Abstract

Introduction

   Canine inflammatory bowel disease (IBD) is characterized by chronic
   intestinal inflammation and dysbiosis. Conventional treatments often
   result in adverse effects and contribute to antibiotic resistance,
   highlighting the need for safe, effective alternatives. Probiotics have
   gained attention for their potential in modulating gut microbiota and
   immune responses. This study investigates the therapeutic mechanisms of
   Enterococcus faecium Kimate-X and Lactobacillus plantarum Kimate-F,
   individually and in combination, in alleviating canine IBD.

Methods

   In vitro antibacterial and anti-inflammatory activities were assessed
   using agar well diffusion assays and LPS-induced RAW 264.7 macrophages,
   respectively. In vivo efficacy was evaluated in dextran sulfate sodium
   (DSS)-induced colitis models in mice and dogs. Metagenomic sequencing
   was performed on canine fecal samples to analyze microbiota composition
   and functional pathways. Short-chain fatty acid (SCFA) levels were
   quantified, and key host signaling pathways were examined.

Results

   Kimate-F showed strong antibacterial effects against Escherichia coli,
   Salmonella enteritidis, and Yersinia enterocolitica. Kimate-X
   significantly suppressed nitric oxide (NO) and TNF-α production in the
   in vitro inflammation model. In both mouse and canine DSS-induced
   colitis models, the probiotic combination significantly reduced weight
   loss, colonic damage, and serum inflammatory cytokines, while
   increasing IL-10 levels. Metagenomic analysis revealed enhanced
   microbial diversity, with enrichment of Bifidobacterium species and
   upregulation of metabolic pathways involved in nutrient absorption and
   immune regulation. The probiotic combination also modulated the PPAR
   and AMPK signaling pathways and promoted SCFA production in canine
   feces.

Discussion

   These findings suggest that E. faecium Kimate-X and L. plantarum
   Kimate-F act synergistically to restore gut homeostasis, reduce
   intestinal inflammation, and enhance host immunity. Their ability to
   modulate gut microbiota composition, host signaling, and metabolic
   output underscores their potential as safe and effective probiotic
   candidates for managing canine IBD.

   Keywords: canine inflammatory bowel disease, probiotics, Enterococcus
   faecium, Lactobacillus plantarum, dysbiosis, inflammation

Introduction

   Dogs are among the earliest domesticated animals and have played a
   significant role in human society for thousands of years ([35]1). With
   economic development and rising living standards, the number of pet
   dogs has significantly increased ([36]2, [37]3). It is estimated that
   the global population of pet dogs has reached between 700 million and 1
   billion, and this number continues to grow ([38]4). In addition to
   providing emotional support and companionship, dogs play crucial roles
   in various fields such as guide work, drug detection, and search and
   rescue operations ([39]5, [40]6). Consequently, canine health has
   become a growing concern, particularly gastrointestinal disorders,
   which significantly impact dogs’ well-being and quality of life
   ([41]7).

   IBD is a chronic and debilitating gastrointestinal disorder in dogs,
   characterized by chronic inflammation and histopathological changes in
   the intestinal mucosa with an unclear etiology ([42]8, [43]9). The
   clinical symptoms of canine IBD include diarrhea, vomiting, reduced
   appetite, and weight loss, which severely impair the affected dogs’
   health ([44]10). Although the exact cause of canine IBD remains
   unclear, it is widely believed to be associated with multiple factors,
   including genetics, immune-mediated mechanisms, environmental factors,
   and gut microbiota dysbiosis ([45]11, [46]12).

   IBD is closely linked to gut dysbiosis, particularly characterized by
   the overgrowth of harmful bacteria such as Escherichia coli and
   Salmonella ([47]13, [48]14). Studies have demonstrated that dogs with
   IBD exhibit significantly reduced gut microbial diversity and an
   increased relative abundance of opportunistic pathogens ([49]15,
   [50]16). The proliferation of these harmful bacteria may exacerbate
   intestinal inflammation, damage the mucosal barrier, and further
   perpetuate the inflammatory cycle ([51]17). Gut dysbiosis is now
   recognized as a key factor in the onset and progression of IBD, making
   the modulation of gut microbiota a promising therapeutic strategy
   ([52]18).

   Currently, traditional treatments for canine IBD mainly rely on
   immunosuppressants (e.g., corticosteroids) and antibiotics ([53]19).
   However, the long-term use of these drugs can lead to various side
   effects, such as immune suppression, increased risk of infection, and
   the development of drug resistance, which limit their prolonged use
   ([54]20, [55]21). As a result, there is increasing interest in
   identifying safe and effective alternative therapies.

   Probiotics, as natural and well-tolerated biological agents, have been
   widely used to promote gut health in both humans and animals ([56]22,
   [57]23). Probiotics are defined as “live microorganisms that, when
   administered in adequate amounts, confer a health benefit on the host”
   ([58]24). Their mechanisms of action include inhibiting the
   colonization and growth of pathogenic microorganisms, enhancing
   intestinal barrier function, modulating the immune system, and
   producing beneficial metabolites, such as SCFAss ([59]25, [60]26). In
   studies involving dogs, probiotics have been shown to improve gut
   microbial composition, alleviate gastrointestinal symptoms, and enhance
   immune function ([61]27, [62]28). Particularly in the treatment of IBD,
   probiotics have demonstrated potential therapeutic effects by
   regulating immune responses and restoring microbial balance in the gut
   ([63]29, [64]30).

   Dextran sulfate sodium (DSS) is a widely used chemical reagent that
   induces colitis in animals by damaging the intestinal epithelial
   barrier, and it is frequently used in IBD animal model research
   ([65]31, [66]32). The DSS-induced colitis model shares similar
   pathological features with canine IBD, such as mucosal damage,
   inflammatory cell infiltration, and the upregulation of
   pro-inflammatory cytokine expression ([67]33, [68]34). However, studies
   on DSS-induced colitis models in dogs are relatively limited, with most
   research focusing on rodent models. This constraint limits our
   understanding of the pathophysiological mechanisms and treatment
   strategies for canine IBD.

   Enterococcus faecium and Lactobacillus plantarum are two commonly
   studied probiotic strains that have demonstrated various biological
   functions, including antimicrobial, anti-inflammatory, and
   immunomodulatory activities ([69]35, [70]36). E. faecium produces
   antimicrobial substances such as lactic acid, hydrogen peroxide, and
   bacteriocins, which inhibit the growth of pathogenic bacteria ([71]37).
   Its cell wall components can also modulate the host’s immune response
   by promoting the production of anti-inflammatory cytokines ([72]38). L.
   plantarum is well-adapted to the gastrointestinal tract, where it
   adheres to intestinal epithelial cells and strengthens barrier function
   ([73]39). It also inhibits pathogens through the production of
   bacteriocins and organic acids ([74]40). Studies have suggested that
   probiotic combinations may exert synergistic effects, enhancing their
   anti-inflammatory and immunomodulatory functions ([75]41).

   This study aims to elucidate the potential benefits of probiotics in
   managing IBD in dogs. Specifically, it evaluates the efficacy of
   Enterococcus faecium Kimate-X and Lactobacillus plantarum Kimate-F in
   alleviating DSS-induced colitis, investigates whether their combination
   exerts synergistic effects enhancing anti-inflammatory and
   immunomodulatory properties, and employs metagenomic sequencing and
   metabolomic analysis to assess the impact of these probiotics on gut
   microbiota composition and metabolic pathways. Based on these
   objectives, we hypothesize that the combined use of these probiotics
   will produce more pronounced anti-inflammatory effects and improved gut
   microbial balance, thereby offering a promising approach for canine IBD
   prevention and management and providing valuable insights for
   understanding human IBD.

Materials and methods

Antibacterial activity of Kimate-X and Kimate-F against intestinal pathogens

   The agar diffusion method was used to evaluate the antibacterial
   effects of Enterococcus faecium Kimate-X and Lactobacillus plantarum
   Kimate-F against common pathogens associated with canine enteritis. The
   pathogens selected for this experiment included Enterotoxigenic
   Escherichia coli ATCC 43888, Salmonella enterica subsp. enterica
   serovar Typhimurium ATCC 14028, and Yersinia enterocolitica ATCC 9610.
   Each pathogen was cultured in suitable media until reaching the
   logarithmic growth phase and then diluted to a concentration of
   1 × 10^8 CFU/mL. One milliliter of each pathogen suspension was spread
   evenly onto LB agar plates, and after standing for 1 min, any excess
   liquid was removed. Three wells were made in each plate using an 8-mm
   sterile punch. The first well was filled with 50 μL of Lactobacillus
   plantarum Kimate-F suspension (1 × 10^8 CFU/mL), the second well was
   filled with the same concentration of Enterococcus faecium Kimate-X
   suspension, and the third well, serving as a control, was filled with
   50 μL of sterile saline. Each condition was tested in triplicate.
   Plates were incubated anaerobically at 37°C for 24 h, and inhibition
   zones were measured in millimeters using calipers to quantify the
   inhibitory effects of Kimate-X and Kimate-F on each pathogen.

Immunomodulatory effects of Kimate-X and Kimate-F in an LPS-induced
inflammation model

   RAW 264.7 macrophages were cultured in Dulbecco’s Modified Eagle’s
   Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1%
   antibiotics. The cells were incubated at 37°C in a 5% CO₂ incubator.
   When cells reached 80–90% confluence, they were digested with 0.25%
   trypsin-EDTA solution and resuspended at 5 × 10^5 cells/mL. Next,
   100 μL of this suspension (approximately 5 × 10^4 cells) was seeded
   into each well of a 96-well plate and incubated for 24 h to allow cell
   adhesion. Inflammation was induced by treating the macrophages with
   0.5 μg/mL lipopolysaccharide (LPS). Six hours after LPS stimulation,
   1 × 10^5 CFU/mL of Kimate-X and Kimate-F were added to the respective
   treatment groups. After 12 h of incubation, supernatants were
   collected, and nitric oxide (NO) levels were measured using a
   commercial ELISA kit (Meilian Bio, Shanghai). NO levels in each group
   were compared to evaluate the effects of the probiotic suspensions on
   LPS-induced NO production.

   For the evaluation of LPS-induced TNF-α production, the same cell
   culture methods and groupings were employed. RAW 264.7 cells were
   treated with 500 ng/mL LPS to induce inflammation, and 1 × 10^5 CFU/mL
   of Kimate-X or Kimate-F was added to the treatment groups. The control
   group received sterile saline without LPS or probiotics, and the
   inflammation model group received only LPS. After 12 h of incubation,
   supernatants were collected and stored at −80°C. TNF-α levels were
   measured using a commercial ELISA kit (Meilian Bio, Shanghai) to assess
   the regulatory effects of probiotics on LPS-induced TNF-α production.

Mouse experiment design

   A DSS-induced colitis model was used to evaluate the effects of
   Kimate-X, Kimate-F, and their combination on colitis in mice. Thirty
   5-week-old male C57BL/6 mice were randomly assigned to five groups
   (n = 6 per group): Control, DSS model, Kimate-X, Kimate-F, and mixed
   probiotics (Kimate-X + Kimate-F). The Kimate-X group received
   Enterococcus faecium Kimate-X, the Kimate-F group received
   Lactobacillus plantarum Kimate-F, and the mixed probiotics group
   received a 1:1 combination of both strains. The Control group received
   PBS. Mice were administered 200 μL of the appropriate suspension
   (1 × 10^9 CFU/mL) daily by oral gavage. After 7 days, all groups except
   the Control were given 3% DSS (molecular weight 36–50 kDa) in their
   drinking water to induce colitis. During the experiment, body weight,
   stool consistency, and bleeding were recorded daily to calculate the
   disease activity index (DAI). Samples were collected once the DSS model
   group exhibited clear colitis symptoms.

Sample collection and analysis

   Blood samples were collected from the orbital venous plexus, and serum
   was separated by centrifugation at 3,000 rpm for 10 min after standing
   at room temperature for 30 min. Serum levels of LPS, IL-10, and TNF-α
   were measured using commercial ELISA kits (Meilian Bio, Shanghai).
   After euthanasia, spleens were collected to calculate the spleen index,
   and colon tissues were harvested for histological analysis, including
   colon length measurement and inflammation scoring.

Canine experiment design

   The experimental design is shown in [76]Figure 3A. Sixteen 5-month-old
   male Beagle dogs were randomly divided into two groups (n = 8 per
   group): Control and probiotic-treated (Kimate-X + Kimate-F). The
   Control group received a basic diet, while the probiotic-treated group
   received a basic diet plus a daily oral administration of mixed
   Kimate-X and Kimate-F suspension (1 × 10^9 CFU/day for each strain) for
   the first 7 days. From days 8 to 14, the Control group was given 4% DSS
   (molecular weight 36–50 kDa) solution to induce colitis, while the
   probiotic-treated group received both probiotics and DSS. Body weight
   was recorded every 2 days, and the DAI was calculated. Blood and fecal
   samples, were collected on day 14. The mental state and fecal scoring
   were assessed according to the method described by Nestler et al.
   ([77]42).

Figure 3.

   [78]Figure 3
   [79]Open in a new tab

   Protective effects of Kimate-X and Kimate-F combination in a
   DSS-induced canine colitis model. (A) Experimental protocol schematic
   for the canine study. (B) Body weight change curves for the two groups.
   (C) Mental state scores of dogs on day 14. (D) Fecal water content on
   day 14. (E) Fecal appearance and scores on day 14. (F–I) Serum levels
   of LPS (F), TNF-α (G), IL-1β (H) and IL-10 (I) on day 14. Data are
   presented as mean ± SEM, statistical significance is indicated by
   *p < 0.05, **p < 0.01, and ***p < 0.001.

Canine blood and fecal sample collection and analysis

   On day 14, 5 mL of blood was collected from the forelimb veins of each
   dog and centrifuged to obtain serum, which was stored at −80°C for
   subsequent analysis. Serum inflammatory cytokines (IL-1β, TNF-α, LPS,
   and IL-10) were measured using commercial ELISA kits (Meilian Bio,
   Shanghai). Fresh fecal samples were collected, immediately frozen at
   −80°C, and analyzed for SCFAs. All experimental procedures were
   approved by the Animal Ethics Committee of Nanjing Agricultural
   University (Approval Number: NJAU. NO20231227193).

Canine fecal short-chain fatty acid analysis

   SCFAs analysis was performed at Shanghai Zhongke New Life Biotechnology
   Co., Ltd. by gas chromatography-mass spectrometry (GC-MS). Fecal
   samples were thawed, resuspended in 20% phosphoric acid, and processed
   for GC-MS analysis, with methylvaleric acid as the internal standard.
   Samples were analyzed using an Agilent DB-FFAP capillary column, and
   SCFAs concentrations were quantified based on calibration curves.

DNA extraction and shotgun metagenomic sequencing

   Genomic DNA was extracted from fecal samples using the QIAamp
   PowerFecal Pro DNA Kit (QIAGEN, United States). DNA quality was
   assessed by agarose gel electrophoresis and quantified using a Qubit
   Fluorometer. Libraries were prepared using the NEBNext DNA Library Prep
   Kit, and sequencing was performed on an Illumina Novaseq 6000 platform.
   Raw reads were filtered, host DNA was removed, and non-host reads were
   assembled for gene prediction.

Microbial taxonomy and functional profiling

   Taxonomic profiling was performed using MetaPhlAn4, providing
   species-level resolution of gut microbial composition, while functional
   annotations were carried out using EggNOG mapper to classify genes into
   metabolic pathways and biological functions. Alpha diversity indices
   (Shannon index, Inverse Simpson index) were calculated to assess
   microbial richness and evenness, while beta diversity was evaluated
   using Bray–Curtis dissimilarity and principal coordinate analysis
   (PCoA) to compare microbiota compositions across experimental groups.
   Group differences in microbial diversity and composition were assessed
   using Wilcoxon rank-sum tests for pairwise comparisons and PERMANOVA
   (permutational multivariate analysis of variance) for overall community
   structure variations. Functional pathway enrichment analysis was
   conducted using KEGG (Kyoto Encyclopedia of Genes and Genomes) and
   LEfSe (Linear Discriminant Analysis Effect Size) analysis to identify
   differentially enriched metabolic pathways between groups.

Correlation analysis

   Spearman correlation analysis was conducted to examine associations
   between gut microbiota, functional gene pathways, SCFAs, and
   inflammatory markers. False discovery rate (FDR) correction was applied
   to control for multiple testing errors, with adjusted p-values <0.05
   considered statistically significant. Correlation heatmaps were
   generated to visualize key relationships, while network analysis using
   Cytoscape identified highly correlated features (r > 0.5 or r < −0.5,
   p < 0.05), mapping microbial taxa, functional genes, and immune
   responses in an integrated network.

Statistical analysis

   All statistical analyses were conducted using SPSS 26.0, and figures
   were generated using GraphPad Prism 8.0. Group comparisons were made
   using Student’s t-test, with statistical significance set at p < 0.05.

Results

In vitro results

   The antibacterial activity of Enterococcus faecium Kimate-X and
   Lactobacillus plantarum Kimate-F was evaluated using the agar diffusion
   method. As shown in ([80]Figure 1A), both probiotic strains inhibited
   the growth of Escherichia coli, Salmonella enteritidis, and Yersinia
   enterocolitica, while no inhibition zones were observed in the control
   group. For E. coli, the inhibition zone diameter of Kimate-F (21 ± 1
   mm) was significantly larger than that of Kimate-X (14.5 ± 0.5 mm,
   P < 0.001), indicating a stronger antibacterial effect of Kimate-F.
   Similar results were observed for S. enteritidis, with Kimate-F
   exhibiting a significantly greater inhibition zone (17.6 ± 0.6 mm) than
   Kimate-X (10.5 ± 0.5 mm, P < 0.001). Against Y. enterocolitica,
   Kimate-F again demonstrated superior antibacterial activity (17.5 ±
   0.46 mm) compared to Kimate-X (14.3 ± 0.52 mm, P < 0.05).

Figure 1.

   [81]Figure 1
   [82]Open in a new tab

   Antimicrobial and immunomodulatory effects of probiotic strains
   Kimate-X and Kimate-F. (A) Inhibitory effects of Kimate-X and Kimate-F
   against Enterotoxigenic Escherichia coli, Salmonella enteritidis, and
   Yersinia enterocolitica, as evaluated by agar well diffusion assay.
   Representative images are shown (top), and inhibition zone diameters
   are quantified (bottom). (B) Effects on NO production: Kimate-X and
   Kimate-F on NO levels in LPS-induced RAW 264.7 cells. (C) TNF-α
   expression: effects of Kimate-X and Kimate-F on TNF-α expression in
   LPS-induced RAW 264.7 cells. Data are presented as mean ± SEM,
   statistical significance is indicated by *P < 0.05, **P < 0.01, ***P <
   0.001.

   In the LPS-induced RAW 264.7 macrophage inflammation model, both
   Kimate-X and Kimate-F significantly reduced NO and TNF-α levels. The NO
   levels in the Kimate-X group were significantly lower than those in the
   Kimate-F group (p < 0.001) ([83]Figure 1B). In terms of TNF-α levels
   ([84]Figure 1C), the Kimate-X group exhibited significantly lower
   levels compared to the Kimate-F group (p < 0.01).

Mouse experiment results

   In the DSS-induced colitis mouse model, the effects of Kimate-X,
   Kimate-F, and their combination on body weight, disease activity index
   (DAI), spleen index, colon length, histopathological changes, and
   inflammatory markers were evaluated. Regarding body weight changes
   ([85]Figure 2A), the DSS group showed significant weight loss, whereas
   the control group experienced continuous weight gain. Kimate-X,
   Kimate-F, and the mixed probiotic group significantly alleviated weight
   loss, with the mixed probiotic group demonstrating the greatest weight
   maintenance (p < 0.001). In the DAI ([86]Figure 2B), the DSS group
   exhibited significantly higher scores, while Kimate-X, Kimate-F, and
   the mixed probiotic group significantly reduced DAI scores, with the
   mixed probiotic group showing the most pronounced improvement
   (p < 0.05).

Figure 2.

   [87]Figure 2
   [88]Open in a new tab

   Protective effects of Kimate-X, Kimate-F, and their combination in a
   DSS-induced colitis mouse model. (A) Body weight changes: left panel
   shows body weight changes under DSS induction; right panel displays
   statistical analysis of body weight gain rates on day 12 for each
   group. (B) DAI scores on day 12 for each group. (C) Spleen index (SI)
   for each group. (D) Colon length comparison among groups. (E)
   Histopathological scores: left panel shows H&E staining of colon
   tissue, highlighting structural damage in the DSS group and protection
   by Kimate-F and the combination group. Right panel presents
   histopathological score results. (F–H) Serum levels of LPS (F), TNF-α
   (G), and IL-10 (H) across groups. Data are presented as mean ± SEM,
   statistical significance is indicated by *p < 0.05, **p < 0.01, and
   ***p < 0.001.

   For the spleen index ([89]Figure 2C), the DSS group exhibited the
   highest index, and Kimate-X, Kimate-F, and the mixed probiotic groups
   significantly reduced spleen indices, with the mixed probiotic group
   showing the greatest reduction (p < 0.05). In terms of colon length
   ([90]Figure 2D), the DSS group exhibited significantly shortened
   colons, while Kimate-X, Kimate-F, and the mixed probiotic groups
   alleviated this shortening, with the mixed probiotic group showing the
   longest colon length, significantly superior to the single probiotic
   groups (p < 0.001).

   In histopathological scoring ([91]Figure 2E), the DSS group exhibited
   the most severe pathological damage, with significantly higher scores
   than other groups. Kimate-X, Kimate-F, and the mixed probiotic groups
   significantly reduced histopathological scores, with the mixed
   probiotic group showing markedly better tissue repair than the single
   probiotic groups (p < 0.01). Inflammatory marker analysis ([92]Figures
   2F–[93]H) revealed that the DSS group exhibited significantly elevated
   serum LPS and TNF-α levels, while IL-10 levels were significantly
   reduced. In contrast, Kimate-X, Kimate-F, and the mixed probiotic
   groups significantly lowered LPS and TNF-α levels and increased IL-10
   levels, with the mixed probiotic group demonstrating superior results
   compared to the single probiotic groups (p < 0.001).

Canine experiment results

   In the DSS-induced colitis experiment in dogs ([94]Figure 3A), the
   weight gain in the mixed probiotic group was significantly higher than
   in the DSS group ([95]Figure 3B, p < 0.01), while mental status scores
   were significantly lower in the mixed probiotic group compared to the
   DSS group ([96]Figure 3C, p < 0.05). Fecal analysis showed that the
   water content and fecal scores in the mixed probiotic group were
   significantly lower than those in the DSS group ([97]Figures 3D,[98]E,
   p < 0.05). Serum inflammatory markers revealed that LPS, TNF-α, and
   IL-1β levels in the mixed probiotic group were significantly lower than
   in the DSS group ([99]Figures 3F–[100]H, p < 0.05, p < 0.01, and
   p < 0.001, respectively). The level of the anti-inflammatory immune
   marker IL-10 in the mixed probiotic group was significantly higher than
   that in the DSS group ([101]Figure 3I, P < 0.01).

Canine gut microbiota analysis

   In the metagenomic analysis of canine fecal samples, microbial
   diversity and community composition were compared between the DSS group
   and the Kimate-X + Kimate-F group. The Shannon diversity index revealed
   significantly higher microbial diversity in the Kimate-X + Kimate-F
   group compared to the DSS group ([102]Figure 4A, p < 0.05), although
   the InvSimpson index did not show a significant difference (p > 0.05).
   Principal coordinate analysis (PCoA) ([103]Figure 4B, left)
   demonstrated partial separation of microbial community structures
   between the two groups, indicating structural differences. However,
   Bray–Curtis distance analysis ([104]Figure 4B, right) did not show
   statistically significant differences between the two groups
   (p > 0.05).

Figure 4.

   [105]Figure 4
   [106]Open in a new tab

   Effects of Kimate-X and Kimate-F combination on gut microbiota in
   DSS-induced colitis dogs. (A) Gut microbiota diversity: Shannon index
   (left) and Invsimpson index (right). (B) Microbiota community
   structure: principal coordinate analysis (PCoA, left) and Bray–Curtis
   distance index (right). (C) Microbial composition: relative abundances
   at the phylum (top left), family (top right), genus (bottom left), and
   species (bottom right) levels. (D) Differential abundance analysis:
   left panel shows linear discriminant analysis (LDA) scores highlighting
   microbial taxa differences between DSS and Kimate-X + Kimate-F groups.
   Right panel shows relative abundance changes. Data are presented as
   mean ± SEM, statistical significance is indicated by *p < 0.05,
   **p < 0.01, and ***p < 0.001.

   Microbial abundance analysis ([107]Figure 4C) revealed distinct
   microbial compositions at the phylum, family, genus, and species levels
   in the Kimate-X + Kimate-F group compared to the DSS group.
   Specifically, the Kimate-X + Kimate-F group exhibited a higher relative
   abundance of Firmicutes, with significant increases in Bifidobacterium.
   LEfSe analysis ([108]Figure 4D) further highlighted that
   Limosilactobacillus oris was significantly enriched in the
   Kimate-X + Kimate-F group, whereas Tyzzerella nexilis, Coprococcus
   comes, and Prevotella copri clade A were more abundant in the DSS
   group.

Canine gut metabolic pathway analysis

   Comparing gut metabolic pathways between the DSS and
   Kimate-X + Kimate-F groups revealed significant differences. PCoA
   results ([109]Figure 5A) showed distinct metabolic pathway compositions
   between the two groups (p = 0.019), indicating differences in metabolic
   pathway diversity and composition. [110]Figure 5B displays functional
   pathway differences across various levels, with the Kimate-X + Kimate-F
   group showing higher relative abundances in pathways related to
   metabolism, environmental information processing, and cellular
   processes, particularly key metabolic pathways such as amino acid,
   carbohydrate, and lipid metabolism. LDA analysis ([111]Figure 5C)
   revealed significant differences in specific metabolic pathways. The
   DSS group was enriched in pathways such as purine metabolism, the
   phosphotransferase system (PTS), glycolysis/gluconeogenesis, and
   pyruvate metabolism, while the Kimate-X + Kimate-F group exhibited
   higher enrichment in fatty acid metabolism, amino acid biosynthesis,
   secondary metabolite biosynthesis, cysteine and methionine metabolism,
   and longevity regulation pathways.

Figure 5.

   [112]Figure 5
   [113]Open in a new tab

   Effects of Kimate-X and Kimate-F on metabolic pathways in DSS-induced
   canine colitis model. (A) PCoA analysis of metabolic pathway data,
   illustrating separation between DSS and Kimate-X + Kimate-F groups.
   Ellipses represent 95% confidence intervals. (B) Functional prediction
   (KEGG database): primary metabolic function classification (left),
   secondary (middle), and tertiary (right) functions with their relative
   abundances. (C) LEfSe analysis: LDA scores (left) and relative
   abundance changes (right) for differential metabolic pathways. (D)
   Correlation heatmap: associations between key microbes and metabolic
   pathways. Red indicates a positive correlation, blue indicates a
   negative correlation, and the deeper the color, the stronger the
   correlation. Data are presented as mean ± SEM, statistical significance
   is indicated by *p < 0.05, **p < 0.01, and ***p < 0.001.

   [114]Figure 5D illustrates the correlations between differential gut
   microbiota and various metabolic pathways in DSS-induced canine
   colitis. Several bacterial species show significant positive or
   negative correlations with specific metabolic pathways. Bifidobacterium
   animalis demonstrates positive correlations with carbohydrate
   metabolism, specifically pentose and glucuronate interconversions
   (p < 0.05), fatty acid biosynthesis, arginine biosynthesis, and several
   amino acid biosynthesis pathways, including those for valine, leucine,
   and isoleucine, as well as phenylalanine, tyrosine, and tryptophan
   (p < 0.05). It is also positively correlated with the PPAR signaling
   pathway while being negatively correlated with the AMPK signaling
   pathway (p < 0.05). Blautia hansenii is positively correlated with
   cysteine and methionine metabolism (p < 0.05), and with the PPAR
   signaling pathway, but shows a negative correlation with the AMPK
   signaling pathway. Coprococcus comes is similarly positively correlated
   with the PPAR signaling pathway and negatively correlated with the AMPK
   signaling pathway. Dorea longicatena is primarily involved in amino
   acid synthesis and degradation, showing positive correlations with
   arginine biosynthesis, valine, leucine, and isoleucine biosynthesis,
   phenylalanine, tyrosine, and tryptophan biosynthesis (p < 0.05), and
   histidine metabolism (p < 0.01), while being negatively correlated with
   lysine degradation (p < 0.05). In terms of inflammatory pathways, it
   also exhibits a positive correlation with the PPAR signaling pathway
   and a negative correlation with the AMPK signaling pathway.
   Fusicatenibacter saccharivoran shows positive correlations with
   arginine biosynthesis, valine, leucine, and isoleucine biosynthesis,
   and phenylalanine, tyrosine, and tryptophan biosynthesis (p < 0.05),
   alongside other amino acid metabolic pathways like cysteine and
   methionine metabolism, and arginine and proline metabolism (p < 0.05).
   It is negatively correlated with lysine degradation (p < 0.05),
   positively correlated with the PPAR signaling pathway (p < 0.01), and
   negatively correlated with the AMPK signaling pathway (p < 0.05).
   Limosilactobacillus oris shows negative correlations with the
   biosynthesis of several amino acids, including phenylalanine, tyrosine,
   and tryptophan, and arginine biosynthesis (p < 0.01), as well as
   valine, leucine, and isoleucine biosynthesis (p < 0.05). It also
   negatively correlates with amino acid metabolic pathways such as
   cysteine and methionine metabolism, histidine metabolism, and
   C5-branched dibasic acid metabolism (p < 0.05). Additionally, it is
   negatively correlated with the PPAR signaling pathway and positively
   correlated with the AMPK signaling pathway. Prevotella copri clade A
   shows positive correlations with histidine metabolism (p < 0.05), and
   with the PPAR signaling pathway, while being negatively correlated with
   the AMPK signaling pathway. Finally, Tyzzerella nexilis is positively
   correlated with the PPAR signaling pathway (p < 0.05) and negatively
   correlated with the AMPK signaling pathway.

Canine fecal short-chain fatty acids analysis

   In the comparison of SCFAs between the two groups of dogs ([115]Figure
   6), the Kimate-X + Kimate-F group showed significantly higher levels of
   acetic acid (p < 0.05), butyric acid (p < 0.05), valeric acid
   (p < 0.01), isovaleric acid (p < 0.01), and hexanoic acid (p < 0.05)
   compared to the DSS group. This indicates that the Kimate-X + Kimate-F
   group produced higher overall amounts of SCFAs. Although no significant
   differences were observed in propionic and butyric acid levels between
   the two groups, the Kimate-X + Kimate-F group exhibited higher overall
   SCFAs concentrations.

Figure 6.

   [116]Figure 6
   [117]Open in a new tab

   Effects of Kimate-X and Kimate-F on short-chain fatty acids (SCFAs) in
   DSS-induced canine colitis model. Relative abundances of major SCFAs
   (acetate, propionate, butyrate, pentanoate, isovalerate, and hexanoate)
   in feces from Kimate-X + Kimate-F and DSS groups. Data are presented as
   mean ± SEM, statistical significance is indicated by *p < 0.05,
   **p < 0.01, and ***p < 0.001.

Correlation between fecal short-chain fatty acids, pro-inflammatory factors,
and microbiota

   In the DSS-induced colitis model in dogs, significant correlations were
   observed between specific microbiota and SCFAs (e.g., acetic acid,
   propionic acid, butyric acid, isovaleric acid, valeric acid, hexanoic
   acid), as well as pro-inflammatory markers (LPS, TNF-α, IL-1β)
   ([118]Figure 7A). For example, Bifidobacterium animalis showed
   significant positive correlations with isovaleric acid and hexanoic
   acid (p < 0.01), and negative correlations with LPS (p < 0.05). Blautia
   hansenii was positively correlated with isovaleric acid, valeric acid,
   LPS (p < 0.05), and TNF-α (p < 0.01). Coprococcus comes was positively
   correlated with isovaleric acid, valeric acid (p < 0.05), and LPS
   (p < 0.05). Dorea longicatena showed significant positive correlations
   with isovaleric acid (p < 0.05) and hexanoic acid (p < 0.01). Both
   Fusicatenibacter saccharivorans and Limosilactobacillus oris showed
   significant positive correlations with hexanoic acid (p < 0.05).
   Prevotella copri clade A showed significant positive correlations with
   isovaleric acid (p < 0.01) and LPS (p < 0.05). Tyzzerella nexilis was
   positively correlated with valeric acid (p < 0.01) and isovaleric acid
   (p < 0.01).

Figure 7.

   [119]Figure 7
   [120]Open in a new tab

   Correlation analysis between key microbes, SCFAs, and inflammatory
   factors. (A) Correlation heatmap: relationships between different
   microbes and SCFAs, along with inflammatory factors (LPS, TNF-α,
   IL-1β). Red indicates positive correlation, blue indicates negative
   correlation. (B) Correlation heatmap: associations between different
   metabolic pathways, SCFAs, and inflammatory factors. Red indicates a
   positive correlation, blue indicates a negative correlation, and the
   deeper the color, the stronger the correlation. Data are presented as
   correlation coefficients, statistical significance is indicated by
   *p < 0.05, **p < 0.01, and ***p < 0.001.

Correlation between fecal short-chain fatty acids, pro-inflammatory factors,
and metabolic pathways

   In the DSS-induced colitis model in dogs, SCFAs are associated with
   various metabolic pathways ([121]Figure 7B). Acetate is positively
   correlated with branched-chain amino acid synthesis, phenylalanine,
   tyrosine, and tryptophan biosynthesis, fatty acid metabolism, fatty
   acid degradation, and glycolysis/gluconeogenesis. Propionate shows
   positive correlations with branched-chain amino acid metabolism and
   fatty acid metabolism. Butyrate is positively correlated with arginine
   biosynthesis and phenylalanine metabolism (p < 0.05), and also with
   branched-chain amino acid synthesis and fatty acid synthesis pathways.
   Isovalerate is positively correlated with phenylalanine, tyrosine, and
   tryptophan biosynthesis, phenylalanine metabolism, branched-chain amino
   acid synthesis, and fatty acid metabolism (p < 0.05). Pentanoate is
   positively correlated with fatty acid synthesis (p < 0.05). Hexanoate
   shows significant positive correlations with fatty acid metabolism and
   amino acid metabolism, including arginine, proline, cysteine, and
   methionine metabolism, as well as branched-chain amino acid synthesis
   (p < 0.05).

   Regarding pro-inflammatory markers, LPS is negatively correlated with
   fatty acid metabolism, cysteine and methionine metabolism, histidine
   metabolism, and arginine biosynthesis (p < 0.05). TNF-α is negatively
   correlated with fatty acid metabolism, glycolysis/gluconeogenesis, and
   several amino acid metabolic pathways (p < 0.05). Similarly, IL-1β is
   negatively correlated with fatty acid synthesis and multiple amino acid
   metabolic pathways (p < 0.05).

Discussion

   The study explores the mechanisms by which probiotics Enterococcus
   faecium Kimate-X and Lactobacillus plantarum Kimate-F, along with their
   combination, alleviate canine inflammatory bowel disease. By employing
   a comprehensive evaluation that includes in vitro antibacterial assays,
   cell models, mouse models, and canine models, we systematically
   assessed the anti-inflammatory effects of these probiotics and
   elucidated their regulatory roles in gut microbiota and metabolic
   pathways.

   In the in vitro antibacterial assays, Kimate-F demonstrated
   significantly stronger antibacterial activity against Escherichia coli,
   Salmonella Enteritidis, and Yersinia enterocolitica compared to
   Kimate-X. This may be attributed to the ability of Lactobacillus
   plantarum to produce higher levels of organic acids, hydrogen peroxide,
   and bacteriocins—potent antimicrobial substances ([122]43). Organic
   acids reduce local pH, inhibiting pathogenic bacteria growth, while
   hydrogen peroxide and bacteriocins disrupt bacterial cell membranes,
   causing lysis ([123]44). In contrast, Kimate-X exhibited relatively
   weaker antibacterial activity, likely due to its limited production of
   antimicrobial substances. However, Kimate-X demonstrated more
   pronounced effects in regulating immune responses.

   In the LPS-induced RAW 264.7 macrophage inflammation model, Kimate-X
   significantly reduced NO and TNF-α production, outperforming Kimate-F.
   This suggests that Kimate-X holds unique advantages in immune
   regulation. Previous studies have indicated that Enterococcus faecium
   can modulate Toll-like receptor (TLR) signaling pathways through its
   cell wall components, such as lipoteichoic acid (LTA) and
   peptidoglycan, thereby inhibiting NF-κB activation and reducing
   pro-inflammatory cytokine secretion ([124]45, [125]46). Additionally,
   Enterococcus faecium may enhance immune tolerance by inducing the
   production of anti-inflammatory cytokines such as IL-10.

   To further investigate the probiotic functions of Kimate-X and
   Kimate-F, we conducted experiments using a DSS-induced colitis model in
   mice. Both Kimate-X, Kimate-F, and their combination significantly
   ameliorated symptoms of inflammation, including weight loss, DAI,
   elevated spleen index, and shortened colon length. Histopathological
   analysis revealed that intestinal mucosal damage in the combination
   group was significantly less than in the single-strain groups,
   indicating a synergistic effect between the probiotics. The combination
   group also showed significantly greater reductions in serum LPS and
   TNF-α levels, alongside an increase in IL-10 levels, compared to the
   single-strain groups. This synergy may arise from complementary
   mechanisms of the two probiotics in regulating gut microbiota, barrier
   function, and immune responses.

   Based on the enhanced probiotic potential observed in the combination
   group from in vitro and mouse experiments, we selected the combination
   of Kimate-X and Kimate-F for further experimentation. In the
   DSS-induced colitis model in dogs, the combination group significantly
   improved weight gain, mental status, and fecal characteristics, while
   markedly reducing serum levels of Pro-inflammatory factors (LPS, TNF-α,
   IL-1β), Significantly increased the levels of the anti-inflammatory
   factor IL-10. These results further confirm the efficacy of probiotics
   in large animal models, bolstering their potential for clinical
   application.

   To explore the mechanisms underlying the effects of the Kimate-X and
   Kimate-F combination in alleviating canine colitis, we performed
   metagenomic analysis on fecal samples from both groups. The results
   indicated significantly higher gut microbial diversity in the
   combination group compared to the DSS group, with increased relative
   abundance of Firmicutes. Notably, the enrichment of Bifidobacterium
   species may contribute to inhibiting pathogen colonization and
   enhancing gut barrier function ([126]47, [127]48).

   LEfSe analysis further highlighted significant differences in
   characteristic microbes between the two groups. The Kimate-X + Kimate-F
   combination not only significantly improved the composition and
   structure of the canine gut microbiota but also affected host metabolic
   functions. Comparative analysis of metabolic pathways revealed notable
   differences between the groups, indicating that the combination
   intervention effectively altered the gut metabolic profile. The
   Kimate-X + Kimate-F group exhibited higher relative abundances in gene
   pathways related to metabolism, environmental information processing,
   and cellular processes, particularly in key metabolic pathways such as
   amino acid metabolism, carbohydrate metabolism, and lipid metabolism.
   These pathways are closely linked to energy supply, nutrient
   absorption, and immune regulation, suggesting that Kimate-X + Kimate-F
   may promote overall health and metabolic function in dogs by enhancing
   these pathways ([128]49, [129]50).

   LDA analysis further revealed differences in specific metabolic
   pathways between the groups. The DSS group was enriched in pathways
   such as purine metabolism, the phosphotransferase system (PTS),
   glycolysis/gluconeogenesis, and pyruvate metabolism, findings that
   align with previous studies on DSS-induced colitis ([130]51, [131]52).
   The upregulation of these pathways may be associated with inflammatory
   responses and metabolic imbalances ([132]53–55). In contrast, the
   Kimate-X + Kimate-F group was enriched in pathways related to fatty
   acid metabolism, amino acid biosynthesis, secondary metabolite
   biosynthesis, cysteine and methionine metabolism, and longevity
   regulation pathways. Activation of these pathways may contribute to
   antioxidant, anti-inflammatory, and cell-protective effects,
   potentially delaying aging and enhancing immune function ([133]56,
   [134]57). Thus, Kimate-X + Kimate-F supplementation may improve canine
   gut health by regulating gut microbiota metabolic functions, promoting
   beneficial metabolite production, and inhibiting inflammation-related
   pathways.

   SCFAs, which are products of gut microbial fermentation, play essential
   roles in regulating immunity and maintaining gut barrier integrity
   ([135]58, [136]59). Many gastrointestinal diseases in dogs, such as
   inflammatory bowel disease and diarrhea, are associated with dysbiosis
   of the gut microbiota and alterations in metabolic by-products. Among
   these, changes in SCFA levels and the microbial populations responsible
   for their production are key characteristics ([137]60, [138]61). Given
   the enrichment of fatty acid metabolism pathways in the
   Kimate-X + Kimate-F group, we compared fecal SCFA levels between
   groups. The combination group exhibited significantly higher levels of
   SCFAs, such as acetic acid, butyric acid, pentanoic acid, isovaleric
   acid, and hexanoic acid. By increasing SCFA levels, the combination
   group demonstrated its potential to promote gut health and metabolic
   regulation.

   In the analysis of the correlation between differential species and
   metabolic pathways. We observed a significant enrichment of
   Limosilactobacillus oris in the DSS group, which exhibited a negative
   correlation with the PPAR signaling pathway and a positive correlation
   with the AMPK signaling pathway. This negative correlation suggests
   that Limosilactobacillus oris may inhibit lipid metabolism and suppress
   anti-inflammatory responses, while its positive correlation with AMPK
   implies a propensity to promote catabolic activity and energy
   production. This regulatory pattern contrasts sharply with the
   microbiota in the probiotic group, where Bifidobacterium animalis,
   Dorea longicatena, and Fusicatenibacter saccharivorans demonstrated
   positive correlations with PPAR signaling and negative correlations
   with AMPK signaling. These findings suggest that these strains tend to
   enhance anabolic metabolism and anti-inflammatory responses, thus
   contributing to intestinal repair and energy homeostasis.

   The complex interaction between microbial metabolism and inflammatory
   pathways in inflammatory bowel disease is well-established ([139]62).
   Our study further revealed correlations between specific microbes,
   SCFAs, and pro-inflammatory factors. Bifidobacterium animalis was
   positively correlated with isovaleric acid and hexanoic acid while
   being negatively correlated with LPS, suggesting that
   Kimate-X + Kimate-F supplementation may exert anti-inflammatory effects
   by increasing SCFA production ([140]63, [141]64). Similarly, Blautia
   hansenii showed positive correlations with isovaleric acid, pentanoic
   acid, LPS, and TNF-α, indicating a role in inflammation regulation via
   SCFA metabolism ([142]65). The positive correlations of Coprococcus
   comes and Dorea longicatena with isovaleric acid and hexanoic acid
   reinforce the pivotal role of SCFAs in regulating inflammation.
   Additionally, Prevotella copri clade A was positively correlated with
   isovaleric acid and LPS, suggesting that some microbes may have dual
   roles in SCFA production and pro-inflammatory responses ([143]66).
   These findings imply that specific microbial groups regulate gut
   inflammation by modulating SCFA metabolism and impacting the expression
   of pro-inflammatory factors. The observed correlations between LPS,
   TNF-α, and IL-1β with key metabolic pathways suggest that
   pro-inflammatory factors exacerbate inflammation by disrupting
   metabolic balance, further weakening gut barrier function and
   triggering immune responses ([144]67). These insights into the
   microbiota-metabolism-inflammation axis provide potential therapeutic
   targets for managing canine inflammatory bowel disease through
   probiotic or microbial interventions.

   Additionally, previous research on canine probiotics has highlighted
   that factors such as administered dose, feeding duration, and specific
   formulation can significantly influence therapeutic outcomes ([145]68,
   [146]69). In our study, we pre-fed the dogs with a combined formula of
   Kimate-X and Kimate-F for 1 week before inducing colitis, during which
   no adverse symptoms or abnormal clinical signs were observed. This
   observation underscores the safety profile of these probiotics in
   canine models. Nonetheless, further research is warranted to
   systematically evaluate varying doses, extend feeding durations, and
   explore different formulations to optimize the benefits of probiotic
   therapy for canine IBD.

   Moreover, safety is a critical consideration in clinical applications
   of probiotics. Before DSS induction, we performed a one-week regimen of
   probiotic feeding, during which the dogs did not exhibit any
   abnormalities, providing preliminary evidence that the administered
   strains do not elicit adverse effects in dogs. In summary, this study
   systematically demonstrated that the combination of Enterococcus
   faecium Kimate-X and Lactobacillus plantarum Kimate-F alleviates canine
   IBD through multiple mechanisms, including direct pathogen inhibition,
   immune modulation, increased SCFA production, and regulation of gut
   microbiota and metabolic pathways. These findings provide strong
   scientific support for the clinical application of probiotics in
   improving canine gut health and treating IBD.

Funding Statement

   The author(s) declare that financial support was received for the
   research and/or publication of this article. This research was funded
   by Diprobio (Shanghai) Co., Limited. The funder was not involved in the
   study design, data collection, analysis, interpretation of data, the
   writing of this article, or the decision to submit it for publication.

Data availability statement

   The sequencing data generated in this study have been submitted to the
   NCBI BioSample database under accession numbers
   SAMN48004212–SAMN48004227.

Ethics statement

   The animal study was approved by Animal Ethics Committee of Nanjing
   Agricultural University. The study was conducted in accordance with the
   local legislation and institutional requirements.

Author contributions

   RZ: Conceptualization, Methodology, Writing – original draft. WH: Data
   curation, Writing – review & editing. SZ: Writing – review & editing.
   WC: Writing – review & editing. SX: Validation, Writing – review &
   editing. MC: Project administration, Writing – review & editing. QY:
   Conceptualization, Writing – review & editing.

Conflict of interest

   WH, SZ, WC, MC, and QY were employed by Diprobio (Shanghai) Co.,
   Limited.

   The remaining authors declare that the research was conducted in the
   absence of any commercial or financial relationships that could be
   construed as a potential conflict of interest.

Generative AI statement

   The authors declare that no Gen AI was used in the creation of this
   manuscript.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

References