Fasting serum samples were collected from all 100 participants during morning rounds following hospital admission. Based on the 2012 Atlanta classification criteria, AP patients were stratified into three clinical severity groups: (1) mild AP (MAP) characterized by absence of both organ failure and local/systemic complications; (2) moderately severe AP (MSAP) defined by transient organ failure (48 hours) and/or localized complications; and (3) severe AP (SAP) featuring persistent organ failure ($>$48 hours) regardless of complication status. Exclusion criteria included patients with rheumatic diseases, neoplastic diseases, gastrointestinal diseases, chronic kidney diseases, or liver diseases, as well as patients whose AP symptoms had persisted for more than 48 h. The healthy control cohort was prospectively matched with AP patients using the following matching criteria: age (within 3-year strata), sex distribution, and BMI categories (WHO classification). Strict exclusion criteria comprised: (a) history of chronic diseases affecting major organ systems; (b) pregnancy/lactation; and (c) use of metabolically active medications within the past 3 months. All collected serum samples were immediately aliquoted into 500 µL cryovials and preserved at –80 °C in ultralow temperature freezers (Thermo Fisher Scientific, Waltham, MA, USA) until subsequent biochemical analyses.

Patients or their families/legal guardians informed consent was obtained for specimen collection, which was approved and supervised by the Ethics Committee of North Sichuan Medical College Affiliated Hospital (Approval Number: 2021ER133-1).

Serum samples (80 µL) were aliquoted into 96-well plates and mixed with 320 µL of methanol:acetonitrile (1:1, v/v) spiked with 50 ng/mL bile acid isotope internal standards. Following 5-min vortexing, samples underwent centrifugation (5300 rpm, 20 min, 4 °C), with 260 µL supernatant aliquoted into 450 µL 96-well plates for drying. The resulting extracts were reconstituted for targeted bile acid metabolomic analysis. Scanning and quantification were performed using an Acquity UPLC (Waters, Milford, MA, USA) and a Sciex 5500+ triple quadrupole mass spectrometer (Sciex, Framingham, MA, USA) in tandem. The bile acid extract was injected at a volume of 2.5 µL and separated on a C18-PFP (3 µm, 2.1 $\times$ 50 mm) chromatographic column. The mobile phase system comprised: (A) 2 mM ammonium acetate in water and (B) acetonitrile. The gradient elution program progressed as follows: 17% to 30% B over 10 min, then to 55% B in 3 min, rapidly increased to 95% B in 1 min (held for 3 min), followed by a 5-min re-equilibration. Chromatographic separation was performed at 0.4 mL/min with metabolites ionized via TurboV-heated electrospray ionization (ESI) and detected in multiple reaction monitoring (MRM) mode, using the following optimized parameters: negative ion spray voltage (–4.5 kV), curtain gas (35 psi), and ion source temperature (550 °C).

The murine macrophage cell line RAW264.7 was obtained from SUNNCELL (Wuhan, China) and maintained in high-glucose DMEM (C3113-0500, VivaCell Biosciences, Shanghai, China) supplemented with 5% (v/v) heat-inactivated fetal bovine serum (FBS, Biological Industries, Kibbutz Beit Haemek, Israel) and 1% (v/v) penicillin-streptomycin solution (Biological Industries, Kibbutz Beit Haemek, Israel). Cell cultures were grown at 37 °C in a 5% CO₂ humidified incubator. Cells at 70% confluence were divided into four groups (n = 6/group): (1) Vehicle (DMSO 10 µL/mL, 12 h); (2) Lipopolysaccharide (LPS, Sigma-Aldrich, St. Louis, MO, USA) (1 µg/mL, 12 h); (3) LPS + NorCA (Sigma-Aldrich, St. Louis, MO, USA, 1 + 10 µg/mL, 12 h); (4) LPS/NorCA$\to$JW74 (MedChemExpress, Monmouth Junction, NJ, USA, 1/10 µg/mL$\to$10 µg/mL, 12 h each phase). The cell line utilized in this study was authenticated by short tandem repeat (STR) profiling and confirmed that the mycoplasma test result was negative.

RAW264.7 cell viability after NorCA treatment was quantified using a commercial CCK-8 kit (K1018, APExBIO Technology LLC, Houston, TX, USA) per the standard protocol. Briefly, RAW264.7 cells were seeded in 96-well plates at a density of 5000 cells/well and treated with six different concentrations of NorCA (0, 2.5, 5, 10, 15, and 20 µg/mL) in culture medium. After 24 h incubation, cells received CCK-8 reagent (10% in medium) for 4 h at 37 °C. Cell viability was quantified by measuring absorbance at 450 nm using a microplate reader (M1000 PRO, Tecan, Männedorf, Switzerland), and the maximum effective concentrations of the two bile acids on RAW264.7 cells were calculated. This experiment was repeated thrice.

RAW264.7 murine macrophages were cultured in DMEM (C3113-0500, VivaCell Biosciences, Shanghai, China) supplemented with 10% heat-inactivated FBS and maintained at 37 °C in a humidified 5% CO₂ incubator. At $>$80% confluence, cells were randomized into four experimental groups: (1) Vehicle control (1% DMSO), (2) LPS stimulation (1 µg/mL), (3) LPS (1 µg/mL) + NorCA (10 µg/mL) co-treatment, and (4) LPS + NorCA (1 + 10 µg/mL) priming followed by JW74 (10 µg/mL) treatment. The cells were maintained under these culture conditions for 24 hours. Subsequently, RAW264.7 macrophages were PBS-washed (3$\times$, 5 min each, pH 7.4) and centrifuged (300 $\times$g, 5 min, 4 °C) for harvesting. Cell pellets were fixed with freshly prepared 4% paraformaldehyde (PFA) (BL539A, Biosharp, Nanjing, China) in PBS for 15 min at room temperature (22–25 °C) with gentle agitation. Following fixation, residual PFA was completely removed through three additional PBS washes (5 min each) to ensure proper cellular preparation for downstream applications. The washed cells were incubated with fluorescence-labeled anti-CD86 (11-0862-82, Invitrogen, Carlsbad, CA, USA) and anti-CD11c antibodies (14-0114-82, Invitrogen, Carlsbad, CA, USA) at room temperature for 30 minutes. After staining, cells were washed three times with PBS to remove unbound antibodies, followed by analysis using a Novocyte flow cytometer (ACEA Biosciences, San Diego, CA, USA) to quantify surface marker expression.

Total RNA from each group of cells was isolated using the TRIzol reagent (R401-01, Vazyme, Nanjing, China). RNA samples underwent reverse transcription with HiScript III RT SuperMix for qPCR (+gDNA wiper) (Vazyme), followed by quantitative PCR employing ChamQ Universal SYBR qPCR Master Mix (Vazyme) per manufacturer’s protocol. Quantitative PCR was carried out on a LightCycler 96 System (Roche Diagnostics, Basel, Switzerland) using gene-specific primers (sequences provided in Table 1).

Finally, the relative expression levels of target genes across experimental groups (DMSO control, LPS, LPS + NorCA, and LPS + NorCA + JW74) were quantified using the 2^{-Δ⁢Δ⁢Ct} method with GAPDH normalization.

RAW264.7 cells were treated for 24 h with DMSO (10 µL/mL), LPS (1 µg/mL), LPS (1 µg/mL) + NorCA (10 µg/mL), or LPS (1 µg/mL) + NorCA (10 µg/mL) + JW74 (10 µg/mL). After treatment, cells were lysed in 1 mL RIPA buffer (R0010, Solarbio, Beijing, China) supplemented with 10 µL PMSF (P0100, Solarbio, Beijing, China) for protein extraction. Cell lysate protein quantification was performed using a commercial BCA assay kit (PC0020, Solarbio, Beijing, China) following standard protocols. Subsequently, 25 µg of protein from each lysate was separated via SDS-PAGE (10% gel) and transferred to PVDF membranes at 240 mA for 1.5 hours for subsequent immunoblotting analysis. Blocked PVDF membranes (protein-free buffer) underwent TBST washing before overnight 4 °C incubation with CD86/CD11c/$\beta$-catenin/$\beta$-tubulin antibodies (DF6332/DF2542/AF7011/AF6266, Affinity Biosciences, Nanjing, China; 1:1000 in Beyotime buffer). Following triple TBST washes, membranes received HRP-anti-rabbit secondary antibody (S0001, Affinity Biosciences, Nanjing, China; 1:3000) for 1 h at room temperature (RT), using $\beta$-tubulin as loading control. The protein signals were visualized using Immobilon® HRP substrate (Millipore Corporation, Burlington, MA, USA) and imaged on an Amersham Imager 600 (GE Healthcare, Chicago, IL, USA).

Transcriptome sequencing of the DMSO, LPS, and LPS + NorCA groups of RAW264.7 cells was performed by Shanghai LingEn Biotechnology Co., Ltd. (Shanghai, China) Total RNA was extracted from the three groups of RAW264.7 cells using the TRIzol method. We next measured the concentration and purity of the extracted total RNA using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Subsequent library preparation was performed following successful quality control assessment. The constructed cDNA libraries underwent paired-end sequencing on an Illumina NovaSeq 6000 system (Illumina, San Diego, CA, USA), generating 150 bp average read lengths. Subsequently, the expression differences between samples for each transcript were analyzed using edgeR (https://bioconductor.org/packages/release/bioc/html/edgeR.html) to identify differentially expressed genes (DEGs). The screening criteria for significant DEGs comprised a minimum 2-fold change (log2 scale) and 0.05 p-value cutoff.

Differential gene expression analysis between the LPS + NorCA-treated group and control groups in LPS-stimulated RAW264.7 cells was conducted using the limma package (R software; https://bioconductor.org/packages/limma/). DEGs were defined as those meeting the threshold criteria of p 0.05 with $|$log2 fold change$|$ $\ge$1. The ggplot2 (version 3.5.2, https://cran.r-project.org/package=ggplot2) and pheatmap R packages (version 1.0.13, https://cran.r-project.org/web/packages/pheatmap/index.html) were employed to visualize the DEGs through volcano plots and hierarchical clustering heatmaps, respectively. Functional enrichment analysis of the DEGs was performed using the clusterProfiler package in R (version 4.16.0, https://bioconductor.org/packages/clusterProfiler) to identify statistically significant pathways. Gene ontology (GO) term categorization (BP/CC/MF) was performed followed by graphical representation. The false discovery rate-adjusted p-value (referred to as the Q-value) was determined, and a cutoff of 0.01 was applied for GO analysis. GO enrichment analysis was performed using the Goatools software (https://github.com/tanghaibao/GOatools) using Fisher’s exact test. DEGs meeting the cutoff criteria (log2-fold change $\ge$2; adjusted p $\le$ 0.05) were considered significant.

Statistical analyses were performed using GraphPad Prism 9.4 (GraphPad Software, San Diego, CA, USA), with data expressed as mean $\pm$ SD. Intergroup differences were analyzed using one-way ANOVA followed by Tukey’s multiple comparisons test (significance threshold: p 0.05).

NorCA has a relative molecular weight of 394.5 g/mol, its molecular structure is shown in Fig. 1a; and its structural formula is shown in Fig. 1a. In addition, the mass-to-charge ratio of the molecular ion during the targeted detection was 393.3; this molecular ion exhibited the highest peak at a retention time of 6.127 min (Fig. 1b). NorCA did not have any structural isomers within the adjacent retention time, making it less prone to interference in peak area calculations, and thus highly reliable for absolute concentration quantification. NorCA demonstrated significant differential expression between healthy controls and AP patients, as well as across AP subgroups (p 0.05), suggesting its potential as a diagnostic biomarker. As shown in Fig. 1c, the serum levels of NorCA in patients with AP were significantly higher than those in the healthy control group. When subdividing patients with AP based on the etiology of the disease, it was evident that the concentrations of NorCA in the serum of patients with biliary AP (BAP) (Fig. 1d), hyperlipidemic AP (HLAP) (Fig. 1e), and idiopathic AP (Fig. 1f) were all significantly higher than those in the healthy control group. Furthermore, NorCA serum levels demonstrated significant upregulation in SAP compared to MSAP cases (Fig. 1g). Therefore, NorCA could serve as a potential biomarker for diagnosing and assessing the severity of AP. ROC curve analysis revealed NorCA’s strong diagnostic value for AP (AUC 0.905), achieving 82.5% sensitivity and 85% specificity (Fig. 1h). For AP diagnosis in amylase-negative patients, the ROC analysis yielded an AUC of 0.847, showing 82.5% sensitivity and 77.8% specificity (Fig. 1i), strongly supporting the feasibility of NorCA as a complementary biomarker to amylase, a commonly used clinical diagnostic marker for AP. Moreover, Moreover, NorCA demonstrated discriminative capacity between MSAP and SAP (AUC = 0.702), with 93.3% sensitivity and 60% specificity (Fig. 1j).

Fig. 1.

NorCA Expression in Pancreatitis Variants and Its Diagnostic Accuracy. (a) The chemical structure of NorCA. (b) The mass-to-charge ratio and chromatographic separation time of molecular ions in NorCA. (c) Comparative serum NorCA concentrations: AP patients vs healthy controls (bar graph). (d) BAP and control serum NorCA quantification (bar graph). (e) Serum NorCA comparison: HLAP vs healthy controls (bar graph). (f) Bar graph showing the difference in serum NorCA levels between idiopathic pancreatitis and healthy controls. (g) Bar graph showing the difference in serum NorCA levels between severe acute pancreatitis (SAP) and moderately severe acute pancreatitis (MSAP) groups. (h) The area under the ROC curve, sensitivity, and specificity of NorCA as a diagnostic biomarker for AP. (i) The area under the ROC curve, sensitivity, and specificity of NorCA for diagnosing AP patients with negative amylase. (j) The area under the ROC curve, sensitivity, and specificity of NorCA for differentiating SAP and MSAP. *p 0.05, ***p 0.001. NorCA, norcholic acid; AP, acute pancreatitis; BAP, biliary AP; HLAP, hyperlipidemic AP; ROC, receiver operating characteristic.

To establish a cellular model of AP, we first induced RAW264.7 cells to differentiate from the M0 state to pro-inflammatory M1 macrophages by administering LPS (Supplementary Fig. 1a,b,d–f). Subsequently, we evaluated the cytotoxic effects of NorCA on the macrophages. We selected a concentration of 10 µg/mL of NorCA for this experiment because it had a minimal impact on cell viability (Supplementary Fig. 1c). At this concentration, flow cytometric analysis revealed markedly elevated CD86 and CD11c expression levels in NorCA-treated cells relative to LPS-only controls Fig. 2a–c). Quantitative RT-PCR analysis revealed significantly elevated mRNA expression of M1 macrophage markers CD86 and CD11c in the LPS + NorCA group compared to LPS-only controls (Fig. 2d,e). Protein imprinting analysis confirmed these results (Fig. 2f–h).

Fig. 2.

NorCA enhances LPS-induced M1 macrophage polarization. (a) Flow cytometric analysis quantified CD86/CD11c expression in LPS and LPS + NorCA groups. (b,c) Flow cytometry-derived CD86/CD11c expression in LPS vs LPS + NorCA groups (bar graphs). (d,e) qRT-PCR analysis quantified CD86/CD11c expression in LPS vs LPS + NorCA groups. (f–h) The expression levels of CD86 and CD11c in the LPS group and LPS + NorCA group were detected by western blotting. *p 0.05, **p 0.01, ***p 0.001. LPS, lipopolysaccharide; qRT-PCR, quantitative reverse transcription PCR; FITC-A, fluorescein isothiocyanate - area; FSC-A, forward scatter - area.

Next, we analyzed the correlation between DEGs and the Wnt pathway based on sequencing data from the LPS and LPS + NorCA groups. As shown in Fig. 3a, we first validated the differences in gene expression in the sequencing data from RAW264.7 cells between the DMSO, LPS, and LPS + NorCA groups using PCA (Principal Component Analysis). Next, we plotted a volcano plot (Fig. 3b) to demonstrate the expression of DEGs between control and NorCA-treated groups. In addition, we generated a heatmap (Fig. 3c) to visualize the extent of the upregulation or downregulation of certain DEGs. Of these genes, CTNNB1, an important gene encoding the Wnt pathway protein $\beta$-catenin, was significantly different between the control and NorCA-treated groups (Fig. 3g).

Fig. 3.

RNA-seq reveals Wnt-related gene alterations, with NorCA activating Wnt/$\beta$-catenin via CTNNB1 upregulation. (a) Principal Component Analysis (PCA) was performed on transcriptomic data to evaluate gene expression variations across DMSO controls, LPS-treated, and LPS + NorCA groups. (b) Volcano plot demonstrating NorCA-modulated gene expression changes in LPS-stimulated conditions. (c) Heatmap showing the expression levels of the top 20 upregulated and downregulated genes in the LPS group and LPS + NorCA group in the sequencing data. Gene Ontology (GO) enrichment analysis of Wnt pathway-associated DEGs in (d) biological processes, (e) cellular components, and (f) molecular functions. (g) Box plot of Ctnnb1 (Wnt/$\beta$-catenin pathway) expression comparing LPS-treated and LPS + NorCA groups. (h) The expression levels of CTNNB1 in the LPS group and the LPS + NorCA group were detected by qPCR-RT. (i,j) Western blot analysis quantified $\beta$-catenin expression in LPS-treated versus LPS + NorCA groups. *p 0.05, **p 0.01, ***p 0.001. DEGs, differentially expressed genes.

Based on these findings, we conducted gene ontology (GO) term analysis of the sequencing data, including biological processes (BPs) (Fig. 3d), cellular components (CCs) (Fig. 3e), and molecular functions (MFs) (Fig. 3f). We identified a significant enrichment of Wnt-related genes. These findings suggest NorCA likely mediates pro-inflammatory responses in AP through canonical Wnt/$\beta$-catenin signaling. To validate this, we performed RT-qPCR (Fig. 3h) and western blotting (Fig. 3i,j), thus confirming that NorCA activated the $\beta$-catenin pathway protein.

To validate our hypothesis, we added a specific Wnt/$\beta$-catenin pathway inhibitor, JW74, to the LPS + NorCA group. We first analyzed the expression levels of CD86 and CD11c in the LPS, LPS + NorCA, and LPS + NorCA + JW74 groups using flow cytometry (Fig. 4a). The analysis showed a significant reduction in the expression levels of both surface markers after addition of the pathway inhibitor (Fig. 4b,c). Next, qRT-PCR was performed to measure the expression levels of CD86 and CD11c in the LPS, LPS + NorCA, and LPS + NorCA + JW74 groups (Fig. 4d,e). Consistent with this pattern, JW74 co-treatment significantly downregulated both CD86 and CD11c surface expression compared to LPS + NorCA controls. Western blot analysis yielded similar results (Fig. 4f–h). Finally, we present the basic experimental section in a flowchart (Fig. 5).

Fig. 4.

Inhibition of Wnt/$\beta$-catenin signaling significantly attenuates NorCA-mediated M1 macrophage polarization. (a–c) Flow cytometric analysis quantified CD86/CD11c expression across LPS, LPS + NorCA, and LPS + NorCA + JW74 groups. (d,e) qRT-PCR quantified CD86/CD11c expression in LPS, LPS + NorCA, and LPS+NorCA+JW74 groups. (f–h) The expression levels of CD86 and CD11c in the LPS group, LPS + NorCA group, and the LPS + NorCA + JW74 group were detected by western blotting. *p 0.05, **p 0.01, ***p 0.001.

Fig. 5.

Related experimental mechanism. By Figdraw.