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Article

Assessing Probiotic Efficacy: Short-Term Impact on Canine Gut Microbiota Using an In Vitro Colonic Fermentation Model

by
Achraf Adib Lesaux
1,*,
Jonas Ghyselinck
2,
Cindy Duysburgh
2,
Massimo Marzorati
2,3,
Jonna E. B. Koper
4 and
Jake Burlet
5
1
Phileo by Lesaffre, 137 Rue Gabriel Péri, 59700 Marcq-en-Baroeul, France
2
ProDigest, Technologiepark 82, 9052 Zwijnaarde, Belgium
3
Center for Microbial Ecology and Technology (CMET), Faculty of Bioscience Engineering, Ghent University, Frieda Saeysstraat 1, 9052 Ghent, Belgium
4
Lesaffre Institute of Science and Technology, 101 Rue de Menin, 59700 Marcq-en-Barœul, France
5
CanBiocin Inc., #4170, 10230 Jasper Ave, Edmonton, AB T5J 0B2, Canada
*
Author to whom correspondence should be addressed.
Pets 2025, 2(4), 33; https://doi.org/10.3390/pets2040033
Submission received: 29 July 2025 / Revised: 24 September 2025 / Accepted: 24 September 2025 / Published: 28 September 2025
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Abstract

In dogs, gut microbiome dysbiosis is associated with several health conditions, including gastrointestinal disease. Probiotic supplementation can support a balanced gut microbiome. This study assessed the impact of a probiotic containing a mixture of Lacticaseibacillus casei, Limosilactobacillus fermentum, Levilactobacillus brevis, and Enterococcus faecium on the gut microbiota of six dogs using short-term colonic simulations. Two groups were included, i.e., blank versus supplementation with the test product, and incubated for 48 h. Probiotic-supplemented reactors had significantly greater fermentative activity compared with the blank, as shown by lower pH levels and higher gas pressure after 6 h, 24 h, and 48 h of incubation (p < 0.05 for all). Saccharolytic fermentation also increased, with a significantly higher level of acetate at 24 h and propionate at 6 h, 24 h, and 48 h with the test product versus blank (p < 0.05 for all). There was no significant effect of the test product on alpha-diversity, but beta-diversity analysis revealed a clear separation in the microbial community composition between the test product and blank. Eight bacterial taxa were enriched with test product supplementation, including the probiotic test strains as well as Megamonas and Bacteroides species. This study, using in vitro short-term colon simulations with six canine donors, provides insights into the probiotic characteristics of the test product.

Graphical Abstract

1. Introduction

Considering the importance of gastrointestinal health in overall well-being, both in humans and animals, research has focused on identifying causes of digestive distress and developing strategies to improve and maintain healthy canine intestinal conditions [1,2]. The gut microbiome has been shown to play a critical role in intestinal health, as it is involved in providing nutrients to the host, defending against intestinal pathogens, aiding in the digestion and absorption of nutrients, maintaining the gut barrier function, and modulating the immune system [3,4,5]. These microbial processes are important for both gastrointestinal and overall health. Gut microbiome dysbiosis has been linked to several health conditions in dogs, including chronic gastrointestinal disease [1] and obesity [6]. Thus, identifying strategies to maintain a balanced and healthy gut microbiome is important for canine health and well-being.
The use of probiotics, which are defined as “live microorganisms that, when administered in adequate amounts, confer a health benefit on the host” [7], is one strategy for improving and maintaining gut health. Studies have demonstrated that probiotics can have beneficial effects in dogs, including improvement of the gut microbiome balance, protection against enteropathogenic organisms, enhancement of the intestinal barrier function, and modulation of the immune system [2,8,9,10]. One mechanism by which probiotics improve gastrointestinal health is by enhancing the production of short-chain fatty acids (SCFAs) by members of the gut microbiome [11]. SCFAs are bacterial metabolites produced during the fermentation of dietary fiber (i.e., saccharolytic fermentation) [12]. They have anti-inflammatory effects in the host and serve as an energy source for colonic epithelial cells, improving the integrity of the intestinal epithelial barrier [12,13,14]. Probiotics also modulate the gut microbiome composition, increasing the relative abundance of potentially beneficial microbes, such as lactic acid bacteria and butyrate-producing micro-organisms, and decreasing the relative abundance of less desirable bacteria such as Escherichia coli or Clostridium spp. [15,16,17,18,19].
Many lactic acid bacteria, particularly those from the genus Lactobacillus, have probiotic activity [17,18,20]. Lactobacillus casei is associated with several probiotic effects, including the prevention of infections caused by enteropathogenic bacteria [21], modulation of the host immune system [22], and maintenance of gut microbiome homeostasis [23]. Lactobacillus fermentum has been shown to modulate the gut microbiota of healthy dogs and to increase fecal SCFA concentrations [24]. Lactobacillus brevis has many probiotic characteristics, including modulation of both the immune system and the gut microbiome, as shown in mice models [25,26,27]. These models furthermore showed a beneficial effect of Lactobacillus brevis in preventing obesity and modulating pro-inflammatory genes in the adipose tissue of obese mice [28], as well as stimulate antihyperuricemic activity [27]. Enterococcus faecium is another lactic acid bacterium with probiotic activity, including the ability to inhibit the growth of certain bacterial pathogens, modulate the host immune system, and enhance fecal SCFA levels [29,30,31].
Considering the previously reported probiotic effects of each of these bacterial species individually, the objective of the current study was to assess the probiotic properties in the canine colon following supplementation of the different lactic acid strains as a mixture. Taking into account the ethical, societal, and economic considerations of initiating studies in live animals, we elected to perform an initial evaluation of this probiotic mixture using an in vitro short-term colonic simulation model adapted to the specific parameters of the canine colon [32,33,34]. This model allows for the study of the processes that drive the canine microbiome and to provide insight into the probiotic effects of test product supplementation by evaluating general fermentative activity, saccharolytic and proteolytic fermentation, and microbial biomass and community composition of healthy dogs in vitro.

2. Materials and Methods

2.1. Fecal Samples

Fecal samples were collected from six healthy canine donors, immediately following defecation. The dogs were Beagles (University of Ghent, Ghent, Belgium) that had a body condition score between 4 and 5 (on a 9-point scale), had no history of chronic disease, and had not received antibiotics in the previous 4 months. Informed consent was received for the use of the fecal samples, and samples were collected and used as approved by the Ethics Committee of the University Hospital Ghent (reference number ONZ-2022-0267; approved on 29 July 2022). The fecal samples were processed under anaerobic conditions. An in-house optimized cryoprotectant (modified from Hoefman et al. [35]) was sparged with nitrogen under anaerobiosis then added to the samples to create fecal suspensions. The suspensions were aliquoted, flash frozen, and stored at −80 °C in an anaerobic atmosphere. Samples were thawed immediately before being added to the reactors for the short-term colonic simulations.

2.2. Test Product

The test product was a mixture of four encapsulated live probiotic bacteria, Lacticaseibacillus casei K9-1, Limosilactobacillus fermentum K9-2, Levilactobacillus brevis WF-1B, and Enterococcus faecium PCEF02, that was provided by Phileo by Lesaffre (Marcq-en-Baroeul, France). The composition of the product provided was a blend of the four strains at 1 × 109 colony forming units (CFU)/g total, with 2.5 × 108 CFU/g of each strain individually, and maltodextrin.

2.3. Short-Term Colonic Simulations

The short-term colonic simulations were set up as follows: First, the test product was added to reactors to obtain a final concentration of 1 × 106 CFU/mL. Next, anaerobic background nutritional medium (PD03 [a modified version of the medium used in Duysburgh et al. [36]]; ProDigest, Ghent, Belgium) supplemented with fiber (3 g/L), L-alanine (0.4 g/L), and ergosterol (10 ppm) was added to each reactor. Blank control reactors received anaerobic nutritional medium without the test product. Finally, each reactor was inoculated with 5 mL of 7.5% canine fecal inoculum suspension (fecal suspensions from each individual donor were used to inoculate both test product and control reactors), to bring the total volume in each reactor to 50 mL. Anaerobiosis was obtained in the reactors by flushing with N2. Reactors were incubated at 39 °C for 48 h in an anaerobic atmosphere with continuous shaking (90 rpm). Samples were collected at the start of the experiment (0 h), and after 6 h, 24 h, and 48 h of incubation.

2.4. Overall Fermentative Activity

The pH of each reactor was measured using a Senseline pH meter F410 (ProSense, Oosterhout, The Netherlands) in a single replicate. Gas pressure was measured during incubation using a pressure meter (WIKA, CPH6200, Lawrenceville, GA, USA) with a transmitter (WIKA, CPT6200). pH and gas pressure measurements were performed in single repetition.

2.5. Microbial Metabolite Production

SCFA (acetate, propionate, butyrate) and branched-chain fatty acid (BCFA; sum of isobutyrate, isovalerate, and isocaproate) levels were quantified using capillary gas chromatography coupled with a flame ionization detector (Shimadzu’s-Hertogenbosch, The Netherlands) following isolation by liquid–liquid extraction using the methods of De Boever et al. [37]. The Enzytec™ kit (R-Biopharm, Darmstadt, Germany) was used to determine lactate concentrations. Concentrations of ammonium were measured using the indophenol blue method and an AQ300 Discrete Analyzer (SEAL Analytical, WI, USA) [38]. Each measurement was performed in single repetition.

2.6. Assessment of Microbial Community Composition

DNA was isolated from the collected samples (after 0 h and 48 h of colonic incubation) using the method described previously [39] Microbial community composition was determined using 16S-targeted Illumina sequencing with the following primers, spanning two hypervariable regions (V3–V4): 341F (5′-CCTACGGGNGGCWGCAG-3′) and 785R (5′-GACTACHVGGGTATCTAAKCC-3′). Sequencing of 2 × 250 bp using a pair-end sequencing approach yielded amplicons of 424 bp (LGC Genomics GmbH, Berlin, Germany). The DADA2 R package (v4.3.2, The R Foundation for Statistical Computing, Vienna, Austria) was used to process the amplicon sequence data according to the pipeline tutorial [40]. For the first quality control step, the primer sequences were removed, and reads were truncated and filtered to eliminate reads containing any ambiguous base calls or reads with high expected errors (using the filterAndTrim function with the following arguments: trimLeft = c(0,0), trun-cLen = c(260,260), maxN = 0, maxEE = c(2,2), truncQ = 2, rm.phix = TRUE). After dereplication, unique reads were further denoised using the Divisive Amplicon Denoising Algorithm (DADA) error estimation algorithm and the selfConsist sample inference algorithm (with the option pooling = TRUE). The obtained error rates were inspected, and after approval, the denoised reads were merged. Finally, the amplicon sequence variants (ASV) table obtained after chimera removal was used for taxonomy assignment, using the Naive Bayesian Classifier and the DADA2 formatted Silva v138.1, and an identity threshold of 97% sequence similarity [41].
Flow cytometry was employed to assess the total number of bacterial cells in each sample. Samples were analyzed on a BC Accuri C6 Plus Flow Cytometer (BD Biosciences, Franklin Lakes, NJ, USA) using the high flow rate setting. A threshold level of 700 was applied to the SYTO channel to separate bacterial cells from medium debris and signal noise. Parent and daughter gates were set to determine the different populations.

2.7. Statistical Analysis

Comparisons between the test product and blank control samples were conducted using paired two-sided Student’s t-tests in Microsoft Excel. Each test product or blank control reading included six replicate measurements (i.e., one measurement per donor).
Four alpha-diversity indices were used: observed taxa (species richness), the Chao1 index (species richness), the Shannon diversity index (species richness and evenness), and the Simpson diversity index (species richness and evenness, with greater weight to common or dominant species). These indices were calculated on relative abundance data (obtained by total sum scaling) using phyloseq v1.44.0 in R (v4.3.1) [42]. Beta diversity analysis was conducted on relative abundance data (obtained by total sum scaling) by hierarchically clustering Euclidean distances between samples using Ward’s minimum variance method. A Discriminant Analysis of Principal Components (DAPC) plot was furthermore constructed with two discriminants and 80% percent of retained variance in the principal components using adegenet v2.1.10 in R (v4.3.1) [43].
Linear discriminant effect size (LEfSe) analysis [44] was conducted on the total-sum scaled taxonomic abundances at genus level, with significant features meeting p ≤ 0.05 for Kruskal–Wallis and Wilcoxon tests. Linear discriminant analysis (LDA) scores between treatment and individual taxon abundances were calculated using MASS v7.3.58-3 in R (v4.3.1). No restrictions were put forward with respect to minimal LDA scores. LDA scores ≥2.0 are generally considered biologically relevant.
Furthermore, a hierarchical differential abundance analysis was conducted to test for statistically significant taxon abundance between control and treatment groups across taxonomic levels (treeclimbR analysis). This was performed on the trimmed mean of M-values library sizes for each ASV using treeclimbR v0.1.5 and edgeR v3.42.4 in R (v4.3.1) [45]. Benjamini–Hochberg multiple testing correction was used, and the alpha-level was set at 0.05, with the cut-off for statistical significance thus set at −log(p-value) > 1.3.
For all of the above analyses, a p-value of <0.05 was considered statistically significant.

3. Results

3.1. Microbial Community Activity

3.1.1. Overall Fermentation

Across donors, both the test product-supplemented and blank control reactors demonstrated a decrease in pH over the course of the 48 h experiment, with the pH decreasing more rapidly following test product supplementation (Figure 1A). Further, at all timepoints (6 h, 24 h, and 48 h) the reduction in pH from baseline (i.e., pH 6.51 on average) was significantly greater following test product supplementation compared with the blank control (p < 0.05 for all timepoints).
Gas pressure, which indicates gas production, increased over time for both the blank control and test product-supplemented reactors; across donors, gas production was significantly greater with the test product versus blank at all timepoints (p < 0.05 for all) (Figure 1B).

3.1.2. Microbial Metabolites

The production of acetate, propionate, and butyrate increased over time with both the test product and blank control (Figure 2A–C). When comparing the two experimental conditions across donors, test product supplementation resulted in significantly higher levels of acetate at 24 h, and propionate at 6 h, 24 h, and 48 h compared with the blank control (p < 0.05 for all). No significant differences in terms of butyrate production were observed. Lactate levels decreased over time for both the blank control and test product condition (Figure 2D). While lactate levels were significantly higher with test product supplementation compared with blank at 6 h, they were significantly lower at 24 h and 48 h (p < 0.05 for all).
There was little change in total BCFA levels from baseline (i.e., 0.0 mM for all donors) at 6 h and 24 h, and the level increased at 48 h in a donor-dependent way for both test product-supplemented and blank control reactors (Figure 3A). The levels of BCFA were therefore not significantly different between the test product and blank control at any timepoint across donors. Levels of ammonium were comparable between the test product and blank control at 6 h and 24 h (Figure 3B). At 48 h, ammonium levels were significantly lower across donors with the test product compared with the blank control (p < 0.05).

3.2. Microbial Community Composition

A general overview of the relative abundance at genus level has been provided in Table S9 (0 h) and Table S10 (48 h). The impact of the test product on alpha-diversity after 48 h of incubation is shown in Figure 4 and Table S11. The test product had no significant impact on species richness, as shown by the Observed taxa and Chao1 results (Figure 4A,B), or on species evenness, as shown by the Shannon and Simpson results (Figure 4C,D). Beta-diversity analysis showed that the test product affected the gut microbiota composition, with a clear segregation between the community composition of test product-supplemented and blank control reactors (Figure S1). Indeed, when compared across donors, the bacterial biomass was significantly enhanced following test product supplementation versus the blank control after 48 h incubation (p < 0.05) (Figure 5).
LEfSe and treeclimbR analyses demonstrated that eight bacterial taxa were specifically enriched with the test product versus the blank control at 48 h (Figure 6). Of those, four taxa were both statistically and biologically enriched, including 78_Lacticaseibacillus, 83_Limosilactobacillus, 121_Levilactobacillus, and 141_Limosilactobacillus (Figure 6A–D). The remaining four enriched taxa were classified as weakly enriched as demonstrated by a fold change ≥2 and <4, and included 2_Megamonas, 61_Megamonas, 76_Enterococcus, and 95_Bacteroides (Figure 6E–H).

4. Discussion

This study evaluated the effects of a mixture of four lactic acid strains (L. casei, L. fermentum, L. brevis, and E. faecium) on the gut microbiota of six individual canine donors using in vitro colonic simulation experiments. Test product supplementation demonstrated probiotic properties compared with the blank control, showing increased fermentative activity, enhanced propiogenic properties which were correlated with reduced lactate levels, and specific bacterial enrichments following test product exposure.
The test product had an effect on saccharolytic fermentation, as demonstrated by the significant increase in propionate production compared with the blank control. The strong propiogenic effect of the test product is likely attributed to the lactogenic properties of the probiotic mixture, particularly of L. casei [46], L. fermentum [47,48,49], and L. brevis [50]. Indeed, microbial lactate utilization for the production of propionate in the gut has been well described [51]. In the current study, the specific enrichment of lactate-converting, propionate-producing Megamonas spp. [52] was probably boosted by the presence of the aforementioned Lactobacillus spp. E. faecium may have also contributed to the increase in propionate levels; a probiotic strain of E. faecium was reported to increase fecal SCFA levels, including propionate [53], and another study showed an increase in lactic acid bacteria following E. faecium supplementation [54]. Interestingly, propionate is reported to have immunomodulatory, anti-inflammatory, and antimicrobial effects [55,56,57]. Furthermore, it has been reported that dogs with inflammatory bowel disease have significantly lower fecal concentration of propionate than healthy dogs [58]. Thus, the propiogenic properties of the probiotic mixture may be considered a health benefit in the canine gastrointestinal environment.
While BCFA levels were similar, levels of ammonium were significantly lower with the test product versus the blank control, thus affecting proteolytic fermentation. Ammonium is a byproduct of proteolytic fermentation, which is generally linked with the less favorable production of toxic end-metabolites [59]. Indeed, in humans, fecal concentrations of ammonia are typically higher in relation to inflammatory bowel diseases [60,61] and in dogs ammonia has already been shown to contribute to fecal odor and have a negative health impact [62]. Thus, the reduction in ammonium levels with the test product is considered a beneficial outcome for canine gastrointestinal health.
Also of interest was that the test product did not cause a shift in the autochthonous gut microbial community of healthy dogs, as demonstrated by the similar alpha-diversity findings between reactors supplemented with the test product and the blank control reactors. This could be considered a positive result, as it implies that test product supplementation does not disrupt the healthy state of gut homeostasis.
Gut homeostasis is typically not maintained in gastrointestinal disorders, such as diarrhea. A number of previous studies have reported beneficial effects of the probiotic strains in dogs, particularly for improvement of diarrhea [63,64,65,66,67,68]. A probiotic mixture containing L. casei was shown to alleviate diarrhea in dogs who were suffering from it before supplementation [63]. Additionally, supplementation improved the composition of the gut microbiota, with a decrease in some pathogenic bacteria, and an increase in bacteria with pathways involved in producing secondary metabolites such as SCFAs [63]. Another study in dogs with acute or intermittent diarrhea found that a probiotic mixture containing L. fermentum, L. rhamnosus, and L. plantarum normalized the consistency of the dog’s stool and reduced pathogenic bacteria [64]. Probiotic supplements containing E. faecium have been shown to improve stools in dogs with diarrhea [65], to shorten the duration of diarrhea [66], and to significantly reduce the incidence of diarrhea in shelter dogs [67]. A study using a canine model of inflammatory bowel disease reported that supplementation with a probiotic containing both E. faecium and L. plantarum significantly reduced colonic damage, weight loss, and serum inflammatory cytokines, and increased IL-10 levels [68]. The mechanistic findings of the present study (e.g., increased propionate production, enrichment of Megamonas spp., etc.) may provide insight into the mechanisms behind the improvement in diarrhea reported for these in vivo studies evaluating L. casei, L. fermentum, and E. faecium.
Studies in dogs have demonstrated that probiotics, including those contained in the test product, have benefits beyond those related to gastrointestinal health. For example, a study found that supplementation with a probiotic mixture of lactic acid bacteria (L. casei, L. plantarum, and Bifidobacterium animalis) promoted the average daily feed intake of elderly dogs, helped dogs of any age to gain weight, and induced changes in the immune system, including an enhancement of serum IgG levels, increased levels of secreted IgA in the feces, and increased serum IFNγ, and reduced levels of the inflammatory cytokine TNFα [9]. Another study showed that supplementation with probiotics containing E. faecium and Bifidobacterium lactis significantly affected energy metabolism in obese dogs, improved glucose and insulin tolerance, and reduced systemic inflammation [69]. It may be of benefit to assess the effects of the probiotic test product beyond the gut in future in vivo studies.
The present study had a few limitations. First, considering that the probiotic strains were tested in combination and not individually, it is not possible to determine the individual contribution of each strain to the observed outcomes. Furthermore, the limited presence of maltodextrin (which would also be present in the final product formulation) could have partially added to the observed results. Second, this study used in vitro colonic simulations, meaning that the results do not directly translate to the in vivo situation. While investigation into interindividual differences was already included by using six different canine donors, the generalizability of the results must be considered critically. As a next step, in vivo studies that use a double-blind, randomized, placebo-controlled approach and include an adequate number of dogs and effective probiotic doses are needed to confirm these effects and ensure the stability of these strains for supplementation in pets.

5. Conclusions

This study demonstrated the potential beneficial effects on the canine microbiome following supplementation with lactic acid probiotics (L. casei, L. fermentum, L. brevis, and E. faecium) using in vitro simulations of the dog colon. While no direct extrapolation to in vivo outcomes can be made based on the current study, further insights into the probiotic characteristics of the test product were generated, including potent propiogenic properties and enrichment of specific beneficial bacterial taxa (e.g., Megamonas). Considering the current need to develop strategies to improve and maintain colonic health in pet dogs, the current data support further research to assess the potential health outcomes following supplementation with the probiotic mixture.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pets2040033/s1, Table S1: Overview of pH; Table S2: Overview of gas pressure; Table S3: Overview of acetate; Table S4: Overview of propionate; Table S5: Overview of butyrate; Table S6: Overview of lactate; Table S7: Overview of branched-chain fatty acids (BCFA); Table S8: Overview of ammonium (NH4-N); Table S9: Overview of taxonomic abundance 0 h; Table S10: Overview of taxonomic abundance 48 h; Table S11: Overview of alpha-diversity; Figure S1: Overview of beta-diversity.

Author Contributions

Conceptualization, A.A.L., J.B. and M.M.; methodology, A.A.L., J.E.B.K. and J.G.; formal analysis, A.A.L. and J.G.; investigation, A.A.L. and J.G.; resources, A.A.L.; data curation, J.G. and C.D.; writing—original draft preparation, J.G. and C.D.; writing—review and editing, A.A.L., J.E.B.K., J.G., C.D. and M.M.; visualization, A.A.L., J.B., J.E.B.K. and C.D.; supervision, A.A.L., J.G. and M.M.; project administration, J.G. and C.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research and the article processing charge were funded by Phileo by Lesaffre. The study sponsor was involved in the study design; approved the final manuscript draft; and agreed to its submission for publication. The study sponsor was not involved in the collection, analysis, and interpretation of data.

Institutional Review Board Statement

Informed consent was received for the use of the fecal samples, and samples were collected and used as approved by the Ethics Committee of the University Hospital Ghent (reference number ONZ-2022-0267; approved on 29 July 2022).

Informed Consent Statement

Informed consent was received for the use of the fecal samples.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author on reasonable request.

Acknowledgments

The authors thank Sarah Bubeck, Bubeck Scientific Communications for providing medical writing support.

Conflicts of Interest

Achraf Adib Lesaux and Jonna E. B. Koper are employees of Phileo by Lesaffre and Lesaffre Institute of Science and Technology, respectively. Author Jake Burlet was employed by the company CanBiocin. Jonas Ghyselinck, Cindy Duysburgh, Massimo Marzorati declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

The following abbreviations are used in this manuscript:
ASVamplicon sequence variant
BCFAbranched-chain fatty acid
CFUcolony forming units
DADADivisive Amplicon Denoising Algorithm
DAPCDiscriminant Analysis of Principal Components
LDAlinear discriminant analysis
LEfSelinear discriminant analysis effect size
PCAprincipal component analysis
SCFAshort-chain fatty acid

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Figure 1. Box plots showing change in pH (A) and gas pressure (B) across six healthy dog donors for the blank control and test product over time (0–6 h, 0–24 h, and 0–48 h). Fermentations were collected from the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). Individual values and t-test results are shown in Supplementary Materials (Tables S1 and S2). * p < 0.05 versus blank control.
Figure 1. Box plots showing change in pH (A) and gas pressure (B) across six healthy dog donors for the blank control and test product over time (0–6 h, 0–24 h, and 0–48 h). Fermentations were collected from the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). Individual values and t-test results are shown in Supplementary Materials (Tables S1 and S2). * p < 0.05 versus blank control.
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Figure 2. Box plots showing levels of acetate (A), propionate (B), butyrate (C), and lactate (D) across six healthy dog donors for the blank control and test product over time (0–6 h, 0–24 h, and 0–48 h). Fermentations were collected from the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). Individual values and t-test results are shown in Supplementary Materials (Tables S3–S6). * p < 0.05 versus blank control.
Figure 2. Box plots showing levels of acetate (A), propionate (B), butyrate (C), and lactate (D) across six healthy dog donors for the blank control and test product over time (0–6 h, 0–24 h, and 0–48 h). Fermentations were collected from the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). Individual values and t-test results are shown in Supplementary Materials (Tables S3–S6). * p < 0.05 versus blank control.
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Figure 3. Box plots showing levels of total BCFA (A) and ammonium (B) across six healthy dog donors for the blank control and test product over time (0–6 h, 0–24 h, and 0–48 h). Fermentations were collected from the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). Individual values and t-test results are shown in Supplementary Materials (Tables S7 and S8). * p < 0.05 versus blank control. BCFA, branched chain fatty acid.
Figure 3. Box plots showing levels of total BCFA (A) and ammonium (B) across six healthy dog donors for the blank control and test product over time (0–6 h, 0–24 h, and 0–48 h). Fermentations were collected from the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). Individual values and t-test results are shown in Supplementary Materials (Tables S7 and S8). * p < 0.05 versus blank control. BCFA, branched chain fatty acid.
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Figure 4. Box plots demonstrating alpha-diversity using the Observed taxa index (A), the Chao1 index (B), the Shannon index (C), and the Simpson index (D) across six healthy dog donors for the blank control and test product at 48 h. Fermentations were collected from the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6).
Figure 4. Box plots demonstrating alpha-diversity using the Observed taxa index (A), the Chao1 index (B), the Shannon index (C), and the Simpson index (D) across six healthy dog donors for the blank control and test product at 48 h. Fermentations were collected from the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6).
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Figure 5. Box plots showing bacterial biomass across six healthy dog donors for the blank control and test product at 48 h. Samples were collected from the reactors of the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). * p < 0.05 versus blank control.
Figure 5. Box plots showing bacterial biomass across six healthy dog donors for the blank control and test product at 48 h. Samples were collected from the reactors of the short-term colonic simulations at the indicated timepoints. Student’s paired t-tests were used to compare changes observed for the test product versus the blank control (across donor comparisons). The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). * p < 0.05 versus blank control.
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Figure 6. Box plots showing the absolute abundances for enriched taxa (FC > 2) 78_Lacticaseibacillus (A), 83_Limosilactobacillus (B), 121_Levilactobacillus (C), 141_Limosilactobacillus (D), 2_Megamonas (E), 61_Megamonas (F), 76_Enterococcus (G), and 95_Bacteroides (H) across six healthy dog donors for the blank control and test product at 48 h. Samples were collected from the reactors of the short-term colonic simulations at the indicated timepoints. Enriched taxa were determined using LEfSe and/or treeclimbR analysis. The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). FC, fold change; LEfSe, linear discriminant analysis effect size.
Figure 6. Box plots showing the absolute abundances for enriched taxa (FC > 2) 78_Lacticaseibacillus (A), 83_Limosilactobacillus (B), 121_Levilactobacillus (C), 141_Limosilactobacillus (D), 2_Megamonas (E), 61_Megamonas (F), 76_Enterococcus (G), and 95_Bacteroides (H) across six healthy dog donors for the blank control and test product at 48 h. Samples were collected from the reactors of the short-term colonic simulations at the indicated timepoints. Enriched taxa were determined using LEfSe and/or treeclimbR analysis. The “X” and horizontal line in the box plot indicate the average and median values, respectively, across donors (n = 6). FC, fold change; LEfSe, linear discriminant analysis effect size.
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Lesaux, A.A.; Ghyselinck, J.; Duysburgh, C.; Marzorati, M.; Koper, J.E.B.; Burlet, J. Assessing Probiotic Efficacy: Short-Term Impact on Canine Gut Microbiota Using an In Vitro Colonic Fermentation Model. Pets 2025, 2, 33. https://doi.org/10.3390/pets2040033

AMA Style

Lesaux AA, Ghyselinck J, Duysburgh C, Marzorati M, Koper JEB, Burlet J. Assessing Probiotic Efficacy: Short-Term Impact on Canine Gut Microbiota Using an In Vitro Colonic Fermentation Model. Pets. 2025; 2(4):33. https://doi.org/10.3390/pets2040033

Chicago/Turabian Style

Lesaux, Achraf Adib, Jonas Ghyselinck, Cindy Duysburgh, Massimo Marzorati, Jonna E. B. Koper, and Jake Burlet. 2025. "Assessing Probiotic Efficacy: Short-Term Impact on Canine Gut Microbiota Using an In Vitro Colonic Fermentation Model" Pets 2, no. 4: 33. https://doi.org/10.3390/pets2040033

APA Style

Lesaux, A. A., Ghyselinck, J., Duysburgh, C., Marzorati, M., Koper, J. E. B., & Burlet, J. (2025). Assessing Probiotic Efficacy: Short-Term Impact on Canine Gut Microbiota Using an In Vitro Colonic Fermentation Model. Pets, 2(4), 33. https://doi.org/10.3390/pets2040033

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