The membrane protein Amuc_1098 from Akkermansia muciniphila alleviates acute pancreatitis via TLR2 signaling and glycerophospholipid metabolism remodeling.

The gene encoding Amuc_1098 was cloned into the pET-26b (+) vector and the resulting construct (pET-26b- Amuc_1098 ) was transformed into Escherichia coli BL21(DE3) for expression. Protein expression was induced during the logarithmic growth phase by adding 1 mM isopropyl-β-D-thiogalactoside (IPTG). After incubation at 37 °C for 4 h, cells were harvested, lysed by ultrasonication, and the His-tagged Amuc_1098 protein was purified under native conditions using Ni-NTA His Bind Resin (Merck Millipore, USA) according to the manufacturer’s protocol, with elution performed using imidazole at varying concentrations. The purified protein (hereafter referred to as Amuc_1098) was analyzed by SDS-PAGE to assess purity. Successful expression and purification were confirmed by Coomassie brilliant blue staining (Supplementary Fig.  1a ) and western blotting (Supplementary Fig.  1b ). To obtain the protein without affinity tags for functional assays, the purified His-tagged Amuc_1098 was subjected to enzymatic cleavage to remove the His-tag. The resulting product is referred to as Amuc_1098 * throughout the manuscript. The proteolytic digestion reaction was set up using a thrombin stock solution prepared according to the manufacturer’s protocol (Solarbio, China). To confirm the cleavage, protein samples before and after digestion were analyzed by SDS-PAGE, and the completion of His-tag removal was verified by western blot analysis. The animal welfare committee of Nanjing University of Chinese Medicine approved all experimental protocols for mice and followed laboratory animal care guidelines (202303A079). We randomly divided six- to eight-week-old C57BL/6 J male mice into three groups (Control, AP, AP+Amuc_1098) of three mice per cage and raised them under specific pathogen-free (SPF) conditions with 12-hour dark/night cycles. Under controlled temperature (22–24 °C) and humidity (50%–55%) conditions, all mice had free access to irradiated laboratory mouse and rat maintenance feed (GB14924.3-2010) and water. Acute pancreatitis in mice was successfully induced by two different models. One mouse model of pancreatitis was induced by intraperitoneal injection with caerulein (50 μg/kg body weight, 10 times at 1-h intervals) (Shanghai Yuanye Bio-Technology Co., Ltd, China) and lipopolysaccharide (LPS, 5 mg/kg body weight, at the same time as the last injection of caerulein) (Sigma-Aldrich, USA), both of which were dissolved in sterile normal saline (NS). Another mouse model of pancreatitis was induced by intraperitoneal injection with 8.8% L-arginine (Shanghai Yuanye Bio-Technology Co., Ltd, China). We administered Amuc_1098 (3 μg/mouse) in sterile phosphate buffer saline (PBS) supplemented with 2.5% glycerol to mice daily for a total of 20 days prior to molding. All mice were weighed daily and sacrificed one day after molding. Global TLR2-knockout (TLR2-KO) mice (B6.129-Tlr2 tm1Kir /J) on a C57BL/6 J background were purchased from The Jackson Laboratory (Bar Harbor, ME, USA). The mice were backcrossed to C57BL/6 J for at least ten generations. Genotypes of the mice were confirmed by PCR using tail genomic DNA. The primers used for genotyping were as follows: TLR2‑KO forward, 5’-ATAGGCCACATCTAGTTCAT-3’; TLR2‑KO reverse, 5’-CAGTATTCAAGACAAAACCC-3’. Both TLR2-KO mice and their wild-type (WT) C57BL/6 J littermates were housed under the same specific pathogen-free (SPF) conditions as described above. For histological assessment, pancreatic and colon tissues were fixed in 4% paraformaldehyde-PBS, dehydrated in a graded series of alcohol solutions, embedded in paraffin, and then sliced to a thickness of 4 μm. Thereafter, these sections were stained with hematoxylin & eosin (H&E) and observed under a light microscope (Zeiss, Germany). Edema, inflammation, hemorrhage, and necrosis were used to calculate the pancreatic histological score 46 . We deparaffinized the paraffin sections of pancreatic tissue, inactivated endogenous enzymes, and thermally repaired antigens before performing IHC. Following blocking and staining with F4/80 antibody, a secondary antibody was added along with Streptavidin Biotin Complex (Boster BioEngineering, China). A detailed description of antibodies for IHC analysis can be found in Supplementary Table  2 . Using Image-Pro-Plus software, we quantified the IHC and determined the mean density through integral optical density sum and area. All the staining of the tissues originated from six biologically independent mice. Sacrificed mice’s blood was centrifuged at 4 °C for 10 min at 4000 x  g after standing at room temperature for 2 h. As a result of this centrifugation, serum was collected as the supernatant. The serum amylase and lipase were measured using a Roche Cobas c 701/702 (Roche Diagnostics) as well as commercial kits (Roche, Switzerland). Serum amylase and lipase levels were measured using samples from six biologically independent mice, with two technical replicates performed for each sample. All cell lines were purchased from the Chinese Academy of Sciences (Shanghai, China) and grown in a humidified incubator at 37 °C with 5% CO 2 . Human colon tissue cell lines HT29 and Caco-2 were cultured in McCoy’s 5a Medium Modified containing 10% fetal bovine serum (FBS) and Eagle’s Minimum Essential Medium mixed with 20% FBS, respectively. HT29 and Caco-2 cells were sorted into Petri dishes and grown to 80–90% density. The cells were stimulated with 1 μg/mL LPS (Sigma-Aldrich, USA) for 24 h and then treated with Amuc_1098 (0.05/0.5/5/10 μg/mL) for another 72 h for subsequent experiments. Rat pancreatic exocrine cells (AR42J) and mouse pancreatic acinar cells (MPC-83) were cultured in Dulbecco’s Modified Eagle Medium containing 20% FBS and RPMI-1640 mixed with 10% FBS, respectively. AR42J cells and MPC-83 cells were stimulated with 10 mg/mL L-arginine (L-Arg) for 12 h and 100 nM caerulein (Cae) for 24 h, respectively. And then they were treated with Amuc_1098 (0.05/0.5/5/10 μg/mL) for another 72 h for subsequent experiments. Total RNA was isolated from cell lines or tissues using TRIzol (TIANGEN, China) according to the manufacturer’s instructions. HiScript II Q RT SuperMix (Vazyme, China) was used to reverse-transcribe collected RNA into cDNA. Then, qPCR was performed on cDNA using SYBRGreen (Vazyme, China) and LightCycler96 (Roche, Switzerland). The specific primers used for qPCR are listed in Supplementary Table  1 . GAPDH was used as an internal reference gene. The qPCR experiment was conducted with three biological replicates and two technical replicates per sample. Liquid nitrogen was used to cryogenically grind the cells and tissues and a RIPA buffer containing protease and phosphatase inhibitors was used to lyse the cells. The protein was separated by SDS-PAGE after heating at 100 °C for 10 min and then the protein on the gel was transferred to PVDF membranes which were placed in 5% bovine serum albumin for 2 h at room temperature to block non-specifically bound proteins. A primary antibody was incubated overnight at 4 °C and a secondary antibody for one hour at room temperature. In Supplementary Table  2 , all antibodies used are listed. Protein bands were developed using a chemiluminescence kit (Beyotime, China), and the density of bands was analyzed using Image Lab. Relative protein levels were standardized to an internal reference β-Tubulin or GAPDH. Results from the densitometric analysis of Western blotting reflect data from three independent biological replicates. The serum concentrations of TNF-α, IL-1β, IL-6, and MCP-1 were measured using commercially available ELISA kits (Dakewe, China). The assays were performed according to the manufacturer’s instructions. The culture supernatants were collected after 96 h and frozen at −80 °C for cytokine detection. The levels of TNF-α, IL-1β, IL-6, and MCP-1 were detected by ELISA. The biological replicate numbers for detecting inflammatory factors in mouse serum and cell culture supernatant were 6 and 3, respectively. Single-cell suspensions were prepared by dividing freshly harvested spleens and intestinal tract into pieces, grinding them, and filtering them through nylon mesh. After removal of erythrocytes, splenic mononuclear cells were resuspended in PBS containing 2% FBS and blocked with purified rat anti-mouse CD16/CD32 (BD Biosciences, USA) for 15 min at room temperature. Splenocytes were stained with Zombie NIR TM Fixable Viability Kit (BioLegend, USA) for 15 min at room temperature. For the analysis of macrophages, splenocytes were stained with FITC anti-mouse/human CD11b Antibody (BioLegend, USA), PE/Cyanine7 anti-mouse F4/80 Antibody (BioLegend, USA), PerCP/Cyanine5.5 anti-mouse Ly6C Antibody (BioLegend, USA), and PE/Dazzle™ 594 anti-mouse CD279 (PD-1) Antibody (BioLegend, USA) for 30 min at 4 °C without light. After washing twice with PBS, the cells were resuspended for analysis by flow cytometry. The data was analyzed by FlowJo_V10. The macrophage detection was conducted with six biological replicates. Stable HT29 and Caco-2 cells were sorted into 6-well plates containing cell slides. The cells were stimulated with 1 μg/mL LPS for 24 h and then treated with Amuc_1098 (0.05/0.5/5/10 μg/mL) for another 72 h. And then they were fixed with 4% paraformaldehyde. After washing with PBS, fixed cells were blocked with 5% bovine serum albumin (Sigma-Aldrich, USA) containing 0.3% Triton X-100 for 60 min. The slides were incubated with Claudin-1 (D5H1D) XP ® Rabbit mAb (dilution, 1:200), CD2AP Antibody (dilution, 1:200), Claudin-3 (D7A3O) Rabbit mAb (dilution, 1:200), and Occludin (E6B4R) Rabbit mAb (dilution, 1:200), respectively, overnight at 4 °C. The next day, cells were washed and incubated with secondary antibody (Cy3-conjugated anti-rabbit IgG) for 60 min at room temperature and the nuclear staining was done using DAPI. Finally, digital IF images were obtained using a THUNDER scanner (Leica Thunder, Germany). Immunofluorescence assays were performed with three biological replicates and three technical replicates. Human serum samples from 11 patients with AP (5 males and 6 females, aged 30–50 years) and 9 healthy controls (4 males and 5 females, aged 28–52 years) were obtained from Zhongda Hospital, Southeast University (Nanjing, China). All participants provided written informed consent, and the study protocol was approved by the Institutional Review Board of Zhongda Hospital (Approval No. 2025ZDSYLL060-Y01). Exclusion criteria for all participants included a history of diabetes, hypertension, coronary heart disease, or any other chronic inflammatory or metabolic disorders. All patients with AP were diagnosed according to the revised Atlanta classification, requiring at least two of the following three criteria: (i) typical abdominal pain, (ii) serum amylase or lipase levels ≥3 times the upper limit of normal, and (iii) imaging findings consistent with AP. Exclusion criteria were as follows: (i) age 48 h) at admission; (v) presence of other inflammatory or infectious diseases; (vi) use of antibiotics, probiotics, or immunosuppressive agents within the 4 weeks prior to enrollment; and (vii) refusal to provide informed consent. Serum metabolic profiling analysis was performed with a UHPLC-Q Exactive HF-X (ThermoFisher, USA). To extract metabolites, serum samples (100 μL) were mixed with 400 μL of acetonitrile-methanol 1:1 solution that contained 0.02 mg/mL of an internal standard (L-2-chlorophenylalanine). In order to precipitate the protein, the samples were vortexed for 30 s and low-temperature sonicated for 30 min (5 °C, 40 KHz). Following centrifugation for 15 minutes (4 °C, 13,000 x  g ), the supernatant was removed and blown dry under nitrogen. After resolving the sample with 100 μL of acetonitrile-water solution (1:1), the sample was ultrasonically extracted for 5 min at 5 °C (40 KHz), followed by centrifugation at 13,000 x  g and 4 °C for 10 min. For LC-MS/MS analysis, the supernatant was transferred to sample vials. An equal volume of all samples was mixed to prepare a pooled quality control sample (QC) as a part of system conditioning and quality control process. To monitor stability, periodic injections of QC samples were carried out at regular intervals (every 5–15 samples) along with the analytic samples. The mobile phases consisted of 0.1% formic acid in water: acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid in acetonitrile: isopropanol: water (47.5:47.5, v/v) (solvent B). The flow rate was 0.40 mL/min and the column temperature was 40°C. A Thermo UHPLC-Q Exactive HF-X mass spectrometer equipped with a positive and negative electrospray ionization source was used for collecting the mass spectrometric data. The optimal conditions were set as followed: source temperature at 425°C; sheath gas flow rate at 50 arb; Aux gas flow rate at 13 arb; ion-spray voltage floating (ISVF) at −3500V in negative mode and 3500 V in positive mode, respectively; Normalized collision energy, 20–40–60 V rolling for MS/MS. Full MS resolution was 60000, and MS/MS resolution was 7500. Data acquisition was performed with the Data Dependent Acquisition (DDA) mode. The detection was carried out over a mass range of 70–1050 m/z. The data were analyzed through the free online platform of majorbio cloud platform (cloud.majorbio.com). Metabolomic analysis was performed using serum samples from mice (n = 6 per group), healthy controls (n = 9), and patients with acute pancreatitis (n = 11). Microbial DNA was extracted from mouse cecal contents (n = 6 per group), followed by amplification of the 16S rRNA V3 hypervariable region. The purified amplicon sequences were conducted by Illumina MiSeq (PE300). The data were analyzed by Majorbio Bio-pharm Biotechnology ( www.i-sanger.com ). Using Mothur, sequences were grouped into operational taxonomic units (OTUs) which reached 97% nucleotide similarity level. Statistical analyses were performed on all independent biological data points; each sample was defined as an independent biological replicate. Data are shown as the mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test was used to test for differences among the three groups. Statistically significant results were defined as P values less than 0.05 ( P  < 0.05). Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

To examine the effects of Amuc_1098 on acute pancreatitis (AP) mice, we administered Amuc_1098 protein (3 μg) or PBS daily for 21 days. On the 21st day, the mice of AP group were injected intraperitoneally every hour (50 μg/kg) for a total of ten injections. The mouse model of AP was established by co-administering LPS (5 mg/kg) with the final intraperitoneal injection of caerulein. Mice in the control group received an equivalent volume of 0.9% normal saline via intraperitoneal injection. All mice were sacrificed after 1 day post injection (Fig.  1a ). The body weight of mice in the three groups (Control, AP, AP+Amuc_1098) showed no significant differences (Fig.  1b ). However, AP mice exhibited increased weight in their pancreas, which were reduced by oral administration of Amuc_1098 (Fig.  1c ). To investigate the effects of Amuc_1098 on the pancreas in AP mice, pancreas pathology was observed by H&E staining. The pancreas of AP mice displayed obvious edema, inflammation, hemorrhage, and necrosis, while Amuc_1098 gavage significantly decreased morphological damage in AP mice (Fig.  1d ), as evidenced by the histologic scores (Fig.  1e ). Moreover, AP mice exhibited significantly increased serum levels of amylase and lipase, which were reduced considerably by Amuc_1098 treatment (Fig.  1f, g ). To evaluate the degree of inflammation in AP, proinflammatory cytokines (TNF-α, IL-1β, IL-6) were detected through qPCR of the pancreatic tissues (Fig.  1h–j ). Results showed high levels of proinflammatory cytokines released in the AP group. However, oral supplementation of Amuc_1098 alleviated pancreatic inflammation. Furthermore, Amuc_1098 pretreatment suppressed the pancreatic infiltration of macrophages in AP mice (Fig.  1k, l ). Western blot analysis of the pancreatic tissues showed that relative protein levels of TLR2, IKKβ, IKBα, p-IKBα, p65, and p-p65 increased in the AP group compared to the control group, while oral supplementation of Amuc_1098 reversed this phenomenon (Fig.  1m, n ). These results suggested that Amuc_1098 intervention could mitigate pancreatic inflammation in AP mice through NF-κB pathway. In addition, in L-Arg-induced AP mouse models (Supplementary Fig.  2a ), we observed that both Amuc_1098 intervention and TLR2 knockdown significantly attenuated weight loss (Supplementary Fig.  2b ), ameliorated pancreatic pathological damage (Supplementary Fig.  2c ), reduced inflammatory infiltration (Supplementary Fig.  2d–f ), and inhibited the NF-κB signaling pathway (Supplementary Fig.  2g–l ) in mice. To further investigate the role of TLR2 in Amuc_1098-mediated protection against acute pancreatitis, we employed global TLR2-knockout (TLR2-KO) mice. Although the therapeutic efficacy of Amuc_1098 was markedly reduced in these mice (Supplementary Fig.  3a–g ), it still suppressed the protein expression of phospho-p65 (p-p65) in the pancreas (Supplementary Fig.  3h ). Overall, the finding demonstrated that oral administration of Amuc_1098 markedly alleviated the severity of AP, and underscore a crucial role for TLR2 in mediating this protective effect. Fig. 1 Amuc_1098 treatment protected against pancreatic injury of AP mice. a A schematic diagram of experimental design among Control, AP, and AP+Amuc_1098. Acute pancreatitis was induced in the mice by intraperitoneal injection with caerulein (50 μg/kg body weight, 10 times at 1-h intervals) and lipopolysaccharide (LPS, 5 mg/kg body weight, at the same time as the last injection of caerulein), both of which were dissolved in sterile normal saline (NS). We administered Amuc_1098 (3 μg/mouse) in sterile phosphate buffer saline (PBS) supplemented with 2.5% glycerol to mice daily for a total of 20 days prior to caerulein treatment. b Body weight. c Pancreas weight. d Representative histopathological sections with H&E staining of pancreatic tissue from mice. All images were amplification 400. e Pathological scores of pancreases among control, AP, and AP+Amuc_1098 groups. The levels of amylase ( f ) and lipase ( g ) in the serum. The mRNA expression of proinflammatory cytokines in the pancreas, including TNF-α ( h ), IL-1β ( i ), and IL-6 ( j ). k IHC staining of F4/80 in the pancreatic tissue. Each image was amplification 200. l The quantitation of F4/80 in the pancreas of mice. Verification of protein expression ( m ) and the grayscale values ( n ) of NF-κB signaling pathway by Western blot. β-tubulin was used as an internal standard. Data from one representative experiment (of three independent experiments with consistent results) are shown as the mean ± SEM, with n = 6 biologically independent mice per group and 2–3 technical replicates per sample. Significance was determined by one-way ANOVA and Tukey’s multiple comparisons test. a A schematic diagram of experimental design among Control, AP, and AP+Amuc_1098. Acute pancreatitis was induced in the mice by intraperitoneal injection with caerulein (50 μg/kg body weight, 10 times at 1-h intervals) and lipopolysaccharide (LPS, 5 mg/kg body weight, at the same time as the last injection of caerulein), both of which were dissolved in sterile normal saline (NS). We administered Amuc_1098 (3 μg/mouse) in sterile phosphate buffer saline (PBS) supplemented with 2.5% glycerol to mice daily for a total of 20 days prior to caerulein treatment. b Body weight. c Pancreas weight. d Representative histopathological sections with H&E staining of pancreatic tissue from mice. All images were amplification 400. e Pathological scores of pancreases among control, AP, and AP+Amuc_1098 groups. The levels of amylase ( f ) and lipase ( g ) in the serum. The mRNA expression of proinflammatory cytokines in the pancreas, including TNF-α ( h ), IL-1β ( i ), and IL-6 ( j ). k IHC staining of F4/80 in the pancreatic tissue. Each image was amplification 200. l The quantitation of F4/80 in the pancreas of mice. Verification of protein expression ( m ) and the grayscale values ( n ) of NF-κB signaling pathway by Western blot. β-tubulin was used as an internal standard. Data from one representative experiment (of three independent experiments with consistent results) are shown as the mean ± SEM, with n = 6 biologically independent mice per group and 2–3 technical replicates per sample. Significance was determined by one-way ANOVA and Tukey’s multiple comparisons test. To establish an acinar cell injury model in vitro, AR42J cells were stimulated using L-Arg. AR42J cells were exposed to L-Arg (10 mg/mL) for 12 h and then treated with several doses of Amuc_1098 * (0.05, 0.5, 5, 10 μg/mL) for 72 h. The production of inflammatory cytokines was detected by ELISA or qPCR. The results showed that L-Arg increased the secretion of pro-inflammatory factors (TNF-α, IL-1β, IL-6, MCP-1) in the AR42J culture supernatant, while administration of Amuc_1098 * decreased the production in a dose-dependent manner (Supplementary Fig.  4a–d ). The data of qPCR further confirmed that Amuc_1098 * reduced the expression of pro-inflammatory gene in vitro (Supplementary Fig.  4e ). Additionally, Amuc_1098 * declined the protein expression of IKKβ, IKBα, p-IKBα, p65, and p-p65 induced by L-Arg in AR42J cells (Supplementary Fig.  4f ). A cellular model for AP was developed using caerulein-treated MPC-83 cells 26 . The inflammatory cytokines secreted by MPC-83 cells were measured after stimulation with 100 nM caerulein for 24 h, followed by treatment with Amuc_1098 for 72 h. Amuc_1098 decreased the production of TNF-α, IL-1β, IL-6, and MCP-1 (Fig.  2a–d ). The results of qPCR were consistent with those of ELISA (Fig.  2e ). Caerulein positively regulated the protein expression of IKKβ, IKBα, p-IKBα, p65, and p-p65, while Amuc_1098 revised this phenomenon (Fig.  2f ). We overexpressed TLR2 in MPC-83 cells using the TLR2 agonist Pam3CSK4 and observed that the expression levels of TLR2, IKKβ, IKBα, p-IKBα, p65, and p-p65 were significantly downregulated as the concentration of Amuc_1098 increased (Fig.  2g , Supplementary Fig.  5a–f ). To further verify the role of Amuc_1098 in alleviating AP by TLR2, we employed TLR2-siRNA to suppress the expression of TLR2 in MPC-83 cells. The results demonstrated that as the concentration of Amuc_1098 increased, the expression levels of TLR2, IKKα, p-IKKα, p65, and p-p65 were significantly downregulated (Fig.  2h , Supplementary Fig.  5g–k ). Collectively, these results demonstrate that Amuc_1098 modulates the NF-κB pathway in a TLR2-partially-dependent manner to alleviate inflammation in AP cellular models. Fig. 2 Amuc_1098 inhibited the release of inflammatory cytokines from pancreatic cells in vitro. To construct the AP cell model, MPC-83 cells were treated with 100 nM caerulein for 24 h. And then the cells were exposed to Amuc_1098 ranging from 0.05 to 10 μg/mL for 72 h. The production of TNF-α ( a ), IL-1β ( b ), IL-6 ( c ), MCP-1 ( d ) in MPC-83 cell culture supernatant. e The mRNA expression of proinflammatory cytokines in MPC-83 cells, including TNF-α, IL-1β, IL-6. f Western blot analyses of NF-κB signal pathway in MPC-83 cells. g The alterations in the NF-κB signal pathway with TLR2 agonist and Amuc_1098 treatment. h The NF-κB signal pathway regulated by TLR2-siRNA and Amuc_1098. Data are presented as the mean ± SEM (n = 3 biologically independent samples per group). Significance was analyzed using one-way ANOVA and Tukey’s multiple comparisons test. To construct the AP cell model, MPC-83 cells were treated with 100 nM caerulein for 24 h. And then the cells were exposed to Amuc_1098 ranging from 0.05 to 10 μg/mL for 72 h. The production of TNF-α ( a ), IL-1β ( b ), IL-6 ( c ), MCP-1 ( d ) in MPC-83 cell culture supernatant. e The mRNA expression of proinflammatory cytokines in MPC-83 cells, including TNF-α, IL-1β, IL-6. f Western blot analyses of NF-κB signal pathway in MPC-83 cells. g The alterations in the NF-κB signal pathway with TLR2 agonist and Amuc_1098 treatment. h The NF-κB signal pathway regulated by TLR2-siRNA and Amuc_1098. Data are presented as the mean ± SEM (n = 3 biologically independent samples per group). Significance was analyzed using one-way ANOVA and Tukey’s multiple comparisons test. To investigate the effects of Amuc_1098 treatment on systemic inflammation in AP mice, spleen and serum indices were measured. Results indicated that spleen weights were heavier in AP mice than control mice, but oral supplementation with Amuc_1098 alleviated splenomegaly in AP mice (Fig.  3a ). The serum concentrations of inflammatory factors, including TNF-α, IL-1β, IL-6, and MCP-1, were increased in AP mice, while they were decreased in mice pretreated with Amuc_1098 (Fig.  3b–e ). qPCR analyses of spleen from AP mice after Amuc_1098 administration revealed significant downregulation of TNF-α, IL-1β, and IL-6 (Fig.  3f–h ). Infiltration of macrophages has increased the gene expression of proinflammatory cytokines such as TNF-α, IL-1β, and IL-6 27 . We found that the percentage of macrophages was significantly increased in the spleens of AP mice, while Amuc_1098 treatment significantly decreased this percentage (Fig.  3i ). However, the proportion of PD-1 + macrophages increased in the spleen upon Amuc_1098 supplementation (Fig.  3j ). Additionally, the proportion of Ly6C + macrophages was significantly elevated in the AP group compared to the control group, and Amuc_1098 treatment inhibited this elevation (Fig.  3k ). However, oral administration of Amuc_1098 upregulated the expression of PD-1 on Ly6C + macrophages in the spleen (Fig.  3l ). Western blot analysis of the spleen demonstrated that relative protein levels of TLR2, IKKα, IKKβ, p-IKBα/β, IKBα, p-IKBα, and p-p65 increased in the AP group compared to the control group, whereas Amuc_1098 treatment reduced these increases (Fig.  3m ). Moreover, in the L-Arg-induced AP mouse model, we observed that Amuc_1098 intervention and TLR2 knockdown did not affect the spleen weight of AP mice (Supplementary Fig.  6a ). However, these interventions significantly reduced the expression of pro-inflammatory cytokines TNF-α, IL-1β, and IL-6 in the spleen (Supplementary Fig.  6b–d ) and inhibited the expression of proteins IKKβ, IKBα, p-IKBα, p65, and p-p65 involved in the NF-κB signaling pathway (Supplementary Fig.  6e–j ). To further validate the role of TLR2 in Amuc_1098-mediated mitigation of splenic inflammation during acute pancreatitis, we established an AP model in global TLR2-KO mice. The results showed that the anti-inflammatory effect of Amuc_1098 was significantly attenuated in these mice (Supplementary Fig.  7a–h ); nevertheless, Amuc_1098 still suppressed the expression of p-p65 in the spleen (Supplementary Fig.  7i ). Taken together, our results demonstrate that oral administration of Amuc_1098 markedly attenuated systemic inflammation in AP mice, and that TLR2 plays a crucial role in mediating its spleen-protective anti-inflammatory effects. Fig. 3 Amuc_1098 intervention in AP mice could improve systemic inflammation. a Spleen weight. The levels of TNF-α ( b ), IL-1β ( c ), IL-6 ( d ), MCP-1 ( e ) in serum of mice. The mRNA expression of TNF-α ( f ), IL-1β ( g ), and IL-6 ( h ) in the spleen. FCM analysis and quantification of macrophage (CD11b + F4/80 + ) ( i ), PD-1 + macrophages ( j ), Ly6c + macrophages ( k ), and PD-1 + Ly6c + macrophages ( l ) in the spleen. m Immunoblot assay of the expression of the NF-κB signaling with antibodies specific for IKKα, IKKβ, p-IKKα/β, IKBα, p-IKBα, p65, and p-p65. β-Tubulin was employed as an internal control. Data from one representative experiment (of three performed) are shown as the mean ± SEM (n = 6 mice per group, assayed in 2–3 technical replicates). Significance was determined by one-way ANOVA and Tukey’s multiple comparisons test. a Spleen weight. The levels of TNF-α ( b ), IL-1β ( c ), IL-6 ( d ), MCP-1 ( e ) in serum of mice. The mRNA expression of TNF-α ( f ), IL-1β ( g ), and IL-6 ( h ) in the spleen. FCM analysis and quantification of macrophage (CD11b + F4/80 + ) ( i ), PD-1 + macrophages ( j ), Ly6c + macrophages ( k ), and PD-1 + Ly6c + macrophages ( l ) in the spleen. m Immunoblot assay of the expression of the NF-κB signaling with antibodies specific for IKKα, IKKβ, p-IKKα/β, IKBα, p-IKBα, p65, and p-p65. β-Tubulin was employed as an internal control. Data from one representative experiment (of three performed) are shown as the mean ± SEM (n = 6 mice per group, assayed in 2–3 technical replicates). Significance was determined by one-way ANOVA and Tukey’s multiple comparisons test. To research the effect of Amuc_1098 on intestinal inflammation, we isolated intestinal lamina propria lymphocytes and subsequently analyzed them using flow cytometry. Our findings revealed that treatment with Amuc_1098 significantly decreased the proportion of intestinal macrophages in AP mice (Fig.  4a ). Compared with the control group, AP mice showed severe disruption of the small intestinal mucosal barrier structure, which was significantly ameliorated by Amuc_1098 intervention (Fig.  4b ). Simultaneously, we found that TLR2 expression was markedly increased in the colon tissue of AP mice, and this increase was reversed by Amuc_1098 treatment (Fig.  4c ). The protein expression levels of ZO-2, Occludin, Claudin-1, and Claudin-3 were assessed in colon tissues due to the strong correlation of tight junction proteins with maintenance of the intestinal mucosal barrier. The results obtained from western blotting demonstrated that the expression of tight junction proteins (ZO-2/Occludin/Claudin-1/Claudin-3) in AP mice exhibited reduced expression level compared to control; however, the expression of tight junction proteins in AP mice was significantly increased after Amuc_1098 pretreatment. Furthermore, colonic TLR2 expression was elevated in AP mice but was markedly reduced following Amuc_1098 treatment (Fig.  4d ). Consistent with the critical role of TLR2, assessment of colonic tight junction proteins (CD2AP, Occludin, and Claudin-1) in TLR2-KO mice revealed that Amuc_1098 treatment failed to upregulate their expression compared to the AP group (Supplementary Fig.  8 ). These results indicate that the protective effect of Amuc_1098 on intestinal barrier integrity is TLR2-dependent. To construct the intestinal injury cell model, HT29 and Caco-2 cells were treated with 1 μg/mL LPS for 24 h. And then the cells were exposed to Amuc_1098 ranging from 0.05 to 10 μg/mL for 72 h. Western blot (Fig.  4e ) and immunofluorescence staining (Fig.  4f ) showed that Amuc_1098 could enhance the expression of tight junction proteins (ZO-3/CD2AP/Claudin-1/Claudin-3/Occludin) in HT29 cells. We overexpressed TLR2 in HT29 cells by treating them with the TLR2 agonist Pam3CSK4 and observed that the expression level of TLR2 was significantly upregulated. However, as the concentration of Amuc_1098 increased, the expression levels of TLR2, ZO-1, ZO-2, ZO-3, CD2AP, Claudin-1, and Occludin were upregulated (Fig.  4g ). Subsequently, we specifically suppressed the expression of TLR2 in HT29 cells using TLR2-specific siRNA, which led to a significant increase in the expression of tight junction signaling pathway proteins (ZO-1/ZO-2/ZO-3/CD2AP/Claudin-1/Occludin) as the concentration of Amuc_1098 increased (Fig.  4h ). We then investigated the changes of other intestinal epithelial cells during AP. Caco-2 cells exposed to Amuc_1098 following LPS exhibited remarkable increased expression of intestinal tight junction proteins (CD2AP/Claudin-1/Claudin-3/Occludin) by western blot (Supplementary Fig.  9a ) and IF staining (Supplementary Fig.  9b ). The overexpression (Supplementary Fig.  9c ) and knockdown (Supplementary Fig.  9d ) assay of TLR2 in Caco-2 cells further confirmed that Amuc_1098 may up-regulate the expression of tight junction proteins regardless of TLR2 overexpression and knockdown. However, in Caco-2 cells overexpressing TLR2, treatment with a high concentration of Amuc_1098 paradoxically reduced Occludin protein levels. This effect may be attributed to off-target activity or the activation of alternative negative feedback pathways under high-dose conditions. The observed discrepancies between in vivo mouse studies and in vitro cellular assays suggest that Amuc_1098 may modulate intestinal barrier integrity through distinct mechanisms in each context. Specifically, the TLR2-dependent effects observed in vivo could involve contributions from additional cell types or systemic factors that are not recapitulated in conventional cell culture systems. These findings imply that Amuc_1098 may alleviate intestinal inflammation by reducing macrophage infiltration and mitigate intestinal permeability through modulation of the tight junction pathway in AP mice, in which TLR2 plays an important regulatory role. Fig. 4 Supplementation with Amuc_1098 improved intestinal permeability. a Contour analysis of the percentage of CD11b + F4/80 + cells in CD45 + lymphocytes and quantification of macrophages. b Representative histological images of small intestinal tissues stained with H&E (original magnification, ×400). c The gene expression of TLR2 in colon. The expression of tight junction proteins was detected using WB in the colon ( d ). To construct the intestinal injury cell model, HT29 were treated with 1 μg/mL LPS for 24 h. And then the cells were exposed to Amuc_1098 ranging from 0.05 to 10 μg/mL for 72 h. e The protein levels of TLR2 and tight junction in HT29 cells. f The expression of Claudin-1, Occludin and Claudin-3 in HT29 cells assessed by IF (original magnification, ×200). Blue, DAPI; Red, Claudin-1 or Occludin or Claudin-3. g The alterations in the tight junction signal pathway with TLR2 agonist and Amuc_1098 treatment. h The tight junction signal pathway regulated by TLR2-siRNA and Amuc_1098. Data are expressed as the mean ± SEM, derived from six biologically independent mice per group (in vivo) or three biologically independent cell culture replicates per group (in vitro). Significance was analyzed using one-way ANOVA and Tukey’s multiple comparisons test. a Contour analysis of the percentage of CD11b + F4/80 + cells in CD45 + lymphocytes and quantification of macrophages. b Representative histological images of small intestinal tissues stained with H&E (original magnification, ×400). c The gene expression of TLR2 in colon. The expression of tight junction proteins was detected using WB in the colon ( d ). To construct the intestinal injury cell model, HT29 were treated with 1 μg/mL LPS for 24 h. And then the cells were exposed to Amuc_1098 ranging from 0.05 to 10 μg/mL for 72 h. e The protein levels of TLR2 and tight junction in HT29 cells. f The expression of Claudin-1, Occludin and Claudin-3 in HT29 cells assessed by IF (original magnification, ×200). Blue, DAPI; Red, Claudin-1 or Occludin or Claudin-3. g The alterations in the tight junction signal pathway with TLR2 agonist and Amuc_1098 treatment. h The tight junction signal pathway regulated by TLR2-siRNA and Amuc_1098. Data are expressed as the mean ± SEM, derived from six biologically independent mice per group (in vivo) or three biologically independent cell culture replicates per group (in vitro). Significance was analyzed using one-way ANOVA and Tukey’s multiple comparisons test. OPLS-DA showed clear differences between healthy controls and AP patients (Fig.  5a ). R2Y and Q2 are used to evaluate the interpretation ability and prediction ability of OPLS-DA model, respectively. The larger the cumulative value of R2Y and Q2, the more stable and reliable the model (Supplementary Fig.  10a ). The abscissa represents the permutation reservation of permutation test. The ordinate represents the values of the substitution tests for R2 (the red dot) and Q2 (the blue triangle), and the two dotted lines represent the regression lines for R2 and Q2 (Supplementary Fig.  10b ). Volcano plot of serum metabolomes identified 1147 differential metabolites (113 increased, 326 decreased) in patients with AP compared with healthy controls (Fig.  5b ). All metabolites were assigned to the human metabolome database (HMDB) which were matched and classified into 12 HMDB super classes. Of these, 246 metabolites were included in the “lipids and lipid-like molecules” term (Fig.  5c ). Within the metabolome, AP samples were largely defined by the depletion of metabolites, with >80% of the identified signature being metabolites present at higher abundances in healthy samples. Pathway analysis revealed that glycerophospholipid metabolism, primary bile acid biosynthesis, steroid hormone biosynthesis, and so on were enriched (Fig.  5d ). To further investigate the differences in lipid metabolism between acute pancreatitis (AP) patients and healthy controls, we quantified lipid metabolites in serum samples. Our analysis revealed significant discrepancies between the control group and the AP group, suggesting that lipid metabolism in AP patients is substantially disrupted (Fig.  5e ). The chord diagram illustrated substantial associations among lipid metabolites in serum, emphasizing distinct variations between AP patients and healthy controls (Supplementary Fig.  10c ). Probing individual lipid species affected by AP, the bubble map visualization highlighted all lipid species exhibiting significant changes. A total of 510 lipid species in AP patients demonstrated significant alterations when conditioned on both P-values and fold change (FC). These findings indicate substantial modifications in the composition and content of lipid species in AP patients relative to healthy controls (Supplementary Fig.  10d ). Volcano plot analysis of serum lipid metabolomes identified 245 differential metabolites (57 up-regulated and 188 down-regulated) in patients with AP compared with healthy controls (Fig.  5f ). According to lipid map, lipid metabolites can be categorized into eight major classes: Fatty Acids (FA), Glycerolipids (GL), Glycerophospholipids (GP), Sphingolipids (SP), Sterol Lipids (ST), Prenol Lipids (PR), Saccharolipids (SL), and Polyketides (PK). Among the various lipid metabolites identified, GP, SP, GL, FA, and ST accounted for 61.18%, 21.37%, 14.12%, 1.96%, and 1.37%, respectively (Fig.  5g ). Based on the VIP scores and P-values derived from multivariate statistical analysis, 30 lipids exhibiting significant differences were identified between the AP group and the Control group (Fig.  5h ). KEGG enrichment analysis of serum lipid metabolites between the AP group and the Control group revealed that glycerophospholipid metabolism was the most significantly differentiated pathway (Supplementary Fig.  10e ). Fig. 5 Metabolic profiling of serum from patients with AP by untargeted LC-MS metabolomics analysis and lipidomics. a OPLS-DA score between healthy control and AP patients by untargeted LC-MS metabolomics analysis. b Volcano plot showing differentially abundant metabolites between control and AP patients. The x-axis represents the log₂ fold change, and the y-axis represents the statistical significance (−log₁₀ P value). Metabolites with |log₂FC | ≥ 1 and P < 0.05 (two-tailed unpaired Student’s t test) were considered significantly altered and are highlighted in red (upregulated) or blue (downregulated). Gray dots indicate metabolites with no significant difference. c Pie chart of classification of metabolites in serum between AP patients and healthy control according to the HMDB Compound Classification. d KEGG enrichment analysis of differentially expressed metabolites between AP patients and healthy control. The x-axis represents the Rich factor (the ratio of the number of metabolites annotated to a pathway to the total number of metabolites in that pathway). The y-axis lists enriched pathways. Dot size indicates the number of metabolites enriched in each pathway, and dot color represents the P -value (Fisher’s exact test, two-sided) with redder colors indicating more significant enrichment. P -values were adjusted for multiple testing using the Benjamini–Hochberg procedure to control the false discovery rate (FDR). Pathways with FDR < 0.05 were considered significantly enriched. OPLS-DA score ( e ) and Volcano Plot ( f ) between healthy control and AP patients by lipidomics. g Pie chart of classification of lipid metabolites in serum. h Heatmap of glycerophospholipid metabolism between healthy control and AP patients. Each row represents a metabolite, and each column represents an individual sample. The color scale indicates the relative abundance of metabolites after Z-score normalization (red: high abundance; blue: low abundance). Clustering was performed using Euclidean distance and Ward’s linkage method. Metabolite selection was based on |log₂FC | ≥ 1 and P  < 0.05 (two-tailed unpaired Student’s t test).The BH method was used to correct the P value. When the corrected P value is less than 0.05, the enrichment was considered to be significant in this pathway. a OPLS-DA score between healthy control and AP patients by untargeted LC-MS metabolomics analysis. b Volcano plot showing differentially abundant metabolites between control and AP patients. The x-axis represents the log₂ fold change, and the y-axis represents the statistical significance (−log₁₀ P value). Metabolites with |log₂FC | ≥ 1 and P < 0.05 (two-tailed unpaired Student’s t test) were considered significantly altered and are highlighted in red (upregulated) or blue (downregulated). Gray dots indicate metabolites with no significant difference. c Pie chart of classification of metabolites in serum between AP patients and healthy control according to the HMDB Compound Classification. d KEGG enrichment analysis of differentially expressed metabolites between AP patients and healthy control. The x-axis represents the Rich factor (the ratio of the number of metabolites annotated to a pathway to the total number of metabolites in that pathway). The y-axis lists enriched pathways. Dot size indicates the number of metabolites enriched in each pathway, and dot color represents the P -value (Fisher’s exact test, two-sided) with redder colors indicating more significant enrichment. P -values were adjusted for multiple testing using the Benjamini–Hochberg procedure to control the false discovery rate (FDR). Pathways with FDR < 0.05 were considered significantly enriched. OPLS-DA score ( e ) and Volcano Plot ( f ) between healthy control and AP patients by lipidomics. g Pie chart of classification of lipid metabolites in serum. h Heatmap of glycerophospholipid metabolism between healthy control and AP patients. Each row represents a metabolite, and each column represents an individual sample. The color scale indicates the relative abundance of metabolites after Z-score normalization (red: high abundance; blue: low abundance). Clustering was performed using Euclidean distance and Ward’s linkage method. Metabolite selection was based on |log₂FC | ≥ 1 and P  < 0.05 (two-tailed unpaired Student’s t test).The BH method was used to correct the P value. When the corrected P value is less than 0.05, the enrichment was considered to be significant in this pathway. To explore whether Amuc_1098 ameliorates acute pancreatitis through glycerophospholipid metabolism, we used spatial metabolomics to identify candidate pathways and metabolites altered in specific pancreas locations. Partial least squares discrimination analysis (PLS-DA) showed a good difference between the AP mice and the Control mice (Supplementary Fig.  11a ). Similarly, in the AP and Amuc_1098 intervention groups, significant differences were observed in the metabolite profiles of pancreatic tissue between the two groups (Supplementary Fig.  11b ). Volcano plot highlighting metabolites differentially abundant between AP and control mice (Supplementary Fig.  11c ). Compared to the AP group, the intervention with Amuc_1098 resulted in a decrease in the content of 168 metabolites and an increase in 108 metabolites within the pancreatic tissue of mice (Supplementary Fig.  11d ). All metabolites were assigned to the HMDB which were matched and classified into 21 HMDB classes. Among them, the proportion of GP metabolites ranked second (Fig.  6a ). The results of the KEGG enrichment analysis further confirmed that glycerophospholipid metabolism plays a crucial role in the pancreatic metabolic pathway of AP mice treated with Amuc_1098 (Fig.  6b ). Subsequently, we analyzed the serum metabolites of mice in the Control group, the AP group, and the Amuc_1098-treated AP group. PLS-DA analysis revealed apparent differences among the control, AP, and AP + Amuc_1098 groups (Supplementary Fig.  11e ). In AP mice, we identified 14 differential metabolites in serum. After Amuc_1098 treatment, serum metabolomics showed 39 differential metabolites (Supplementary Fig.  11f ). The HMDB compound classification of 179 identified metabolites. According to the number of metabolites, the name of selected HMDB hierarchy (superclass) and the percentage of metabolites are displayed from high to low. Different colors in each pie chart represent different HMDB classification, and the area represents the relative proportion of metabolites in the classification. The proportion of lipids and lipid-like molecules in the serum of the three groups (Control, AP, AP+Amuc_1098) is the most in the superclass (Supplementary Fig.  11g , Fig.  6c ). KEGG enrichment analysis showed that glycerophospholipid metabolism, serotonergic synapse, steroid hormone biosynthesis and so on were enriched (Fig.  6d ). The data confirmed that glycerophospholipid metabolism was the only enriched pathway with significant differences in serum of AP patients and mice. Specifically, PC (15:0/0:0) and 2-steroylglycerophosphoglycerol had higher abundance in AP mice than controls. However, Amuc_1098 supplementation decreased the serum levels of PC (15:0/0:0) and 2-steroylglycerophosphoglycerol in AP mice (Supplementary Fig.  11h–i ). To elucidate the role of Amuc_1098 in glycerophospholipid metabolism in AP mice, a comprehensive lipidomics analysis of mouse serum was conducted. The results demonstrated that, compared with the control group, 252 lipid metabolites were upregulated and 142 lipid metabolites were downregulated in AP mice (Supplementary Fig.  11j ). In comparison to the AP group, 41 lipid metabolites were upregulated and 70 lipid metabolites were downregulated in the Amuc_1098 intervention group (Supplementary Fig.  11k ). KEGG topological analysis revealed that glycerol phospholipid metabolism exhibited the most significant difference among the three groups (Supplementary Fig.  11l–m ). The lipid radar map illustrated the alterations in serum lipid content among the three groups of mice (Supplementary Fig.  11n ). Serum lipid metabolites in control group, AP group and Amuc_1098 treated AP group showed significant changes in GL, GP, and SP. Amuc_1098 significantly reduced the proportions of CL (23:0/16:0/16:0/18:1) and PE (28:0/20:4) in the serum glycerophospholipid metabolism of AP mice, while increasing the abundance of dMePE (16:1/20:4), MePC (8:1e/18:4), and PIP2 (18:3e/19:0) (Fig.  6e ). These results demonstrate that Amuc_1098 could significantly improve lipid metabolism in AP mice, particularly exerting a substantial influence on glycerophospholipid metabolism. Fig. 6 Effects of Amuc_1098 on serum metabolic profiling in mice with AP by lipidomics and spatial metabolome. a Pie chart of classification of metabolites in pancreas of AP mice according to the HMDB Compound Classification. b KEGG enrichment analysis of differentially expressed metabolites by spatial metabolome. c Classify total serum metabolites into superclass between AP+Amuc_1098 and AP group. d KEGG enrichment analysis of differentially expressed serum metabolites between AP+Amuc_1098 and AP group. The bubble plot displays enriched metabolic pathways, with the Rich factor on the x-axis and pathway names on the y-axis. Dot size corresponds to the number of metabolites annotated to each pathway, and dot color reflects the statistical significance determined by two-sided Fisher’s exact test. P -values were adjusted for multiple comparisons using the Benjamini–Hochberg method. Pathways with adjusted P  < 0.05 were considered significantly enriched. e The relative abundance of glycerophospholipid metabolism of serum among the three groups. Data are presented as the mean ± SEM (n = 6 biologically independent mice per group). Significance was determined by one-way ANOVA and Tukey’s multiple comparisons test. a Pie chart of classification of metabolites in pancreas of AP mice according to the HMDB Compound Classification. b KEGG enrichment analysis of differentially expressed metabolites by spatial metabolome. c Classify total serum metabolites into superclass between AP+Amuc_1098 and AP group. d KEGG enrichment analysis of differentially expressed serum metabolites between AP+Amuc_1098 and AP group. The bubble plot displays enriched metabolic pathways, with the Rich factor on the x-axis and pathway names on the y-axis. Dot size corresponds to the number of metabolites annotated to each pathway, and dot color reflects the statistical significance determined by two-sided Fisher’s exact test. P -values were adjusted for multiple comparisons using the Benjamini–Hochberg method. Pathways with adjusted P  < 0.05 were considered significantly enriched. e The relative abundance of glycerophospholipid metabolism of serum among the three groups. Data are presented as the mean ± SEM (n = 6 biologically independent mice per group). Significance was determined by one-way ANOVA and Tukey’s multiple comparisons test. To explore the relationship between gut microbiota and the progression of AP and the effect of Amuc_1098 treatment, we analyzed the abundance and composition of intestinal flora in cecal contents among the control, AP, and AP+Amuc_1098 groups using 16S rRNA sequencing. PLS-DA analysis of sequencing data shows clear separation of community composition among the groups (Fig.  7a ). The rank-abundance curves characterize species abundance and species uniformity (Supplementary Fig.  12a ). Different samples are represented by curves of different colors. The pan analysis is used to observe the increase of the total number of OTUs (Supplementary Fig.  12b ), whereas the core analysis represented the decrease in the number of OUTs (Supplementary Fig.  12c ). The community richness, based on sobs, chao, ace, Shannon, simpson and coverage indices, was not significantly altered between AP and AP+Amuc_1098 group (Supplementary Fig.  12d–i ). The rarefaction curves indicated near complete sampling of the community (Supplementary Fig.  12j ). Beta diversity index for bacterial communities declined in AP mice compared with control, but it increased after Amuc_1098 supplementation (Supplementary Fig.  12k ). We analyzed the relative abundances of phyla in microbial communities among the three groups to identify differences. The results showed that the abundance of Proteobacteria, Campilobacterota, Verrucomicrobiota, and Deferribacterota increased in mice with AP in comparison to controls, while it tended to decrease in AP mice after Amuc_1098 treatment (Fig.  7b, c ). At the genus level, heatmap and bar plot diagrams revealed the abundance of Colidextribacter , Eubacterium coprostanoligenes , Desulfovibrionaceae , and Enterococcus increased dramatically in AP mice compared to controls, while the proportion of them was reduced in Amuc_1098-treated mice relative to AP mice (Fig.  7d, e ). These findings suggest that Amuc_1098 treatment improved gut dysbiosis in the mice with AP induction. Fig. 7 Bacterial abundance and community composition differences across control, AP, and AP+Amuc_1098 groups. 16S rRNA gene sequencing was performed on cecal contents obtained from mice to analyze the abundance and microbiome composition. a Partial Least-Squares Discriminant Analysis (PLS-DA) among control, AP and AP+Amuc_1098 groups. b The community bar plot analysis of intestinal microbiota at phylum level. c Comparison of the gut microbiota at the phylum level among control, AP, and AP+Amuc_1098 groups. Data are presented as mean ± SEM. Statistical significance was assessed using the Kruskal–Wallis H test (non-parametric one-way ANOVA) followed by Dunn’s post hoc test for pairwise comparisons. P -values from Dunn’s test were adjusted for multiple comparisons using the Bonferroni correction. Significance levels are indicated as P  < 0.05, * P  < 0.05, and ** P  < 0.01. d Community heatmap analysis on genus level. e Kruskal-Wallis H test bar plot of intestinal content at the genus level. 16S rRNA gene sequencing was performed on cecal contents obtained from mice to analyze the abundance and microbiome composition. a Partial Least-Squares Discriminant Analysis (PLS-DA) among control, AP and AP+Amuc_1098 groups. b The community bar plot analysis of intestinal microbiota at phylum level. c Comparison of the gut microbiota at the phylum level among control, AP, and AP+Amuc_1098 groups. Data are presented as mean ± SEM. Statistical significance was assessed using the Kruskal–Wallis H test (non-parametric one-way ANOVA) followed by Dunn’s post hoc test for pairwise comparisons. P -values from Dunn’s test were adjusted for multiple comparisons using the Bonferroni correction. Significance levels are indicated as P  < 0.05, * P  < 0.05, and ** P  < 0.01. d Community heatmap analysis on genus level. e Kruskal-Wallis H test bar plot of intestinal content at the genus level. We integrated both 16S rRNA and metabolomic data into multiomic features. The correlation analysis of 78 metabolites identified in the 6 enriched pathways mentioned above between altered (richness top 100) bacteria at the genus level revealed significant associations, and many of the involved genera were reduced in mice with AP. There were three genera were positively associated with the 2 metabolites involved in the glycerophospholipid metabolism pathway. Among these genera, Eubacterium coprostanoligenes , Desulfovibrionaceae , and Enterococcus were identified as significantly differential among the three groups. Network analysis based on the integration of 16S rRNA sequencing and metabolomic data using genera (top 100) and metabolites involved in the glycerophospholipid metabolism pathway, as described above, was performed to investigate the associations of the broader microbiome and AP-linked metabolites. Akkermansia muciniphila was positively correlated with PC (15:0/0:0), whereas it was negatively correlated with 2-steroylglycerophosphoglycerol level (Fig.  8 ). Fig. 8 AP-associated genera correlate with metabolites differentiating mice with AP and AP+Amuc_1098. The genera (top 100) identified as significantly differential between mice with AP and AP+Amuc_1098 and serum metabolites were included. The serum metabolites possess significant differences between the AP + Amuc_1098 and AP groups. Higher taxonomy of genera (phyla) and metabolites are indicated by color bars. The genera (top 100) identified as significantly differential between mice with AP and AP+Amuc_1098 and serum metabolites were included. The serum metabolites possess significant differences between the AP + Amuc_1098 and AP groups. Higher taxonomy of genera (phyla) and metabolites are indicated by color bars.

Acute pancreatitis is an acute gastrointestinal disorder characterized by increased systemic inflammation, bacterial translocation, and disrupted intestinal barrier. AP involved pancreatic inflammation caused by inflammation signaling pathways, oxidative stress, and apoptosis activation 28 . In the present experiment, Amuc_1098 exerted protective effects against AP risk by decreasing pathological scores and the activities of lipase in AP mice. Oral administration of Amuc_1098 also elevated anti-inflammatory capacity by reducing TNF-α, IL-1β, and IL-6 in vivo and in vitro. Several reports have shown that inhibiting NF-κB signaling pathway could ameliorate acute pancreatitis 29 , 30 . Our results are consistent with previous reports that Amuc_1098 intervention could exert its function by impacting protein levels in the NF-κB signaling pathway. The data suggest that Amuc_1098 attenuated inflammatory properties through inactivation of NF-κB signaling. In the canonical NF-κB signaling pathway, activation typically leads to phosphorylation and subsequent proteasomal degradation of IKBα, thereby allowing NF-κB nuclear translocation. However, in our study, we observed a reduction in IKBα levels in both pancreatic and splenic tissues of AP mice treated with Amuc_1098. This seemingly paradoxical decrease suggests that Amuc_1098 may activate unknown pathways that enhance either the resynthesis or stabilization of IKBα. This represents a limitation of the present study, and future investigations into the anti-inflammatory mechanisms of Amuc_1098 should aim to elucidate its regulatory network involving IKBα dynamics. Although our findings implicate TLR2 as a key mediator of Amuc_1098‑induced suppression of the NF‑κB pathway, results from TLR2 knockdown and knockout experiments suggest that the anti‑inflammatory effects of Amuc_1098 involve both TLR2‑dependent and TLR2‑independent mechanisms. The mechanism differs notably from that of another Akkermansia muciniphila outer-membrane protein, Amuc_1100, which exerts its anti-inflammatory action primarily via TLR2. Our findings thus broaden the understanding of the diverse anti-inflammatory strategies employed by Akkermansia muciniphila . These results indicate that Amuc_1098 is a potential drug for preventing AP progression. The immune responses during AP and recovery, including neutrophil infiltration, expansion of dendritic cells, and a substantial change in macrophage transcriptome due to both resident and macrophage activation and monocyte expansion 5 . Macrophages are vital to the regulation of immune responses and the development of inflammation by NF-κB signaling pathway which plays a central role in host defense and acute responses 31 , 32 . Amuc_1100 pretreatment decreased the inflammatory infiltration in the spleen of AP mice, accompanied by the reduction of Ly6C + macrophages 30 . In our study, we found that the percentage of macrophages increased in the spleen and pancreas of AP mice, while oral administration of Amuc_1098 significantly decreased this proportion. Moreover, Amuc_1098 reduced the number of Ly6C + macrophages in the spleen of AP mice, while the proportion of PD-1 in macrophages and Ly6C + macrophages increased. Ultimately, Amuc_1098 improved the population and function of macrophages in AP mice. Hence, our findings indicated that Amuc_1098 protect against AP by alleviating inflammation through NF-κB signaling pathway and modulating macrophage inflammation cascade. However, the ability of Amuc_1098 to suppress IKBα expression within the NF-κB pathway opens plausible avenues for future research into its anti-inflammatory mechanisms. Intestinal barrier injury often accompanied by intestinal mucosal barrier injury and led to serious consequences, which played a key role in the onset and development of AP 33 , 34 . Mucin and tight junction protein expression were increased by FMT, alleviating intestinal barrier injury and reducing intestinal permeability 35 . The tight junction protein indicators were tested include ZO-1, Occludin, Claudin-1 and so on 36 . However, there is no effective therapeutic method available for intestinal barrier dysfunction in AP. This study found that Amuc_1098 could restore the expression of tight junction protein (ZO-2, Occludin, Claudin-1, Claudin-3) to improve intestinal permeability via TLR2 in AP mice. Furthermore, analysis of colon tissues from TLR2-KO mice confirmed that the ability of Amuc_1098 to improve intestinal permeability in AP mice is largely TLR2-dependent. However, in TLR2-siRNA experiments, Amuc_1098 was still able to upregulate tight junction proteins despite TLR2 knockdown, indicating that its protective effects may be mediated, at least in part, via TLR2-independent mechanisms. This phenomenon could potentially be explained by Amuc_1098-mediated regulation of other signaling molecules or microbial components. Additionally, immunofluorescence (IF) and western blot (WB) analyses revealed that Amuc_1098 effectively restored tight junction protein expression in colonic cell lines. Interestingly, we observed a discrepancy between in vivo and in vitro results regarding the TLR2 dependency of tight junction regulation. While the protective effect of Amuc_1098 on colonic tight junction proteins was absent in TLR2-KO mice, suggesting an in vivo TLR2 dependency, the upregulation of these proteins by Amuc_1098 in isolated HT29 and Caco-2 cells was independent of TLR2 manipulation. This discrepancy might be attributable to the complex in vivo environment, where Amuc_1098 could act indirectly through other cell types (e.g., immune cells) or systemic factors that are not recapitulated in a monoculture of epithelial cells. Therefore, our interpretation that Amuc_1098 restores intestinal barrier integrity via TLR2 is primarily supported by our in vivo data, and the direct TLR2-dependent regulation on epithelial cells in vitro appears to be limited. Collectively, these observations offer a plausible perspective for investigating the Amuc_1098–tight junction axis. In summary, our results demonstrate that Amuc_1098, operating through the TLR2-modulated intestinal tight junction pathway, repairs intestinal barrier injury by restoring tight junction protein expression. This restoration reduces intestinal inflammation, improves intestinal function, and ultimately alleviates the progression of AP. Our findings thus provide mechanistic and therapeutic insights into intestinal barrier injury in AP mice, highlighting the interplay between inflammatory regulation and tight junction integrity. The severity of AP may be aggravated by early dysbiosis of the gut microbiota 16 . Through intestinal barrier disruption, local or systemic inflammation, bacterial translocation, and the regulation of microbial metabolites, dysbiosis of gut microbiota influences the etiology 15 . As compared to healthy volunteers, acute pancreatitis patients had more Bacteroidetes and Proteobacteria and fewer Firmicutes and Actinobacteria 37 . However, the gut microbiome, potential pathobionts, and host metabolic profile of individuals with AP remain poorly understood. The findings of this study indicate that the prevalence of Proteobacteria, Deferribacterota, Campilobacterota, and Verrucomicrobiota exhibited an increase in AP mice, while supplementation with Amuc_1098 was observed to reduce their relative abundance. The proteins encoded by the Amuc_1098 gene cluster, which play a role in pilus formation and assembly, exhibit high levels of expression in the membrane protein components of Akkermansia muciniphila 20 . Akkermansia muciniphila could modulate lipid and immune homeostasis, improve gut microbiota, and promote the overall health 38 . There was significant improvement in lipid metabolism, inflammation, energy balance, and blood parameters following treatment with Akkermansia muciniphila 39 . Studies have shown that Akkermansia muciniphila colonizes the mucosa layer in the gut and modulates basal metabolism 40 . Lipids were lowered and immune function was modulated by four weeks of treatment with Akkermansia muciniphila 41 . Metabolism of glucose and lipids is improved by cell-free supernatants from Akkermansia muciniphila 42 . The primary mode of interaction between gut microbiota and the host is through metabolites; however, there remains a significant gap in understanding their respective roles within biological networks associated with AP. A high correlation exists between AP severity and lipid metabolism disorders 22 . The current investigation mainly examined the metabolism impact of Amuc_1098 in AP mice and explored the possible underlying mechanism. We found that the proportion of lipids and lipid-like molecules in serum of AP patients and mice was higher than that of the control group. KEGG enrichment analysis showed that oral administration of Amuc_1098 markedly restored the dysfunction of glycerophospholipid metabolism in AP mice, especially PC (15:0/0:0) and 2-steroylglycerophosphoglycerol. Furthermore, correlation analysis of metabolic profiling and intestinal flora showed that the richness values of Desulfovibrionaceae , Enterococcus , and Eubacterium coprostanoligenes were positively correlated with PC (15:0/0:0) and 2-steroylglycerophosphoglycerol in AP mice vs Amuc_1098 treatment. Bacteria translocated to the serum from the gut were primarily opportunistic pathogens like Escherichia coli , Shigella flexneri , Enterobacteriaceae bacterium , Acinetobacter lwoffii , Bacillus coagulans , and Enterococcus faecium 43 . Gut microbiome analyses further revealed that the dysfunction of pancreatic-gut was correlated with pathogenic bacteria Escherichia coli and Enterococcus penetration into pancreas, accompanied by increased abundance of Desulfovibrionaceae 44 , 45 . Our study examined how Amuc_1098 administration suppressed AP aggravation by enterobacterial metabolites in mice, which provided evidence that suggested Amuc_1098 could effectively prevent and treat AP. In summary, our findings indicated that Amuc_1098 protect against AP by alleviating inflammation, rescuing intestinal injury, reshaping intestinal flora structure, and reprograming glycerophospholipid metabolism. Hence, Amuc_1098 can be considered an alternative medical supplementary agent for maintaining health and preventing AP via different mechanisms. This study is limited by its primary focus on the NF-κB pathway; the role of other TLR2-dependent signaling cascades, including MAPK, in the action of Amuc_1098 remains to be determined. Unbiased screening approaches (e.g., phospho-protein arrays) are warranted to delineate the full spectrum of downstream effectors. Moreover, the precise mechanism by which Amuc_1098 engages TLR2—whether through direct binding or indirect regulation—requires further investigation.

Acute pancreatitis (AP) is a complex inflammatory disease of the pancreas that involves overstimulation of acinar cells stemming from pancreatic zymogen activation, with a high morbidity and mortality rate. The global incidence of AP ranges from 13 to 45/100,0000 persons/year 1 . Pancreatic necrosis and systemic inflammation caused by acinar cell death are closely related to the prognosis and severity of AP, and can generate systematic inflammation and multi-organ dysfunction from mild to severe acute pancreatitis (SAP) 2 , 3 . Although therapeutic strategies have been developed, there have yet to be any effective drugs applied clinically for the treatment or prevention of AP 4 . During the onset and recovery phase of acute pancreatitis, the immune response includes early neutrophil infiltration, dendritic cell expansion and macrophage activation 5 . Macrophages, including alveolar microphages, Kupffer cells, and peritoneal macrophages are extensively activated at different stages of acute pancreatitis 6 , 7 . Normally activated macrophages (M1 macrophages) trigger the release of pro-inflammatory cytokines during the initial phase of the inflammatory response, although other forms of macrophages (alternative activated macrophages) are also involved in limiting the activity of pro-inflammatory cytokines 8 , 9 . The exertion of pro-inflammatory and anti-inflammatory role mediate the progression of AP, and the activation of NF-κB promote the proliferation of macrophages 10 , 11 . The gut plays a vital role in both digestion and immunity, and its balance is essential for overall health. This balance relies on dynamic interactions among intestinal epithelial cells, macrophages, and crypt stem cells 11 . Intestinal epithelial cells play a key role in protecting and regulating the intestinal tract. They form important barriers, regulate immune responses, participate in pathogen defense and cytokine secretion 12 . The intestinal mucosa barrier is key to maintain homeostasis in the gut because it can prevent intestinal microbes and endotoxins from migrating into the circulatory system. AP can cause intestinal mucosa barrier dysfunction, which may lead to bacterial translocation, secondary infection and multiple organ failure 13 . The pathogenesis of AP is complex and it is related to inflammation, blood fat, and gut microbiota 14 . Therefore, it is critical to seek further effective therapeutic strategies to protect intestinal barrier function to reduce mortality in AP patients. Microbiota dysbiosis contributes to the etiology and severity of AP through intestinal barrier disruption, local or systemic inflammatory responses, and bacterial translocation. The interaction between gut microbiota and the pancreas has opened new avenues for AP, and new therapeutic interventions that target the bacteria community have been studied extensively 15 . Early dysbiosis in the gut microbiota may exacerbate the severity of AP. However, a comprehensive understanding of the gut microbiota, potential pathogens, and host metabolome in patients with AP remains unclear 16 . Recent studies have shown that gut microbes are closely related to the pathogenesis of pancreatitis. The richness and composition of intestinal microorganisms in mice with pancreatitis altered significantly. Akkermansia muciniphila is a kind of beneficial microorganism colonizing human intestinal mucosal layer and belongs to the phylum Verrucomicrobia, which is closely associated with many diseases 17 . Akkermansia muciniphila prevents weight gain associated with high-fat diets, repairs the damage to intestinal epithelial barriers, reduces endotoxin levels in the blood and improves insulin sensitivity 18 . There was a decrease of butyrate-producing bacteria and an increased Akkermansia muciniphila following pancreatitis 19 . The protein encoded by Amuc_1098 gene cluster is highly expressed in the membrane protein components of Akkermansia muciniphila , which is related to the formation of fimbriae 20 . Intestinal permeability and gut microbiota play an important role in AP. However, it is unclear whether Amuc_1098 affects AP. Hence, this study aimed to investigate whether Amuc_1098 protect against AP by inhibiting inflammatory response and intestinal barrier injury, as well as potential targets. In acute pancreatitis, gut microbiota predict severity and reveal novel metabolic signatures 21 . Host-microbiota interactions are primarily mediated by metabolites, but their roles in AP networks are poorly understood. Lipid metabolism disorders are highly correlated with AP occurrence and severity 22 . Akkermansia muciniphila has been shown to affect intestinal immunity, glucose metabolism, and lipid metabolism 23 . It is believed that Akkermansia muciniphila plays a crucial role in maintaining gut health, energy homeostasis, and lipid metabolism 24 . By reducing fat storage and promoting browning of white adipocytes, Akkermansia muciniphila can alleviate lipid metabolic disorders 25 . In this study, we show that glycerophospholipid metabolism is significantly disturbed in patients and mice with AP. Oral supplementation with Amuc_1098 effectively attenuates AP progress by repressing inflammation, alleviating intestinal permeability, reshaping gut microbiota, and regulating glycerophospholipid metabolism via regulation of the NF-κB and tight junction signal pathway, which provides a plausible strategy for prevention and treatment of AP.

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