Inhibition of CD53 Reduces the Formation of ROS-Induced Neutrophil Extracellular Traps and Protects Against Inflammatory Injury in Acute Pancreatitis

Introduction

Acute pancreatitis (AP) is a pancreatic inflammatory condition characterized by the inappropriate activation of digestive enzymes, resulting in autodigestion of pancreatic acinar cells.^1,2 Most cases of mild acute pancreatitis (MAP) are self-limiting. Repeated or excessive acinar cell injury releases a large number of pro-inflammatory mediators and inflammatory chemokines while causing pancreatic necrosis, and secondary pancreatic necrosis infection, systemic inflammatory response and multiple organ failure can progress to SAP, which is a dangerous condition with a mortality rate of up to 20–40%.^3,4 The dysregulation of the immune system is pivotal in the progression and pathology of SAP.⁵ Early studies indicate that immune cells help control pancreatic inflammation by capturing microorganisms and releasing bactericidal enzymes. Failure to regulate this immune response promptly can exacerbate pancreatic injury, leading to systemic inflammatory reactions.⁶ Therefore, identifying targets that modulate immune cell infiltration and the release of inflammatory mediators is crucial to attenuate the severity of AP.

Neutrophils are the primary effectors of acute inflammation and play a pivotal role in clearing extracellular pathogens.⁷ During AP-induced pancreatic tissue damage, neutrophils are the first responders to migrate from circulation to the injured site, where they form neutrophil extracellular traps (NETs).⁸ NETs significantly contribute to AP development, mediating inflammation and intensifying its severity.⁹ The level of NETs formation was also positively correlated with the clinical prognosis of AP. Previous research by our group identified CD177 and gasdermin D as mediators of NET formation. Notably, the levels of peripheral neutrophil CD177 and gasdermin D in AP patients closely correlate with NETs levels and clinical prognosis.¹⁰, ¹¹ Targeting NETs formation and its key components can mitigate pancreatic tissue injury, inflammatory response in AP mice, and minimize distant organ damage.^12–14 Regrettably, current intervention lacks effective targets that can precisely address NETs formation to prevent and treat AP-related inflammatory injuries.

CD53,also known as MOX44 or TSPAN25, belongs to a novel tetraspanin family and is a pan-leukocyte antigen expressed across various white blood cells, including B cells, T cells, monocytes, and granulocytes.¹⁵ CD53 serves critical roles in the immune system, playing parts in B-cell development, lymphocyte trafficking, and inflammatory and tumor diseases.^16–18 Recent studies demonstrate CD53’s involvement in early B-cell development and lymphocyte recycling by stabilizing L-selectin on lymphocyte surfaces.^19,20 In addition, CD53 influences cell activation by inhibiting α3 integrin, thereby enhancing cell adhesion and promoting white blood cell aggregation at inflammatory sites.²¹

To date, it has not been reported whether CD53 promotes inflammation of AP through the release of NETs. Therefore, this study aims to explore the immunomodulatory role of CD53-mediated NETs formation in pancreatic necrosis and inflammatory injury of AP.

Materials and Methods

Animals

Male wild-type C57BL/6J mice, weighing approximately 20–25g, were procured from GemPharmatech Co.Ltd., Nanjing, China. The mice were housed in a standard pathogen-free room with a 12-hour light / 12-hour dark cycle, maintained at a temperature of 20–25°C. They were provided with standard rodent food and ad libitum access to water.

Patients

All patient samples were sourced from the Department of Gastroenterology, Affiliated Hospital of Yangzhou University, between October 2022 and October 2023. The study included a total of 61 AP patients and 20 healthy volunteers. Clinical characteristics of AP patients and healthy volunteers are detailed in Supplementary Table 1 and Supplementary Table 2.

AP diagnosis was based on the revised Atlanta Classification, 2012, requiring the presence of at least two of the following three criteria:²² (1) Abdominal pain: acute, persistent, radiating to the back; (2) Serum lipase activity (or amylase activity) at least three times the upper limit of normal; (3) Radiological findings (USG/CT/MRI) indicating acute pancreatitis.²³ Exclusion criteria included: (1) age <18 or >80 years; (2) pregnancy period; (3) Previous history of tumors; (4) Recurrent pancreatitis; (5) Incomplete information. Patients were categorized into AP without multiple organ dysfunction syndrome (MODS) and AP with MODS groups based on the modified Marshall score system,²³ defining MODS as failure of two or more organs (cardiovascular, lung, kidney) for 48 hours or longer. Subgroups were also created based on the presence of local complications and systemic inflammatory response syndrome. AP patients were divided into AP without local complications (Non-LC-AP) and AP with local complications (LC-AP), AP without systemic inflammatory response syndrome (Non-SIRS-AP) and AP with systemic inflammatory response syndrome (SIRS-AP).

Processing of Human Peripheral Blood Samples

Blood samples of AP patients were collected using anticoagulant-containing collection vessels. Samples were mixed with sterile PBS solution in a 1:1 ratio. Lymphoprep (Alere Technologies AS, Oslo, Norway) was added to a 15mL centrifuge tube, followed by slow addition of the blood-PBS mixture along the tube wall. Centrifugation at a gradient of 800g for 20 minutes allowed for collection of cells in the lower layer. Neutrophils were isolated following red blood cell lysis.

Cell Culture

Mouse bone marrow-derived neutrophils: The femur and tibias of male C57BL/6J mice aged 6–8 weeks were isolated, muscle tissue was excised, the femur and tibia were placed in pre-cooled 1640 medium (BC-M-017, BIO-Channel). Both ends of the bones were trimmed, exposing the bone marrow cavity. The cavity was rinsed with 1640 medium, filtered using a 70μm cell filter (CLS431751, MERCK), and centrifuged at 450g for 10 minutes at room temperature. The supernatant was discarded, and neutrophils were isolated using the mouse peripheral blood neutrophil isolation kit (P9201, Solarbio), carefully collecting the neutrophilic layer fluid. After suspension dilution in 1640 medium, centrifugation was done at 4°C, 1200 rpm for 5 minutes. Following removal of the supernatant, cells were suspended in 1640 complete medium (90% 1640 + 10% FBS) and incubated in a 5% CO₂ cell incubator at 37°C for subsequent use.

Reagents and Antibodies

Details of all reagents and antibodies utilized in the study are provided in Supplementary Table 3 and Supplementary Table 4.

Acute Pancreatitis Model Preparation and Drug Intervention

For the Anti-CD53 (124701, BioLegend) intervention experiment, mice were divided into a caerulein-induced AP group (Cae) and various dose gradients of Anti-CD53 treatment group (Cae/Anti-CD53, 40μg/kg, 80μg/kg, 160μg/kg), along with a normal control group. The acute pancreatitis model was induced by intraperitoneal caerulein injections (100μg/kg at hourly intervals for 10 times) in the AP and Anti-CD53 groups. The normal control group received intraperitoneal injections of the same volume of phosphate-buffered saline (PBS) as the other groups, while the AP group received an equivalent amount of IgG based on the maximum Anti-CD53 dose.

Sample Collection

The mice were anesthetized with Zoletil and euthanized 12 hours after the initial injection of caerulein. Blood samples were collected from the eye at 12 hours and analyzed for serum amylase and lipase levels following the kit instructions. Pancreatic tissue was extracted, fixed in 4% paraformaldehyde, and embedded in paraffin blocks for hematoxylin and eosin staining. Any remaining pancreatic tissue was preserved at −80°C.

Histological Analysis

Pancreatic tissue was fixed in 4% paraformaldehyde for 48 hours, embedded in paraffin blocks and sliced. The tissue sections were stained with hematoxylin eosin (H&E), dehydrated with gradient ethanol, transparent with xylene, sealed with neutral gum, and interpreted under an optical microscope. Histological scoring of pancreatic tissue was conducted by two independent pathologists in a blinded manner as previously described.²⁴

Immunofluorescence Staining

Mouse pancreatic tissues were fixed, embedded, and sectioned. Sections were subjected to antigen retrieval by boiling in a sodium citrate buffer. The tissue sections were then blocked with normal goat serum, incubated overnight at 4°C with Anti-MPO (1:100, Abcam) and Anti-Cith3 (1:300, Abcam) or Anti-MPO (1:100, Abcam) and Anti-CD53 (1:100, Senta) in the dark. The next day, the slices were washed three times with PBS for three minutes each time. They were then incubated at room temperature for 2 hours with biotinized secondary antibody (1:1000 dilution). Finally, the tissue sections were incubated at room temperature in 4, 6-Diamino-2-phenylindole (DAPI) solution and sealed. After drying, the sample was observed using a confocal microscope (Leica TCS Sp8 sted, Germany), images were acquired and analyzed using its corresponding LAS X imaging software.

Measurement of NETs Related Indicators in vitro

Isolated mouse bone marrow neutrophils were seeded in 12-well plates and cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) at 37°C and 5% CO₂ for 30 minutes. In vitro NETs model was constructed by treating 100 nM PMA or PBS for 4 hours. The formation of NETs in vitro was visualized by immunofluorescence. The cells were fixed with 4% paraformaldehyde, washed with PBS, incubated with Anti-MPO (1:100, Abcam) and Anti-Cith3 (1:300, Abcam) at 4°C overnight, stained with a biotinylated secondary antibody, and counterstained with DAPI. The samples were then examined under a confocal microscope (Leica TCS Sp8 sted, Germany) and analyzed using LAS X imaging software.

Elisa

Cell supernatants and serum from experimental animals were collected. 96-well plates were coated with specific antibodies and incubated for 12 hours. The levels of TNF-α (88–7324-77, Affymetrix Ebioscience), MCP-1 (88–7391-77, Affymetrix Ebioscience), and IL-6 (88–7064-77, Affymetrix Ebioscience) were determined following the kit instructions.

Flow Cytometry

MPO determination: The treated mouse bone marrow neutrophils were exposed to surface antibodies (Anti-CD45.2, Anti-CD11b and Anti-Ly6G) at 4°C for 30 minutes, washed with PBS, fixed with fixative at 4°C for 20 minutes, and then washed with PBS. Anti-MPO antibody was stained at 4°C for 20 minutes. After washing with PBS, the cells were suspended with 200μL PBS. Finally, we collected cells at Beckman DxFlex B5-R3-V3 (Indianapolis, IN, United States), CytExpert for DxFlex was used for analysis.

ROS determination: DHE fluorescent probe (Sigma, D70N8, 1:2000) was used to detect the content of intracellular reactive oxygen species.¹² The treated mouse bone marrow neutrophils were incubated with DHE solution at 37°C for 30 minutes without light. The cells were washed with PBS and then stained with surface antibodies (Anti-CD45.2, Anti-CD11b, and Anti-Ly6G) at 4°C for 30 minutes. After washing the cells with PBS, the cells were suspended with 200μL PBS. Finally, cells were collected as above for analysis.

CD53⁺ neutrophils determination:The extracted human peripheral blood neutrophils were stained with surface antibodies (Anti-CD45, Anti-CD11b, Anti-CD66b, and Anti-CD53) at 4°C for 30 minutes. After washing the cells with PBS, the cells were re-suspended with 200μL PBS. Finally, the cells were collected and analyzed as above.

RNA Sequencing and Bioinformatics Research Methods

mRNA expression profiles pre and post PMA-induced NETs formation were analyzed using RNA-seq technique. Total RNA from the control and PMA-treated groups was extracted using TRNzol universal reagent (TIANGEN, China) and processed for RNA-seq by Novogene (Tianjin, China). Differential gene expression analysis was conducted using volcano plot and heatmap analysis in R software to identify differentially expressed genes (DEGs) between the two groups. To identify differentially expressed genes (DEGs), we used the “DESeq2” software package with adjusted p-value < 0.05 and |log2FC|>1.²⁵ We used online tools (https://www.bioinformatics.com.cn/static/others/jvenn/example.html) to draw Venn diagrams. The R packages “Enhanced Volcano” and “pheatmap” were used to visualize the differential expression and clustering of genes in the sequencing dataset.

Furthermore, mRNA expression in PMA group and PMA+Anti-CD53 group was assessed using RNA-seq. Total RNA from these groups was isolated using Trzol universal reagent (TIANGEN, China) and then subjected to RNA-seq by Novogene Company (Tianjin, China). Subsequently, KEGG enrichment analysis²⁶ was performed to using R software uncover gene pathway associations.

Protein Extraction and Western Blot Analysis

Cells were lysed using ultrasonic homogenization, and the resulting supernatant was collected by centrifugation (12,000 rpm, 30 minutes). Protein concentration was determined using a BCA protein kit (P0012, Beyotime). The remaining supernatants were added with 5X protein loading buffer (P0015, Beyotime), denatured at 100°C for 10 minutes, and stored at −80°C.

The protein samples underwent 10% SDS-polyacrylamide gel electrophoresis (PAGE), followed by transfer onto a PVDF membrane and blocking with 5% skim milk at room temperature for 2 hours. The membrane was then probed with primary antibodies, including Anti-PAD4 (1:1000, Abcam), PI3 Kinase p85 (19H8) Rabbit mAb (1:1000, CST), Phospho-PI3 Kinase p85 (Tyr458)/p55 (Tyr199) (E3U1H) Rabbit mAb (1:1000, CST), Akt (pan) (C67E7) Rabbit mAb (1:1000, CST), Phospho-Ak t(Ser473) (D9E) Rabbit mAb (1:1000, CST) and Anti-β-actin (1:1000, CST) at 4°C overnight. On the second day, use TBST washing film 3 times for 15 minutes each time. The membrane was then incubated with the second antibody coupled with horseradish peroxidase at 37°C for 2 hours. Finally, use TBST washing film 3 times, each time for 15 minutes. After washing, protein bands were detected using the ECL Plus chemiluminescence system. Image intensity was analyzed using ImageJ software.

Scanning Electron Microscope (SEM) Observation

The sample underwent fixation at 4°C for 24 hours with 2.5% glutaraldehyde, followed by standard dehydration, critical point drying, heavy metal sputter coating, and enhancement of electrical conductivity. Subsequently, the morphology of NETs was visualized using a scanning electron microscope (GeminiSEM 300, UK).

Statistical Analysis

Statistical analysis was performed using GraphPad Prism 9.0, R Project, SPSS and Mengte Cloud Statistical Platform. A Student’s t-test was utilized for comparison of means between two groups, while one-way analysis of variance (ANOVA) was employed to assess statistical differences between multiple groups. Results are presented as mean ± standard error (SE), and histogram data is expressed as mean ± standard error of the mean (SEM). A significance level of P < 0.05 indicates a two-tailed difference. Each experiment was replicated three times for robustness.

Results

CD53 Is Significantly Highly Expressed in PMA-Induced NETs and AP Mice

In the PMA-induced NETs formation model, the RNA of mouse bone marrow neutrophils pre and post PMA induction was extracted for RNA sequencing analysis. To reduce batch differences, we jointly analyzed the two batches of NETs model RNA-Seq database of our research group and found that a total of 16,118 differential genes overlapped in the two sets of data, and further analysis revealed significant changes in various cell surface molecules. We combined the change of P-value and Log2FoldChange, and finally screened through volcanic map and heat map analysis and found that CD53 significantly increased after PMA-induced NETs formation (Figure 1A-B). In order to further verify the changes in CD53 expression during the formation of NETs, WB, flow cytometry and RT-PCR experiments were respectively used to verify the significantly high expression of CD53 in NETs (Figure 1C-F).

Figure 1 CD53 is significantly highly expressed in PMA-induced NETs and AP mice. (A) Flow chart of combined analysis of two sets of transcriptome sequencing data cultured from bone marrow neutrophils isolated from C57BL/6J male mice aged 6 to 8 weeks and stimulated with PMA (100 nM) for 4 hours. (B) Overlapping of the two sets of data for differential surface molecular volcano maps and heat map analysis. (C-D) Western blotting was used to analyze the protein levels of CD53 in neutrophils and the corresponding semi-quantitative analysis (N=3 per group). β-actin is used as a control for proteins. (E) Mouse bone marrow derived neutrophils were stimulated with PMA (100 nM) for 4 hours and then stained for flow cytometry analysis. Representative flow cytometry of C57BL/6 J mouse bone marrow neutrophils (CD45.2+CD11b+Ly6G+). Representative flow cytometry and bar charts describing neutrophil proportions and CD53 expression levels. Data are expressed as mean ± SE (N=6 per group). (F) Mouse bone marrow derived neutrophils were stimulated with PMA (100 nM) at different time gradients to analyze CD53 mRNA levels by RT-PCR. (G) The AP mouse model was induced by caerulein, and the mouse pancreatic tissue was stained with CD53 and MPO immunofluorescence, the scale = 50μM. (H) Optical density analysis of CitH3 and MPO fluorescence (N=6 per group). Statistical significance was indicated by *P<0.05,** P<0.01,***P<0.001.

Moreover, the AP mouse model was also induced by caerulein. Immunofluorescence detection results showed that CD53 and MPO co-localization in damaged pancreatic tissue of AP group was significantly higher than that of control group, suggesting that CD53 was mainly highly expressed in neutrophils after AP (Figure 1G-H). These findings suggest a significant overexpression of CD53 in both NETs formation and AP mice in vitro.

CD53 is Highly Expressed in Peripheral Blood Neutrophils of AP Patients and is Closely Related to Disease Severity

61 AP patients were included in the study, peripheral blood samples were collected for analysis. The clinical characteristics of AP patients are shown in Supplementary Table 1. Peripheral blood neutrophils from AP patients and healthy controls were analyzed by flow cytometry. As shown in Figure 2A, polymorphonuclear leukocytes (PMN) and CD53⁺PMN were significantly increased in AP patients compared to Control. AP patients were divided into AP non-MODs (N=47) and AP with MODS (MODS AP) groups (N=14) according to the modified Marshall score. According to the occurrence of local complications or systemic inflammation, AP patients were divided into two groups: Non-LC group (N=39) and LC group (N=22), Non-SIRS group (N=40) and SIRS group (N=21). We found that the expression level of peripheral neutrophils CD53 in AP patients with poor prognosis was significantly higher than that in the control group, and was correlated with the severity of the disease (Figure 2B-D).Correlation heat map analysis demonstrated a positive correlation between peripheral neutrophil CD53 levels and serum C-reactive protein,White Blood Cell, and other indicators (R=0.43, P=0.0002) (Figure 2E). In addition, ROC curve analysis showed that the area under the curve of CD53⁺PMN(%) was 0.83 (P < 0.001, Sensitivity 76.92%, Specificity 86.33%) (Figure 2F). We found that CD53⁺PMN was more accurate and sensitive than PMN and CRP in predicting progression to MODS in AP patients.

Figure 2 CD53 is highly expressed in peripheral blood neutrophils of AP patients and is closely related to disease severity. (A) Flow cytometry gated (CD45+CD11b+CD16+) of a representative sample of human peripheral blood neutrophils, with representative flow cytometry graphs and bar plots depicting CD53 expression levels in neutrophils. Data are expressed as mean ±SE. (B) Percentage of CD53+ neutrophils (CD45+CD11b+CD16+) in peripheral blood in control, Non-SIRS and SIRS groups. (C) Percentage of CD53+ neutrophils (CD45+CD11b+CD16+) in peripheral blood in control, Non-MODS, and MODS groups. (D) Percentage of CD53+ neutrophils (CD45+CD11b+CD16+) in peripheral blood in control group, Non-LC group and LC group. (E) Heat maps of correlations between neutrophil CD53 levels and other clinical indicators in AP patients. (F) Diagnostic value of CD53+PMN, PMN, CRP level ROC curve analysis for MODS. Statistical significance was indicated by ***P<0.001.

To further analyze the changes of NETs in patients with acute pancreatitis. We used flow cytometry to analyze NETs-related indicators (MPO) in healthy controls and AP, and the expression of NETs-related indicators (MPO) and CD53 was significantly increased in AP patients compared with controls, and the two were positively correlated (Supplementary Figure 1). In summary, CD53 was significantly overexpressed in AP patients and correlated with disease severity.

Anti-CD53 Significantly Reduces PMA-Induced NETs Formation

In Figure 3A, different concentrations of Anti-CD53 (10, 20, and 40 ng/mL) were assessed for MPO and ROS production in mouse bone marrow neutrophils post PMA stimulation using flow cytometry. MPO and ROS are important substances for the early formation of NETs.²⁷ Significant protection against MPO and ROS was observed in the Anti-CD53 40 ng/mL group compared to other doses. Subsequently, immunofluorescence and scanning electron microscopy confirmed (Figure 3B-C) that PMA-induced neutrophils formed extensive NETs structures, which were significantly inhibited by treatment with Anti-CD53. In addition, Pro-inflammatory cytokine levels in PMA-induced neutrophil supernatant were also inhibited by Anti-CD53 (Figure 3D). As shown in Figure 3E-F, WB confirmed that the expression of PAD4, a key enzyme involved in NETs production,²⁸ was also significantly reduced after treatment with Anti-CD53.

Figure 3 Inhibition of CD53 significantly reduces NETs formation in vitro. (A) Bone marrow neutrophils isolated from C57BL/6J male mice aged 6 to 8 weeks were cultured with PMA (100 nM), incubated with different doses of Anti-CD53 (10, 20, and 40 ng/mL), and then stained for flow cytometry analysis. Representative flow cytometry of C57BL/6 J mouse bone marrow neutrophils (CD45.2+ CD11b+ Ly6G+). Representative flow cytometry and bar charts describing neutrophil proportions and MPO and ROS expression levels. Data are expressed as mean ± SE (N=4 per group). (B) Representative immunofluorescence images of CitH3 and MPO at 400X and 1000X magnifications (N=6 per group). Scale=50μM. Representative SEM images of neutrophils at 5000X magnification (N=6 per group). Scale = 1μM. Red arrows represent NETs under immunofluorescence, white arrows represent normal neutrophils, and yellow arrows represent NETs under SEM. (C) Optical density analysis of CitH3 and MPO fluorescence (N=6 per group). (D) The levels of IL-6, TNF-α and MCP-1 were determined by ELISA (N=6 per group). (E-F) The protein level of PAD4 in neutrophils (N=3 per group) and the relative protein expression histogram of PAD4 were analyzed by Western blot. β-actin is used as a control for proteins. Statistical significance was indicated by *P<0.05,** P<0.01,***P<0.001. No significance was indicated by ‘NS’.

Anti-CD53 Inhibits the Formation of NETs Through the PI3K/AKT Signaling Pathway

To elucidate the mechanism of CD53 in the formation of NETs, we extracted RNA from mouse bone marrow derived neutrophils in the PMA group and the PMA+Anti-CD53 group for RNA-Seq (Figure 4A). PCA analysis was performed on the sequencing data of PMA group and PMA+Anti-CD53 group (Figure 4B). The differences between the PMA group and the PMA+Anti-CD53 group are reflected in the volcano map (Figure 4C). KEGG enrichment analysis showed significant changes in the PI3K/AKT signaling pathway (Figure 4D). Previous studies have shown that the PI3K/AKT signaling pathway can activate NADPH enzyme, thereby mediating the production of ROS and promoting the formation of NETs, which is one of the key pathways in the formation of NETs.²⁹ Subsequently, we examined the effect of CD53 on the PI3K/AKT pathway. As shown in Figure 4E, we found that the expression of P-PI3K and P-AKT protein in neutrophils in the PMA group was increased, but the expression of P-PI3K and P-AKT protein was significantly decreased after Anti-CD53 administration (Figure 4F). Subsequently, we sought to verify whether Anti-CD53 inhibited the formation of NETs only through the PI3K/AKT signaling pathway. In Figure 4G, flow cytometry was used to assess MPO and ROS production in mouse bone marrow neutrophils after PMA stimulation with different concentrations of LY094002 (1, 10, and 100 μM). LY094002 is a PI3K/AKT pathway inhibitor. Compared with other doses, LY094002 100 μM group had a significant protective effect on MPO and ROS. Proceed to the next step, the addition of Anti-CD53 did not further improve the formation of NETs during LY094002 treatment (Figure 4H). These results support the conclusion that CD53 inhibits the formation of NETs through the PI3K/AKT signaling pathway.

Figure 4 Anti-CD53 inhibits the formation of NETs via the PI3K/AKT signaling pathway. (A)RNA was extracted from mouse bone marrow derived neutrophils in PMA group and PMA+Anti-CD53 group for RNA-seq. (B) PCA analysis of sequencing data. (C) Volcanic map analysis of differential genes. (D) Differential gene response group enrichment analysis of PMA group and PMA+Anti-CD53 group (N=3 per group). (E) The expression levels of P-PI3K and P-AKT in neutrophils were detected by Western method. (F) The relative protein expression of P-PI3K and P-AKT in neutrophils, with PI3K and AKT as the protein load control (N=3 per group). (G-H) Representative flow cytometry of C57BL/6 J mouse bone marrow neutrophils (CD45.2+ CD11b+ Ly6G+). Representative flow cytometry and bar charts describing neutrophil proportions and MPO and ROS expression levels. Data are expressed as mean ± SE (N=4 per group). Statistical significance was indicated by *P<0.05,** P<0.01,***P<0.001. No significance was indicated by ‘NS’.

Anti-CD53 Efficacy in Reducing Pancreatic Injury in AP Mice

Based on the prior dose-response experiment (Supplementary Figure 2), a dose of 160μg/kg Anti-CD53 was validated in subsequent experiments. As shown in Figure 5A, Anti-CD53 intervention significantly alleviated pancreatic tissue edema, inflammatory cell infiltration and acinar cell necrosis in mice, and also significantly reduced serum amylase and lipase levels and inflammatory factors (Figure 5B and Figure 5DFigure 5E). In addition, immunofluorescence detection showed that the formation of NETs (Cith3/MPO colocalization) in pancreatic tissue of mice treated with Anti-CD53 was significantly reduced compared with the control group (Figure 5A and Figure 5C). These findings suggest the efficacy of Anti-CD53 in protecting against pancreatic injury, reducing inflammation, and inhibiting NETs formation in the pancreatic tissue of AP mice.

Figure 5 Anti-CD53 significantly alleviates pancreatic injury in AP mice. (A) Representative HE staining of pancreatic tissue at 100X and 400X magnifications. Scale=50μM. Representative immunofluorescence images of CitH3 and MPO of pancreatic tissue at 400X and 1000X magnifications (N=6 per group). Scale=50μM. Red arrows represent NETs under immunofluorescence. (B) Serum amylase and lipase levels. (C) Optical density analysis of MPO and CitH3 fluorescence (N=6 per group). (D) Pathological scores of pancreatic tissue (N=6 per group). (E) Serum IL-6,TNF-α and MCP-1 levels were measured by ELISA (N=6 per group). Statistical significance was indicated by *P<0.05,** P<0.01,***P<0.001. No significance was indicated by ‘NS’.

Discussion

This study provides new insights that inhibition of CD53 can reduce the formation of NETs and protect against inflammatory injury in AP through the PI3K/AKT pathway. These findings elucidate the mechanism of CD53 in NETs formation and highlight that CD53 is a key factor in AP-targeting inflammatory injury.

Studies have shown that neutrophils rapidly infiltrate pancreatic tissue during the early inflammatory response, forming NETs that contribute to the inflammatory process.³⁰ Activated neutrophils form NETs that participate in the inflammatory process.³¹ Excessive release of NETs may have proinflammatory effects,³² then aggravated acute pancreatitis. To investigate further, we recruited 61 AP patients and 20 healthy controls, revealing significantly elevated CD53, WBC, and CRP levels in the serum of AP patients, correlating with disease severity. Subsequently, in a mouse AP model, Anti-CD53 intervention significantly reduced pancreatic injury and inflammatory infiltration, offering strong support for targeting CD53 as a potential therapeutic approach in AP. Next, we established a mouse AP model and applied Anti-CD53 intervention, and the results proved that Anti-CD53 could significantly reduce pancreatic injury and inflammatory infiltration of AP. Taken together, these results strongly support the potential therapeutic effect of targeting CD53 in AP.

Recent research has highlighted that the four-transmembrane protein CD53, which is widely expressed in immune cells and positively correlated with the level of immune cell infiltration,³³ plays a crucial role in cellular immune responses.^15,34 CD53 has a variety of roles in immune cells, such as response to pathogens, adhesion, migration²¹ and signaling.³⁵ The co-localization of CD53 and MPO confirmed the regulatory role of CD53 in neutrophil-forming NETs, so we focused on the role of CD53 in NETs. The data presented here extend previous literature showing that neutrophil CD53, but not lymphocyte CD53, mediates inflammatory responses. Existing reports have demonstrated a multifunctional role for CD53 in different cell types and processes.^16,36,37 Future studies will better distinguish the relative contribution of CD53 from different cell sources to the body’s inflammatory response. Understanding these data in the light of the previous literature on CD53 function raises interesting points for further investigation, particularly regarding the relationship between CD53 and neutrophils. Beinert et al proposed that the expression of CD53 is increased in senescent neutrophils,³⁸ Neutrophils have traditionally been considered short-acting effector cells in the innate immune system with a half-life of about 19 hours.³⁹ Our methodologic observation window was 4 hours, well before the accepted half-life of aging, and our experiments were rigorously controlled. Thus our findings do not conflict with the conclusions of Beinert et al. Previous studies by Mollinedo et al found⁴⁰ that when neutrophils were stimulated by PMA, the expression level of CD53 was reduced. This conclusion seems inconsistent with our results, and we consider that several factors may have contributed to this difference: first, the specific timing of PMA processing is different, different methodologies may lead to neutrophils displaying different cell biological functions in different states. Second, our study is focused on discussing the expression levels of CD53 in NETs, which have been shown to be released by most neutrophils upon death 2–4 hours after activation.^41,42 Finally, we also verified that the expression of CD53 in NETs was up-regulated by RNA sequencing, flow cytometry, WB, and PCR. Through a series of in vitro and in vivo experiments, we verified that Anti-CD53 effectively inhibits NETs formation in AP, and reducing inflammatory cytokine levels such as IL-6, TNF-α, and MCP-1. The regulatory effect of CD53 on NETs formation via neutrophils was confirmed by CD53 and MPO colocalization.

In addition, the PI3K/AKT pathway in myeloid immune cells is implicated in regulating immune cell activation by modulating anti-inflammatory cytokines and pro-inflammatory cytokines.⁴³ Guo.G et al demonstrated that DNase-I@V2C shifts neutrophil function from NETs to phagocytosis by activating the PI3K/AKT pathway in the diabetic microenvironment, thereby eliminating biofilm infections.⁴⁴ Zhu.C.L et al verified that NETs are overreleased during ARDS and that knocking down PD-L1 can inhibit the release of NETs through the PI3K/AKT pathway.⁴⁵ In this study, through RNA-Seq and subsequent functional validation, we found that CD53 promotes the formation of NETs through the PI3K/AKT pathway. Targeted inhibition of CD53 may inhibit the formation of NETs through PI3K/AKT signaling.

Conclusion

In conclusion, our findings represent the initial confirmation that CD53 triggers the PI3K/AKT pathway and fosters the formation of NETs formation. By modulating CD53, the generation of NETs can be thwarted, consequently mitigating inflammatory injury in AP mice. This study contributes to the understanding of NETs formation mechanisms and introduces innovative therapeutic approaches for managing AP and other inflammatory conditions associated with NETs.