Akkermansia muciniphila alleviates metabolic disorders through gut microbiota-mediated tryptophan regulation

doi:10.21203/rs.3.rs-7211958/v1

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Akkermansia muciniphila alleviates metabolic disorders through gut microbiota-mediated tryptophan regulation

https://doi.org/10.21203/rs.3.rs-7211958/v1

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published 22 Nov, 2025

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High-fat diet (HFD) consumption poses a significant public health threat due to its strong association with obesity, hyperlipidemia, cardiovascular diseases, and metabolic syndrome. While Akkermansia muciniphila has emerged as a promising probiotic for ameliorating metabolic disorders, the underlying mechanisms-specifically its interplay with intestinal tryptophan metabolism-remain poorly understood. Here, we demonstrate that A. muciniphila supplementation mitigates HFD-induced metabolic dysfunction in zebrafish by orchestrating a gut microbiota-tryptophan metabolite-AhR signaling axis. Firstly, A. muciniphila treatment effectively reduced the final weight of zebrafish, partially mitigated weight gain caused by HFD, and improved survival rate. In addition, A. muciniphila treatment reduced zebrafish adiposity, hepatic steatosis, and endotoxemia while enhancing the antioxidant capacity of the liver. We further found that A. muciniphila exerted anti-apoptotic effects, improved chronic low-grade inflammatory responses, enhanced antioxidant capacity, and repaired intestinal barriers induced by HFD. These effects were partially abolished by antibiotic pretreatment, confirming microbiota dependency. Subsequent 16S rRNA sequencing analysis revealed that A. muciniphila supplementation reshaped gut microbiota, enriching Staphylococcus and Vibrionaceae, while depleting Acinetobacter, Perlucidica, and Massilia. In addition to reducing lipid synthesis, metabolomic profiled the substantial changes in microbiota-regulated tryptophan metabolism, including increased levels of indole-3-lactic acid, indole-3-acetaldehyde, and 5-hydroxyindole acetic acid in Amuc group. These metabolites activated aryl hydrocarbon receptor (AhR), upregulating downstream IL-22 and improving intestinal integrity and chronic inflammatory response. Pathway analysis and enzyme gene expression detection indicated that A. muciniphila may inhibit the KP pathway while promoting the microbiota-dependent tryptophan metabolic pathway. This study establishes a novel link by which A. muciniphila exerts protective effects against HFD-induced metabolic syndrome through microbiota-mediated enhancement of tryptophan metabolism.

Akkermansia muciniphila

hepatic steatosis

intestinal barrier

intestinal microbiota

tryptophan metabolism

Obesity prevalence has doubled in over 70 countries since 1980, and it has been steadily rising in most countries (Organization WH, 2021). Epidemiological studies show that obesity and overweight are often accompanied by cardiovascular diseases (CVD), diabetes mellitus, non-alcoholic fatty liver disease (NAFLD), chronic kidney disease (CKD), multiple cancers, etc (Gaiţă et al., 2017; Stasi et al., 2022). These illnesses rank among the top 10 global causes of death. In recent decades, besides genetic factors contributing to obesity, the imbalance between energy intake and energy expenditure caused by overnutrition is the dominant factor (Golay and Bobbioni, 1997). Systemic persistent low-grade inflammation and a substantial accumulation of body fat are characteristics of obesity (Esser et al., 2014). The gut microbiota is a major mediator in the relationship between the development of obesity and the consumption of high-fat diets (HFDs), according to a number of studies (Liu et al., 2020).

Gut bacteria and host must be able to talk to each other in order to keep host metabolism and immunity in balance (Marchesi et al., 2016). Current research suggests that a HFD is highly correlated with the dysbiotic gut microbial structure, this change occurs within a day. Obese individuals have less microbial diversity, accompanied by large variation in core microorganisms (Turnbaugh et al., 2009). Meanwhile, prior studies noted that HFD-fed mice have abundant gut Pseudomonadota, whose increased abundance may indicate dysbiosis and higher disease risk (Zhu et al., 2023; Duan et al., 2024). Specific intestinal bacteria taxa (Lachnospiraceae, Desulfovibrio) have been suggested to exacerbate HFD-induced metabolic disorders and obesity, while others are negatively associated with these diseases (Yoon et al., 2021;Takeuchi et al., 2023; Qi et al., 2024; Aron-Wisnewsky et al., 2020). Gut microbiota plays a major role in the development and progression of fatty liver disease. The transfer of microbiota from obese animals to germ-free animals leads to increased body fat accumulation, while the germ-free animal model is safeguarded against obesity's effects (Turnbaugh et al., 2006). Fatty liver disease induced by HFD can be prevented by repairing the disruption of microbiota (Porras et al., 2017; Ding et al., 2020). The gut microbiota intervention has demonstrated significant therapeutic advantages for the clinical treatment of NAFLD (Malesza et al., 2021; Wu et al., 2024). Bacteroides vulgaris reduces fat absorption and inhibits 5-HT production to mitigate the effects of HFD-induced obesity (Wen et al., 2024). By triggering the liver receptor acetate/GPR43/IL-6/JAK1/STAT3 axis, Bifidobacterium pseudolongum can alter the gut microbiota's composition and prevent the growth of NAFLD-HCC (Song et al., 2023). Bacteroides uniformis has been identified to improve the progression of MAFLD through novel bile acid 3-sucCA, and its beneficial effects depend on the presence of gut microbiota (Nie et al., 2024). Furthermore, nicotinamide, a metabolite of B. uniformis, promotes the abundance of Lactobacillus Johnsonii through bacterial cross-feeding. Its release of indole-3-lactic acid (ILA) is a key bioactive substance that activates AhR (Zhang et al., 2024). The direct or indirect effects of potential probiotics render them desirable therapeutic target for HFD-induced metabolic syndrome.

The gut microbiota and its metabolites play a major role in the physiological metabolism of the host. The three most researched intestinal bacterial metabolites are tryptophan metabolites, bile acids generated, and short-chain fatty acids (SCFA). Among them, the intestinal microbiota can metabolize 4–6% of tryptophan through the indole pathway to produce indole and its derivatives (IDs) (Agus et al., 2018). By activating AhR, IDs, which are AhR's natural ligands, have been demonstrated to enhance the intestinal barrier (Liu et al., 2023). It has been reported that HFD leads to the consumption of tryptophan metabolites that depend on microbial communities (Krishnan et al., 2018). The impairment of AhR activity and HFD-induced metabolic syndrome are significantly positively correlated (Natividad et al., 2018). Supplementing with AhR agonists and ligands can lead to improvements in metabolic disorders, including inflammatory responses and hepatic steatosis (Postal et al., 2020). A. muciniphila is a symbiotic bacterium in the mucous layer, as a next-generation candidate probiotic, it has garnered a lot of attention due to its role in treating obesity, Type 2 Diabetes, and oxidative stress of aging (Everard et al., 2013; Estefanía et al., 2021). Yin et al. (2021) found a reciprocal interaction between tryptophan and A. muciniphila in vitro (Yin et al., 2021). It can metabolize tryptophan to indole, IAA, indole-3-carboxaldehyde, and ILA, these IDs, in turn, promote its growth. According to a preliminary investigation, A. muciniphila relieves colitis in mice by blocking the kynurenine pathway's (KP) activation for tryptophan metabolism (Gu et al., 2021). Further, it may inhibit the progression of CRC by downregulating AhR (Zhang et al., 2023). Shi et al. (2021) linked AhR ligands and A. muciniphila abundance to impeding saccharin/sucralose-induced NAFLD (Shi et al., 2021). However, there has been no evidence on the direct intervention of A. muciniphila in regulating microbial-tryptophan metabolism pathway to improve metabolic syndrome induced by HFD. Therefore, further clarification is needed on whether A. muciniphila improves metabolic syndrome by regulating gut microbiota composition, and restoring HFD-induced depletion of the tryptophan metabolites in indole pathway.

As a model organism, the zebrafish shares similar mechanisms for regulating metabolism with mammals and has been extensively applied in almost all fields of human diseases, including lipid metabolism (Clifton et al., 2010). In the current study, we determined whether A. muciniphila could regulate hepatic metabolic dysfunction and intestinal inflammation in HFD-induced zebrafish. And we further used antibiotic intervention was to evaluate whether this prebiotic effect is mediated by intestinal microbiome. Moreover, we utilized omics techniques to investigate the impact of A. muciniphila on intestine metabolism profile, and utilized correlation analysis to explain how gut microbiota alleviates metabolic disorders and immune response caused by HFD through tryptophan metabolism. Moreover, after A. muciniphila intervention, the three major tryptophan metabolic pathways, as well as the expression of AhR and its downstream genes, were evaluated. This study preliminarily clarifies that A. muciniphila corrects HFD-induced metabolic abnormalities through gut microbiota-dependent regulation of tryptophan metabolism.

Fish husbandry

All experiments and fish care procedures were approved by the Experimental Animal Welfare Ethics Committee of Zhejiang Ocean University (Assurance No. 2024050). Adult zebrafish (1-month-old) for Experiment 1 and Experiment 2 were cultured in 5L tanks in a recirculating system (n = 6 tanks/diet treatment, 20 fish/tank). Feeding conditions were as follows: temperature 27 ± 1 ℃; inlet rate 120 L/h; dissolved oxygen (DO) > 6.0 mg/L; nitrogen < 0.02mg /L; nitrite < 0.05 mg/L; pH 7 ± 0.5.

Feeding experiments and diets

Experiment 1.

In this experiment, A. muciniphila (ATCC BAA-835, BioSci, Hangzhou, China) was supplemented to investigate its role in improving HFD-induced metabolic syndrome such as hepatic steatosis, intestinal homeostasis disorders of zebrafish. The crude lipid content of the basal diet (LFD) was 6%, and the high-fat diet (HFD) was used to induce fatty liver model with lipid content of 16%. On the basis of the HFD, 10^{^7} cfu/g, 10^{^8} cfu/g, 10^{^9} cfu/g of A. muciniphila were added respectively (Amuc) (Supplementary Table 1). The experiment lasted for 4 weeks, the suitable effective dosage of A. muciniphila was determined firstly. Blood was extracted by tail amputation, placed at 4 ℃ for 30 min, then centrifuged (2000 g, 4 ℃, 2 min) subsequently and the serum was obtained for detection. After the liver and intestinal tissue were taken out, then rinsed with PBS for detection of biochemical parameters. After the last feeding for 12 h, intact liver and intestine were taken carefully and fixed with 4% paraformaldehyde immediately for morphological observation. Liver and intestinal samples were transferred into 1.5 ml RNA-free tube filled with Trizol reagents, for detecting mRNA expression level. Intestinal content (12 tails/sample) was submitted to the detection of Untargeted metabolomics and 16s rRNA high-throughput sequencing.

Experiment 2.

In order to find out if the gut microbiota mediates the impact of A. muciniphila on HFD-induced fatty liver in zebrafish, antibiotics (0.25% polymyxin and 0.33% neomycin) were added to each of the three diets in Experiment 1. LFD and HFD with antibiotics served as negative and positive controls, respectively. The feeding period and conditions were consistent with those described in Experiment 1. After the completion of intervention culture, the body weight of the antibiotic-treated (Abx) zebrafish was measured, as were the triacylglycerol (TAG) levels in the liver and muscles. Moreover, the morphology of the intestines and liver was detected by hematoxylin-eosin (H&E) staining.

Hematoxylin-eosin (H&E) staining, Alcian blue-PAS (AB-PAS)

Liver and intestinal tissues from adult zebrafish in Experiments 1 and 2 were extracted, washed twice with PBS, and fixed in 4% paraformaldehyde for H&E and AB-PAS staining, as described by Zhang et al (2019). Intestinal tissues were embedded in paraffin and prepared for AB-PAS staining. Subsequently, the sections were dehydrated and made transparent and rinsed with PBS buffer first. Then it was stained with alcian blue solution (sigma, America), followed by three wash steps every 5 min with distilled water. Then the sample as immersed in schiff reagent and re-stained with hematoxylin. The slice was differentiated with acid solution, reversed blue with Scott's blue solution, and dehydrated with ethanol. The representative image of each group was microscopically observed using a microscope (Leica DMIL-LED, Germany).

Biochemical parameters analysis

The collection of zebrafish serum samples in Experiment 1 was used for detecting biochemical indicators. Alanine transaminase (ALT) and aspartate aminotransferase (AST) were measured with commercial kits (Nanjing Jiancheng Bioengineering Inc., China) following the manufacturer’s instructions. The diluted serum was successively added with matrix solution, dinitrophenylhydrazine, and sodium hydroxide, and OD₅₁₀nm was determined after the reaction was completed. The standard curve was fitted and the enzyme activity of each sample was calculated, expressed as U/L.

Zebrafish liver from four fish was pooled as a sample (about 10mg) for TAG determination in Experiment 1 and Experiment 2. The method was referred to as previously described with minor modifications (Zhang et al., 2019). The TAG content was normalized by the protein (mg) of liver and muscle, and expressed as ug/mg prot. The total cholesterol (T-CHO) (Nanjing Jiancheng Bioengi-neering Inc., China) content of liver was also measured. The working liquid containing cholesterol esterase, cholesterol oxidase and peroxidase was pre-prepared and mixed with the sample (100:1) at 37 ℃ for 10 min, and the OD₅₀₀ was determined with the microplate reader. Hepatic Malondialdehyde (MDA) was measured by Lipid Peroxidation MDA Assay Kit (Beyotime, China). The homogenate of the liver sample was mixed with TBA diluent, TBA storage solution and the mixture of antioxidants at a ratio of 1:2, heated at 100 ℃ for 15 min, centrifuged at 1000 g for 10 min, and determined at 532 nm with a microplate reader.

Serum endotoxin levels were determined using the ToxinSensor™ Chromogenic LAL Endotoxin Assay Kit (GenScript) following the manufacturer's protocol, with results reported in endotoxin units per milliliter (EU/ml). Intestinal diamine oxidase (DAO) activity was assessed via a commercially available ultraviolet spectrophotometric assay (Grace Biotechnology Co.,Ltd). The intestinal tissue was weighed and homogenated in PBS at 4 ℃. The determination method of DAO activity follows the manufacturer's instruction, which is based on its catalytic formation of aldehyde and ammonia by diamine, and the formation of NADH by ammonia under the action of glutamate dehydrogenase. The change of NADH at OD₃₄₀ was determined by ultraviolet light method to calculate the activity of DAO. The oxidation of 1 nmol NADH per milligram of tissue per minute is defined as a unit of enzyme activity. Intestine and liver samples of homogenates were centrifuged and the supernatant was used to evaluate Total antioxidant capacity (T-AOC) activity (Nanjing Jiancheng Bioengi-neering Inc., China). Peroxidase and ABTS working liquid were added to the reaction system successively, and the reaction was carried out at room temperature for 6 min. The wavelength of 410 nm was measured, and the standard curve of Trolox was used to calculate the antioxidant capacity of each sample (mmol/g). Liver superoxide dismutase (SOD) activity was further tested according to the instructions of the kit (Beyotime Biotechnology).

Intestine microbiota analysis

Within 4–6 h of the last feeding after the 4-week, intestinal contents were collected. And the genomic DNA was extracted with Fast DNA spin kit (MP, Biomedicls). The quality and concentration of DNA extraction were assessed prior to sequencing. The V3-V4 region of 16S rRNA was amplified with U341F and U806R, and amplification results were delivered to Illumina HiSeq platform (Majorbio, China). The paired-end sequences of each sample were deposited with NCBI SRA and are available under accession no. PRJNA1094899. The raw data was processed by concatenation and quality control, denoised with DADA2, the representative sequences were output and then grouped into ASV (Amplicon Sequence Variants). Mothur 1.30.2 was used for α-diversity (ACE, Chao, Shannon, Simpson indices). QIIME 1.9.1 assessed β-diversity via PCoA based on Bray Curtis distance to test microbial community similarity across groups. Bar charts visualized A. muciniphila's effects on intestinal community relative abundance at phylum and genus levels using taxonomic analysis results. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) used the representative sequences from ASV to predict the metagenomic function. Furthermore, based on the KEGG pathway in the PICRUSt2 module at the level 3 classification, the gut microbiota was predicted to specifically compare the differences in tryptophan metabolism among different groups.

Untargeted metabolomics of intestine

After 4 h of feeding, the intestine of zebrafish (5 fish pooled as a sample, 6 replicates per group) was collected for nontargeted metabolomics analysis. The pre-treatment process of the sample was performed as follows: 25 ± 5 mg sample was weighed and homogenized with 200 µl extraction solution (methanol:water = 4:1). The supernatant was obtained by centrifugation (13000 g, 4 ℃, 15 min) for LC-MS (UHPLC-Q Exactive HF-X) detection. The chromatographic column was ACQUITY UPLC HSS T3 (100mm×2.1mmi.d., 1.8µm; Waters, Milford, USA), mobile phase A was 95% water + 5% acetonitrile (containing 0.1% formic acid), and mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid), with an injection volume of 3 µl, and a column temperature of 40 ℃. The sample was ionized by electrospray, and the mass spectrum signals were collected by positive and negative ion scanning modes respectively. Quality control samples were used to assess the stability of the entire testing process. And the raw data was imported into the metabolomics processing software ProgenesisQI v3.0 (Waters Corporation, Milford, USA) for raw data matrix analysis and metabolites identification. VIP values were calculated, with metabolites having VIP ≥ 1 considered significant. Differential metabolites (DMs) were screened by VIP ≥ 1 and p < 0.05. Fold change indicated DM differences between HFD vs LFD and Amuc vs HFD. The volcano plot was used to visualize the changes in differential metabolites between the Amuc vs HFD and HFD vs LFD groups (Fold Change ≥ 1, p-value < 0.05). A supervised PLS-DA (partial least-squares discriminant analysis, PLS-DA) was performed on MajorBio Cloud to reveal global metabolic changes among the three dietary groups. Subsequently, the differential metabolites of different groups were mainly mapped to which KEGG pathways they belonged to, and pathway enrichment analysis was performed between different groups. The Heatmap and clustering tree were constructed to analyze the global differential metabolites among the different groups.

Correlation analysis between gut microbiota and differential metabolites

The Spearman correlation analysis was utilized to explore the correlation between gut microbiota and differential metabolites. Metabolites, as subsets of environmental factors, were analyzed alongside bacterial genera using the Majorbio cloud platform (https://cloud.majorbio.com). All statistical analyses employed two-sided tests with p < 0.05 as the significance threshold. Correlations were assessed and adjusted for multiple comparisons via false discovery rate (FDR).

RNA extraction, and qRT-PCR analysis

Total RNA of zebrafish intestinal and liver tissue was extracted with TRIzol reagent (Cowin, China). The samples were homogenized and centrifuged at 13000 g and 4 ℃ for 15 min. The supernatant was mixed with 200 µl chloroform and vortexed, then centrifuged as above. The supernatant was transferred to new RNA-free EP tube, 400 µl isopropyl alcohol was added and gently mixed, and placed at -20 ℃ for 1 h. The RNA was reverse-transcribed into cDNA with one-step method using FastKing RT reagent kit (TIANGEN, China). Subsequent qRT-PCR analysis using cDNA as a template was carried out by SYBR Green FsatReal RT-PCR premix (TIANGEN, China). The reaction procedure followed the method described previously, and the annealing temperature was modified according to each specific primer (Supplementary Table 2). Using Rps11 as the reference gene, the results were normalized, and the relative expression of genes at mRNA level was analyzed by 2^−ΔΔCT method. The LFD group served as the control, with its value set to 1, and the other two groups are presented as fold of control.

Statistics analysis

Statistical data reported here are shown as mean ± SEM. All statistical analyses were performed by GraphPad Prism 8.0.1. Comparisons between the two groups were performed using Student’s t -tests (and nonparametric tests). Tukey's post hoc tests were conducted after a one-way analysis of variance (ANOVA) to evaluate multiple sample differences. The data of Experiment 1 and Experiment 2 were compared with the HFD or corresponding antibiotic intervention group, and the significance was marked directly above the corresponding group column (* represents p < 0.05, **represents p < 0.01, ***represents p < 0.001).

A. muciniphila altered growth performance and alleviated HFD-induced hepatic steatosis

Firstly, we discovered that a dosage of 10^{^8} cfu/g could significantly reduce the liver TAG content by introducing A. muciniphila at varying concentrations (p < 0.05; Supplementary Figure 1). In addition, we also conducted a comprehensive evaluation considering the effects on growth performance, and thus determined to use this dose in all subsequent experiments. Firstly, the growth performance and feed utilization of zebrafish were evaluated (Supplementary Table 3). HFD significantly reduced the survival rate (SR) of zebrafish compared to LFD (p < 0.05), while the A. muciniphila supplementation reduced the mortality induced by HFD. Compared with LFD, HFD showed a showed a trend of an increased of weight gain rate (WGR, p = 0.06), while the Amuc group did not. Similarly, HFD had a significant tendency towards increasing specific growth rate (SGR) of zebrafish (p = 0.09), while the addition of Amuc had no effect on SGR. These data show A. muciniphila administration improves zebrafish survival without affecting growth or feed conversion.

We further investigated the effect of A. muciniphila addition on HFD-induced lipid metabolism disorders and liver injury. This experiment found that HFD significantly increased the liver fat accumulation of zebrafish, and there was a distinct tendency to reduce liver fat accumulation due to the supplementation of A. muciniphila (p = 0.08; Figure 1A). While there was no significant effect on muscle TAG content (Figure 1B). A. muciniphila could significantly reduce the content of T-CHO in liver of zebrafish (p < 0.05; Figure 1C). HFD significantly increased oxidative stress of zebrafish liver manifested as higher level of MDA, and the addition of A. muciniphila can effectively reverse the negative effects (p < 0.01, p < 0.05; Figure 1D). Liver H&E staining confirmed A. muciniphila's role in improving hepatic steatosis, as its supplementation notably reduced HFD-induced liver fat droplet accumulation and enlargement (Figure 1E).

To further investigate how A. muciniphila regulates hepatic lipid metabolism, mRNA expression of lipolysis- and lipogenesis-related genes was assessed. Consistently, A. muciniphila significantly downregulated peroxisome proliferator-activated receptor γ (PPARγ) (p < 0.05), CCAAT enhancer binding proteins (C/EBPα) (p < 0.01), fatty acid synthetase (FAS) (p < 0.05), Diacylglycerol-O-Acyltransferase Homolog 2 (DGAT2) (p < 0.05), and upregulate the expression of uncoupling protein 2 (UCP2) (p = 0.06; Figure 1F). However, it had no significant effect on acetyl-CoA carboxylase1 (ACC1, p = 0.08) or peroxisome proliferator-activated receptor α (PPARα, p = 0.09). HFD impairs hepatic antioxidant capacity, which is manifested by a significant compensatory increase in T-AOC (p < 0.05, Figure 1G). Supplementation with A. muciniphila can weaken this effect, accompanied by an enhancement in SOD activity (p < 0.05, Figure1H). The addition of A. muciniphila can effectively restore HFD-induced liver injury, manifested by a significant decrease in serum ALT and AST levels (p < 0.05, p < 0.01; Figure 1I, J). The above results indicate that A. muciniphila could improve HFD-induced hepatic steatosis via inhibiting lipid synthesis and promoting lipolysis by regulating key genes. Moreover, it can boost antioxidant capacity while alleviating liver oxidative stress and damage.

A. muciniphila mitigates HFD-induced gut injury and improves barrier function

Acidic mucin secreted by goblet cells is stained blue by AB-PAS stain. Supplementation with A. muciniphila significantly reversed the HFD-induced reduction in goblet cells (Figure 2A). HFD induced intestinal chronic inflammation, as zebrafish on HFD showed upregulated intestinal pro-inflammatory factors TNFα and IL-1β compared with LFD. A. muciniphila supplementation downregulated IL-1β versus HFD (p < 0.05; Figure 2C). The expression of anti-inflammatory cytokines IL-10 and TGFβ was increased (p = 0.07, p < 0.05; Figure 2D, E). Consuming high levels of fat induces endoplasmic reticulum (ER) stress of intestines, which further activates apoptosis in fish (Ling et al., 2019). As shown in Figure 2F, oral administration of A. muciniphila significantly down-regulated pro-apoptotic genes bid and bax, up-regulated anti-apoptotic gene bcl-2, and increased the bcl-2/bax ratio. (p < 0.05; Figure 2F). The activities of caspase3 and caspase9 further confirmed that it alleviated intestinal cell apoptosis in HFD-fed zebrafish (p < 0.001, p < 0.05; Figure 2G, H).

HFD leads to a compromised intestinal barrier and increased serum endotoxin levels. Oral administration of A. muciniphila evidently decreased serum endotoxin levels (p = 0.06, Figure 3B). Meanwhile, the it could improve the damage to the intestinal morphological structure (Figure 3A). qRT-PCR showed that A. muciniphila upregulated intestinal barrier-related genes, including Hif-1α, muc2, Tjp-1α, claudin1, occludin (p < 0.05, Figure 3E). Intestinal antimicrobial peptides play an important role in intestinal barrier function. The expression of intestinal defb11 was significantly down-regulated by HFD, and the addition of A. muciniphila could significantly reverse it (p < 0.05, Figure 3F). But there was no discernible difference in the expression of lysozyme and hepcidin in both the HFD and Amuc groups (Figure 3G, H). Moreover, A. muciniphila has the ability to greatly increase DAO enzyme activity and improve the antioxidant capacity in the intestine (p < 0.05, Figure 3C, D).

A. muciniphila alleviates metabolic disorders partially depending on gut microbiota

To determine whether gut microbiota supports the probiotic effect of A. muciniphila-induced metabolic phenotype, we performed a 4-week broad-spectrum antibiotics cocktail treatment on zebrafish. The impact of A. muciniphila in lowering HFD-induced weight gain was eliminated following a 4-week intervention (Figure 4A). Specifically, its effect in alleviating and excessive fat deposition of liver and intestine was also eliminated (Figure 4B, C). And the liver H&E section showed no change in the alleviation of steatosis after antibiotic intervention (Figure 4E). At the same time, we also found that A. muciniphila cannot effectively reverse the intestinal damage caused by HFD after co-administration of antibiotics (Figure 4D). This indicates that the metabolic regulation of A. muciniphila is affected after depletion of the gut microbiota.

A. muciniphila application regulated HFD-induced gut microbiota community dysbiosis, elevated the tryptophan metabolizing-related genus

A. muciniphila colonizes the intestinal mucus layer, digesting mucus to interact with symbiotic microbiota via trophic relationships. It enhances microbial gene richness and ecosystem abundance, and its gut crosstalk may improve obesity-related metabolism (Ling et al., 2019). Intestinal community composition was analyzed via 16S rRNA high-throughput sequencing, yielding 1,430,214 optimized sequences clustered into 2,655 OTUs. Ace and Chao index showed that microbial richness had an obvious increase, while Shannon index was significantly reduced in the Amuc group (Figure 5A, Supplementary Table 4). Principal Coordinate Analysis (PCoA) showed that the intestine microbiota structure of LFD and HFD had relatively high similarity, while there is obvious shifts from the Amuc group (Figure 5D). The above results suggested that the administration of A. muciniphila may exert a strong regulating effect on the intestinal microbial community. We next analyzed the composition of intestinal microbial community in different groups. At the phylum level, the types of core microbiota were identical in different dietary groups, including Pseudomonadota, Actinobacteriota, Bacillota, Verrucomicrobiota, Bacteroidota (Figure 5B, Supplementary Table 5). In contrast to the HFD group, the Amuc group exhibited a significant reduction in the relative abundance of Pseudomonadota (41.1% vs. 58.6%). Conversely, the relative abundance of Bacillota was markedly higher in the Amuc group (29%) than in the HFD group (9.72%). There were no obvious changes in Bacteroidota among the three dietary groups. PICRUSt2 combined with the KEGG prediction found that the addition of A. muciniphila increased the relative abundance bacteria related to Biosynthesis of Secondary Metabolites (ko01110, p < 0.01) and acetate synthesis (ko02020) (Figure 5E). Further exploration found that the number of ASV related to tryptophan metabolism had a obvious increase in the Amuc group compared to the HFD group (p = 0.06, Figure 5F). Analysis at the genus level found that A. muciniphila increased the relative abundance of Staphylococcus (p < 0.01), Vibrionaceae (p = 0.2), unclassified_f_Vibrionaceae (p = 0.2) and significantly downregulated the relative abundance of Acinetobacter, Perlucidica, Massilia, Acidovoorax and Bradyrizobium (p < 0.01, p < 0.05; Figure 5C, Supplementary Table 6).

A. muciniphila altered the gut metabolite profile and pathways

The gut microbiota affects the metabolites. Therefore, the changes of zebrafish gut metabolites after A. muciniphila intervention were investigated by untargeted transcriptomics. We identified a total of 4345 metabolites, which were parameterized (p < 0.05, VIP 1.0 and fold change (FC) > 1,) to construct a differential metabolite dataset. The volcano plot showed that HFD had elevated 486 and reduced 162 metabolites when compared to LFD, while the addition of A. muciniphila upregulated 180 and downregulated 308 metabolites when compared to the HFD group (Figure 6A, B). This indicates that A. muciniphila administration could significantly alter the gut metabolic profile. Further two-component PLS-DA models comprehensively showed the metabolome profiles evidently discriminated among the three dietary groups (Figure 6E). More specifically, the first two components account for 29.1% of the total variation, with the first component explaining 17.1% and the second 12%, respectively. Hierarchical cluster analysis (HCA) was performed for the top 30 differential metabolites (Figure 6C). HCA presented two main clusters, LFD and HFD samples, Amuc and LFD samples clustered separately in a sub-cluster. This indicated the differences in accumulated metabolites as a result of the addition of A. muciniphila. The key metabolic pathways caused by different diets were further enriched through KEGG enrichment pathway analysis. As depicted in Figure 6D, the top 15 metabolic pathways with the highest enrichment were selected from the KEGG database in Amuc group versus HFD group. Among the enriched KEGG pathways, Lipid metabolism (Primary bile acid biosynthesis, Steroid hormone biosynthesis, Linolenic acid metabolism, alpha-Linolenic acid metabolism, Glycerophospholipid metabolism), Endocrine system (Insulin/GnRH signaling pathway), Signal transduction (Apelin/Hedgehog/MAPK signaling pathway), and Gap junction, Ferroptosis pathway were significantly enriched (p < 0.05). Furthermore, in the Amuc group, the overall expression of the pathways of Primary bile acid biosynthesis and Insulin signaling pathway tended to be upregulated, while the overall expression of the pathways related to lipid synthesis, such as Linolenic Acid/alpha-Linolenic acid/Glycerophospholipid metabolism, tended to be downregulated.

A. muciniphila shifted tryptophan regulation, activating the AhR

A. muciniphila modulates the community structure of the gut microbiota. It has been demonstrated that three gut microbiota metabolites, including secondary bile acids metabolism, SCFA metabolism, and tryptophan are crucial to the host's physiological metabolism (Nicolas et al., 2019). Given that HFD can induce drastic changes in tryptophan metabolism, we conducted a study to investigate whether the administration of A. muciniphila has an impact on the pathways associated with the tryptophan-related differential metabolites (Figure 7). Three main metabolic processes catabolized tryptophan when it enters the gastrointestinal tract: the host kynurenine and serotonin pathway, and the gut microbial system which directly converts tryptophan into its distinct metabolites, indole and IDs (Agus et al., 2018). Tryptophan metabolites were therefore examined both before and after the administration of A. muciniphila. We found the metabolites of the AhR pathway, which represents the microbial metabolism of tryptophan, have increased, including indole (p < 0.05), indole-3-acetaldehyde (IAAId) (p < 0.05), 5-hydroxylindoleacetylglycin (p < 0.05), ILA (p < 0.05), while indoleacetaldehyde and indolepyruvate had no notable alteration. The metabolites in KP such as kynurenic acid and 5-hydroxykynurenine showed no discernible differences, while quinic acid (p = 0.056) exhibits a considerable downward trend. Serotonin levels significantly decreased although the 5-hydroxy-L-tryptophan in serotonin pathway remained unchanged. Consistently, the Amuc group showed a marked reduction in the expression of key enzymes in the KP and 5-HT pathways compared to the HFD group, including IDO1 (p < 0.05), TDO2a (p < 0.05), Kynu (p = 0.067), and TPH1 (p = 0.074) (Figure 7). In summary, the intake of A. muciniphila inhibited several key enzymes in the KP and 5-HT pathway, consequently driving the tryptophan metabolic pathway towards a microbiota-dependent direction.

Metabolomics studies confirmed that mucin phage significantly altered tryptophan metabolites. These metabolites, in turn, activate AhR, which plays a role in maintaining intestinal homeostasis and regulating metabolism (Yano et al., 2015). To determine its effect on intestinal AhR signaling, we first conducted detection using qRT-PCR. It is as expected that A. muciniphila can successfully counteract the HFD-induced decrease of AhR1 and AhR2 at the mRNA level (p = 0.06, p < 0.05; Figure 8A, B). To clarify AhR's effect on downstream targets, we detected IL-17 and IL-22 expression (Figure 8D, E). HFD tended to reduce IL-17 (p = 0.06) and IL-22 (p = 0.32) levels, while A. muciniphila addition significantly increased IL-22 levels (p < 0.05). AhR plausibly contributes to the interaction between metabolism and the proinflammatory state during the onset of obesity and in T2D patients (Carcia-Villatoro et al., 2017).Moreover, IL-22 maintains intestinal integrity and barrier functions, and is linked to insulin resistance in obesity. Therefore, A. muciniphila alleviates HFD-induced intestinal inflammation and barrier by activating AhR and downstream target gene IL-22.

Correlation analysis among intestinal microbiota and metabolites

Subsequently, Figure 9 provides a clear representation of the outcomes derived from the pathway analysis. We discovered that the treatment with A. muciniphila enhanced the gut microbiota's capacity to metabolize tryptophan. This enhancement potentially led to the generation of higher levels of 5-hydroxyindole acetic acid (5-HIAA), ILA, IAAId and indole-3-acetic acid (IAA). Consequently, the A. muciniphila intervention potentially augmented the metabolism of tryptophan along the serotonin pathway (5-HT) and the AhR pathway. In line with the latest and most comprehensive literature accounts, the tryptophan metabolites elevated through these two metabolic pathways are intrinsically dependent on the gut microbiota (Zhang et al., 2024; Du et al., 2024). Therefore, A. muciniphila's activation of AhR and its target gene depends on its regulation of gut microbiota-mediated tryptophan metabolism.

For a more profound exploration of the microorganisms involved in the regulatory effect of A. muciniphila on tryptophan metabolism, Spearman analysis was employed to establish the connection between the altered bacteria and tryptophan-related differential metabolites. As depicted in Figure 10,

treatment with A. muciniphila up-regulated indole, indolepyruvate, IAAId, and 5-HIAA, which positively correlated with Plesiomonas, Vibrio, unclassified_f_Vibrionaceae, and Staphylococcus. Specifically, relative abundances of these bacterial taxa were elevated in the Amuc group compared to HFD group (p = 0.1; p < 0.05; p < 0.05; p < 0.01). Nevertheless, quinic acid and kynurenic acid, which are products of the host's tryptophan metabolic pathway, either had no association or a negative correlation with these bacteria. In contrast, they showed a positive correlation with chloroplasts. Furthermore, a negative correlation was detected between ILA and both Bradyrhizobium and Massilia. Remarkably, the relative abundances of these two bacterial taxa were substantially reduced in the Amuc group (p < 0.01; p < 0.05). In contrast, serotonin levels were down-regulated in the Amuc group. This neurotransmitter displayed a significant positive correlation with Massilia, Bradyrhizobium, Acinetobacter, Perlucidibaca, and Sphingobium. Significantly, the Amuc group demonstrated considerably reduced relative abundances of these bacterial taxa in comparison to the HFD group (p < 0.05). In summary, A. muciniphila may promote the abundance of Plesiomonas, Vibrio, unclassified_f_Vibrionaceae, and Staphylococcus while reducing that of Bradyrhizobium, Massilia, thereby regulating tryptophan metabolism.

With the improvement of living standards and changes in lifestyle, the increasing intake of carbohydrates and lipids has led to a rising incidence of chronic metabolic diseases. Currently, the global prevalence of NAFLD in the adult population has reached approximately 32%, making it the primary chronic liver disease at present (Teng et al., 2023). Over the past two decades, the incidence of NAFLD has increased seven-fold, becoming a primary and prevalent driver of global liver-related deaths (Allen et al., 2018).

NAFLD is primarily driven by overnutrition and insulin resistance, leading to hepatic lipid droplet accumulation, ectopic fat, and metabolic abnormalities (Paik et al., 2020). Current research indicates that in addition to hepatic steatosis, NAFLD is accompanied by a series of systemic metabolic disorders, including endoplasmic reticulum stress, inflammatory response, bacterial translocation caused by gut microbiota dysbiosis, and the consequent endotoxemia (Zhao et al., 2020; Zimmermann et al., 2019; Utzeri et al., 2017). Thus, the pathogenesis of NAFLD has gradually moved beyond the "two-hit hypothesis" proposed by British scholar Day in 1998 to the current “multiple-hit hypothesis” (Day and James, 1998; Loomba et al., 2021). The disease in NAFLD patients progresses slowly, making it difficult to conduct continuous clinical dynamic observation and sampling. Therefore, research on the pathogenesis of NAFLD, drug screening, and target evaluation all rely on appropriate experimental animal models. Currently, the pathogenic factors of diet-induced models in rodents are similar to those of human NAFLD. Rats and mice have been widely used in NAFLD efficacy evaluation experiments, but it takes a long time to establish the models (Van et al., 2017; Zhao et al., 2022).

In recent years, zebrafish, as a new type of model organism, has been widely used in research on human diseases (Liu, 2022; Chang et al., 2023). Zebrafish are small in size, easy to breed, and have a high reproductive rate, which can significantly shorten the test cycle. Zebrafish embryos develop rapidly and are transparent throughout the development process, which is conducive to in vivo research on development and nutritional metabolism (Siriyappagouder et al., 2020). Notably, zebrafish share 87% genomic homology with humans, with their genomes fully sequenced. At present, researchers have established zebrafish fatty liver models through gene knockout and diet-induced methods (Zhao et al., 2022; Asaoka et al., 2013; Fan et al., 2020). In our prior study, a zebrafish model of nutritional hepatic steatosis was successfully established by feeding 1-month-old zebrafish a 16% high-fat diet for 4 consecutive weeks (Li et al., 2024; Zhang et al., 2022; Zhang et al., 2023). Using this zebrafish model, we are systematically exploring A. muciniphila's therapeutic potential in ameliorating hepatic steatosis and enhancing gut barrier health, aiming to elucidate novel protective mechanisms via integrated molecular biology and multi-omics approaches.

In this work, therapeutic supplementation with A. muciniphila at 10^{^8} cfu/g significantly attenuated pathological TAG accumulation, and it reduced the HFD-induced body weight gain and mortality. To explore the molecular mechanism by which A. muciniphila regulates hepatic lipid metabolism, we found that it significantly reduced the expression of key enzymes in lipid synthesis, such as PPARγ, C/EBPα, FAS, ACC1, and DGAT2. However, it had no effect on β-oxidation. Its regulatory effect on lipid metabolism in zebrafish closely resembles that in HFD-induced mice (Everard et al., 2013; Plovier et al., 2017). In addition, we found that the treatment with A. muciniphila had no effect on muscle fat. Zhao et al. (2017) showed that it could reduce muscle fat by reducing the gene expression related to fatty acid synthesis (Zhao et al., 2017). This difference might be due to the use of a different model. Meanwhile, the administration of A. muciniphila can significantly alter the gut metabolic profile. Consistent with hepatic metabolic remodeling, ‌A. muciniphila‌ intervention induced significant enrichment of intestinal lipid catabolism pathways.

The gut-liver axis's role in NAFLD pathogenesis is elaborated, emphasizing that the intestinal barrier-an integral part of this axis-protects the liver by blocking intestinal noxious substance translocation to it (Albillos et al., 2020). The intestinal barrier comprises four interconnected layers: (a) mechanical barrier (epithelial tight junctions), (b) chemical barrier (mucus and antimicrobial peptides), (c) immune barrier (sIgA and immune cells), and (d) biological barrier (gut microbiota) (Scaldaferri et al., 2012). Oral A. muciniphila degrades intestinal mucus, compensatorily normalizes inner mucus layer thickness, and enhances intestinal barrier function (Grander et al., 2018). However, it has also been reported that A. muciniphila can exacerbate mucosal barrier damage during intestinal infection or inflammation (Marcella et al., 2021). In our study, A. muciniphila effectively reversed HFD-induced damage to intestinal mechanical and chemical barriers, significantly upregulating tight junction proteins Tjp-1α, claudin1, and occludin, as well as that of muc2 and defb11. This is consistent with morphological observations. Overall, this indicates that in the context of chronic inflammation induced by HFD, A. muciniphila improve the intestinal barrier and simultaneously reduce the inflammatory response.

Increasing evidence suggests that gut dysbiosis increases the risk of obesity and NAFLD. Moreover, Gram-negative (G-) bacteria are prevalent in NAFLD, especially Pseudomonadota (Filipovic et al., 2021; Vasques-Monteiro et al., 2021). Furthermore, given that Pseudomonadota are recognized as one of the premier sources of lipopolysaccharide (LPS), the increase in their proportion further leads to the occurrence of endotoxemia in mice fed HFD (Miura et al., 2017). As endotoxin travels through the bloodstream to reach the liver, it influences the liver's inflammatory and metabolic dysfunction. LPS stimulates pro-inflammatory cytokines, thereby perpetuating low-grade inflammation in metabolic disorders (Mohammad et al., 2021). Furthermore, inflammation-induced intestinal leakage of gut microbial endotoxins serves as a key driver of hepatic steatosis. This process suppresses hepatic fatty acid β-oxidation, impairing metabolism, promoting hepatic accumulation, and exacerbating lipid deposition (Henao-Mejia et al., 2023). In line with prior research, our study found that HFD led to a marked enrichment of Pseudomonadota (58.5%) in zebrafish, whereas A. muciniphila supplementation reversed this trend, achieving a 17% reduction in its relative abundance. At the genus level in particular, a significant decrease was mainly manifested in Acinetobacter and Perlucidibaca. Correspondingly, HFD significantly increased the serum endotoxin level, which was significantly improved after A. muciniphila treatment. The findings indicated that A. muciniphila mitigates hepatic steatosis and inflammation by enhancing the integrity of the intestinal barrier, particularly by restoring gut microbiota dysbiosis. Moreover, through the intervention of antibiotics, we further demonstrated that this probiotic effect is mediated by the gut microbiota. Of course, this inference needs to be further confirmed by subsequent studies using germ-free zebrafish and zebrafish larval microbiota transplantation models.

Studies show that the gut microbiota can metabolize tryptophan in three different manners, ‌KP‌: ~90% is catabolized by intestinal epithelial/immune cells; 5-HT pathway‌: ~3% converted to 5-hydroxytryptamine in enterochromaffin cells; Indole pathway‌: 4%-6% directly metabolized to indole derivatives (Gao et al., 2020). Dynamic equilibrium among these pathways maintains tryptophan metabolic homeostasis. ‌Notably, HFD significantly reduces microbial production of tryptophan-derived metabolites‌, including tryptamine and indole-3-acetate (I3A), thereby aggravating‌ HFD-associated chronic inflammatory responses (Krishnan et al., 2018). ‌‌Furthermore, fecal analyses revealed reduced IDs and elevated kynurenine levels in patients with obesity and metabolic syndrome. Consistently, HFD-fed mice exhibited persistent gut microbiota dysbiosis, followed by a reduction in tryptophan-derived microbial metabolites (Aron-Wisnewsky et al., 2021). This indicates that gut microbiota-dependent metabolites (IDs) contribute to metabolic disease pathogenesis, exhibiting an inverse association (Bock, 2020). Our gut microbiota sequencing results show that A. muciniphila treatment effectively reverses HFD-induced dysbiosis. Thus, we speculate that it may improve HFD-induced tryptophan-indole pathway metabolism in zebrafish. Metabolomic analysis showed that A. muciniphila could indeed reverse the decrease in IDs induced by HFD, such as indole, indole-3-acetaldehyde, 5-hydroxyindoleacetylglycine, 5-hydroxyindole-3-acetic acid and ILA. Meanwhile, the levels of quinic acid and serotonin decreased. This indicated that the A. muciniphila intervention impacts the balance and proportion of the three major tryptophan metabolic pathways, enhancing the microbiota-dependent indole pathway while inhibiting the KP and partly 5-HT pathways.

IDs act as a ligands, with binding activating AhR signaling. As expected, qRT-PCR showed significant upregulation of two AhR transcripts. As a nuclear transcription factor, it regulates energy metabolism, inflammatory response, lipid and intestinal homeostasis via controlling the transcriptional processes of downstream genes. It can regulate the expression of key enzymes involved in hepatic gluconeogenesis (such as PEPCK), activate thermogenesis in brown adipose tissue, and improve obesity-related metabolic syndromes (Xu et al., 2015). Activation of AhR also leads to increased expression of the IL-22-STAT3 axis in innate lymphoid cells (ILCs) and T cells, enhances epithelial barrier function and the production of antimicrobial peptides (Qiu et al., 2012). Moreover, AhR activation regulates immune cell balance (e.g., Treg/Th17 ratio) to inhibit inflammation, stimulates goblet cells to secrete mucin, and upregulates claudin4 and occluding (Gu et al., 2024). Meanwhile, it promotes the secretion of IL-22 to maintain barrier integrity, reduce intestinal permeability, and inhibit the intestinal NF-κB inflammatory pathway (Shinde et al., 2018). In our study, we found that A. muciniphila exhibited significant upregulation of IL-22 expression, a cytokine essential for maintaining epithelial integrity. Collectively, A. muciniphila's protection against HFD-induced metabolic disruptions is partly due to AhR activation via these tryptophan metabolites, enhancing barrier integrity-related gene differentiation and expression (muc2, Tjp-1α, claudin1, occludin). Certainly, future studies should include AhR overexpression and in vivo knockdown experiments to determine the role of A. muciniphila in this pathway.

Recent studies indicate that Burkholderia species carry out the microbial conversion of tryptophan into 5-HIAA via a non-traditional pathway, and then activates the AhR/TSC2/mTORC1 axis to improve glucose intolerance and obesity (Du et al., 2024). Bifidobacterium bifidum and Lactobacilli (Lactobacillus spp.) encode indole lactate dehydrogenase (aldh), thus having the ability to synthesize ILA (Cui et al., 2023; Wang et al., 2023). ILA activates AhR, promoting tight-junction proteins (ZO-1, occludin) to enhance intestinal tight junctions, while inducing Treg differentiation and upregulating inhibitory cytokines (IL-10, TGFβ) to alleviate intestinal inflammation. A. muciniphila mediates the gut microbiota and regulates tryptophan metabolism, thus alleviating the metabolic syndrome induced by HFD. To further investigate which specific microbial species are associated with tryptophan metabolism, Spearman correlation analysis was applied. We found that the upregulated indole derivatives such as indole, indolepyruvate, indoleacetamide (IAAId), ILA and 5-HIAA were positively correlated with Plesiomonas, Vibrio, unclassified_f_Vibrionaceae, and Staphylococcus. Meanwhile, the downregulated serotonin level was significantly positively correlated with Acinetobacter, Massilia. Consistently‌, Nuidate et al. (2015) demonstrated that Vibrio cholerae utilizes the ‌tryptophanase (TrpAse)‌ encoded by the tnaA gene to catalyze the conversion of tryptophan to indole, a key metabolic pathway in bacterial signaling and host interactions (Nuidate et al., 2015). However, bacterial strains associated with ILA‌ and 5-HIAA exhibit discrepancies compared to previous studies. ‌This inconsistency may arise from the distinct composition of the zebrafish gut microbiota‌, which lacks certain dominant taxa (e.g., Bacteroides). Certainly, correlation analysis alone has limitations. Further experimental validation-such as in vitro isolation of bacterial strains and spectrometric identification-is required to confirm the specific microbial origin of metabolites, thereby establishing precise correlations between specific intestinal microbes and metabolites.

This study demonstrates that A. muciniphila alleviates HFD-induced metabolic dysfunction, such as disturbances in host lipid homeostasis, intestinal integrity, and inflammatory responses, through gut microbiota-dependent regulation of tryptophan. Specifically, A. muciniphila reshapes gut microbiota composition (enriching Staphylococcus and Vibrionaceae while depleting Acinetobacter and Massilia), enhances microbiota-dependent tryptophan metabolism (elevating ILA and 5-HIAA). Concurrently, it inhibits the pro-inflammatory kynurenine pathway, shifting tryptophan metabolism toward beneficial microbial-derived metabolites. These metabolic shifts ‌subsequently activate‌ AhR, driving the upregulation of IL-22 and muc2, Tjp-1α, claudin1, and occludin, thereby restoring the barrier function, alleviating chronic inflammation and improving hepatic steatosis. These findings establish a novel mechanistic link between A. muciniphila-mediated microbiota remodeling, tryptophan metabolic reprogramming, and AhR activation. However, further studies on the mechanism are still needed using germ-free zebrafish models, microbiota transplantation, in vitro microbiota isolation, as well as gene knockout or overexpression approaches.

Author Contribution

Fengli Zhang took on the roles of conceptualization, in-depth investigation, meticulous visualization, formal analysis, drafting the original manuscript, and successfully acquiring the necessary funding. Xiao Wang conducted comprehensive review and editing, skillfully supervising the entire research process. Jiajun Tian, JiTong Li and Tengyuan Zhang are responsible for investigation, methodology, formal analysis, data curation. This author has also read and consented to the published version of the manuscript.

Acknowledgement

This work was supported by the Natural Science Foundation of Zhejiang Province under Grant No. TGD24C190001, the Bureau of Science and Technology of Zhoushan (No: 2023C41023), Provincial Discipline Construction Project-Degree Program Construction (Fisheries) (No: 110340602211), Talent Introduction Research Fund of Zhejiang Ocean University (JX6311032023).

Data Availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below:https://www.ncbi.nlm.nih.gov/search/all/?term= PRJNA1094899And further enquires can be made to the corresponding author.

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Akkermansia muciniphila alleviates metabolic disorders through gut microbiota-mediated tryptophan regulation

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