Fermented Lacticaseibacillus Paracasei Cultures Ameliorate Colitis by Modulating Microbiota‐Derived Tryptophan Metabolism and Macrophage Polarization
Heng Zhang, Jingzhou Sun, Xin Zheng, Huiqing Yang, Aowen Xie, Yuxuan Ding, Yuxia Mei, Jinshan Li, Yuanliang Hu, Min Ren, Yangyang Liu, Yunxiang Liang
TL;DR
A fermented probiotic product reduces colitis by altering gut bacteria and tryptophan metabolism, offering a new treatment for inflammatory bowel disease.
Contribution
A high-density fermented Lacticaseibacillus paracasei culture is shown to alleviate colitis through microbiota-dependent mechanisms involving tryptophan metabolites and AhR signaling.
Findings
PYW restores gut microbiota structure and tryptophan metabolism, enriching ILA and IAA.
PYW's effects are microbiota-dependent and replicable via fecal microbiota transplantation and ILA/IAA supplementation.
Viable bacterial load in PYW is essential for its anti-colitic efficacy.
Abstract
High‐density solid‐state fermented probiotic products, combining live bacteria with microbial and substrate‐derived bioactives, offer a potential solution to address dysregulation of gut microbiota–immune homeostasis associated with inflammatory bowel disease (IBD). However, their synergistic efficacy against IBD remains elusive. Here, we discuss our high‐density solid‐state fermented Lacticaseibacillus paracasei culture (PYW) and its effects on dextran sulfate sodium (DSS)‐induced colitis. Comparison of the effects of PYW, enriched with viable cells and bioactive metabolites—obtained via fermentation with wheat bran—with those of its thermally inactivated postbiotic (SPYW) shows superior efficacy of PYW than SPYW, with a viable bacterial load of ≥ 5 × 1010 CFU g−1 being indispensable. PYW effectively restores microbiota structure, restructures the gut tryptophan metabolic network,…
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FIGURE 8| Hematochezia | Diarrhea | Score |
| No observable blood (negative) | Normal | 0 |
| Trace blood (light blue) | Loose stool | 1 |
| Slight blood (blue) | Mild diarrhea | 2 |
| Obvious blood (dark blue) | Diarrhea | 3 |
| Gross blood (black) | Gross diarrhea | 4 |
- —Hubei Provincial Agricultural Science and Technology Research Project
- —Natural Science Foundation of Hubei Province, China
- —Key Research and Development Program of Hubei Province
- —Fundamental Research Funds for the Central Universities10.13039/501100012226
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TopicsGut microbiota and health · Probiotics and Fermented Foods · Inflammatory Bowel Disease
Introduction
1
Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), is a chronic relapsing inflammatory disorder of the gastrointestinal tract. Recent epidemiological studies indicate that IBD affects over 6.8 million people worldwide, with China experiencing a 15‐fold increase in incidence over the past decade, making it one of the fastest‐growing regions for this disease [1]. The core pathological features of IBD include disruption of the intestinal barrier, immune dysregulation, and gut microbiota dysbiosis. Approximately 30% of patients ultimately require surgical intervention to manage complications such as transmural fibrosis and strictures [2]. However, the efficacy of current therapeutic strategies, such as anti‐tumor necrosis factor‐alpha (TNF‐α) antibodies and 5‐aminosalicylic acid derivatives, remains limited, with approximately 40% of patients showing primary non‐response and relapse rates exceeding 70% within five years [3]. These limitations are the reason that existing treatments cannot adequately address the fundamental microbiota‐driven immune imbalance and mucosal repair defects underlying IBD.
IBD is marked by profound alterations in gut microbiota composition, characterized by reduced abundance of beneficial commensal bacteria, such as Lacticaseibacillus and Bifidobacterium, and increased populations of pathogenic bacteria, including Escherichia coli and Enterococcus [4]. This dysbiosis disrupts microbial metabolic networks, leading to diminished production of short‐chain fatty acids (SCFAs) and tryptophan‐derived aryl hydrocarbon receptor (AhR) ligands, such as indole‐3‐lactic acid (ILA) and indole‐3‐acetic acid (IAA), alongside abnormal accumulation of secondary bile acids [2]. Tryptophan metabolism plays a central role in maintaining gut homeostasis through three primary pathways: the microbial indole pathway, which produces AhR ligands that regulate immune and barrier functions [5]; the host kynurenine pathway, which generates neuroactive metabolites that exacerbate inflammation [6]; and the serotonin pathway, which modulates gut–brain axis signaling [7]. Disruption of these pathways compromises mucosal barrier integrity and neuroimmune homeostasis, underscoring the therapeutic potential of targeting the microbiota–tryptophan metabolic axis in IBD management.
Although probiotics and fecal microbiota transplantation (FMT) can modulate gut microbiota composition and alleviate IBD symptoms [8], their clinical efficacy remains limited. Challenges include loss of bacterial viability due to gastric acid and bile exposure, and variability in host microbiome composition that affects colonization and therapeutic responses [9, 10]. To overcome these limitations, postbiotics—comprising inactivated microbial cells and metabolites—have emerged as promising alternatives due to their stability, safety, and independence from colonization requirements [11, 12].
Solid‐state fermentation (SSF) is widely used for postbiotic production because of its operational simplicity, broad substrate adaptability, and environmental sustainability [13, 14]. However, conventional SSF systems typically yield low bacterial densities (1.0 × 10^6^–1.0 × 10^8^ CFU g^−1^ or CFU mL^−1^) due to substrate fiber content and anti‐nutritional factors. Moreover, thermal inactivation used for postbiotic production eliminates potential synergistic effects between live bacteria and their metabolites. To address these challenges, high‐density solid‐state fermented probiotic products have been proposed. These products contain viable bacteria (≥ 1.0 × 10^10^ CFU g^−1^ or CFU mL^−1^), microbial metabolites such as SCFAs and indole derivatives, and matrix‐derived bioactive compounds like ferulic acid and γ‐oryzanol. This approach offers multi‐target synergistic regulation by simultaneously remodeling gut microbiota composition, enriching beneficial metabolic pathways, and enhancing antioxidant defenses [14, 15], aligning with principles of sustainable biotechnological development. However, direct experimental evidence evaluating the synergistic therapeutic effects of live bacteria and their metabolites in high‐cell‐density SSF probiotic products for IBD treatment is lacking.
The present study aimed to investigate the protective effects and underlying mechanisms of Lacticaseibacillus paracasei‐fermented postbiotics against dextran sulfate sodium (DSS)‐induced colitis in mice. Specifically, it evaluated the anti‐inflammatory, antioxidant, and gut barrier protective activities of active (PYW) and heat‐inactivated fermented (SPYW) products by exploring their effects on modulation of gut microbiota composition, metabolomic profiles, and Ahr signaling. It also quantified key tryptophan‐derived metabolites and elucidated the associated molecular pathways using transcriptomic analyses. Additionally, the immunomodulatory effects of PYW and SPYW on macrophage activation and polarization were also assessed. The findings of this study may support the development of novel postbiotic‐based interventions for IBD.
Results
2
PYW Demonstrates Superior Efficacy Over SPYW in Ameliorating DSS‐Induced Colitis
2.1
The anti‐inflammatory efficacies of PYW and SPYW were assessed in a DSS‐induced murine colitis model with daily oral gavage interventions. Both treatments significantly alleviated colitis symptoms, including body weight loss, disease activity index (DAI), and colon shortening (p < 0.05; Figure 1A–C). Histopathological assessment of colon tissues demonstrated distinct responses among groups (Figure 1D). The MC group exhibited severe crypt architectural distortion and dense inflammatory cell infiltration, and treatment with SPYW moderately alleviated these injuries. The PYW group showed near‐normal crypt architecture and markedly reduced inflammatory infiltration, presenting a pathology closely resembling that of the NC group. This superior protective effect of PYW was further confirmed using quantitative analysis, showing a significantly lower histological score than that for the SPYW group (p < 0.05; Figure 1D). Serum biomarker quantification showed significantly reduced inducible nitric oxide synthase (iNOS), myeloperoxidase (MPO), TNF‐α, and interleukin (IL)‐6 levels. It increased IL‐10 concentrations in the PYW and SPYW groups than those in the model control (MC) group (p < 0.05) (Figure 1E, F). Furthermore, the reductions in MPO, TNF‐α, and IL‐1β were higher in the PYW group than those in the SPYW group (p < 0.05). These findings indicate the superior capacity of PYW for comprehensive regulation of oxidative stress and inflammatory responses.
PYW and SPYW ameliorate DSS‐induced colitis in mice. (A) Body weight dynamics during DSS challenge (days 0–12); (B) DAI; (C) Colon length; (D) H&E‐stained transverse sections of the colon and histological scoring; (E, F) Serum concentrations of iNOS, MPO, TNF‐α, IL‐6, IL‐1β, and IL‐10. Data are presented as mean ± SEM; the sample size for panels A–C is 8 and for panels D–F is 6. Different lowercase letters indicate significant differences (p < 0.05, one‐way ANOVA with Tukey's test).
Viable L. paracasei in PYW Leads to Enhanced Anti‐Inflammatory Effects
2.2
To determine the contribution of viable bacteria in PYW, SPYW was supplemented with gradient doses of live L. paracasei (SP1 [5×10^8^ CFU g^−1^], SP10 [5 × 10^9^ CFU g^−1^], and SP100 [5 × 10^10^ CFU g^−1^ ]; Figure 2A). Low‐dose supplementation groups (SP1/SP10) showed no significant difference from SPYW in alleviating colitis symptoms (body weight loss, colon shortening, and DAI). In contrast, the high‐dose group (SP100) recapitulated the colonic protective effects of PYW, exhibiting comparable efficacy (p > 0.05; Figure 2B–D). Histopathological scoring confirmed significantly reduced tissue damage in PYW and SP100 groups compared with those in SPYW and low‐dose groups (p < 0.05; Figure S1). Serum cytokine analysis showed significantly lower IL‐6 and IL‐1β levels along with elevated IL‐10 levels in PYW and SP100 groups than those in the other groups (p < 0.05; Figure 2E–G). These findings indicate that a sufficient viable bacteria load (≥5 × 10^10^ CFU g^−1^) is critical for the anti‐colitis efficacy of PYW, potentially acting synergistically with its fermentation metabolites to enhance anti‐inflammatory effects in DSS‐induced colitis.
A sufficient viable bacterial load is essential for the anti‐colitis efficacy of PYW to alleviate DSS‐induced colitis in mice. (A) Experimental schema; (B) Body weight; (C) DAI; (D) Colon length; (E) Serum concentrations of iNOS, MPO, TNF‐α, IL‐6, IL‐1β, and IL‐10. Data are presented as mean ± SEM; the sample size for panels B–D is 8 and for panel E is 6. Different lowercase letters indicate significant differences between groups (p < 0.05, one‐way ANOVA with Tukey's test).
PYW is Superior to SPYW in Regulating the Structure of Intestinal Flora in Mice With Enteritis
2.3
Intestinal microbiota imbalance, characterized by reduced α‐diversity and structural disruption, is common in patients with IBD [4]. In this study, the Sobs index (α‐diversity) of the MC group was significantly lower than that of the NC group (p < 0.001). SPYW intervention significantly increased the Sobs index (p < 0.01). In contrast, PYW intervention did not induce a significant increase compared with DSS treatment (Figure 3A), potentially due to high viable bacteria in PYW causing transient ecological disturbance. Moreover, β‐diversity analysis based on principal coordinates analysis revealed that PYW intervention resulted in greater overall structural modulation of gut microbiota than SPYW, indicated by farther separation from MC group and closer clustering with NC group (Figure 3B). At the phylum level, Bacteroidota, Firmicutes, Verrucomicrobiota, and Proteobacteria were the dominant in each group (Figure 3C). DSS treatment significantly increased the abundance of Proteobacteria in the MC group (vs. NC group, p < 0.001), which was significantly inhibited by PYW and SPYW (vs. MC group, p < 0.01; Figure 3D). At the genus level, DSS significantly reduced beneficial genera, such as norank_f_Muribaculaceae and Lacticaseibacillus (vs. NC p < 0.05; Figure 3E–G). PYW intervention significantly increased the abundance of norank_f_Muribaculaceae and Lacticaseibacillus (vs. MC p < 0.05), whereas SPYW failed to improve these genera (Figure 3F, G). Additionally, PYW significantly reduced the relative abundance of Parasutterella and Enterococcus (vs. MC p < 0.05). In contrast, both PYW and SPYW prevented the enrichment of Escherichia–Shigella, although neither was significant (Figure 3H–J). In summary, PYW demonstrated significantly superior efficacy over SPYW in regulating gut microbiota composition in DSS‐induced colitis.
PYW intervention demonstrates superior efficacy over SPYW in modulating the gut microbiota composition in DSS‐induced colitis mice. (A, B) Sobs index (α‐diversity) and principal coordinates analysis plots of gut microbiota based on 16S rRNA sequencing; (C) community bar plot at the phylum level; (D) relative abundance of Proteobacteria; (E) community bar plot at the genus level; (F–J) relative abundance of differential bacterial genera including norank_f_Muribaculaceae, Lacticaseibacillus, Parasutterella, Escherichia–Shigella, and Enterococcus. Data are presented as mean ± SEM; n = 5. Different lowercase letters indicate significant differences between groups (p < 0.05, one‐way ANOVA with Tukey's test).
PYW Modulates the Gut Microbiota‐Derived Tryptophan Metabolic Network in Colitis Mice
2.4
To investigate the bioactive components of PYW beyond viable bacteria, scanning electron microscopy was performed to assess the structural alterations in PYW compared with those of unfermented bran (KZ). The analysis revealed a compact morphology in KZ, whereas PYW displayed a characteristic porous architecture (Figure S2A). Composition analyses showed significantly reduced levels of insoluble dietary fiber (p < 0.01) and increased levels of soluble dietary fiber (p < 0.001) and flavonoids (p < 0.01) in PYW compared with those in KZ (Figure S2B, C). Additionally, PYW showed superior DPPH and ABTS^+^ radical scavenging capacities compared with KZ (p < 0.05; Figure S2D, E), indicating microbial liberation of antioxidant compounds during fermentation.
Untargeted metabolomics of KZ and PYW identified 1,679 upregulated and 1,205 downregulated metabolites in PYW relative to KZ. Principal component analysis (PCA) revealed pronounced separation along PC1, indicating significant metabolic profile shifts post‐fermentation (Figure 4A). Specifically, tryptophan derivatives (such as ILA) and flavonoids (such as didymin) were enriched in PYW (Figure 4B). Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis highlighted significant alterations in tryptophan metabolism (Figure 4C). Heatmaps further confirmed elevated levels of key tryptophan metabolites (ILA, kynurenic acid, 3‐indoleacrylic acid) and flavonoids (didymin, 6‐C‐β‐glucopyranosyl‐8‐C‐α‐arabinopyranosyl apigenin, 6‐glucosylprocyanidin B2, and saponarin) in PYW (Figure 4D, E).
PYW restructures the tryptophan metabolic network in colitis mice. (A) 2D PCA score plot; (B) Radar chart of characteristic metabolites; (C) KEGG pathway enrichment analysis; (D, E) Heatmap of tryptophan‐related metabolites and flavonoids; (F) PCA of mouse fecal metabolome; QC, quality control; (G) Volcano plot of differential metabolites. (H) Bubble plot of KEGG enrichment analysis of differential metabolites, PYW vs MC. (I) Clustering heatmap of differential metabolites in the KEGG tryptophan metabolism pathway, PYW vs MC. (J) Schematic of tryptophan metabolism highlighting significantly upregulated (green) and downregulated (red) metabolites in PYW. Individual data points (KZ1‐3, PYW1‐5, and MC1‐5) represent biological replicates. The sample size for panels A–E is 3 and for panels F–I is 5.
Based on these findings, we hypothesized that viable bacteria and metabolites in PYW would ameliorate gut microbial metabolic dysregulation in colitis mice. To test this hypothesis, we performed untargeted metabolomic analysis of intestinal contents. PCA confirmed distinct metabolite clustering among groups (Figure 4F). The volcano plot identified 1,556 differentially abundant metabolites between the NC and MC groups (701 upregulated, 855 downregulated in the NC group), whereas PYW intervention induced alterations in 1,164 metabolites (902 upregulated, 262 downregulated vs MC) (Figure 4G). KEGG pathway enrichment analysis revealed significant modulation of tryptophan metabolism, phenylpropanoid biosynthesis, and tyrosine metabolism pathways in the PYW group (Figure 4H). Pathway‐specific analysis (Figure 4I, J) indicated that in the indole pathway, PYW significantly increased indole, IAA, ILA, and indole‐3‐acetaldehyde (p < 0.05). In the serotonin pathway, PYW elevated 5‐hydroxytryptophan and 5‐hydroxyindoleacetate (p < 0.05) while reducing 5‐hydroxyindoleacetaldehyde (p < 0.01). In the kynurenine pathway, PYW enhanced conversion to L‐formylkynurenine and 3‐hydroxyanthranilate (p < 0.05). Furthermore, fecal ILA (59 ng per mouse) and IAA (14 ng per mouse) concentrations substantially exceeded daily administered doses (0.4 ng and 0.3 ng per mouse, respectively; Table S1 and Figure S2F), confirming predominant de novo synthesis by gut microbiota.
PYW Alleviates Colitis by Activating the AhR Signaling Pathway
2.5
Recent literature indicates that tryptophan metabolites such as ILA and IAA, which are key AhR agonists, exert anti‐inflammatory effects via AhR pathway activation [16]. Therefore, we hypothesized that PYW alleviates intestinal inflammation through AhR signaling. Our transcriptomic analysis of colonic tissue samples revealed significant gene expression differences between MC and PYW groups (Figure 5A), identifying 1,610 differentially expressed genes (DEGs) at |log_2_ fold change (FC)| > 1 and adjusted p‐value (q) < 0.05 thresholds—611 upregulated and 990 downregulated by PYW intervention (Figure 5B). KEGG pathway analysis showed significant modulation of inflammation‐related pathways, including cytokine‐cytokine receptor interaction, IL‐17 signaling, and TNF signaling pathways in the PYW group (Figure 5C). Specifically, PYW upregulated AhR‐associated (AhR and Cyp1a1) and barrier‐related genes (Muc2, Reg3b, Cdh1, Cldn, B3gnt7, and zbp1), while downregulating pro‐inflammatory mediators (Cxcl1, Cxcl2, Cxcl3, Cxcl5, Ccl2, Ccl7, Il1b, Il6, Ptgs2, and Csf3) compared with MC (Figure 5D). Validation experiments corroborated these findings of transcriptomic analyses: qPCR confirmed significantly increased AhR and Cyp1a1 mRNA expression (p < 0.05; Figure S3A).
PYW alleviates intestinal inflammation through the AhR signaling pathway. (A) Principal component analysis of transcriptomes (PYW vs MC); (B) Volcano plot of DEGs; (C) Bubble plot of KEGG enrichment analysis of differential metabolites; (D) Heatmap of AhR‐, barrier‐, and inflammation‐related genes; (E) PAS staining and immunohistochemistry (ZO‐1, Claudin‐1, Occludin). The quantitative analyses for the immunofluorescence results are provided in Figure S3B, C. (F) Body weight; (G) DAI; (H) colon length; (I) RT‐qPCR validation of AhR‐, barrier‐, and inflammation‐related genes. Data are expressed as mean ± SEM; the sample size for panels A–D is 3, for panels E and I is 6, and for panels F–H is 8. Different lowercase letters indicate significant differences (p < 0.05, one‐way ANOVA with Tukey's test).
Our transcriptomic analyses suggested improved intestinal barrier function. To validate these findings, we performed histochemical and immunofluorescence staining. Periodic acid‐Schiff (PAS) staining revealed a significant increase in the number of goblet cells and enhanced mucin secretion in the PYW‐treated group compared to those in the MC group, indicating restoration of the chemical barrier (Figure 5E). Moreover, immunofluorescence staining of tight junction proteins clearly demonstrated markedly stronger fluorescence signals for ZO‐1, Claudin‐1, and Occludin in the PYW group relative to those in the MC group, confirming the upregulation of these critical junctional components (Figure 5E; Figure S3B, C). To further quantify these findings, western blot analysis was performed. The representative blot images and quantitative analysis of band intensity consistently showed that the relative protein expression of ZO‐1, Claudin‐1, and Occludin was significantly higher in the PYW group than in the MC group (Figure 5F), providing additional evidence at the protein level for the enhanced intestinal barrier integrity following PYW treatment. Additionally, qPCR demonstrated significant suppression of Cxcl1, Cxcl2, Cxcl3, and Ccl2 (p < 0.05; Figure S3A), consistent with reduced serum TNF‐α, IL‐1β, and IL‐6 levels as described above (Figure 1F). To confirm AhR pathway dependency, mice were pretreated with the AhR antagonist CH223191 before PYW administration. Pretreatment with CH223191 markedly attenuated the therapeutic efficacy of PYW, as evidenced by reduced weight recovery, increased DAI scores, and diminished improvement in colon shortening (Figure 5G–J). Molecular analyses further revealed significantly decreased colonic mRNA expression levels of AhR, Cyp1a1, Muc2, Zo1, claudin‐1, and occludin (p < 0.05), along with elevated Il6 and IL1b levels (p < 0.05) in antagonist‐treated groups. Collectively, these data demonstrate that PYW alleviates colitis by enhancing intestinal barrier function and suppressing inflammation through activation of the AhR signaling pathway.
PYW Alleviates DSS‐Induced Colitis by Elevating Intestinal ILA and IAA Levels
2.6
To elucidate the indispensable role of gut microbiota contributing to the anti‐inflammatory efficacy of PYW, antibiotic‐mediated microbiota depletion was performed before the interventions. The findings demonstrated that microbiota‐depleted mice (PA group) receiving PYW exhibited no significant improvement in pathological phenotypes—including weight loss, DAI scores, and colon shortening—compared with the MC mice (p > 0.05; Figure S4A–C), suggesting that the efficacy of PYW is dependent on an intact gut microbiota. Further validation through FMT revealed that colitis mice receiving fecal transplants from PYW‐treated donors (PFMT group) showed significantly less weight loss, hematochezia, diarrhea, and colon shortening compared with those receiving transplants from normal donors (NFMT group) (p < 0.05; Figure 6A–C; Figure S4D). PFMT treatment significantly ameliorated DSS‐induced colonic damage, showing preserved crypt architecture and reduced inflammatory cell infiltration on hematoxylin & eosin (H&E) staining, alongside a marked increase in Muc2‐positive areas on PAS staining. These morphological improvements were confirmed by the significantly lower histopathological scores and greater Muc2‐positive areas relative to those of the NFMT group (p < 0.05; Figure 6D; Figure S4E). Concurrently, at the molecular level, PFMT upregulated barrier‐related (Muc2, Zo1, claudin1, and occludin) and AhR pathway (AhR and Cyp1a1) genes (p < 0.05) and suppressed the pro‐inflammatory cytokines IL‐6 and IL‐1β (p < 0.05; Figure S4F, G).
PYW alleviates DSS‐induced colitis in mice by enhancing intestinal levels of ILA and IAA. (A) Bodyweight; (B) DAI; (C) Colon length; (D) Representative H&E and PAS staining (100× magnification); (E) Heatmap of differentially expressed tryptophan metabolites (VIP > 1.5) across groups; (F–I) ILA and IAA supplementation experiment in DSS‐induced colitis mice models. Effects on (f) body weight, (g) DAI, and (h) colon length; (I) Representative H&E and PAS staining (100× magnification) images of colon tissue from MC, ILA‐treated, and IAA‐treated mice. The quantitative analyses for the immunofluorescence results are provided in Figure S4D, E, H, I. (J) mRNA levels of AhR, Cyp1a1, Il6, and Il1b. Data expressed as mean ± SEM; the sample size for panels A–C and F–H is 8, for panels D, I, and J is 5, and for panel E is 3 (n = 3–8). Different lowercase letters indicate significant differences (p<0.05, one‐way ANOVA with Tukey's test). Individual data points (PFMT1‐3 and NFMT1‐3) represent biological replicates.
Targeted metabolomic analysis confirmed elevated colonic ILA and IAA levels in PFMT mice (p < 0.05; Figure 6E). Exogenous supplementation of ILA or IAA independently replicated therapeutic effects: reducing weight loss (p < 0.05), lowering DAI scores (p < 0.05), and ameliorating colon shortening (p < 0.05; Figure 6F–H). Comparative histology demonstrated that the MC group suffered severe mucosal erosion, inflammatory infiltration, and architectural disruption. Both ILA and IAA supplementation groups showed remarkable tissue repair, with well‐restored crypt architecture and significantly diminished inflammatory responses. PAS staining confirmed that both metabolite treatments significantly enhanced Muc2 secretion, as evidenced by the substantially increased positive‐stained areas compared to those of the MC group (p < 0.05). This restoration was mechanistically linked to the activation of the AhR signaling pathway, evidenced by the significant upregulation of AhR and Cyp1a1(p < 0.05), concomitant with a marked suppression of inflammation, as indicated by the downregulated expression of the pro‐inflammatory cytokines IL‐6 and IL‐1β (p < 0.05; Figure 6J). Collectively, these findings demonstrate that PYW alleviates colitis by enriching gut microbiota‐derived ILA and IAA.
ILA/IAA Regulate Macrophage Polarization by Inhibiting the TLR4/NF‐κB/MAPK Pathway
2.7
The pivotal role of macrophages in IBD pathogenesis is well‐established, and modulation of their polarization phenotypes represents a promising therapeutic strategy for alleviating intestinal inflammation. In this study, intervention with either PYW or its microbial metabolites ILA or IAA significantly upregulated expression of M2 macrophage markers (CD206 and Arg1; p < 0.05) while downregulating M1 markers (CD86 and iNos) in colonic tissue (Figure S5A‐C). To elucidate the underlying mechanism, an LPS‐induced RAW264.7 macrophage inflammation model was established (Figure 7A). Dose–response experiments confirmed that ILA and IAA maintained >90% cell viability (cytotoxicity <10%) at concentrations up to 50 µM (Figure 7B, C). Treatment with 50 µM ILA or IAA significantly inhibited nitric oxide production (p < 0.05) (Figure 7D). Immunofluorescence and qPCR analyses further demonstrated reduced CD86 expression (p < 0.05) alongside elevated CD206 levels (p < 0.05) (Figure 7E, F; Figure S5D), indicating phenotypic switching toward anti‐inflammatory M2 macrophages. Given the central role of TLR4/NF‐κB and MAPK pathways in LPS‐driven macrophage activation, subsequent protein analyses revealed that ILA or IAA treatment significantly downregulated TLR4 expression (p < 0.05), suppressed NF‐κB p65 subunit phosphorylation (p < 0.05), increased p‐IκBα levels (p < 0.05), and reduced p38 MAPK phosphorylation (P‐p38/p38 ratio; p < 0.05) (Figure 7G). Collectively, these findings demonstrate that ILA and IAA regulate macrophage polarization by suppressing the TLR4/NF‐κB/MAPK signaling axis, thereby promoting an anti‐inflammatory M2 phenotype.
ILA and IAA regulate macrophage polarization and suppress inflammatory signaling pathways in RAW264.7 cells. (A) Schematic of cell treatment protocol; (B, C) Cell viability assay showing survival rates of RAW264.7 cells following PBS (CK), LPS stimulation, or ILA/IAA treatments; (D) Nitric oxide (NO) concentration measurements indicating concentration‐dependent suppression of LPS‐induced inflammation; (E, F) Immunofluorescence staining for CD206 (M2) and CD86 (M1). Bar graphs show the relative proportions of CD86 and CD206 macrophages, quantified by calculating the ratio of immunofluorescence intensity to the positively stained area in tissue sections. (G) Western blot analysis of TLR4‐NFκB pathway proteins, including TLR4, p‐P65, p‐IκBα, and p‐P38. The bar charts display the relative expression levels of the proteins, which were quantified by analyzing the grayscale values of the corresponding western blot bands. RAW264.7 cells were pretreated with ILA (10–200 µmol L−1) or IAA (10–200 µmol L−1) for 1 h, followed by stimulation with LPS (1 µg mL−1) for 24 h. Data represent mean ± SEM; the sample size for panels B–E is 5 and that for panel G is 3. Different lowercase letters indicate significant differences (p < 0.05, one‐way ANOVA with Tukey's test).
Discussion
3
Postbiotics are defined by the International Scientific Association for Probiotics and Prebiotics as “preparations of inanimate microorganisms and/or their components that confer health benefits on the host,” where the specific formulation, matrix, and inactivation methods critically determine their efficacy [12]. Within this framework, SSF has emerged as a core strategy for postbiotic production due to its environmental sustainability and ability to generate diverse bioactive compounds. For example, the porous structure of wheat bran, as a substrate, facilitates efficient oxygen transfer, creating a microaerophilic environment conducive to Lacticaseibacillus proliferation while simultaneously enriching the media with proteins, carbohydrates, and trace elements that serve as essential carbon–nitrogen sources and growth factors to support high‐density bacterial growth [17]. During this process, lactic acid bacteria continuously synthesize cellular components, metabolic enzymes, and bioactive substances, forming postbiotic complexes with superior bioactivity compared to liquid‐fermented pure cultures [18]. Consistent with these studies, in the present study, we showed that integrating L. paracasei with wheat bran matrix through SSF yielded PYW containing high‐density viable bacteria (≥10^10^ CFU g^−1^), released matrix‐bound flavonoids, and generated microbial indole metabolites (ILA/IAA). This tripartite complex (viable bacteria–phytochemicals–microbial metabolites) exerts multi‐target synergistic effects [19, 20]. Probiotics mediate ecological niche competition [21], flavonoids enhance antioxidant defense [22], and indole derivatives regulate immune signaling [23]. Moreover, its dynamic metabolite profile can adapt to substrates to produce additional bioactive components such as SCFAs and phenolic acids [24, 25]. These findings suggest that the multi‐layered bioactive synergistic mechanism could demonstrate superior anti‐inflammatory potential than traditional single‐component microbial preparations.
Although heat‐inactivated postbiotics (SPYW) retained some anti‐inflammatory activity—likely due to thermally stable components such as flavonoids and dietary fiber released from the wheat bran matrix [26, 27]—their efficacy was markedly lower than that of PYW containing viable bacteria. This indicates that live bacteria and their metabolites act synergistically to achieve optimal therapeutic effects. Moreover, this synergy was dose‐dependent, with significant benefits observed only at bacterial concentrations ≥5 × 10^1^⁰ CFU g^−1^. High‐density viable bacteria remodel the gut microbiota through ecological competition while continuously producing active metabolites such as SCFAs and indole derivatives, thereby establishing functional complementarity [28, 29]. Multi‐omics analyses confirmed that although PYW did not significantly increase microbial α‐diversity, it induced substantial structural remodeling, surpassing the effects of heat‐inactivated postbiotics. This ecological transformation, driven by transient bacterial perturbation [30, 31], facilitated functional normalization of the gut environment.
We identified several bacterial species, including Lactobacillus reuteri, Lactobacillus johnsonii, Bifidobacterium pseudolongum, and Bacteroides acidifaciens, that were significantly enriched in the PYW treatment group. As representative strains of Lactobacillus, L. reuteri and L. johnsonii have been confirmed to directly synthesize ILA through the tryptophan transaminase pathway and further participate in the generation of IAA [5]. B. pseudolongum reportedly shows a positive correlation with IAA concentration [32]. Although B. acidifaciens is not directly associated with indole metabolism, this species may participate in the regulation of the tryptophan metabolic network by degrading complex polysaccharides to produce SCFAs [33]. Furthermore, microbial community restructuring led to significant reorganization of the tryptophan metabolic network, increasing anti‐inflammatory metabolites such as IAA, ILA, 5‐hydroxyindoleacetate, and 3‐hydroxyanthranilate [16, 34, 35]. These effects likely resulted from the combined action of wheat bran, known to modulate tryptophan metabolism [36], and L. paracasei, which is both correlated with indole‐derived tryptophan metabolites [37] and possesses intrinsic tryptophan metabolic activity [38]. Overall, PYW regulates tryptophan metabolism not only through direct metabolite production but also by enhancing endogenous microbial synthesis. These findings highlight the therapeutic potential of targeting microbial metabolic pathways in IBD management [39, 40, 41].
Both antibiotic‐mediated depletion of the microbiota and AhR antagonism significantly reduced the therapeutic efficacy of PYW, demonstrating that its anti‐inflammatory effects are dependent on microbiota‐mediated AhR pathway activation. Given that tryptophan metabolites such as IAA, ILA, and indole‐3‐acetaldehyde are endogenous AhR agonists [42, 43, 44], the ability of PYW to increase AhR ligand levels through modulation of tryptophan metabolism forms the basis of its anti‐inflammatory mechanism. This was further supported by the results of the FMT experiments, which showed that transferring gut microbiota from PYW‐treated donors to recipient mice reproduced the anti‐inflammatory effects and significantly elevated intestinal ILA/IAA concentrations. Moreover, exogenous supplementation with ILA/IAA alone ameliorated colitis, confirming that PYW exerts its therapeutic effects by remodeling microbial community structure and enhancing tryptophan metabolic pathways to boost AhR ligand biosynthesis. These findings not only establish ILA and IAA as key mediators of microbiota–host crosstalk but also suggest new avenues for developing next‐generation probiotic formulations targeting tryptophan metabolism.
The LPS‐stimulated RAW264.7 macrophage model is widely used in research on IBD innate immunity to investigate macrophage phenotypic plasticity, given its ability to induce both M1 polarization (CD86^+^/iNOS^+^ pro‐inflammatory phenotype) and M2 polarization (CD206^+^/Arg‐1^+^ anti‐inflammatory phenotype) [45, 46, 47]. In the present study, we found that the microbial metabolites ILA and IAA modulate macrophage polarization by suppressing M1 marker expression while enhancing M2 markers, thereby promoting an anti‐inflammatory phenotype. Mechanistically, ILA and IAA downregulated TLR4 receptor expression and simultaneously inhibited downstream signaling by preventing phospho‐IκBα degradation, thus blocking NF‐κB p65 phosphorylation, and by reducing MAPK p38 phosphorylation levels. This coordinated inhibition of TLR4 signaling initiation and its NF‐κB/MAPK effector pathways underlies the ability of ILA and IAA to prevent M1 polarization in macrophages [47, 48].
Although this study demonstrated the significant efficacy of PYW in alleviating colitis, the potential safety risks associated with administering high doses of live bacteria warrant careful evaluation. Previous clinical studies have indicated that although certain probiotic strains exhibit good therapeutic effects at high doses, the incidence of adverse events is comparable to, or even higher than, that of low‐dose or control groups [30, 49, 50]. Therefore, the potential risks of introducing ultra‐high doses of live bacteria warrant heightened vigilance, particularly in individuals with severely compromised or highly suppressed immune systems.
This study also observed that although high‐dose PYW significantly improved colitis symptoms and remodeled the gut microbiota structure, it did not significantly enhance the α‐diversity of the microbial community. This suggests that the introduction of high‐dose probiotics induces a “constructive perturbation” of the indigenous microbiota. We hypothesize that this perturbation is a key step in the efficacy mechanism: high‐dose live bacteria, through mechanisms such as competitive exclusion, rapidly suppress inflammation‐associated pathogenic or conditionally pathogenic bacteria, thereby creating ecological space for the subsequent colonization and recovery of beneficial bacteria. This regulatory approach of “disruption preceding reconstruction” may hold greater therapeutic significance than merely pursuing an increase in diversity. Nevertheless, the long‐term ecological consequences of such perturbations require further evaluation through longitudinal studies. Therefore, although high‐dose PYW shows promising therapeutic potential, a comprehensive assessment of its long‐term biosafety, the full ecological implications of the microbiota remodeling, and the precise identification of the key bacterial strains responsible for the beneficial effects remain critical challenges for future research.
Conclusion
4
PYW exerts its anti‐inflammatory effects through a multi‐tiered mechanism involving synergistic interactions between high‐density L. paracasei, microbial tryptophan metabolites, and wheat bran bioactive components, such as dietary fibers/flavonoids (Figure 8). This integrated system operates in three sequential phases: (1) viable bacteria directly remodel gut microbiota architecture by promoting beneficial bacterial proliferation while suppressing pro‐inflammatory species; (2) the optimized microbial community subsequently enhances tryptophan metabolic pathways to boost biosynthesis of specific indole metabolites (ILA or IAA); and (3) these metabolites concurrently activate the AhR signaling pathway to strengthen intestinal barrier integrity and inhibit macrophage M1 polarization. Overall, this solid‐state fermented multicomponent synbiotic—comprising live bacteria, metabolites, and matrix components—offers a novel therapeutic strategy for IBD by precisely targeting the microbial tryptophan metabolic pathway.
Proposed mechanism of PYW‐mediated anti‐colitis effects.
Materials and Methods
5
Fermentation Procedure
5.1
For preparation of the probiotic culture (PYW), L. paracasei (Preservation No. CCTCC M 20241648 LP‐Liang) was cultured in a solid‐state medium comprising wheat bran (20 g), yeast extract (0.34 g), soy peptone (0.17 g), calcium carbonate (6 g), thermostable α‐amylase (9 × 10^3^ U), glucoamylase (2.5 × 10^7^ U), and distilled water (44 mL). The medium was sterilized at 121°C for 30 min before inoculation. The fermentation was carried out under the following optimized conditions: incubation temperature 37°C, substrate‐to‐water ratio 1:1.4 (w/v), bed height 10.4 cm, inoculum size 1.92 × 10⁸ CFU g^−1^ (dry substrate), and static cultivation (48 h). To control the pH in our SSF system, calcium carbonate (CaCO_3_) was added as a buffering agent. CaCO_3_ effectively neutralizes the organic acids produced by lactic acid bacteria metabolism, thereby autonomously maintaining pH stability throughout the fermentation process. Thus, no external pH adjustment was required prior to fermentation. The oxygen gradient was primarily controlled by the bed height (10.4 cm), a parameter optimized as the best condition through response surface methodology. This height creates a suitable aerobic/microaerobic gradient for bacterial growth under static cultivation conditions. Furthermore, to ensure system homogeneity and reproducibility, the following crucial operational steps were implemented: Raw wheat bran was ground and passed through an 80‐mesh sieve to ensure uniform substrate particle size. The fermentation substrate was thoroughly mixed both before and after inoculation to ensure even distribution of bacterial cells, moisture, and nutrients.
For preparation of the postbiotic culture (SPYW), fermented cultures were subjected to three sequential freeze–thaw cycles involving freezing at −20°C for 5 h, followed by thermal treatment in a 60°C water bath for 2 h per cycle. The final products (PYW, SPYW, or unfermented bran) were freeze‐dried and pulverized for subsequent analyses. The comparative analysis of basic nutritional components and active substances, covering crude protein, carbohydrates, dietary fiber, ash, moisture, SCFAs, indole derivatives (ILA/IAA), flavonoids, and phenolic acids, among KZ, PYW, and SPYW is detailed in Supplementary Table S3.
Animal Experiments
5.2
All animal procedures were approved by the Institutional Animal Care Committee of Huazhong Agricultural University (approval number SYXK2020‐0084). Specific‐pathogen‐free male C57BL/6 mice (6–8 weeks) were acclimated for 7 days under controlled conditions (25°C, 12 h light–dark cycle) with ad libitum access to standard diet (License No. HZAUMO‐2025‐0207) and sterile water.
The animal experiments were carried out in four experimental models: colitis intervention, AhR antagonism, FMT, and metabolite intervention. For the colitis intervention model, mice were randomly assigned to normal control (NC; saline gavage + normal water), MC (saline + 3% DSS water), PYW (1 mg g^−1^ bodyweight PYW + 3% DSS), SPYW (1 mg g^−1^ bodyweight heat‐inactivated PYW + 3% DSS), SP1 (1 mg g^−1^ bodyweight SPYW + 5 × 10^8^ CFU g^−1^ L. paracasei + 3% DSS), SP10 (1 mg g^−1^ bodyweight SPYW + 5 × 10^9^ CFU g^−1^ L. paracasei + 3% DSS), and SP100 (1 mg g^−1^ bodyweight SPYW + 5 × 10^10^ CFU g^−1^ L. paracasei + 3% DSS), probiotic control (A100; 5 × 10^10^ CFU g^−1^ L. paracasei + 3% DSS). For the AhR antagonism model, mice received PYW (1 mg g^−1^ body weight) with 3% DSS and topical CH223191 (20 µL; 10 µM) on auricular and dorsal skin. For the FMT model, recipient mice were pretreated with antibiotic cocktail (ampicillin 1 g L^−1^ + neomycin 1 g L^−1^ + metronidazole 1 g L^−1^ + vancomycin 0.5 g L^−1^) for 7 days, followed by daily gavage of 0.2 mL fecal supernatant prepared by vortexing 300–400 mg feces from PYW (PFMT) or NC (NFMT) donors in 4 mL reduced PBS (0.5 g L^−1^ cysteine + 0.2 g L^−1^ Na_2_S) and centrifuged at 1000 x g for 1 min. For the metabolite intervention model, mice received ILA or IAA at 40 mg kg^−1^ body weight daily along with 3% DSS. The pure ILA and IAA reagents used for treatment were sourced from Shanghai Yuanye Bio‐Technology Co., Ltd. (China), with product codes I157602 (for ILA) and B21801 (for IAA). Each group comprised 10 mice. At study termination, colon and cecum tissues were harvested, rinsed with PBS, snap‐frozen in liquid nitrogen, and stored at −80°C for a maximum of 12 weeks before analysis.
The experimental groups were designated as follows: NC (normal control), MC (DSS model control), PYW (probiotic preparation of L. paracasei fermented on wheat bran via SSF treatment), and SPYW (heat‐inactivated PYW treatment).
Disease Activity Index
5.3
Ulcerative colitis progression was evaluated based on the DAI scoring of clinical parameters as follows: body weight loss (0, 1%; 1, 1–5%; 2, 6–10%; 3, 11–15%; 4, >15%). Fecal samples were analyzed using a fecal occult blood test kit (Nanjing Jiancheng Bioengineering Institute). Hematochezia and stool consistency were assessed using a scoring system (Table 1).
Histopathological Analysis of Colon Tissue
5.4
Colonic tissues were harvested, and colon lengths were measured. Distal colon tissues were rinsed with 0.9% normal saline, blotted dry on filter paper, and formalin‐fixed. Tissues were then washed under running water, ethanol‐dehydrated, paraffin‐embedded, cut into 5 µm slices, and stained using H&E. Histopathological analysis of colonic tissue samples was performed using light microscopy. PAS staining of colonic tissue was performed by Servicebio Technology Co. (Wuhan, China). Histopathological damage was scored using the following four categories based on inflammation severity, crypt disappearance, and pathological changes: 0, normal intestinal mucosa; 1, mild inflammation and edema in mucosal layer, disappearance of one‐third of basal crypts; 2, moderate mucosal inflammation, disappearance of ⅔ of crypts; 3, moderate mucosal inflammation, complete disappearance of crypts, epithelium remains intact; 4, severe inflammation of mucosa, submucosa, and muscularis mucosa, disappearance of crypts and epithelium.
Enzyme‐Linked Immunosorbent Assay (ELISA)
5.5
Blood samples were collected from the orbital venous plexus and centrifuged at 2500 × g (4°C, 10 min). The supernatant (serum) was harvested and stored at −80°C until analysis. The levels of inflammatory cytokines (TNF‐α, IL‐6, IL‐1β, and IL‐10) and oxidative stress kinases (iNOS and MPO) were measured using ELISA kits following the manufacturer's protocol.
Gut Microbiota Analysis
5.6
Microbial DNA was extracted from fecal samples using the E.Z.N.A. Soil DNA Kit (Omega Bio‐Tek; Norcross, GA, USA). The V3–V4 regions of bacterial 16S rRNA genes were amplified using 338F (5′‐ACTCCTACGGGAGGCAGCAG‐3′) and 806R (5′‐GGACTACHVGGGTWTCTAAT‐3′) primers on a GeneAmp 9700 thermal cycler (ABI; Carlsbad, CA, USA). Purified amplicons were pooled in equimolar concentrations and subjected to paired‐end sequencing (2 × 300 bp) on an Illumina MiSeq platform (San Diego, CA, USA) following the standard protocols provided by Majorbio Bio‐Pharm Technology Co. (Shanghai, China). The raw sequence data have been deposited in the NCBI Sequence Read Archive database (accession # PRJNA1114914).
Untargeted Metabolomics
5.7
Samples stored at −80°C were thawed on ice, and 20 mg aliquots were homogenized in 400 µL methanol‐water (7:3, v/v) containing internal standards. The homogenates were vortexed for 3 min, sonicated in an ice bath for 10 min, vortexed again for 1 min, and incubated at −20°C for 30 min. Subsequently, the homogenate was centrifuged at 14,000 × g (4°C, 10 min), supernatants were collected and re‐centrifuged under the same conditions for 3 min. The resulting supernatant (200 µL) was subjected to liquid chromatography‐mass spectrometry (LC‐MS) analysis using a Shimadzu LC‐30A UHPLC system coupled to a SCIEX TripleTOF 6600+ mass spectrometer. Data acquisition was performed in an information‐dependent acquisition mode (Analyst TF 1.7.1). Raw data files were converted to mzXML format via ProteoWizard (v3.0.8789). Peak extraction, alignment, and retention time correction were conducted using XCMS (v3.18.0), and peak areas were corrected using the SVR method. Peaks detected in less than 50% of samples within each group were excluded. Metabolite identification was carried out using a combination of in‐house, public, Artificial Intelligence (AI)‐based databases, and metDNA, with data processed on Metware Cloud (https://cloud.metware.cn).
Targeted Analysis of Tryptophan Metabolites
5.8
Targeted quantification of tryptophan metabolites was performed by Metware (http://www.metware.cn) using an AB Sciex QTRAP 6500 LC‐MS/MS platform. Tissue samples (50 mg) were homogenized in 500 µL methanol spiked with internal standards (250 ng mL^−1^), vortexed (3 min), incubated at −20°C for 30 min, and centrifuged twice (14,000 × g, 4°C for 10 and 5 min, respectively). The chromatographic separation was carried out on a Waters ACQUITY UPLC HSS T3 C18 column (100 × 2.1 mm, 1.8 µm) maintained at 40°C using the following gradient of the mobile phases [0.1% formic acid in water (A) and acetonitrile (B)]: 10% B from 0–1 min, linear increase to 95% B from 1–6 min, held at 95% B from 6–7 min, then returned to 10% B by 10 min. The flow rate was 0.35 mL min^−1^. Mass spectrometric detection was carried out in both positive and negative electrospray ionization modes (spray voltage: ±5500 or 4500 V, respectively; source temperature: 550°C) with scheduled multiple reaction monitoring. All analyses were carried out using HPLC‐grade solvents (Merck, Frankfurt, Germany) and standards (Sigma‐Aldrich and OlChemim). Data were acquired using Analyst 1.6.3 software and quantified using Multiquant 3.0.3 (https://cloud.metware.cn). The method was validated using spiked internal standard recovery.
Transcriptomics Analysis
5.9
Total RNA was extracted from samples and assessed for purity using a KaiaoK5500 spectrophotometer (Kaiao, China). RNA integrity (RIN > 8.0) and concentration were verified using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). For library preparation, 2 µg of high‐quality RNA per sample was processed using the NEBNext Ultra RNA Library Prep Kit (NEB, USA). Polyadenylated mRNA was enriched with oligo(dT) beads and fragmented in a divalent cation‐containing 5× fragmentation buffer. Double‐stranded cDNA synthesis was performed with random hexamer priming and RNase H digestion. cDNA fragments were end‐repaired, adenylated at the 3′ ends, ligated to sequencing adapters, and amplified using PCR. Library quality and concentration were validated using a Qubit 3.0 fluorometer and size distribution analysis on an Agilent 2100Bioanalyzer. Libraries were clustered on an Illumina cBot system and sequenced as 150 bp paired‐end reads on an Illumina platform. Raw sequencing data underwent adapter removal and trimming of low‐quality bases (Q20 threshold). De novo transcriptome assembly was performed using Trinity. Assembled unigenes were functionally annotated against Gene Ontology and KEGG databases. DEGs were identified using DESeq2 with thresholds of |log_2_ FC| > 1 and q < 0.05. Hierarchical clustering was conducted using Cluster 2.1.0, and functional enrichment analysis was performed using the clusterProfiler R package.
Immunohistochemical Analysis
5.10
Colon tissue sections were deparaffinized, rehydrated, and subjected to antigen retrieval in citrate buffer (pH 6.0), and washed with PBS. Endogenous peroxidase activity was blocked with 3% H_2_O_2_, and sections were incubated with goat serum to prevent nonspecific binding, followed by overnight incubation with primary antibodies against ZO‐1, claudin‐1, or occludin (Servicebio) at 4°C. After PBS washes, sections were incubated with HRP‐conjugated secondary antibodies at 25°C for 50 min. Immunoreactivity was visualized using 3,3′‐diaminobenzidine staining, followed by H&E counterstaining, and evaluated under light microscopy.
Cell Culture and Treatment
5.11
RAW264.7 macrophages (American Type Culture Collection) were cultured in Dulbecco's Modified Eagle Medium (DMEM; Gibco) supplemented with 10% (v/v) fetal bovine serum (Gibco) and 100 U mL^−1^ penicillin‐streptomycin at 37°C in a humidified 5% CO_2_ incubator. For treatments, cells were seeded at 5 × 10^4^ cells per well in 96‐well plates (for viability) or 2 × 10^5^ cells per well in 6‐well plates (for immunofluorescence and western blot analyses) and incubated for 24 h. ILA (Shanghai Yuanye, B27400) and IAA (Shanghai Yuanye, B21810) were dissolved in Dimethyl Sulfoxide (DMSO) to prepare 50 mM stock solutions stored at −20°C and diluted to working concentrations (10–200 µM)in serum‐free DMEM (final DMSO concentration ≤ 0.1% v/v). Cells were pretreated with ILA or IAA, followed by co‐stimulation with LPS (1 µg mL^−1^; Shanghai Yuanye, S11060) for 24 h. Experimental groups included: CK (PBS), LPS (1 µg mL^−1^), LPS+ILA (10–200 µM), and LPS+IAA (10–200 µM).
Nitric Oxide (NO) Quantification
5.12
The release of NO from RAW264.7 macrophage supernatants was quantified using a Griess reagent‐based assay kit (Solarbio, Cat# BC5485). Following 24 h treatments with LPS (1 µg mL^−1^) in the presence or absence of ILA or IAA (10–50 µM), cell culture supernatants were collected and centrifuged at 10,000 × g for 15 min at 4°C. According to the manufacturer's protocol, 100 µL of each sample, standard, or blank was mixed with 50 µL reagent 1, incubated at 25°C for 5 min, and centrifuged again at 10,000 × g for 5 min at 4°C. Subsequently, 100 µL of the resulting supernatant was mixed with 100 µL of freshly prepared chromogenic agent (Solution A: B = 1:1) and incubated at 25°C for 10 min. Absorbance was measured at 550 nm using a BioTek Synergy H1 microplate reader. NO concentration (mM) was calculated as: (ΔA_sample/ΔA_standard) × 0.05, where ΔA represents the absorbance difference (sample or standard −blank). All measurements were performed in triplicate.
Immunofluorescence Staining
5.13
RAW264.7 macrophages cultured on glass coverslips were treated with LPS (1 µg mL^−1^) and ILA or IAA (50 µM) for 24 h. Cells were fixed with 4% paraformaldehyde at 25°C for 15 min, permeabilized with 0.1% Triton X‐100 for 10 min, and blocked with 5% Bovine Serum Albumin (BSA) at 37°C for 1 h. Cells were incubated overnight at 4°C with primary antibodies against CD86 or CD206 (Servicebio) diluted in blocking buffer, followed by incubation with fluorophore‐conjugated secondary antibodies (1:500, 1 h, room temperature, protected from light). Nuclei were counterstained with DAPI (1 µg mL^−1^, 5 min), and coverslips were mounted with ProLong Diamond antifade reagent. Fluorescence images were acquired using a Zeiss LSM 900 confocal microscope and quantified using ROI‐based analysis in ImageJ‐Fiji.
Statistical Analyses
5.14
All experiments were conducted independently in triplicate with sample sizes ranging from 3 to 8 per group. Data are presented as means ± SEM. Statistical analyses were performed using GraphPad Prism v8.0.1 (GraphPad Software, CA, USA). Two‐tailed Student's t‐tests and one‐way ANOVA were used to evaluate differences between groups. Differences were considered statistically significant at p < 0.05 and highly significant at p < 0.01.
Author Contributions
H.Z., J.S., Y.L., and Y.L. performed conceptualization. H.Z., H.Y., A.X., and J.S. performed data curation. H.Z. and X.Z. performed formal analysis. Y.L., Y.M., and Y.H. performed funding acquisition. H.Z. performed investigation. H.Z., Y.D., X.Z., Y.H., and Y.L. performed methodology. Y.L. performed project administration. Y.L. performed supervision. Y.L. performed validation. H.Z., R.M., Y.L., and Y.L. performed visualization. H.Z. wrote the original draft. J.L., Y.M., Y.L., and Y.L. wrote, review and edited the final manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supporting File: advs73494‐sup‐0001‐SuppMat.pdf.
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