group; however, the I/R + DFE group showed significantly reduced BUN and Scr levels compared to the I/R group ([61]Fig. 1A–B), suggesting that DFE pretreatment influenced the functional changes in the kidney post I/R. Fig. 1. [62]Fig. 1 [63]Open in a new tab The effect of DFE pretreatment on renal I/R. Note: (A) Urea measurement reagent kit was used to detect urea levels in mouse serum. The results indicated a significant increase in urea content in the I/R group compared to the Sham group (P < 0.01), while the I/R + DFE group, which receivedDFE pretreatment, showed a significant decrease in urea levels compared to the I/R group; (B) Creatinine detection kit was used to measure Scr levels in mouse serum. The data showed a significant elevation of creatinine content in the I/R group compared to the Sham group, whereas the I/R + DFE group exhibited a significant reduction in creatinine levels compared to the I/R group; (C–E) ELISA kits were utilized as per the manufacturer's instructions to detect the levels of inflammatory factors TNF-α, IL-1β, and IL-6 in renal serum. Compared to the Sham group, the I/R group exhibited a significant upregulation of these inflammatory factors, whereas the levels of inflammatory factors in the I/R + DFE group were lower than those in the I/R group; (F–J) Commercial assay kits were used according to the manufacturer's instructions to measure the levels of oxidative stress-related factors (MDA, MPO) and antioxidant-related factors (GSH, SOD, CAT) in renal tissue. The levels of MDA and MPO in the I/R group were significantly higher than in the Sham group, while GSH, SOD, and CAT levels were significantly lower than in the Sham group. These parameters showed improvement in the I/R + DFE group compared to the I/R group; (K) HE staining was used to evaluate the histological damage in renal tissues of each group, with a scale of 50 μm. The renal tissue structure of the Sham group and Sham + DFE group remained intact, whereas significant damage was observed in the renal tissue of the I/R group, with less severe damage in the I/R + DFE group; (L) Assessment of ATN in various groups under an optical microscope, with scoring criteria based on the percentage of damaged renal tubules in the sample, ranging from 0 (normal kidney) to 4 (severe necrosis); (M − O) RT-qPCR results of TNF-α, IL-1β, and IL-6. Compared to the Sham group, the ATN score in the I/R group significantly increased, indicating severe renal tissue damage, while the ATN score in the I/R + DFE group was lower than in the I/R group. Compared to the Sham group, ∗P < 0.05, ∗∗P < 0.01; Compared to the I/R group, #P < 0.05, ##P < 0.01, with a sample size of n = 8 mice in each group. Subsequently, we measured the levels of inflammatory factors (TNF-α, IL-1β, IL-6) in kidney serum using commercial assay kits. Results revealed a substantial increase in the levels of inflammatory factors in the serum of the I/R group compared to the Sham group; conversely, the I/R + DFE group exhibited significantly lower levels of inflammatory factors compared to the I/R group ([64]Fig. 1C–E), indicating that DFE pretreatment could suppress the kidney's inflammatory response induced by I/R. Additionally, we evaluated the levels of oxidative stress-related factors (MDA, MPO) and antioxidant-related factors (GSH, SOD, CAT) in kidney tissues. Results showed a significant increase in oxidative stress-related factors and a decrease in antioxidant-related factors in kidney tissues of the I/R group compared to the Sham group; in contrast, the I/R + DFE group demonstrated reduced oxidative stress-related factor levels and elevated antioxidant-related factor levels compared to the I/R group. There were no significant differences between the Sham and Sham + DFE groups ([65]Fig. 1F–J), indicating that DFE pretreatment inhibited renal oxidative stress induced by I/R. Furthermore, we used HE staining combined with the ATN score to assess the extent of kidney tissue damage. The findings revealed that the kidney tissue structure in the Sham and Sham + DFE groups remained unchanged, with normal glomerular and tubular structures. In the I/R group, kidney tissue damage was significantly exacerbated, particularly in the outer medulla, with evident vacuolization and luminal dilation in the tubules, which significantly increased the ATN score. Conversely, the I/R + DFE group showed a reduction in the severity of pathological and outer medullary tissue damage to some extent, with relatively intact tubular tissue and a corresponding decrease in the ATN score compared to the I/R group ([66]Fig. 1K–L). RT-qPCR results showed that compared with the Sham group, the levels of inflammatory cytokines in renal tissues of the I/R group were significantly elevated, while those in the I/R + DFE group were significantly reduced compared with the I/R group ([67]Fig. 1M-O). These findings indicate that DFE pretreatment alleviated the histological damage caused by I/R in the kidney. In conclusion, the above results demonstrate the protective effect of DFE on renal I/R. 3.2. Single-cell transcriptomic analysis reveals key cell subpopulations in renal I/R tissues Renal I/R is a common pathological process in clinical settings, and the kidney is particularly sensitive to I/R due to its unique structure and function [[68]19]. To delve deeper into the protective mechanisms of DFE on renal I/R, we conducted a detailed single-cell transcriptomic analysis of the pathogenesis of renal I/R. We obtained human renal I/R-related scRNA-seq data from the GEO database. Following data integration using the Seurat package, we first analyzed the gene counts (nFeature_RNA), mRNA molecules (nCount_RNA), and mitochondrial gene percentage (percent. mt) of all cells. Results showed that most cells had nFeature_RNA less than 5000, nCount_RNA less than 20000, and percent.mt less than 20 % ([69]Fig. S1A). Our data quality control criteria were 200 < nFeature_RNA <5000 percent.mt < 20 %. After filtering out low-quality cells based on these standards, we obtained an expression matrix of 18767 genes and 20693 cells. Correlation analysis revealed a coefficient of −0.54 between nCount_RNA and percent. mt in the filtered data, and a coefficient of 0.93 between nCount_RNA and nFeature_RNA ([70]Fig. S1B). Additionally, cell cycle distribution across samples was relatively consistent ([71]Fig. S1C), indicating good data quality post-filtering for subsequent analysis. Subsequently, by selecting highly variable genes, we chose the top 2000 variance-ranked genes for downstream analysis ([72]Fig. 2A). After data normalization, we reduced linear dimensionality using PCA based on the selected highly variable genes. Expression heatmaps of major correlated genes from PC_1 to PC_6 revealed the expression patterns of key genes ([73]Fig. S1D). Due to the significant batch effects identified during initial analysis, we performed batch correction on the data using the Harmony package ([74]Fig. S1E), and the corrected results demonstrated the successful elimination of batch effects ([75]Fig. 2B). Furthermore, ElbowPlot analysis of PCs for variance ranking ([76]Fig. 2C) indicated that the first 20 PCs sufficiently captured information from the selected highly variable genes, aiding in better cell discrimination. Fig. 2. [77]Fig. 2 [78]Open in a new tab Overview of single-cell transcriptome analysis: from gene expression variability to cell clustering and batch effect correction. Note: (A) Variance analysis was conducted to select highly variable gene expression, with red representing the top 2000 highly variable genes and black representing low variable genes, labeling the top 10 genes in the highly variable gene list; These genes have high variability in expression in different cells, suggesting that they have specific functions in different cell types; (B) Distribution of cells in PC_1 and PC_2 before (left) and after (right) Harmony batch correction, where each point represents a cell. After correction, cells from different batches are more uniformly distributed, indicating effective correction of batch effects. (C) Standard deviation distribution of PCs, where important PCs exhibit larger standard deviation, the standard deviation trend of the top 50 PCs was shown, and the top 20 PCs were screened out for further analysis; (D) UMAP clustering visualization, illustrating cell clustering in Control and I/R samples in a two-dimensional setting, with each color representing a cluster; The results showed that the distribution of different cell populations in the I/R group was significantly different from that in the control group, indicating that I/R treatment resulted in significant changes in the cell population; (E) Expression patterns of known cell lineage-specific marker genes in different clusters, with red indicating high average expression levels and blue indicating low average expression levels, where larger circles represent more cells expressing that gene; The results showed that there were significant differences in the expression patterns of specific marker genes in different clusters, P < 0.05.; (F) Visualization of cell annotation results based on UMAP clustering, with each color representing a cell subpopulation, these include TECs, macrophages, neutrophils, iymphocytes, endothelial cells, and stromal cells. The results showed that the distribution of cell types in the I/R group was significantly different from that in the control group, P < 0.05. Finally, the UMAP algorithm was employed to perform nonlinear dimensionality reduction on the top 20 PCs, and clustering analysis yielded 25 cell clusters. The gene expression profile for each cluster is depicted in [79]Fig. 2D, and annotation of cells was conducted by referencing relevant literature and utilizing the online database CellMarker ([80]Fig. 2E). Our analysis identified six major cell categories in human renal I/R tissues: TECs, Macrophages, Neutrophils, Stromal cells, Lymphocytes, and Endothelial cells ([81]Fig. 2F), shedding light on crucial cell subpopulations and their potential functions in human renal I/R tissues. 3.3. Role of renal TECs in renal I/R and analysis of the protective effect of DFE To further investigate the main pathogenic mechanisms of renal I/R, we computed the proportions of various cell types in individual samples based on Seurat analysis. The results revealed that TECs are the predominant cell type in the kidney, with a significant decrease in TEC cell proportion in the I/R group and a notable increase in macrophages and neutrophils ([82]Fig. 3A). This indicates that TECs undergo cell death or necrosis after renal I/R, leading to increased infiltration of macrophages and neutrophils in the kidney tissue post I/R. Fig. 3. [83]Fig. 3 [84]Open in a new tab Enrichment analysis reveals the BP and signaling pathways primarily involved in differential gene expression in TECs between the control and I/R groups. Note: (A) Proportions of different cell subpopulations in each sample, represented by different colors; renal TECs are the main cell type in the kidney, but their proportion significantly decreases in the I/R group, while the proportions of macrophages and neutrophils increase; (B) Cell interaction network in Control group samples, where different colors represent different cell types, with darker colors indicating stronger cell interactions; The results showed that TECs had strong interactions with other cell types.; (C) Cell interaction network in I/R group samples, similar to above with stronger interactions observed between macrophages, neutrophils, and other cell types after renal I/R damage, while TEC interactions weaken; (D) Expression levels of necrosis, apoptosis, and ferroptosis-related genes in TECs of different treatment groups, with each point representing a cell. In the I/R group, the expression levels of these genes are generally elevated; (E) Expression of inflammatory factors in TECs of different treatment groups, with each point representing a cell. In the I/R group, there is a significant increase in the expression of these inflammatory factors, consistent with the enhanced cell death mechanisms observed in Figure D; (F–G) Immunohistochemical staining to detect the expression levels of the TEC marker protein Cytokeratin-18 in mouse model kidney tissues, scale = 25 μm. Compared to the Sham group, the expression of Cytokeratin-18 significantly decreases in the I/R group, while it shows some recovery in the I/R + DFE group; ∗∗ indicates significance compared to the Sham group (P < 0.01), # indicates significance compared to the I/R group (P < 0.05), with n = 8. To explore changes in the interactions between cells following renal I/R, we examined the gene expression correlations of receptors and ligands between different cell types using the “cellcall” package in R software ([85]Figs. S2A–B). Subsequently, we evaluated the interactions between different cells. The results demonstrated that after renal I/R, the pathway activities of macrophages and neutrophils with other cells significantly increased, while the interaction of TECs with other cells markedly decreased ([86]Fig. 3B–C). At the molecular mechanism level, TEC injury is primarily associated with activating death pathways such as pyroptosis, necroptosis, and ferroptosis. Literature reports suggest that TECs are susceptible to toxins, hypoxia, mechanical injury, and aging, leading to cell death and necrosis, thereby promoting the release of inflammatory mediators and infiltration of inflammatory cells, further exacerbating kidney damage and dysfunction [[87]20,[88]21]. We visualized the expression levels of pyroptosis-related genes (Casp1, Gsdmd, Nlrp3, Il1b), necroptosis-related genes (Ripk3, Mlkl), and ferroptosis-related genes (Gclc, Acsl4) in TECs from the Control and I/R groups. The results showed a significant increase in the expression of ferroptosis-related genes in TECs from the I/R group ([89]Fig. 3D). Ferroptosis is caused by intracellular iron accumulation and lipid peroxidation, leading to cellular structural damage. Understanding these mechanisms can help explore therapeutic strategies to mitigate kidney injury and promote renal function recovery. Subsequently, the expression levels of inflammatory factors (Tnf, Il6, Ccl3, Cxcl11) were examined, revealing a higher expression of inflammatory factors in TECs from the I/R group compared to the Control group ([90]Fig. 3E). Furthermore, immunohistochemical staining was performed to detect the expression of the TEC marker protein Cytokeratin-18 in the kidney tissues of the mouse model of I/R. The results showed no significant difference in protein expression of Cytokeratin-18 between the Sham and Sham + DFE groups; however, Cytokeratin-18 was significantly reduced in the I/R group compared to Sham, while the I/R + DFE group exhibited significantly higher Cytokeratin-18 protein expression than the I/R group ([91]Fig. 3F–G). This suggests that DFE pretreatment can alleviate TEC death induced by I/R. Overall, these results indicate that TECs play a crucial role in renal I/R. 3.4. Revealing the molecular mechanisms of TECs in renal I/R: the ITGB1/CD9 pathway To investigate the impact pathway of TECs mediated by DFE on renal I/R, we studied the specific molecular mechanisms of TECs involved in I/R. Based on Seurat analysis, we extracted TEC cell clusters from the Control and I/R groups and analyzed the gene expression differences. The results revealed 241 differentially expressed genes, including 84 downregulated and 157 upregulated genes ([92]Fig. 4A). Enrichment analysis of the differentially expressed genes showed that they were mainly enriched in BP such as small molecule catabolic processes, anion transport, and alpha-amino acid metabolism ([93]Fig. S3A); while the KEGG enrichment results indicated enrichment in pathways like amino acid synthesis, carbon metabolism, and lysosomes ([94]Fig. S3B). By constructing a PPI network using the String online platform, we identified ITGB1 as a central gene in the I/R group and its crucial role under similar pathological conditions was supported by literature ([95]Fig. 4B–C). Among the top three genes regarding connectivity, ITGB1 was upregulated in the I/R group, while Pex and Sdhb were upregulated in the Control group ([96]Figs. S3C–E). Furthermore, we validated the tissue-level expression of ITGB1, Pex, and Sdhb using meta-analysis from renal I/R-related transcriptome chips in the GEO database ([97]Figs. S3F–H). The results showed a high expression of ITGB1 in the I/R group consistent with its cellular expression level, but the results for Pex and Sdhb were not statistically significant. This finding prompted further exploration of the biological function of ITGB1 and its potential impact on TECs, although direct experimental validation was not conducted. After obtaining the gene expression matrix of TECs from the I/R group, we divided the cells into high and low-expression groups based on the median expression level of ITGB1. Differential analysis of these two groups identified 109 differentially expressed genes ([98]Fig. 4D). Using the online analysis tool GeneMANIA, we predicted the target genes of ITGB1 and obtained 20 genes closely related to ITGB1 ([99]Fig. 4E). By intersecting the differentially expressed genes with the predicted target genes, we found CD9 and Dab2 to be differentially expressed target genes ([100]Fig. 4F). Among these, ITGB1 showed a positive correlation with CD9 and a negative correlation with Dab2 ([101]Fig. 4G–I). Literature review revealed that CD9 is closely associated with the activation of renal epithelial cells and glomerular diseases, with a larger correlation coefficient between ITGB1 and CD9 [[102]22]. Therefore, we selected CD9 as the focus of our study. Fig. 4. [103]Fig. 4 [104]Open in a new tab Gene expression, PPI, and key regulatory factors in TECs injury response. Note: (A) Differential gene expression in TECs of Control and I/R groups in single-cell data, blue indicates genes with P-value<0.05, red indicates genes that simultaneously meet P-value<0.05 and │logFC│>0.5; (B) PPI network of differential genes in TECs of Control and I/R groups in single-cell data, where connections represent interactions between two genes; (C) Statistical graph of connected nodes in the PPI network, genes with more connections likely occupy central positions in the PPI network; (D) Differential gene expression related to ITGB1 expression levels in high and low expression groups of I/R group TECs, with red indicating significantly high expression and blue indicating significantly low expression; (E) GeneMANIA tool analysis of co-expression and interaction relationships between p53 and PTEN, with red indicating interaction relationships, purple representing co-expression relationships, green showing genetic interactions, and yellow indicating predicted interactions; (F) Significant differentially expressed genes intersecting with predicted target genes of high and low expression groups of ITGB1 in I/R group TECs; CD9 and Dab2 are two important differentially expressed target genes identified in the analysis; (G) Expression patterns of CD9 and Dab2 in Control and I/R groups; (H) Expression correlation between CD9 and ITGB1; (I) Expression correlation between Dab2 and ITGB1; Potential regulatory relationships among ITGB1 and these genes in TECs injury response. 3.5. DFE inhibits CD9 expression by regulating ITGB1 To explore the role of ITGB1 in renal I/R injury, we conducted relevant experimental analyses on clinical samples and human renal TECs (HK-2). The expression levels of ITGB1 and CD9 in clinical tissue samples were detected using RT-qPCR and Western blot. The results showed that ITGB1 and CD9 expression were significantly higher in the I/R group than in the control group, indicating that ITGB1 was significantly upregulated under I/R conditions ([105]Fig. 5A). In HK-2 cells, an I/R model was used to simulate I/R, with DFE added as an intervention. The experimental results revealed that ITGB1 and CD9 expression were significantly upregulated in the I/R group compared to the control group, while their expression was significantly reduced in the I/R + DFE group compared to the I/R group. This suggests that DFE may alleviate I/R injury by suppressing ITGB1 and CD9 expression ([106]Fig. 5B). In HK-2 cells overexpressing ITGB1, RT-qPCR and Western blot results showed that the expression levels of ITGB1 and CD9 were significantly increased in the I/R + DFE + oe-ITGB1 group compared to the I/R + DFE + oe-NC group, further confirming the regulatory effect of ITGB1 on CD9 ([107]Fig. 5C). In HK-2 cells with ITGB1 knockdown, RT-qPCR and Western blot results showed that ITGB1 and CD9 expression levels were significantly reduced in the I/R + DFE + si-ITGB1 group compared to the I/R + DFE + si-NC group, indicating that ITGB1 knockdown could significantly suppress CD9 expression ([108]Fig. 5D). Additionally, we examined the interaction between ITGB1 and CD9 proteins using Co-IP experiments and found that they have the ability to form a complex ([109]Fig. S4). Fig. 5. [110]Fig. 5 [111]Open in a new tab Analysis of the upregulation of ITGB1 and CD9 expression under I/R conditions and the intervention effect of DFE. Note: (A) RT-qPCR and Western blot were used to detect the expression levels of ITGB1 and CD9 in clinical tissues. Under ischemia-reperfusion (I/R) conditions, the expression levels of ITGB1 and CD9 were significantly increased, n = 5; (B) RT-qPCR and Western blot were performed to evaluate the expression levels of ITGB1 and CD9 in HK-2 cells. (C) RT-qPCR and Western blot were conducted to examine ITGB1 and CD9 expression in HK-2 cells overexpressing ITGB1; (D) RT-qPCR and Western blot were used to assess the expression levels of ITGB1 and CD9 in HK-2 cells with ITGB1 knockdown. Under DFE intervention, the protein levels of ITGB1 and CD9 changed significantly. ∗∗∗ indicates P < 0.001 compared to the Control group; ### indicates P < 0.001 compared to the I/R group; && indicates p < 0.01, &&& indicates P < 0.001 compared to the I/R + DFE + oe-NC group. Cell experiments were repeated 3 times. These results suggest that ITGB1 plays an important role in renal I/R by regulating CD9 expression, while sevoflurane may exert protective effects by downregulating ITGB1 and CD9 expression. 3.6. DFE improves TEC function via the ITGB1/CD9 pathway, providing protective effects against renal I/R Changes in TEC function are a key factor influencing renal damage and functional decline [[112]22]. We used lentiviral transduction to interfere with the expression of ITGB1 and CD9 ([113]Figs. S5A–B), further investigating the impact of DFE on TEC function. To study the effect of DFE on ITGB1 regulating glomerular epithelial cell apoptosis, we assessed the apoptosis of HK-2 cells treated with DFE using TUNEL. The results showed that compared to the control group, apoptosis of HK-2 cells in the I/R group significantly increased. However, in the I/R + DFE group, compared to the I/R group, cell apoptosis significantly decreased. There was no significant difference between the I/R + DFE + oe-NC + sh-NC group and the I/R + DFE group. Additionally, compared to the I/R + DFE + oe-NC + sh-NC group, apoptosis in HK-2 cells of the I/R + DFE + oe-ITGB1+sh-NC group significantly increased; whereas compared to the I/R + DFE + oe-ITGB1+sh-NC group, cell apoptosis in the I/R + DFE + oe-ITGB1+sh-CD9 group significantly decreased ([114]Fig. 6A–B). Subsequently, we evaluated the effect of DFE regulating ITGB1 on glomerular epithelial cell migration ability through scratch assay. The results revealed that compared to the control group, TEC migration ability in the I/R group significantly increased. Conversely, in the I/R + DFE group, cell migration ability significantly decreased compared to the I/R group. There was no significant difference between the I/R + DFE + oe-NC + sh-NC group and the I/R + DEF group. Furthermore, compared to the I/R + DFE + oe-NC + sh-NC group, migration ability in the I/R + DFE + oe-ITGB1+sh-NC group significantly increased; whereas compared to the I/R + DFE + oe-ITGB1+sh-NC group, migration ability of the I/R + DFE + oe-ITGB1+sh-CD9 group significantly decreased ([115]Fig. 6C–D). Fig. 6. [116]Fig. 6 [117]Open in a new tab Effects of DFE regulation of ITGB1 on the proliferation, migration, and apoptosis of TECs. Note: (A) TUNEL assay was used to detect the apoptosis of HK-2 cells after DFE treatment. DFE treatment significantly reduced the apoptosis of HK-2 cells induced by I/R, especially in the I/R + DFE + oe-ITGB1+sh-CD9 group, where cell apoptosis was significantly reduced compared to the group overexpressing ITGB1; There were almost no apoptotic cells in the control group. The I/R group showed a significant increase in apoptotic cells with a significant increase in green dots. DFE treatment significantly reduced I/R-induced apoptosis. In the I/R + DFE + oe-ITGB1+sh-CD9 group, compared with overexpressing ITGB1 only (I/R + DFE + oe-ITGB1+sh-NC), the green apoptotic cells were significantly reduced, indicating that CD9 knockdown further reduced apoptosis. n = 3; (B) Statistical graph of cell apoptosis; Compared with the control group, the apoptosis rate was significantly increased in the I/R group (∗P < 0.05). The apoptosis rate of the DFE treatment group was significantly lower than that of the I/R group. In the combination of DFE and ITGB1 overexpression (I/R + DFE + oe-ITGB1+sh-NC), the apoptosis rate was further decreased, while this effect was significantly enhanced when CD9 was knocked down under the same background. n = 5. (C) Scratch test was used to detect the change in migration ability of HK-2 cells after DFE treatment. DFE treatment significantly slowed down the migration of HK-2 cells induced by I/R, and the overexpression of ITGB1 enhanced cell migration ability in the absence of sh-CD9; (D) Statistical graph of relative cell migration distance, DFE can control the migration ability of renal glomerular epithelial cells by affecting the expression of ITGB1 and CD9; Compared with the control group, the cell migration distance in the I/R group increased (∗P < 0.05). The migration distance of the DFE treatment group was significantly lower than that of the I/R group. In the combination of DFE and ITGB1 overexpression (I/R + DFE + oe-ITGB1+sh-NC), the migration distance increased, but decreased significantly when CD9 was knocked down under the same background. n = 3. (E) MTT assay was used to detect the change in proliferation ability of HK-2 cells after DFE treatment. DFE treatment increased the proliferation ability of HK-2 cells under the influence of I/R; DFE treatment improved cell proliferation under I/R conditions. The proliferative capacity of the I/R + DFE + oe-ITGB1+sh-NC group was significantly higher than that of the other groups. n = 3. ∗ indicates P < 0.05 compared to the Control group; # indicates P < 0.05 compared to the I/R group; & indicates P < 0.05 compared to the I/R + DFE + oe-NC + sh-NC group; $ indicates P < 0.05 compared to the I/R + DFE + oe-ITGB1+sh-NC group. Cell experiments were repeated 3 times. To understand the impact of DFE regulating ITGB1 on glomerular epithelial cell proliferation ability, we evaluated the proliferation ability of HK-2 cells treated with DFE or lentiviral transduction using the MTT assay. The results indicated that compared to the control group, proliferation ability of HK-2 cells in the I/R group significantly decreased; however, in the I/R + DFE group compared to the I/R group, cell proliferation ability significantly increased. There was no significant difference between the I/R + DFE + oe-NC + sh-NC group and the I/R + DEF group. Moreover, compared to the I/R + DFE + oe-NC + sh-NC group, the proliferation ability of HK-2 cells in the I/R + DFE + oe-ITGB1+sh-NC group significantly decreased, whereas compared to the I/R + DFE + oe-ITGB1+sh-NC group, cell proliferation ability in the I/R + DFE + oe-ITGB1+sh-CD9 group significantly decreased ([118]Fig. 6E). These results indicate that DFE promotes TECs proliferation, reduces cell apoptosis and migration by inhibiting the expression of ITGB1/CD9, thereby protecting against renal I/R. 3.7. The protective effect of DFE on renal I/R via ITGB1/CD9 pathway To validate the protective effect of DFE on renal I/R through ITGB1/CD9, we established a mouse model of renal I/R and modulated the protein levels of ITGB1 or CD9 by injecting recombinant proteins/antibodies. Upon completing the breeding experiment, we collected blood and kidney tissue samples to assess the functional changes, inflammatory response, and degree of injury in renal tissues. Initially, we detected the expression of ITGB1 and CD9 in kidney tissues using immunohistochemical staining. The results showed that compared to the Sham group, the expression of ITGB1 and CD9 in the I/R group significantly increased, while in the I/R + DFE group, the expression of ITGB1 and CD9 significantly decreased. There was no significant difference in the indicators between the I/R + DFE group and the I/R + DFE + PBS + IgG group. Compared to the I/R + DFE + PBS + IgG group, the expression of ITGB1 and CD9 in the I/R + DFE + r-ITGB1+IgG group significantly increased; whereas in the I/R + DFE + r-ITGB1+CD9-mAb group compared to the I/R + DFE + r-ITGB1+IgG group, the expression of ITGB1 showed no significant difference while CD9 expression significantly decreased ([119]Fig. 7A–D). Fig. 7. [120]Fig. 7 [121]Open in a new tab In vivo experiments confirmed that DFE exerts a protective effect on renal I/R via ITGB1/CD9. Note: (A) Immunohistochemical staining was used to detect the positive expression of ITGB1 in mouse renal tissues. Dark brown color indicated positive protein staining, scale bar = 25 μm. ITGB1 expression was significantly upregulated in the I/R group, indicating successful induction of TECs injury. The expression level of ITGB1 was lower in the Sham group. In the I/R group, the expression of ITGB1 was significantly up-regulated, indicating that the model successfully induced renal tubular epithelial cell (TECs) injury. DFE treatment significantly reduced ITGB1 expression, additional injection of ITGB1 protein reversed the protective effect of DFE, while the use of CD9 antibody inhibited ITGB1 expression; (B) Quantitative results of ITGB1 protein expression; Compared with the Sham group, the expression of ITGB1 in the I/R group was significantly increased. DFE treatment significantly reduced the expression level of ITGB1 in the I/R group. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased the expression of ITGB1 again, while the use of CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group) significantly inhibited the expression of ITGB1. (C) Immunohistochemical staining detected the positive expression of CD9 in mouse renal tissues, with a pattern similar to ITGB1, where dark brown color represented positive protein staining, scale bar = 25 μm; CD9 expression was lower in the Sham group. In the I/R group, CD9 expression was significantly up-regulated. DFE treatment significantly reduced CD9 expression in the I/R group. Additional injection of ITGB1 protein reversed the protective effect of DFE and increased CD9 expression again, while the use of CD9 antibody inhibited CD9 expression. (D) Quantitative results of CD9 protein expression; Compared with the Sham group, the expression of CD9 in the I/R group was significantly increased. DFE treatment significantly reduced the expression level of CD9 in the I/R group. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased the expression of CD9 again, while the use of CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group) significantly inhibited the expression of CD9. (E) BUN levels in mouse serum were measured using a urea detection kit, showing a significant decrease with DFE treatment; In the I/R group, the level of BUN was significantly increased; DFE treatment significantly reduced the BUN level in the I/R group. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased BUN levels again, while CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group) significantly reduced BUN levels. (F) Serum creatinine (Scr) levels in mouse blood were measured using a creatinine detection kit, showing a significant decrease with DFE treatment; In the I/R group, the level of Scr was significantly increased; DFE treatment significantly reduced the level of Scr in the I/R group; Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased Scr levels again, while the use of CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group) significantly reduced Scr levels. (G–I) ELISA kits were used to detect the levels of inflammatory factors TNF-α, IL-1β, IL-6 in renal tissues following the manufacturer's instructions; The levels of these inflammatory cytokines were significantly increased in the I/R group (∗P < 0.05). DFE treatment significantly reduced the levels of inflammatory cytokines in the I/R group. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased inflammatory factor levels again, while CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group) significantly reduced inflammatory factor levels. (J–L) Commercial kits were used to measure the levels of oxidative stress-related factors MDA, MPO, SOD in mouse renal tissues; In the I/R group, the levels of MDA and MPO increased significantly, while the levels of SOD decreased significantly. DFE treatment significantly reduced the levels of MDA and MPO in the I/R group, and increased the level of SOD. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased MDA and MPO levels again and decreased SOD levels, while CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group) significantly reduced MDA and MPO levels and increased SOD levels. (M) Western blot experiments detected the expression of cleaved-caspase 3 and Caspase 3 in mouse renal tissues, where the upregulation of Cleaved-caspase 3 indicated the occurrence of apoptosis, and DFE treatment significantly reduced the expression of apoptotic proteins; In the I/R group, the expression of cleaved-caspase 3 was significantly increased, indicating an increase in apoptosis. DFE treatment significantly reduced cleaved-caspase 3 expression in the I/R group. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased the expression of cleaved-caspase 3 again, while the expression of cleaved-caspase 3 was significantly decreased by CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group). (N) Quantitative results of cleaved-caspase 3 protein expression; In the I/R group, the expression of cleaved-caspase 3 was significantly increased. DFE treatment significantly reduced the expression level of cleaved-caspase 3 in the I/R group. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased the expression of cleaved-caspase 3 again, while the expression of cleaved-caspase 3 was significantly decreased by CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group). (O) Statistics of kidney weight for each group of mice. (P) ATN in various groups under a light microscope, where the upregulation of ITGB1 reversed the protective effect of DFE, and CD9 inhibition further reduced cell migration. Evaluation criteria: based on the percentage of damaged renal tubules in the sample, the higher the score, the more severe the damage (maximum score: 4 points): 0-normal kidney, 1-mild necrosis (<5 %), 2-moderate necrosis (5–25 %), 3-moderate necrosis (25–75 %), 4-severe necrosis (>75 %), the ATN value in the I/R group was significantly increased, indicating that the kidney injury was aggravated. DFE treatment significantly reduced the ATN values in the I/R group. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) increased the ATN value again, while the use of CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group) significantly decreased the ATN value. (Q) Assessment of renal histological damage in various groups using HE staining, the renal tissue structure in the Sham group was normal, and no obvious damage was observed. In the I/R group, the renal tissue structure was disordered, and the tubular necrosis was significantly increased. DFE treatment significantly reduced the renal tissue injury in the I/R group. Additional injection of ITGB1 protein (I/R + DFE + r-ITGB1+IgG group) aggravated renal tissue damage, while the use of CD9 antibody (I/R + DFE + r-ITGB1+α-CD9 group) significantly reduced kidney tissue damage. scale bar = 50 μm ∗ indicates P < 0.05 compared to the Sham group, # indicates P < 0.05 compared to the I/R group; & indicates P < 0.05 compared to the I/R + DFE + PBS + IgG group; $ indicates P < 0.05 compared to the I/R + DFE + r-ITGB1+IgG group. Subsequently, we used commercial assay kits to measure the levels of BUN and Scr in mouse kidney serum and the levels of inflammatory factors (TNF-α, IL-1β, IL-6). The results indicated that compared to the Sham group, the levels of BUN, Scr, and inflammatory factors in the I/R group significantly increased; however, in the I/R + DFE group, these levels significantly decreased. There was no significant difference in these indicators between the I/R + DFE group and the I/R + DFE + PBS + IgG group. Moreover, compared to the I/R + DFE + PBS + IgG group, the levels of BUN, Scr, and inflammatory factors significantly increased in the I/R + DFE + r-ITGB1+IgG group, while in the I/R + DFE + r-ITGB1+CD9-mAb group compared to the I/R + DFE + r-ITGB1+IgG group, the levels of BUN, Scr, and inflammatory factors significantly decreased ([122]Fig. 7E–I). Furthermore, we studied oxidative stress-related indicators (MDA, MPO, SOD) in mouse kidney tissues. The results revealed that compared to the Sham group, MDA and MPO significantly increased in the I/R group, while SOD significantly decreased; in the I/R + DFE group, MDA and MPO significantly decreased, and SOD increased significantly. There was no significant difference in these indicators between the I/R + DFE group and the I/R + DFE + PBS + IgG group. Compared to the I/R + DFE + PBS + IgG group, MDA and MPO significantly increased, while SOD significantly decreased in the I/R + DFE + r-ITGB1+IgG group, and in the I/R + DFE + r-ITGB1+CD9-mAb group compared to the I/R + DFE + r-ITGB1+IgG group, MDA and MPO significantly decreased, while SOD increased significantly ([123]Fig. 7J–L). Moreover, we assessed the expression of apoptosis-related proteins Cleaved-caspase 3 and Caspase 3 and the injury status of kidney tissues by HE staining through Western blot experiments. The results demonstrated that compared to the Sham group, the expression of Cleaved-caspase 3 and the degree of kidney tissue injury significantly increased in the I/R group, whereas in the I/R + DFE group, the expression of Cleaved-caspase 3 and the degree of kidney tissue injury significantly decreased. There was no significant difference in these indicators between the I/R + DFE group and the I/R + DFE + PBS + IgG group. Compared to the I/R + DFE + PBS + IgG group, the expression of Cleaved-caspase 3 and the degree of kidney tissue injury significantly increased in the I/R + DFE + r-ITGB1+IgG group, while in the I/R + DFE + r-ITGB1+CD9-mAb group compared to the I/R + DFE + r-ITGB1+IgG group, the expression of Cleaved-caspase 3 and the degree of kidney tissue injury significantly decreased ([124]Fig. 7M–Q). In conclusion, the above results indicate that DFE protects renal I/R through the ITGB1/CD9 pathway. 3.8. Analysis of the role of ITGB1 and the protective effect of DFE in renal I/R In the previous sections, we discussed the protective mechanism of DFE via the ITGB1/CD9 pathway in renal I/R. Subsequently, we delved deeper into how ITGB1 deficiency affects renal function in mice treated with DFE by utilizing a mouse model with ITGB1 gene knockout generated through CRISPR/Cas9 technology. This study aimed to explore the potential protective effect of DFE in regulating renal I/R through the ITGB1/CD9 pathway. We successfully established a mouse model with ITGB1 gene knockout using CRISPR/Cas9 technology and designed a series of experimental groups to simulate and evaluate the renal protective effects of DFE. The experiment was divided into five main groups, with 10 mice in each group, repeated three times to ensure the reliability of the results. The experimental groups included the untreated wild-type control group, wild-type mice group treated with only I/R, wild-type mice group treated with DFE following I/R, ITGB1 gene knockout group plus I/R, and the I/R mouse group undergoing both ITGB1 knockout and DFE treatment. Immunohistochemical staining results revealed the crucial role of ITGB1 under I/R conditions, with a significant upregulation of CD9 expression in the ITGB1 knockout mice, possibly indicating a compensatory mechanism by cells attempting to compensate for the loss of ITGB1 function. Immunohistochemical staining showed I/R-induced increased CD9 expression in ITGB1-deficient mice, suggesting activation of the compensatory pathway ([125]Fig. 8A). The significant increase in SCr and BUN levels was more pronounced in the ITGB1 knockout mice, especially without DFE treatment. In ITGB1 knockout mice, I/R induces a significant increase in BUN levels, indicating increased kidney damage. DFE pretreatment reduced the level of BUN to a certain extent, suggesting that it had a protective effect on I/R ([126]Fig. 8B). Similar to BUN levels, serum creatinine levels were significantly increased after I/R in ITGB1 knockout mice, while DFE pretreatment significantly reduced elevated serum creatinine, demonstrating the nephroprotective potential of DFE ([127]Fig. 8C). While DFE treatment improved these biochemical parameters somewhat, its effectiveness was limited in the ITGB1 knockout mice, highlighting the crucial role of normal ITGB1 expression in DFE's protective effect. The corresponding inflammatory response, reflected by increased TNF-α, IL-1β, and IL-6 levels, was notably higher in the ITGB1 knockout group, surpassing all other groups. I/R significantly increased the serum levels of TNF-α in mice, especially in ITGB1 knockout mice. Deflurane treatment significantly reduced TNF-α levels, suggesting that I/R may be attenuated through anti-inflammatory mechanisms ([128]Fig. 8 D). I/R significantly increased the level of IL-1β and was more pronounced in ITGB1 knockout mice. DFE pretreatment effectively reduced IL-1β levels, showing its potential anti-inflammatory effects ([129]Fig. 8 E). Similar to TNF-α and IL-1β, upregulation of IL-6 in ITGB1 knockout mice reflects significant inflammation caused by I/R, while DFE pretreatment reduced its expression ([130]Fig. 8F). From an oxidative stress perspective, the significant increase in MDA and MPO activity, coupled with decreased SOD activity, was more pronounced in the ITGB1 gene knockout mice, further confirming the essential role of ITGB1 in modulating oxidative stress response. I/R resulted in significant lipid peroxidation, especially in ITGB1 knockout mice, while DFE pretreatment reduced MDA levels, demonstrating its antioxidant damage potential ([131]Fig. 8G). I/R significantly increased MPO activity, especially in ITGB1 knockout mice, indicating enhanced oxidative stress. DFE pretreated mice had lower MPO activity, indicating a protective effect ([132]Fig. 8H). I/R reduced SOD activity, and this effect was more pronounced in ITGB1 knockout mice. DFE-treated mice showed relatively increased SOD activity, suggesting antioxidant protection ([133]Fig. 8I). The antioxidative effect of DFE was more prominent in mice expressing ITGB1, while in the knockout model, the improvement in these oxidative stress markers was less significant, emphasizing the crucial facilitating role of ITGB1 in the protective effect provided by DFE. Kidney histopathological evaluation, assessed through ATN scoring with HE staining under an optical microscope, further corroborated these findings. Observation by light microscopy showed severe tubular damage in ITGB1 knockout mice after I/R, characterized by tubular dilation, epithelial cell shedding, and cell vacuolization. In contrast, DFE pretreated ITGB1 knockout mice showed milder pathological changes, suggesting that DFE had a modest alleviating effect on I/R-induced kidney injury ([134]Fig. 8J). ITGB1 knockout mice exhibited higher ATN scores after I/R based on HE staining analysis, while DFE treatment attenuated tubular injury, even in the context of ITGB1 knockout ([135]Fig. 8K). The ITGB1 knockout mice exhibited more severe histological damage following I/R, which could be alleviated by DFE intervention, albeit with reduced effectiveness under ITGB1 knockout conditions. Fig. 8. [136]Fig. 8 [137]Open in a new tab Molecular mechanisms and biomarker analysis of renal injury affected by ITGB1 knockout. Note: (A) Immunohistochemical detection of CD9 expression under different treatment conditions. Immunohistochemical staining shows that CD9 expression induced by I/R is increased in ITGB1-deficient mice, implying the activation of compensatory pathways. In the Sham group, ITGB1 and CD9 were normally expressed; In the I/R group, the expression of ITGB1 and CD9 increased; In the ITGB1-KO group, ITGB1 was missing, and the normal expression of CD9 was slightly changed. In the I/R + ITGB1-KO group, ITGB1 was deleted and CD9 expression was significantly increased. (B) Detection of SCR changes. In ITGB1 knockout mice, I/R-induced BUN levels significantly increased, indicating aggravated renal injury. Pretreatment with DFE moderately reduced BUN levels, suggesting a protective effect against I/R. (C) Detection of Scr levels. Similar to BUN levels, ITGB1 knockout mice showed a significant increase in Scr levels after I/R, while DFE pretreatment significantly decreased the elevation in Scr levels, demonstrating the kidney-protective potential of DFE. (D) Detection of TNF-α levels. I/R significantly elevated TNF-α levels in mouse serum, especially in ITGB1 knockout mice. DFE treatment markedly reduced TNF-α levels, indicating a potential anti-inflammatory mechanism to alleviate I/R. (E) Detection of IL-1β response. I/R markedly increased IL-1β levels, particularly in ITGB1 knockout mice. DFE pretreatment effectively decreased IL-1β levels, exhibiting its potential anti-inflammatory effect. (F) Detection of IL-6 expression. Similar to TNF-α and IL-1β, the upregulation of IL-6 in ITGB1 knockout mice reflects significant inflammation induced by I/R, which was reduced by DFE pretreatment. (G) Detection of MDA levels. I/R resulted in significant lipid peroxidation, especially in ITGB1 knockout mice, while DFE pretreatment lowered MDA levels, indicating its potential for antioxidant damage protection. (H) Detection of MPO activity. I/R significantly increased MPO activity, especially in ITGB1 knockout mice, suggesting enhanced oxidative stress. Mice pretreated with DFE showed lower MPO activity, indicating a protective effect. (I) Trend of SOD activity: I/R reduced SOD activity, particularly pronounced in ITGB1 knockout mice. Mice treated with DFE exhibited relatively increased SOD activity, implying antioxidant protection. (J) Comparison of renal pathology slices: Observations under an optical microscope showed severe renal tubular injury in ITGB1 knockout mice after I/R, characterized by tubular dilation, epithelial cell shedding, and cell vacuolization. In contrast, DFE-pretreated ITGB1 knockout mice displayed milder pathological changes, suggesting the mitigating effect of DFE on I/R-induced renal injury. (K) Detection of ATN scores. Analysis based on HE staining revealed higher ATN scores in ITGB1 knockout mice after I/R, which were alleviated by DFE treatment, even in the context of ITGB1 knockout. n = 8, ∗ indicates P < 0.05 compared to the wild-type control group, # indicates P < 0.05 compared to the wild-type I/R group, & indicates P < 0.05 compared to the wild-type I/R + DFE group, $ indicates P < 0.05 compared to the ITGB1 knockout I/R group. The results showed that DFE could alleviate the damage caused by renal ischemia-reperfusion by modulating the ITGB1/CD9 pathway, but the protective effect was significantly reduced in the case of ITGB1 knockout. Upregulation of CD9 may be a compensatory response, but it fails to fully restore the protective status of the kidneys in the absence of ITGB1. Schematic diagram of CRISPR/Cas9 knockout of the ITGB1 gene ([138]Fig. S6A) and mouse cross-propagation ([139]Figure S6 B) illustrating the construction process of ITGB1 knockout mice. PCR results ([140]Fig. S6C) further verified the successful knockout of the ITGB1 gene in knockout mice, and sequence alignment analysis ([141]Fig. S6D) showed significant base deletions and insertions, confirming the effectiveness of CRISPR/Cas9 technology. These findings provide an in-depth molecular mechanism for understanding the nephroprotective role of DFE in ischemia-reperfusion and highlight the value of ITGB1 as a potential therapeutic target. In conclusion, this study demonstrates that DFE can alleviate the damage caused by renal I/R by modulating the ITGB1/CD9 pathway, but the protective effect significantly diminishes in ITGB1 knockout conditions. The upregulation of CD9 may be a compensatory response, but in the absence of ITGB1, it fails to fully restore the renal protective state. 3.9. The regulatory role of CD9 knockout on the protective effect of DFE in renal I/R Following elucidating the profound impact of ITGB1 deletion on renal damage in mice, we further explored the role of CD9 as another key membrane protein in the DFE-mediated renal protective effect. This portion of the study focused on analyzing the potential regulatory role of DFE in renal I/R under CD9 deficiency. We employed a methodology similar to that of ITGB1 to ensure the comparability of research results and expanded our understanding by investigating the unique contribution of CD9 in renal protective mechanisms. In this study, we thoroughly examined the role of CD9 in the protective effect mediated by DFE and evaluated the biochemical and pathological changes after renal I/R ([142]Fig. 9A). By comparing the responses of wild-type mice with CD9 gene knockout mice, we observed a significant increase in SCr and BUN levels induced by I/R, indicating renal dysfunction. However, DFE treatment markedly alleviated the elevation of these injury markers, demonstrating its protective effect against I/R ([143]Fig. 9B–C). Importantly, although under CD9 knockout conditions, DFE still exhibited a protective trend, its effect was diminished compared to the wild type, possibly indicating the necessity of CD9 in the renal protective mechanism mediated by DFE. The inflammatory response induced by I/R was also reflected in the significant increases in IL-1β, IL-6, and TNF-α levels ([144]Fig. 9D–F). DFE pretreatment effectively reduced the expression of inflammatory factors, mitigating the inflammatory response, although its efficacy was slightly reduced in CD9 knockout mice. These results highlight the central role of the inflammatory pathway in DFE's renal protective effect. Oxidative stress, as another key factor in I/R, was confirmed by the significant increase in MDA and MPO activity, especially in CD9 knockout mice, suggesting more severe oxidative damage in the absence of CD9. DFE intervention had a positive effect on alleviating the increase in these oxidative stress markers, and the changes in SOD activity further corroborated this. SOD activity decreased post-I/R, but DFE treatment was able to partially restore its activity in CD9 knockout mice, albeit not as significantly as in the wild type ([145]Fig. 9G–I). Through optical microscopic evaluation of kidney tissues, we observed that ATN induced by I/R had the highest score in CD9 knockout mice, while DFE treatment significantly reduced the ATN score, indicating a certain degree of alleviation of tissue damage. However, compared to wild-type mice, the protective effect of DFE was still limited in the background of CD9 knockout ([146]Fig. 9J–K). In conclusion, this study elucidated the protective role of CD9 in renal I/R and demonstrated the limitations of the protective effect of DFE in the absence of CD9, suggesting the need to consider the regulation of CD9 in future therapeutic strategies. Fig. 9. [147]Fig. 9 [148]Open in a new tab Modulation of DFE protective effect by CD9 knockout in renal I/R. Note: (A) Immunohistochemical staining diagram of kidney tissues: Shows the expression of KRT8 in normal kidneys and the changes in SDHA in CD9 knockout mice's kidneys, reflecting the physiological changes in the kidneys under CD9 deficiency; In normal kidneys, the expression of KRT8 was normal, while in the kidneys of CD9 knockout mice, the expression of SDHA was significantly reduced, reflecting the physiological changes of the kidneys in the state of CD9 deletion.(B) Detection of Scr levels: Demonstrates a significant elevation of Scr levels after I/R in CD9 knockout (CD9-KO) mice, which was reduced by DFE pretreatment (IR + DFE); Scr levels were the lowest in the Sham group and the CD9-KO control group. In the I/R group, the level of Scr was significantly increased; DFE pretreatment significantly reduced the level of Scr in the I/R group. In the context of CD9 knockout, the level of Scr in the I/R group was also significantly increased, while the level of Scr was decreased by DFE pretreatment (I/R + DFE + CD9-KO group). (C) Detection of BUN levels: I/R-induced BUN significantly increased in CD9-KO mice, and DFE pretreatment reduced this elevation; BUN levels were the lowest in the Sham group and the CD9-KO control group. In the I/R group, the level of BUN was significantly increased; DFE pretreatment significantly reduced the level of BUN in the I/R group. In the context of CD9 knockout, the BUN level in the I/R group was also significantly increased, while DFE pretreatment (I/R + DFE + CD9-KO group) could reduce the BUN level. (D) Detection of TNF-α levels: I/R significantly increased TNF-α levels in the CD9-KO group, which were decreased by DFE pretreatment; The levels of TNF-α were the lowest in the Sham group and the CD9-KO control group. In the I/R group, the level of TNF-α was significantly increased. DFE pretreatment significantly reduced the level of TNF-α in the I/R group. In the context of CD9 knockout, the level of TNF-α in the I/R group was also significantly increased, while the level of TNF-α was decreased by DFE pretreatment (I/R + DFE + CD9-KO group). (E) Detection of IL-1β levels: I/R led to a significant increase in IL-1β levels in CD9-KO mice, which was significantly reduced by DFE pretreatment; IL-1β levels were lowest in the Sham group and the CD9-KO control group. In the I/R group, IL-1β levels were significantly increased; DFE pretreatment significantly reduced the level of IL-1β in the I/R group. IL-1β levels were also significantly increased in the I/R group under the background of CD9 knockout, while DFE pretreatment (I/R + DFE + CD9-KO group) could reduce IL-1β levels. (F) Detection of IL-6 levels: In CD9-KO mice, I/R caused a significant increase in TNF-α levels, while DFE pretreatment effectively decreased their levels; IL-6 levels were lowest in the Sham group and the CD9-KO control group. In the I/R group, IL-6 levels were significantly increased; DFE pretreatment significantly reduced IL-6 levels in the I/R group; In the context of CD9 knockout, IL-6 levels were also significantly increased in the I/R group, while DFE pretreatment (I/R + DFE + CD9-KO group) could reduce IL-6 levels. (G) Detection of MDA levels: Reflects a significant increase in lipid peroxidation induced by I/R in CD9-KO mice, which was reduced by DFE pretreatment; The level of MDA in the Sham group and the CD9-KO control group was the lowest, the level of MDA in the I/R group was significantly increased, and the level of MDA in the I/R group was significantly reduced by DFE pretreatment. In the context of CD9 knockout, the level of MDA in the I/R group was also significantly increased, while the level of MDA was reduced by DFE pretreatment (I/R + DFE + CD9-KO group). (H) Detection of MPO activity: I/R significantly increased MPO activity in CD9-KO mice, which was slightly reduced after DFE pretreatment; The lowest MPO activity was found in the Sham group and the CD9-KO control group. In the I/R group, MPO activity was significantly increased; DFE pretreatment significantly reduced MPO activity in the I/R group. In the context of CD9 knockout, MPO activity in the I/R group was also significantly increased, while DFE pretreatment (I/R + DFE + CD9-KO group) could reduce MPO activity. (I) Detection of SOD activity: SOD activity decreased in CD9-KO mice undergoing I/R, but was partially restored by DFE pretreatment; The activity of SOD was the highest in the Sham group and the CD9-KO control group. In the I/R group, SOD activity was significantly reduced. DFE pretreatment significantly increased the SOD activity in the I/R group. In the context of CD9 knockout, the SOD activity in the I/R group was also significantly reduced, while the SOD activity was increased by DFE pretreatment (I/R + DFE + CD9-KO group). (J) Detection of renal pathology slices: Shows the renal structural damage induced by I/R in the background of CD9-KO (middle) and the optical microscope image of renal tissue protection after DFE pretreatment (right); The renal tissue structure in the Sham group was normal, and no obvious damage was observed. In the I/R group, the renal tissue structure was disordered, and the tubular necrosis was significantly increased. DFE pretreatment significantly reduced kidney tissue damage in the I/R group. Under the background of CD9 knockout, the renal tissue injury in the I/R group was aggravated, while DFE pretreatment (I/R + DFE + CD9-KO group) could significantly reduce the renal tissue injury. (K) Detection of ATN scores: Reveals a significant increase in ATN scores after I/R in CD9-KO mice, which was alleviated by DFE pretreatment; The ATN score was the lowest in the Sham group and the CD9-KO control group. In the I/R group, the ATN score was significantly increased; DFE pretreatment significantly reduced the ATN score in the I/R group. In the context of CD9 knockout, the ATN score in the I/R group was also significantly increased, while the ATN score was reduced by DFE pretreatment (I/R + DFE + CD9-KO group). n = 8, ∗ indicates P < 0.05 compared to the wild-type control group, # indicates P < 0.05 compared to the wild-type I/R group, & indicates P < 0.05 compared to the CD9 knockout I/R group, $ indicates P < 0.05 compared to the CD9 knockout control group. 4. Discussion By utilizing scRNA-seq and transcriptome analysis, we identified the significant role of ITGB1 in renal I/R [[149]23]. This finding aligns with previous research on the role of ITGB1 in other diseases, thus further validating its critical involvement in pathophysiological processes [[150]24]. ITGB1 plays a crucial role in tissue regeneration, oncology, cardiovascular diseases, and immune responses. It serves as a key molecule in connecting cells to the extracellular matrix, essential for wound healing and organ regeneration. In the context of tumor growth and metastasis, ITGB1 influences tumor behavior by regulating cell adhesion strength. Its impact on heart development and function is associated with various cardiovascular diseases, particularly in myocardial cells. Moreover, ITGB1 participates in regulating T cell activation and migration on the surface of immune cells, affecting the entire immune response. When discussing the impact of DFE on ITGB1/CD9 and the mechanisms underlying renal protection, researchers explored the application of cerium oxide (CeO[2]) and DFE anesthesia in a rat liver I/R model. Their findings indicated that the combined use of cerium oxide and DFE significantly reduced oxidative stress and liver damage [[151]25]. This corresponds with the results of our study, suggesting that cerium oxide may have potential organ-protective effects, especially during DFE anesthesia. This study also demonstrates the potential protective effects of DFE. Although its mechanisms differ from cerium oxide, DFE provides a protective effect in the I/R model, attenuating oxidative stress and tissue damage. These discoveries offer a broader perspective on the role of DFE in I/R and may lay the theoretical foundation for future clinical strategies. The novelty of our study lies in elucidating the potential mechanism of inhaled anesthetic DFE in protecting against renal I/R by modulating ITGB1 and its downstream target gene CD9, affecting the function of glomerular epithelial cells. Firstly, the role of ITGB1 and glomerular epithelial cells in renal I/R: TECs, the primary cell type in the kidney, significantly decrease in abundance after renal I/R, indicating their potentially critical role in the injury. Furthermore, the study reveals that ITGB1 is one of the genes significantly altered in these cells. Importantly, the study further reveals that the inhaled anesthetic DFE can protect against renal I/R by modulating ITGB1 and downstream target gene CD9, affecting glomerular epithelial cell function. This finding presents a new perspective on the role of ITGB1 in renal I/R and aligns with previous research descriptions of ITGB1 roles [[152]8,[153]24,[154]26,[155]27]. Next, the protective effect of DFE on renal I/R and its potential mechanisms: both in vitro and in vivo experiments show that DFE can regulate ITGB1 expression, impacting TEC proliferation, migration, and apoptosis. These results suggest that DFE may protect against renal I/R by influencing TEC function through regulating ITGB1. This novel finding emphasizes a potential mechanism of the protective effect of DFE on renal I/R. Additionally, the role of DFE in regulating ITGB1/CD9: through further analysis, we found that DFE may reduce CD9 expression by inhibiting ITGB1 expression, which could be a key mechanism for DFE's protective effect on renal I/R. CD9 plays crucial roles in reproductive physiology, virus infections, intercellular communication, and tumor suppression. It is essential for oocyte maturation, fertilization, and embryo implantation and regulates virus infection and spread in HIV and HBV processes. As a component of extracellular vesicles, CD9 participates in intercellular communication, impacting cancer, immune regulation, and the progression of neurological diseases. Regarding tumor suppression, CD9 acts as a tumor suppressor by regulating cell migration and invasion in certain contexts. This discovery enriches our understanding of the mechanisms of action of DFE and provides new theoretical foundations for the clinical application of DFE. The preliminary conclusions drawn from the above experiments are as follows: DFE may exert its effects on inhibiting the migration and apoptosis of TECs, as well as enhancing their proliferation, possibly by regulating the expression of ITGB1 and CD9. This effect may further sustain renal function, reduce the release of inflammatory mediators, and thereby potentially confer a protective effect against renal IPI ([156]Fig. 10). While this study holds scientific significance and clinical prospects to some extent, further in-depth research is required for validation. Specifically, through the integration of scRNA-seq and transcriptome analysis, we have revealed preliminary evidence that DFE may influence the potential molecular mechanisms of renal I/R by modulating ITGB1. This provides a new perspective for understanding the complex mechanisms of the disease, yet these findings need further validation in a broader range of experimental conditions and models [[157]23]. Moreover, we emphasize the importance of glomerular epithelial cells in renal I/R, which may contribute to improving the pathophysiological state of patients and enhancing the quality of life; however, rigorous clinical studies are still needed to verify the actual clinical outcomes [[158]28]. Our conclusions are in line with the viewpoints of previous studies [[159]28], underscoring the critical role of TECs in renal I/R; nevertheless, we have further elucidated the potential specific molecular pathways through which DFE may regulate this process. It is important to note that our research findings have not yet been validated in human cases, thus the conclusions should be approached with caution. Concerning the protective mechanisms of DFE on TECs, our study aligns with observations from previous research [[160]29] but also presents differences. Prior studies have suggested the protective effects of DFE on various I/R, whereas our study delves deeper into the molecular mechanisms by which DFE regulates TEC function through ITGB1 and CD9. Specifically, we observed that DFE significantly downregulates the expression of ITGB1 and CD9, reduces the migration and apoptosis of TECs, and enhances their proliferation, a mechanism that is reported for the first time in current literature. Fig. 10. [161]Fig. 10 [162]Open in a new tab Potential molecular mechanisms of DFE impacting renal I/R through regulating ITGB1. DFE regulates ITGB1 and CD9 to influence glomerular epithelial cell function, demonstrating a protective effect on renal I/R. This not only reveals the potential molecular mechanisms of DFE in renal protection but also provides new insights for the treatment of kidney diseases. In particular, the regulation of ITGB1 and CD9 plays a key role in maintaining TEC function, suppressing cell apoptosis and migration, and promoting cell proliferation, thereby preserving renal function and relieving inflammation. This discovery holds significant clinical translational potential, suggesting that DFE and its molecular targets, ITGB1 and CD9, may serve as new targets for the treatment of kidney diseases, especially I/R. Future research should validate the universality of these molecular mechanisms in different kidney disease models and explore their application value in clinical treatment, devising therapeutic strategies based on these new findings to enhance the treatment efficacy and quality of life of patients with kidney diseases. Our study suggests that DFE through the ITGB1/CD9 pathway may have a protective effect on renal I/R, providing new avenues for drug development and treatment strategies, although further research support is needed for its effectiveness and safety as a clinical intervention [[163]29]. In this study, we systematically analyzed the roles of ITGB1 and CD9 in renal I/R and the protective effects mediated by DFE. Renal I/R is a complex multifactorial disease process involving oxidative stress, inflammation, and cell apoptosis. We found that ITGB1 plays an essential maintenance role not only in normal renal function but also seems to be a self-protective response to I/R-induced injury. In contrast, CD9 knockout mice exhibited more severe renal damage after I/R, indicating a potential protective role of CD9. Furthermore, we found that the protective effect of DFE on I/R-induced kidney injury was significantly affected under conditions of ITGB1 or CD9 knockout. This suggests that DFE's alleviation of I/R-induced renal injury may depend on the normal expression and function of ITGB1 and CD9. Although DFE still exhibited a certain protective effect in models where these key factors were knocked out, its effect was not as pronounced as in wild-type mice. This finding suggests that DFE may act through multiple cellular signaling pathways, and the involvement of ITGB1 and CD9 is crucial for the complete functionality of these pathways. Through these findings, we enhance our understanding of the potential value of ITGB1 and CD9 as therapeutic targets in preventing and treating I/R. The maintenance of ITGB1 and the protection of CD9 may provide important clues for designing new intervention measures. For example, maintaining ITGB1 activation or promoting CD9 function may be an effective strategy to enhance the renal protective effect of DFE. In the comprehensive analysis of the effects of ITGB1 and CD9 knockout on DFE action, we conclude that ITGB1 and CD9 not only synergize in the protective effect mediated by DFE but also may be crucial for strategies to prevent I/R-induced renal damage. Therefore, future research needs to delve deeper into how DFE interacts with these molecules and other potential pathways to develop more effective treatment plans against kidney diseases caused by I/R. While this study reveals a novel mechanism by which DFE influences TECs through the regulation of ITGB1 and CD9 to exert a protective effect on renal I/R, there are limitations that must be acknowledged. Firstly, this study mainly relied on transcriptome data, mouse models, and in vitro experiments, and the results may vary to the complexity of human diseases. Different animal models and broader clinical studies help confirm the universality and clinical relevance of these findings. Therefore, further research is needed to validate the protein expression levels of these genes. Secondly, although we validated the protective effect of DFE on renal I/R in both in vitro and in vivo models, whether the same results can be achieved in clinical practice requires more clinical trials. Future studies should explore the impact of DFE on other crucial renal cell types and its interaction with other protective or damaging signaling pathways. Lastly, this study did not comprehensively explore all potential mechanisms by which DFE affects renal I/R, necessitating further research [[164]29], and actual clinical translation requires more preclinical and clinical trials to assess the safety and efficacy of DFE or its targeted strategies in human kidney diseases, particularly in renal I/R. Future studies can be expanded in several aspects: first, further investigate the specific molecular mechanisms through which DFE regulates renal I/R via ITGB1 to provide a more comprehensive theoretical basis. Secondly, conduct more clinical trials to verify the protective effect of DFE on renal I/R to better guide clinical practice. Additionally, further research on the therapeutic effects of DFE on other types of kidney diseases, such as chronic kidney disease and nephritis, can broaden its clinical application. In conclusion, this study offers new insights and potential therapeutic strategies for the treatment of kidney diseases, but further research is necessary to overcome current limitations and fully evaluate the potential of these strategies in clinical application. 5. Conclusion DFE exhibits a protective effect against renal I/R by modulating the ITGB1/CD9 signaling pathway and reducing oxidative stress in TECs. By inhibiting apoptosis and enhancing cell proliferation, DFE significantly improves renal function and reduces tissue damage. These findings highlight DFE's potential as a therapeutic agent for alleviating oxidative stress-induced kidney injury, offering new insights into the molecular mechanisms involved in renal protection and recovery. Further investigation into the clinical application of DFE for kidney injury is warranted. CRediT authorship contribution statement Qiaoling Wu: Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Dongbo Zhang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review & editing. Siqi Dai: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing. Feifei Liu: Writing – original draft, Validation, Supervision, Methodology, Investigation, Conceptualization. Wei Zhang: Writing – original draft, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization. Tu Shen: Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation. Ethics approval and consent to participate All animal experiments were conducted in accordance with the guidelines set by the Animal Ethics Committee of The First Affiliated Hospital of Jinzhou Medical University, with approval number 2023DR542. The clinical aspects of this study were approved by the Institutional Review Board of The First Affiliated Hospital of Jinzhou Medical University, adhering to the principles outlined in the Declaration of Helsinki. Informed consent was obtained from all human participants involved in the clinical part of this study. Consent for publication Not applicable. Availability of data and materials The data supporting the findings of this study are available from the corresponding author upon reasonable request. Funding This study was supported by the Natural Science Foundation Program of Liaoning Province (Grant No.2024-MS-203) and Liaoning Provincial Science and Technology Program Joint Program (2024-MSLH-138). Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments