Introduction
A high-fat diet (HFD) is an environmental risk factor associated with increase for intestinal inflammatory (Gruber et al. Reference Gruber, Kisling and Lichti2013; Paik et al. Reference Paik, Fierce and Treuting2013; Xie et al. Reference Xie, Sun and Wu2018). This is due to HFD-induced impairments in mitochondrial bioenergetics, which exacerbate epithelial hypoxia to support the proliferation of Escherichia coli and other Enterobacteriaceae (Lee et al. Reference Lee, Cevallos and Byndloss2020; Yoo et al. Reference Yoo, Zieba and Foegeding2021). These pathogenic bacteria further accelerate the risk of intestinal barrier impairment, mucosal infections and systemic inflammation (Genser et al. Reference Genser, Aguanno and Soula2018; Thaiss et al. Reference Thaiss, Levy and Grosheva2018), posing a threat to both human and animal intestinal health.
Probiotics, as live microbial food components, are recognized for their beneficial effects on animal and human health. L. reuteri strains are part of commensal microbiota in the intestines of piglets and play important roles in microbiota maturation (Wang et al. Reference Wang, Wang and Ma2022) and physical function regulation (Lin et al. Reference Lin, Wu and Wang2023). Previous studies found that L. reuteri produce lactic acid (Behare et al. Reference Behare, Ali and Mishra2023), reutericyclin (Huang et al. Reference Huang, Shi and Yang2022) and exopolysaccharides (Kšonžeková et al. Reference Kšonžeková, Bystrický and Vlčková2016), exhibiting antibacterial properties against certain intestinal pathogens in vitro (Al-Nabulsi et al. Reference Al-Nabulsi, Osaili and Oqdeh2021; Bertin et al. Reference Bertin, Habouzit and Dunière2017; Kšonžeková et al. Reference Kšonžeková, Bystrický and Vlčková2016; Muthukumarasamy et al. Reference Muthukumarasamy, Han and Holley2003). The intestinal mucosa functions as a conduit for absorbing food-derived nutrients and microbiome-derived metabolites, while also acting as a barrier to prevent microbial invasion and regulate inflammatory responses to the diverse contents of the lumen. L. reuteri has been shown to repair gut damage and alleviate intestinal inflammation after E. coli K88 infection (Gao et al. Reference Gao, Cao and Xiao2022; Xie et al. Reference Xie, Song and Wang2021). However, few studies have evaluated the effects of L. reuteri on gut barrier function and systemic inflammation in HFD-fed individuals exposed to pathogenic bacteria, nor have the illustrate the potential protective mechanism involved.
In previous studies, we isolated and cultured a high adhesive L. reuteri ZJ617 from piglet intestines (Zhang et al. Reference Zhang, Wang and Liu2013), which demonstrated the ability to regulate intestinal barrier and reduce inflammatory in piglet or mouse model challenged with lipopolysaccharide under a normal diet (Cui et al. Reference Cui, Liu and Dou2017; Zhu et al. Reference Zhu, Mao and Zhong2021). In the current study, we used Shiga-toxin-producing E. coli O157:H7 as a challenge for HFD-fed mice, a well-known zoonotic pathogen that cause gastroenteritis. Through combined complete genome sequencing, metabolomics, and functional prediction of microorganisms, we aimed to explore the mechanism by which L. reuteri ZJ617 mitigates E. coli O157:H7 infection-induced intestinal barrier impairment and systemic inflammatory in obese mice.
Materials and methods
Animals and experiment design
Four-week-old male C57BL/6J mice, specific pathogen-free, were purchased from Shanghai SLAC Laboratory Animal Co., Ltd. (Shanghai, China). Prior to the experiment, the mice were acclimatized to the environment (23 ± 1℃, 12-h light-dark cycle) with unrestricted access to food and drinking water for 1 week. Following acclimation, the mice were randomly assigned into two groups: HFD (containing 60% kcal from fat; PD6001) (n = 18) and HFD supplement with L. reuteri ZJ617 (n = 9, one of which died unexpectedly). The mice were maintained on an HFD for 14 weeks. 2 days before being sacrificed, half of the HFD mice and all HFD mice supplemented with L. reuteri ZJ617 were challenged with 200 μL E. coli O157:H7 (109 cfu/mL) for 48 h. All diets were sourced from Changzhou SYSE Bio-Tec. Co., Ltd. (Changzhou, China). Animal experiments were conducted in accordance with the “Regulation for the Use of Experimental Animals” of Zhejiang Province, China and approved by the Animal Care and Use Committee of Zhejiang University (ETHICS CODE Permit no. ZJU20240770).
L. reuteri ZJ617 had previously been isolated from piglet small intestines and stored in 20% glycerol at −80℃ until usage. The strain was anaerobically cultured in sterilized de Man Rogosa and Sharpe (MRS) medium at 37℃ for 18 h. The final concentration of L. reuteri ZJ617 used was 109 cfu/mL in drinking water. The drinking water was refreshed every day.
Serum biochemical analysis
Blood samples were centrifuged at 3000 g for 15 min at 4℃ to produce serum samples. The levels of serum IL-1β, TNF-α and endotoxin were determined using corresponding assay kits (H002-1-2, H052-1-1 and A054-1-1, respectively; Nanjing Jiancheng Bioengineering Institute, Nanjing, China). All processes were performed according to manufacturer’s instructions.
Histological analysis
After euthanasia, the ileum and colon of the mice were preserved in 4% paraformaldehyde. The fixed, paraffin-embedded samples sections were stained with hematoxylin and eosin (H&E) to evaluate the villus height and crypt depth of ileum or colon, respectively, followed by microscopical examination. All processes were carried out according to manufacturer’s instructions. The intestinal villus height and crypt depth were quantified using Image J.
Alcian blue staining
The above fixed paraffin-embedded colon samples sections were stained with Alcian blue to assess mucus secretion of colon (Riva et al. Reference Riva, Kuzyk and Forsberg2019), according to manufacturer’s instructions. Then the samples under glass slide were observed with a microscope. The Image J was applied to quantify the mucus secretion.
Western blotting
Ileum tissue were lyzed in RIPA buffer supplemented with phenylmethylsulfonyl fluoride (PMSF), proteinase inhibitor and phosphatase inhibitors (Beyotime Technology, Shanghai, China) while kept on ice. The lysates were then centrifuged at 12,000 rpm for 10 minutes. The supernatant was collected, denatured in sample buffer and separated using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Samples were transferred onto polyvinylidene fluoride (PVDF) membranes with a pore size of 0.45 μM. For proteins detection, the following primary antibodies were used at a 1:1000 dilution: zonula occludens1 (ZO-1) (Abcam, ab276131), occludin (Abcam, ab216327) and β-actin (Cell Signaling Technology, 4970). Visualization of proteins was performed on film using horseradish peroxidase-conjugated secondary antibodies diluted at 1:5000.
16S rRNA amplicon sequencing
Microbial DNA was extracted from intestinal contents, and the total DNA was quantified by Nanodrop 2000 (Thermo Fisher, USA). The V3–V4 region of the bacteria 16S rRNA genes were amplified with primers 341 F 5ʹ-CCTACGGGRSGCAGCAG-3ʹ and 806 R 5ʹ-GGACTACVVGGGTATCTAATC-3ʹ (Takahashi et al. Reference Takahashi, Tomita and Nishioka2014). PCRs were conducted, and the library was created using Qubit. The purified amplicons were combined in equal amounts and sequenced in paired-end on an Illumina MiSeq PE250 platform (Illumina, San Diego, USA) following the manufacturer’s protocols. Raw reads were first removed barcode to generate high-quality reads and then were assembled. Amplicon Sequence Variant (ASVs) were identified and generated through standard clustering at 100% similarity using Deblur. Each representative sequence was classified to a taxon with the RDP Classifier. ASV profiling was carried out with QIIME2. Based on the ASV table, a Venn diagram was used to visualize common and unique features between the three groups. Alpha diversity was evaluated using chao1 and the corresponding significance was determined using the Kruskal–Wallis test. Beta diversity was estimated using principal coordinate analysis (PCoA) on an unweighted unifrac distance matrix using QIIME2 software. QIIME2 was utilized for calculating each species abundance and taxonomic distributions across different levels.
Co-occurrence network analysis
Based on the genus abundances, Spearman’s rank correlation analysis was performed to identify the differences among the different group. Data with a correlation coefficient |ρ-value| > 0.5 and a P-value < 0.01 were selected to construct a co-occurrence network. Network graphs and indices, such as average degree and average clustering coefficient, were displayed and calculated using the Cytoscape software (3.9.0).
PICRUSt2 for prediction of metagenome functions
PICRUSt2 was utilized to predict metagenome functions (Douglas et al. Reference Douglas, Maffei and Zaneveld2020). Kyoto Encyclopedia of Gene and Genomes (KEGG) was used to support pathway profiles. All predicted pathways were obtained using a Wilcoxon-test.
Scanning electron microscope
L. reuteri ZJ617 was anaerobically cultured in sterilized MRS medium at 37℃. After 18 h cultured, the supernatant was removed, and the bacteria were washed three times with PBS. Then the bacteria were preserved in 2.5% glutaraldehyde for more than 4 h and then were postfixed with 1% OsO4 for 1 h. After dehydration with ethanol, the samples were dried with a critical point dryer, the sections were observed with a scanning electron microscope (Hitachi SU-8010). All processes were performed according to the instructions of the bio-ultrastructure analysis Laboratory of Analysis center of Agrobiology and environmental sciences, Zhejiang University.
DNA extraction and genome sequencing, assembly and annotation
L. reuteri ZJ617 genomic DNA was extracted according to manufacturer’s instructions. The DNA quantity and quality were tested by the NanoDrop 2000 (Thermo Fisher Scientific), and then the qualitied DNA was further purified. The whole genome was sequenced using the Pacific Biosciences platform and the Illumina NovaSeq 6000 platform. Quality control was performed by using Trimmonmatic (Bolger et al. Reference Bolger, Lohse and Usadel2014). All good qualitied data were assembled using ABySS software (Simpson et al. Reference Simpson, Wong and Jackman2009). The gene function annotation using various databases, including NR (non-redundant protein database), Swiss-Prot (http://uniprot.org), KEGG, GO (Gene Ontology), COG (Clusters of Orthologous Groups), CAZy (Carbohydrate-Active EnZymes Database), CARD (The Comprehensive Antibiotic Resistance Database), TCDB (Transporter Classification Database), PHI (Pathogen–Host Interactions), VFDB (virulence factors of pathogenic bacteria).
The anti-SMASH analysis
To understand the secondary metabolites production capacities of L. reuteri ZJ617, assembled genome was analyzed for the presence of secondary metabolite biosynthetic gene clusters (BGCs) using antibiotics and Secondary Metabolites Analysis Shell (anti-SMASH) 7.0 (Blin et al. Reference Blin, Shaw and Augustijn2023) MIBiG database was applied to identify both known and putatively novel BGCs that may be involved in the biosynthesis of major compound classes, such as polyketides, terpenoids, non-ribosomal peptides (NRPs) and beta-lactones (Kautsar et al. Reference Kautsar, Blin and Shaw2020).
Metabolomic profiling
After 18 h of culture, the bacteria were removed, and supernatant was harvested. The metabolites were analyzed by liquid chromatography time-of-flight mass spectrometer (LC-TOF-MS). Briefly, adding 1.2 mL methanol to 300 μL culture medium and vortex for a while. Incubating the mixture on ice for 30 min and centrifuging 15,000 g at 4℃ for 15 min. Transfer 1.2 mL supernatant for a new clean tube and recentrifuge for once. Transfer 900 μl supernatant into two aliquots and dry with freeze dryer. After drying, resolving the aliquot with 150 μl 60% acetonitrile, and incubating on the ice for 3 min and centrifuge 15,000 g at 4℃ for 15 min. Then transfer supernatant for a new clean tube and recentrifuge for once. The supernatant was filtered with a nylon filter and injected onto the LC-TOF-MS system for detection and analysis.
For the data of the metabolites of intestinal contents, the abundance differences were obtained using a student t-test with Benjamini-Hochberg’s method corrected P-values. Differential metabolites were defined as those with variable importance in the projection >1.0 obtained from partial least squares discriminant analysis (PLS-DA), |Log2 (Foldchange)| > 1 and P < 0.05.
Statistical analysis
A one-way analysis variance was performed to identify the differences in animal study, followed by Dunnett tests. All data are shown as means ± SD. Statistical analyses were performed using GraphPad Prism (USA). P < 0.05 was considered statistically significant.
Results
Fuctional annotation of complete genome unravels probiotic characteristics of L. reuteri ZJ617
Scanning electron microscope images show that L. reuteri ZJ617 was rod-like with secretions on its surface (Fig. 1). Then, we conducted a genomic analysis to obtain a comprehensive understanding of the potential probiotic characteristics of L. reuteri ZJ617. The strain contains a single circular chromosome of 2,538,072 bp and two plasmids (plasmid 1:143, 069 bp; plasmid 2:14, 763 bp) with 38.33% in average GC content (Fig. 2A). The various genes coding for acid tolerance, bile tolerance, adhesion and biofilm formation was presented in genome of L. reuteri ZJ617 (Table S1). Furthermore, annotation of KEGG database showed that metabolic pathway is the dominant functional pathway, and approximately 12.9% (150 genes) were classified into biosynthesis of secondary metabolites in the top functional pathways (Fig. 2B). Notably, the genome of L. reuteri ZJ617 has a set of genes involved in the de novo synthesis two of essential amino acids for animal production, including Threonine and L-lysine (Fig. 2C). In addition, the anti-SMASH analysis predicted the type III polyketide synthase is the single secondary metabolite BGCs (Fig. 2D). The MIBiG algorithm grouped the BGCs predicted by anti-SMASH into 280 gene cluster families (GCFs), with 141 polyketides, 41 NRPs, 61 NRP-polyketides, 20 terpenes, 7 saccharides, 6 alkaloids and 4 ribosomally synthesized and posttranslationally modified peptide (RiPPs) (Fig. 2E).
Figure 1. The characterization of L. reuteri ZJ617. The representative sem-images of L. reuteri ZJ617. The scale bar of left is 5 μm, the right is 1 μm.
Figure 2. Genome information of L. reuteri ZJ617. (A) Circular genomic map of L. reuteri ZJ617 chromosome and two plasmids. from the outermost to innermost circle, circle 1 represents genome size; circles 2 and 3 are forwards CDSS and reverse CDSS, respectively, color-coded according to the COG classification; circles 4 is rRNA genes and tRNA genes; circles 5 represents GC content; circles 6 is GC skew. (B) The bar of KEGG pathway. (C) threonine and l-lysine biosynthesis pathway. (D) the secondary metabolite BGCs. (E) the classification of GCFs.
Meabolomics analysis of L. reuteri ZJ617
We next performed an untargeted metabolomic profiling on probiotic-cultured supernatant to further identify metabolic patterns of L. reuteri ZJ617. The PLS-DA showed a significant change of metabolites before and after culture (Fig. 3A). A total of 788 metabolites were detected in the supernatant, among which 99 were downregulated and 135 were up-regulated relative to those in the MRS medium (Fig. 3B). These metabolites were further divided into primary metabolites and secondary metabolites (Fig. 3C). Amino acids, peptides and analogues are the dominant up-regulated metabolites (Fig. S1). The KEGG pathway enrichment analysis also suggested that L. reuteri ZJ617 is mainly responsible for amino acid metabolism (Fig. 3D). Consistence with genomic information, the L-threonine and lysine were detected, as well as their precursor (L-homoserine) (Fig. 3E). For the secondary metabolites, ursodeoxycholic acid, gallate, trans-2-hydroxycinnamate, trans-3-hydroxycinnamate and cis-2-hydroxycinnamate were significantly up-regulated in the supernatant (Fig. 3F).
Figure 3. Metabolic pattern of L. reuteri ZJ617. (A) PLS-DA score plot for discriminating the metabolic profile of L. reuteri ZJ617 before and after culture. (B) Volcano plots of significant changed metabolites of L. reuteri ZJ617 before and after culture. (C) The detected classification of metabolites. (D) The enriched KEGG pathway. (E) Heatmap of significant changed amino acids metabolites of L. reuteri ZJ617 before and after culture. (F) The part of significantly up-regulated secondary metabolites.
L.reuteri ZJ617 prevents obese mice from E. coli O157:H7-induced serum inflammation
The experimental design and workflow are shown in Fig. 4A. HFD mice challenged with E. coli O157:H7 for 48 h experienced a slight decline in body weight, however, supplementation with L. reuteri ZJ617 contributed to maintaining their weight (Fig. 4B). There is no significant difference in serum IL-1β, TNF-α and endotoxin between HFD and HE mice, and pretreated with L. reuteri ZJ617 significantly decrease the serum inflammation (Fig. 4C–E).
Figure 4. L. reuteri ZJ617 prevents obese mice from E. coli-induced serum inflammation. (A) Study design of L. reuteri ZJ617 intervention experiment. Mice were randomly assigned to three groups. After a week of adaption, all mice were fed with a high-fat diet, HZE mice were additionally supplemented with L. reuteri ZJ617 for 14 weeks. Two days before sacrifice, a half of HFD mice and all obese mice supplemented with L. reuteri ZJ617 were challenged with E. coli for 48 h, referred to as the HE mice and HZE mice, respectively. (B) Body weight (g). (C–E) Serum inflammation index, including il-1β, tnf-α and endotoxin.
L.reuteri ZJ617 attenuates obese mice from E. coli O157:H7-induced intestinal barrier damages
As shown in pathological section, there is inflammatory cell infiltration in the ileum and colon tissues of HFD-fed mice both before and after E. coli O157:H7 challenge (Fig. 5A and B). However, supplementation with L. reuteri ZJ617 provides protection against this infiltration (Fig. 5A and B). Additionally, there is no significant difference in villus height, crypt depth and the ratio of villus height to crypt depth in HFD and HE mice (Fig. 5A and B). Regarding the intestinal barriers in the colon, E. coli O157:H7 challenge did not affect mucus secretion in HFD mice (Fig. 5C), but significantly downregulated the expression of ZO-1 (Fig. 5D) and caused a numerical decline in occludin (Fig. 5E). Notably, compared with the HE mice, L. reuteri ZJ617 supplementation significantly increased villus height and the ratio of villus height to crypt depth, promoted mucus secretion, and up-regulated colon tight junction protein ZO-1 and occludin (P = 0.08) (Fig. 5A–E).
Figure 5. L. reuteri ZJ617 attenuates obese mice from E. coli-induced intestinal barrier damages. (A–B) Representative H&E images, villus height, crypt depth and their ration of ileum and colon, respectively (scale bars, 500 μm). (C) Representative images of abstained colonic sections and mucus secretion (%) (Scale bars, 500 μm). (D–E) Protein level of ZO-1 and occludin.
L. reuteri ZJ617 induces alterations in gut microbiome structure and functions
To evaluate the impacts of L. reuteri ZJ617 on the gut microbiota, we performed 16S ribosomal RNA gene amplicon sequencing on intestinal contents from three groups. Venn diagram showed common and unique features between three groups, from which 32, 30 and 23 unique features in the HFD, HE and HZE mice, respectively (Fig. 6A). Chao1 indices supported that E. coli O157:H7 infection significantly increased the diversity of the microbiota in HFD-fed mice (Fig. 6B), whereas no difference was found between HFD mice and HZE mice (Fig. 6B), suggesting that L. reuteri ZJ617 supplementation stabilized microbial diversity. The unweighted UniFrac-based PCoA revealed a clear clustering of the gut microbiota for each group (Analysis of Similarity, P = 0.002) (Fig. 6C). Additionally, the E. coli O157:H7 challenge decreased the relative abundance of Romboutsia, Enterothabdus, Lactobacillus and Dubosiella compared to HFD mice. However, L. reuteri ZJ617 supplementation prevented them from decreasing (Fig. 6D).
Figure 6. L. reuteri ZJ617 induces alterations in gut microbiome structure and functions. (A) Venn diagram. (B) Chao1 index. (C) Unweighted unifrac PCoA analysis of gut microbiota. (D) Relative abundance. (E-G) Co-occurrence network of HFD, HE and HZE, respectively. (H) Degree. (I) Clustering coefficient. (J) Bar chart represents functional pathways in intestinal contents predicted using PICRUSt2.
Large modules (more than 3 nodes) within the HFD, HE and HZE networks were identified by focusing on the relevance of genera and the proportion of major phyla to uncover potential changes in gut microbial interactions following L. reuteri ZJ617 supplementation. The HFD mice exhibited compact and larger networks featured with many more nodes (55) and edges (105) (Fig. 6E). In contrast, E. coli O157:H7 challenge led to looser and smaller networks, with fewer nodes (40) and edges (37) (Fig. 6F). HE mice (27.03%) showed a lower ratio of positive correlations compared to HFD mice (72.97%) (Table S2), indicating that bacteria genera in HE mice tended to co-exclude (negative correlation) rather than co-occur (positive correlation). However, the number of negative edges is not reduced (Table S2). Furthermore, the reduced average degree (Fig. 6H) and average clustering coefficient (Fig. 6I) in HFD mice challenged with E. coli O157:H7 reflected an impaired network complexity. Of note, pre-treatment with L. reuteri ZJ617 prevented the reduction in nodes, percentage of positive edges, degree, and clustering coefficient, as well as the loosening of the network (Fig. 6G–I).
We applied PICRUSt2 to predict metagenome functions between HE and HZE mice. Among the 414 identified pathways, a total of 66 differential pathways were changed by L. reuteri ZJ617 supplementation, like preventing L-lysine, L-tryptophan, gallate, hydroxycinnamate degradation, reducing lipopolysaccharide (LPS) biosynthesis and other functional pathways (Fig. 6J).
Discussion
A HFD favors the emergence of E. coli in the ileal, cecal and colonic mucosa, thereby increasing host susceptibility to chronic inflammatory bowel disease. This dietary pattern further supports E. coli colonization or enhances the risk or chemically induced colitis (Agus et al. Reference Agus, Denizot and Thévenot2016; Martinez-Medina et al. Reference Martinez-Medina, Denizot and Dreux2014). Here, we found that E. coli O157:H7 challenge exhibited an increased serum inflammatory factor compared to sole HFD-fed mice, although the increase was not statistically significant. One possible reason is that wild-type C57BL/6 J mice do not exhibit a strong genetic predisposition to bacterial infection (Martinez-Medina et al. Reference Martinez-Medina, Denizot and Dreux2014). Additionally, the HFD compromises the intestinal barrier function and creates a low-grade inflammation environment in the host (Li et al. Reference Li, Huang and Zhang2023), which may have masked the incremental effect of E. coli O157:H7 infection on serum inflammatory markers. Pretreatment with L. reuteri ZJ617 significantly up-regulated intestinal tight junction proteins and promotes mucin secretion, both of which may contribute to its protective effect against intestinal and systemic inflammation induced by HFD, while also enhancing resistance to subsequent E. coli O157:H7 challenge.
Probiotic biofilms can resist pathogen colonization in the gut by occupying ecological niches (Ghosh et al. Reference Ghosh, Nag and Lahiri2022). The genomic traits of L. reuteri ZJ617 reveal numerous factors for acid and bile tolerance, which consistent with our previous obtained phenotypic traits (Zhang et al. Reference Zhang, Wang and Liu2013). The ability of probiotics to colonize and survives in the gastrointestinal tract by resisting acidic and bile conditions is the very first step in exerting their antibacterial effects. Notably, these resistances are believed to be partially dependent on their ability to form biofilms (Liu et al. Reference Liu, Wu and Zhang2018). Studies have indicated that the formation of biofilm by L. reuteri reduced E. coli O157:H7 survive in vitro (Speranza et al. Reference Speranza, Liso and Russo2020). L. reuteri ZJ617 encodes both of genes related to EPS biosynthesis and LuxS, which are considerate to involved in biofilm formation (Deng et al. Reference Deng, Hou and Valencak2022; Lin et al. Reference Lin, Chen and Zhou2021), and has been demonstrated to have an inhibition effect on E. coli O157:H7 (Jelcić et al. Reference Jelcić, Hüfner and Schmidt2008; Liu et al. Reference Liu, Zhang and Qiu2017; Ma et al. Reference Ma, Xu and Peng2022). Therefore, the ability of L. reuteri ZJ617 to form solid biofilm is one of factor to control pathogen infection.
Consistent with the other strains of L. reuteri derived from bovine or murine (Huang et al. Reference Huang, Shi and Yang2022; Montgomery et al. Reference Montgomery, Eckstrom and Lile2022), the genome of L. reuteri ZJ617 also participates in multiple amino acids biosynthesis pathways. In vivo, the microbiome function prediction suggested that supplementation with L. reuteri ZJ617 may prevent lysine degradation. This phenomenon might be contributed to the potential of L. reuteri ZJ617 to produce lysine as indicated by its metabolic profile, which has been confirmed to be beneficial for modulating immune tolerance in colitis (Zhang et al. Reference Zhang, Tu and Ji2024). Another study showed that high dietary ration of lysine could increase the gene expression of tight junction proteins in Clostridium perfringens-challenged turkeys (Ognik et al. Reference Ognik, Konieczka and Mikulski2020). In addition, the microbial prediction suggests that E. coli O157:H7 challenged accelerate L-tryptophan degradation, which is consistent with phenomenon in DSS-induced ulcerative colitis (Cheng et al. Reference Cheng, Liu and Zhang2022). Conversely, L. reuteri ZJ617 can prevent it, which may result in more L-tryptophan accumulate in intestine. Numerous studies have reported that L. reuteri can convert tryptophan into indole derivatives to regulate host immune (Bender et al. Reference Bender, McPherson and Phelps2023; Zhao et al. Reference Zhao, Hu and Bao2021). Among these derivatives, indole-3-propionic acid has been shown to enhance intestinal barrier function by promoting the expression of tight junction proteins and upregulating the expression of mucins such as MUC2 and MUC4 (Li et al. Reference Li, Zhang and Wu2021). Furthermore, both the genomic information and metabolic profile of L. reuteri ZJ617 monocultures revealed an increase of L-threonine production, which has an ability to optimize intestinal barrier function (Dong et al. Reference Dong, Azzam and Zou2017; Koo et al. Reference Koo, Choi and Yang2020). Therefore, the amino acids derived from L. reuteri ZJ617 or its influenced microbiome may serve as one contributor to mitigate E. coli O157:H7-induced inflammatory and gut barrier impairment. Furthermore, lysine, threonine and tryptophan produced by L. reuteri ZJ617, as essential nutrients, may not only serve as immunoregulators but also support the growth and development of various organism, including microorganisms, animals and humans.
Microbial secondary metabolites and their active compounds are another considerable antibacterial candidate, which has been extensively reported in recent study (Cheng et al. Reference Cheng, Wu and Chen2021; Xiao et al. Reference Xiao, Zhang and Gao2014). Although our previous study and others have reported that the supernatant of L. reuteri monocultures play important roles in protecting from pathogen, regulating intestinal barrier and inflammatory (Cui et al. Reference Cui, Qi and Zhang2019; Geraldo et al. Reference Geraldo, Batalha and Milhan2020), few studies have explored and discussed the compositions and potential functions of Lactobacillus-derived secondary metabolites. The genomic information shows that L. reuteri ZJ617 is mainly involved in biosynthesis of polyketide, a common characteristic observed in L. reuteri as confirmed by other studies (Özçam et al. Reference Özçam, Tocmo and Oh2019). We also detected an increase of hydroxycinnamate, one of polyketide, in supernatant after monocultures. Interestingly, previous study has demonstrated this polyketide compound can protect against LPS-induced intestinal epithelial cells injury (Liu et al. Reference Liu, Zhang and Chen2023b). And its methyl ester derivative, methyl p-hydroxycinnamate, has an same anti-inflammatory effects in RAW264.7 cells challenged with LPS (Vo et al. Reference Vo, Lee and Shin2014). The possible mechanism may involve in L. reuteri derived polyketide activate the aryl hydrocarbon receptor (Özçam et al. Reference Özçam, Tocmo and Oh2019), which is a potential target for the control of intestinal inflammation (Ganapathy et al. Reference Ganapathy, Saha and Wang2023; Li et al. Reference Li, Wang and Su2022). In addition, L. reuteri ZJ617 can produce other secondary metabolites, like gallate (benzonoids) and ursodeoxycholic acid (terpenoids), which have been confirmed to be beneficial for protecting from pathogeny infection, alleviating intestinal barrier dysfunction and related inflammation (Liu et al. Reference Liu, Liu and Wang2023a; Ward et al. Reference Ward, Lajczak and Kelly2017; Wu et al. Reference Wu, Shen and Xu2022; Zuo et al. Reference Zuo, Chen and Zuo2024). Given the antibacterial properties of secondary metabolites, L. reuteri ZJ617 may have the potential to replace antibiotics in treating diseases caused by pathogenic infections in animal production or human therapy.
Since L. reuteri ZJ617 produces numerous metabolites, which serve as interactive mediators for complex inter-microbial communication (Wang et al. Reference Wang, Fan and Zhang2024). The cooperating networks of gut microbes are considered metabolically efficient (Coyte et al. Reference Coyte, Schluter and Foster2015). This means that one bacterium provides nutrients for another, supporting its survival and colonization. Conversely, competitive relationships are believed to stabilize microbial structure (Coyte et al. Reference Coyte, Schluter and Foster2015). This is supposed as a rescue measure, where adaptive immunity helps reestablish a healthy microbiome by suppressing species whose existence is causing harm during pathogen infection (Lee and Mazmanian Reference Lee and Mazmanian2010; Mazmanian et al. Reference Mazmanian, Round and Kasper2008). Indeed, we observed that E. coli O157:H7 challenge shifted microbial networks in HFD mice by reducing the positive links while maintaining the number of negative links, which suggesting that E. coli O157:H7 disrupts the metabolic efficiency of the gut microbiome, and attempts to stabilize microbial structure to exert health benefits to host. Major alteration of microbial community composition is often associated with ill conditions. One supposed that the disturbed microbial structure leads to an increase in LPS release (Khafipour et al. Reference Khafipour, Krause and Plaizier2009). Correspondingly, pretreated with L. reuteri ZJ617 remain maintain complicated and balanced networks. The microbiome prediction showed that L. reuteri ZJ617 reduced LPS biosynthesis from bacteria. Long term consumption of a HFD decreases goblet cell numbers, impairs mucus secretion and compromises gut barrier integrity both in small intestine and colon, leading to increased LPS translocation into the bloodstream, contributing to systemic inflammation (Yang et al. Reference Yang, Wei and Zhou2022), which is consistent with the phenomenon we detected in serum of HE mice. However, supplementation with L. reuteri ZJ617 appears to promote mucus production and reduce the level of serum inflammatory factors. In addition, the mucus layer covering the intestinal surface effectively keeps the majority of gut bacteria at a safe distance from the epithelial cells lining the intestine (Chassaing et al. Reference Chassaing, Koren and Goodrich2015), which is may another potential approach to defend against E. coli O157:H7 challenge.
In summary, our study found that L. reuteri ZJ617 alleviates gut barrier injury, intestinal inflammatory cell infiltration and systemic inflammatory induced by E. coli O157:H7 in HFD mice. We comprehensively analyzed the possible mechanisms involved, including the properties of biofilm, amino acids and secondary metabolites biosynthesized by L. reuteri ZJ617 and balanced microbiome. In the future, specific functional metabolites need to be identified and studied for their precise contributions, as well as their mechanism in preventing E. coli O157:H7 infection.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/anr.2025.3.
Acknowledgements
We specially thank Professor Yizhen Wang from College of Animal Sciences, Zhejiang University for providing the E. coli O157:H7.
Author contributions
Y.M. and H.W. planned the project and the experiments. Y.M. analyzed the data; Y.M., Y.M., Z.D., S.W., J.L. performed the experiments; Y.M., and H.W. wrote the original draft; Y.M. and H.W. were responsible for writing review and editing. Funding acquisition was the responsibility of H.W.
Financial support
This study was supported by grants from the Key R & D Projects of Zhejiang Province (2022C04034; 2022C02015).
Conflict(s) of interests
The authors declare no competing interests.
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