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. 2022 Dec 27;101:skac424. doi: 10.1093/jas/skac424

Effects of a Saccharomyces cerevisiae fermentation product on fecal characteristics, metabolite concentrations, and microbiota populations of dogs subjected to exercise challenge

Patrícia M Oba 1, Meredith Q Carroll 2, Kelly M Sieja 3, Juliana P de Souza Nogueira 4, Xiaojing Yang 5, Tammi Y Epp 6, Christine M Warzecha 7, Jessica L Varney 8, Jason W Fowler 9, Craig N Coon 10, Kelly S Swanson 11,12,13,✉
PMCID: PMC9890449  PMID: 36573478

Abstract

The objective of this study was to determine the fecal characteristics, microbiota, and metabolites of dogs fed a Saccharomyces cerevisiae fermentation product (SCFP) and subjected to exercise challenge in untrained and trained states. Thirty-six adult dogs (18 male, 18 female; mean age: 7.1 yr; mean body weight: 29.0 kg) were randomly assigned to control or SCFP-supplemented (250 mg/dog/d) diets and fed for 10 wk. After 3 wk, dogs were given an exercise challenge (6.5 km run), with fresh fecal samples collected pre- and post-challenge. Dogs were then trained by a series of distance-defined running exercise regimens over 7 wk (two 6.4 km runs/wk for 2 wk; two 9.7 km runs/wk for 2 wk; two 12.9 km runs/wk for 2 wk; two 3.2 km runs/wk). Dogs were then given exercise challenge (16 km run) in the trained state, with fresh fecal samples collected pre- and post-challenge. Fecal microbiota data were evaluated using QIIME2, while all other data were analyzed using the Mixed Models procedure of SAS. Effects of diet, exercise, and diet*exercise were tested with P < 0.05 considered significant. Exercise challenge reduced fecal pH and ammonia in both treatments, and in untrained and trained dogs. After the exercise challenge in untrained dogs, fecal indole, isobutyrate, and isovalerate were reduced, while acetate and propionate were increased. Following the exercise challenge in trained dogs, fecal scores and butyrate decreased, while isobutyrate and isovalerate increased. SCFP did not affect fecal scores, pH, dry matter, or metabolites, but fecal Clostridium was higher in controls than in SCFP-fed dogs over time. SCFP and exercise challenge had no effect on alpha or beta diversity in untrained dogs. However, the weighted principal coordinate analysis plot revealed clustering of dogs before and after exercise in trained dogs. After exercise challenge, fecal Collinsella, Slackia, Blautia, Ruminococcus, and Catenibacterium were higher and Bacteroides, Parabacteroides, Prevotella, Phascolarctobacterium, Fusobacterium, and Sutterella were lower in both untrained and trained dogs. Using qPCR, SCFP increased fecal Turicibacter, and tended to increase fecal Lactobacillus vs. controls. Exercise challenge increased fecal Turicibacter and Blautia in both untrained and trained dogs. Our findings show that exercise and SCFP may affect the fecal microbiota of dogs. Exercise was the primary cause of the shifts, however, with trained dogs having more profound changes than untrained dogs.

Keywords: 16S rRNA sequencing, canine microbiota, yeast product

Thirty-six adult dogs were fed a control diet or Saccharomyces cerevisiae fermentation product-supplemented diet and then subjected to an exercise challenge in an untrained and trained state, with fresh fecal samples collected pre- and post-challenge. Many fecal metabolites and microbiota were affected, but our findings show that exercise was the primary cause of the shifts and trained dogs had more profound changes than untrained dogs.

Introduction

Saccharomyces cerevisiae fermentation product (SCFP) is a dry product produced via S. cerevisiae fermentation that includes residual yeast cells, yeast cell wall fragments, fermentation metabolites, and media used during fermentation. Previous research has demonstrated that SCFP consumption may improve the health and performance of many livestock species, including broiler chicks (Gao et al., 2008), young dairy calves (Magalhães et al., 2008), and growing piglets (Shen et al., 2009). SCFP supplementation may also benefit companion animals, as it has been shown to improve diet palatability, positively alter fecal microbiota populations, decrease fecal phenol concentrations, and alter circulating immune cell function in healthy adult dogs (Lin et al., 2019). Moreover, a study that examined the effects of SCFP in a working Labrador Retriever exercise model demonstrated that SCFP improved stool quality, exercise performance, immune response after exercise, and oxidative stress markers (Varney et al., 2019). Because SCFP consumption has also been shown to result in less paw swelling and arthritis in mice and rats (Evans et al., 2012), its use may be expected to benefit animals undergoing exercise challenge.

Exercising intensely increases oxidative stress and inflammation, both of which can affect the gastrointestinal and muscular systems (Criswell et al., 1993; Finaud et al., 2006; Powers and Jackson, 2008; Lambert, 2009; de Oliveira et al., 2014; Janssen Duijghuijsen et al., 2016). Due to this response, dogs often suffer from gastrointestinal problems during bouts of exercise (Davis et al., 2003). An association between these symptoms and alterations in intestinal permeability and decreased barrier function have been identified (Øktedalen et al., 1992; Pals et al., 1997). Additionally, the changes in gastrointestinal transit rate, blood flow to the gastrointestinal tract, and other factors may impact the gut microbiota populations during intense exercise. The intestinal microbial ecosystem of professional athletes exhibits an overall increase in biodiversity, with some compositional changes mainly in mucin degraders, lactate utilizers, and short-chain fatty acid (SCFA) producers (Mailing et al., 2019). These shifts may benefit the host by assisting in the metabolism of excess lactate produced by exercise and by promoting gut barrier integrity (Mailing et al., 2019). Because microbiota metabolism affects intestinal barrier function, SCFP supplementation may be able to counteract exercise-induced intestinal barrier dysfunction.

Monitoring gastrointestinal microbiota during exercise may help elucidate the mechanisms by which exercise affects gastrointestinal permeability and microbiota and how SCFP supplementation may provide a benefit. Therefore, the objective of the current study was to determine the fecal characteristics, microbiota populations, and metabolite concentrations of dogs subjected to exercise challenge. We hypothesized that SCFP would beneficially shift and maintain the stability of the fecal microbiota and metabolites, and avoid poor fecal scores, following an exercise challenge, and that trained dogs would had a more diverse microbiota.

Materials and Methods

The animal study was conducted at Four Rivers Kennel, Inc. (Walker, MO). All experimental procedures were approved by the Four Rivers Kennel, Inc. IACUC (FRK-20) prior to experimentation, with all procedures being performed in accordance with the United States Public Health Service Policy on Humane Care and Use of Laboratory Animals.

Animals and housing

All dogs were housed in individual kennels overnight and during inclement weather. Each kennel measured 1.22 m × 1.83 m × 1.83 m. Each kennel had a metal nipple drinker available for access to water ad libitum. All dogs were fed in their individual kennels each morning (9 am). The temperature of the kennel was controlled with the use of heated floors, tunnel ventilation, ceiling fans, and HVAC units. All dogs were placed in social groups in outside airing yards each day, weather permitting, which contain shade/shelter and automatic waterers. Thirty-six healthy and working Labrador Retrievers [18 intact males and 18 intact females; mean age: 7.1 yr, mean body weight (BW): 29.01 kg] were utilized for this study. Dogs were randomly divided into two groups (SCFP; control), blocked on the basis of age, sex, BW, body condition score, and body composition data (via dual-energy X-ray absorptiometry scans).

Diets and experimental timeline

Dogs in the SCFP group were given a standard dose (250 mg/dog/day) contained in a TruMune (Diamond V Mills, Inc., Cedar Rapids, IA) tablet each day for the duration of the study. The 0.5 g tablet contained 0.25 g of active ingredients and 0.25 g of inactive ingredients (dicalcium phosphate, powdered cellulose, magnesium stearate). The dosage was based on the results of a previous study (Lin et al., 2019). All dogs consumed the same diet (Gold-N-Pro, MFA Inc., Columbia, MO) throughout the study to maintain BW and body condition score. This diet is the standard kennel diet, and is fed to all dogs from the age of one year of age. The analyzed chemical and ingredient composition of this diet is presented in Supplementary Table 1.

The exercise regimen was the following: week 1–3: no exercise; week 4–5: 6.4 km, 2x/wk; week 6–7: 9.7 km, 2x/wk; week 8–9: 12.9 km, 2x/wk; week 10: 3.2 km, 2x/wk; week 11: 16 km, 1x/wk (final run). Dogs ran at a rate of 10 min per mile (1.61 km).

Fecal sample collection

Fresh fecal samples were collected on wk 0 (morning), wk 3 (d 21 and 22; morning before exercise challenge and next morning after the exercise challenge, 6.4 km run took an average of 40 min), and wk 11 (d 70 and 71; morning before exercise challenge and next morning after the exercise challenge, 16 km run took an average of 1 h, 40 min) for the measurement of fecal characteristics (fecal scores; pH; dry matter percentage), microbiota populations, and metabolites at baseline (prior to treatments), after SCFP supplementation, and before and after exercise challenges. At each time point, fecal samples were scored according to the following scale: 1, hard, dry pellets, small hard mass; 2, hard, formed, dry stool; remains firm and soft; 3, soft, formed, and moist stool, retains shape; 4, soft, unformed stool, assumes shape of container; and 5, watery, liquid that can be poured.

Fresh fecal samples (within 15 min of defecation) were collected at each time point. Fecal pH were measured immediately using a pH meter (Denver Instrument, Bohemia, NY) equipped with an electrode (Beckman Instruments Inc., Fullerton, CA) and then samples were divided into several aliquots. Aliquots for analysis of phenols and indoles were frozen at −20 °C immediately after collection. One aliquot was collected and placed in 2 N hydrochloric acid and then −20 °C for ammonia, SCFA, and branched-chain fatty acid (BCFA) analyses. One aliquot was immediately transferred to 4 sterile cryogenic vials (Nalgene, Rochester, NY) and placed on dry ice until being transferred to a −80 °C freezer and stored until microbial analyses. A final aliquot was collected for measurement of dry matter (105 °C in oven for two d).

Fecal metabolite concentrations

Fecal SCFA and BCFA concentrations were determined by gas chromatography according to Erwin et al. (1961) using a gas chromatograph (Hewlett-Packard 5890A series II, Palo Alto, CA) and a glass column (180 cm × 4 mm i.d.) packed with 10% SP-1200/1% H3PO4 on 80/100 + mesh Chromosorb WAW (Supelco Inc., Bellefonte, PA). Nitrogen were the carrier with a flow rate of 75 mL/min. Oven, detector, and injector temperatures were 125, 175, and 180 °C, respectively. Fecal ammonia concentrations were determined according to the method of Chaney and Marbach (1962). Fecal phenol and indole concentrations were determined using gas chromatography according to the methods described by Flickinger et al. (2003).

Fecal DNA extraction and MiSeq Illumina sequencing of 16S rRNA gene amplicons

Total DNA from fecal samples were extracted using Mo-Bio PowerSoil kits (MO BIO Laboratories, Inc., Carlsbad, CA). Concentrations of extracted DNA were quantified using a Qubit 3.0 Fluorometer (Life Technologies, Grand Island, NY). 16S rRNA gene amplicons were generated using a Fluidigm Access Array (Fluidigm Corporation, South San Francisco, CA) in combination with Roche High Fidelity Fast Start Kit (Roche, Indianapolis, IN). The primers 515F (5ʹ-GTGCCAGCMGCCGCGGTAA-3ʹ) and 806R (5ʹ-GGACTACHVGGGTWTCTAAT-3ʹ) that target a 252 bp-fragment of the V4 region of the 16S rRNA gene were used for amplification (primers synthesized by IDT Corp., Coralville, IA) (Caporaso et al., 2012). CS1 forward tag and CS2 reverse tag were added according to the Fluidigm protocol. Quality of the amplicons was assessed using a Fragment Analyzer (Advanced Analytics, Ames, IA) to confirm amplicon regions and sizes. A DNA pool was generated by combining equimolar amounts of the amplicons from each sample. The pooled samples were then be size selected on a 2% agarose E-gel (Life technologies, Grand Island, NY) and extracted using a Qiagen gel purification kit (Qiagen, Valencia, CA). Cleaned size-selected pooled products were run on an Agilent Bioanalyzer to confirm appropriate profile and average size. Illumina sequencing was performed on a MiSeq using v3 reagents (Illumina Inc., San Diego, CA) at the Roy J. Carver Biotechnology Center at the University of Illinois.

16S rRNA microbial data analysis

Illumina 16S rRNA gene amplicon sequencing produced a total of 7,734,786 sequences, with an average of 43,947 sequences per sample. Forward reads were trimmed using the FASTX-Toolkit (version 0.0.13) and QIIME 2 (Caporaso et al., 2011) were used to process the resulting sequence data. Briefly, high-quality (quality value ≥ 20) sequence data derived from the sequencing process were demultiplexed. Sequences then were clustered into operational taxonomic units (OTU) through a closed-reference OTU picking strategy against the Greengenes 13_8 reference database (DeSantis et al., 2006) with a 97% similarity threshold. Singletons (OTU that are observed fewer than two times) and OTU that had less than 0.01% of the total observation were discarded. An even sampling depth (25,000 reads) was used for assessing alpha and beta diversity measures. Beta diversity was calculated using weighted and unweighted UniFrac distance measures.

Quantitative polymerase chain reaction (qPCR) and dysbiosis index

qPCR was performed according to previous methods (Panasevich et al., 2015). Briefly, primers targeting specific bacterial genera (Lactobacillus, Bifidobacterium, Blautia, Faecalibacterium, and Fusobacterium) and species (Escherichia coli, Clostridium perfringens, and Akkermansia muciniphila) were used. qPCR data were expressed as the log amount of DNA (fg) for each particular bacterial group/10 ng of isolated total DNA according to Suchodolski et al. (2012). Dysbiosis index (DI), which is based on the abundances of the bacterial taxa mentioned above, was calculated according to Alshawaqfeh et al. (2017).

Statistical analyses

All data were analyzed using the Mixed Models procedure of SAS (version 9.4; SAS Institute, Cary, NC). Substrates were considered to be a fixed effect and dogs were considered a random effect. Tukey’s multiple comparison analysis was used to compare LS means and control for experiment-wise error. Differences were considered significant when P < 0.05 and trends when P < 0.10.

Results

Throughout the experiment, dogs were offered an average of 688.9 ± 47.1 g of diet for males and 563.9 ± 47.9 g for females. Overall, 98.1% of the food was consumed. Compared with baseline, the consumption of SCFP for 21 d did not affect fecal scores, pH, dry matter, or metabolite concentrations (Table 1). However, period (baseline vs. d 21) affected several fecal characteristics, with fecal scores and ammonia, phenol, indole, and total phenol and indole concentrations being lower (P < 0.05) and fecal pH and butyrate being higher (P < 0.05) after 21 d in both groups. Fecal isobutyrate and isovalerate concentrations also tended to be lower (P < 0.10) after 21 d on study in both groups.

Table 1.

Fecal characteristics and metabolite concentrations of dogs before and after 21 d of SCFP consumption

Control SCFP1 P-value
Day 0 Day 21 Day 0 Day 21 SEM Treatment Period Trt*P2
Fecal score3 3.36 2.92 3.36 3.03 0.118 0.6501 0.0014 0.6313
pH 5.82 6.05 5.90 6.11 0.093 0.5500 0.0044 0.8314
Dry matter (%) 30.34 30.54 30.40 29.95 0.642 0.6716 0.8983 0.5810
µmol/g DM basis
Ammonia 97.17 87.49 97.35 87.00 4.946 0.9347 0.0056 0.9804
Phenols and indoles
 Phenol 1.06 0.63 1.06 0.61 0.101 0.9256 <0.0001 0.9090
 Indole 2.55 1.50 2.78 1.49 0.340 0.9739 0.0028 0.7782
 Total phenols/indoles 3.61 2.13 3.84 2.10 0.415 0.8538 0.0002 0.7983
Short-chain fatty acids
 Acetate 476.81 479.02 478.07 465.30 17.521 0.7494 0.7356 0.6322
 Propionate 242.65 245.15 236.61 241.44 10.010 0.6340 0.7123 0.9070
 Butyrate 66.36 75.90 68.01 78.68 4.540 0.6386 0.0278 0.8987
 Total SCFA4 785.73 800.07 782.69 785.42 28.422 0.7737 0.7460 0.8257
Branched-chain fatty acids
 Isobutyrate 7.11 5.97 6.69 6.33 0.580 0.8931 0.0602 0.2167
 Isovalerate 10.78 9.09 10.10 10.07 0.896 0.9326 0.0788 0.1556
 Valerate 2.78 3.63 2.82 7.17 1.733 0.3521 0.1023 0.2659
 Total BCFA4 20.49 18.69 19.60 23.57 2.715 0.5083 0.6564 0.2405
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; P, period.

3Fecal samples were scored according to the following scale: 1, hard, dry pellets, small hard mass; 2, hard, formed, dry stool; remains firm and soft; 3, soft, formed, and moist stool, retains shape; 4, soft, unformed stool, assumes shape of container; and 5, watery, liquid that can be poured.

4SCFA, short-chain fatty acids; BCFA, branched-chain fatty acids.

Based on 16S rRNA gene amplicon data, a significant treatment*period interaction (P < 0.05) was noted for the relative abundance of fecal Clostridium over the first 21 d of study (Table 2). In control dogs, the relative abundance of fecal Clostridium increased from baseline to day 21. In dogs fed SCFP, however, fecal Clostridium was not altered during that time. Based on 16S rRNA gene amplicon data, a trend for a treatment*period interaction (P = 0.054) was noted for the relative abundance of fecal Lactobacillus over the first 21 d of study. The consumption of SCFP did not affect any other bacterial populations, but period (baseline vs. d 21) affected several bacterial genera. The relative abundances of fecal Prevotella (Prevotellaceae family), Lactobacillus, Butyricicoccus, and Sutterella were lower (P < 0.05) and the relative abundance of fecal Parabacteroides tended to be lower (P < 0.10) after 21 d compare with baseline. In contrast, the relative abundances of fecal Streptococcus, Blautia, Dorea, Faecalibacterium, Clostridium (Erysipelotrichaceae family), and Eubacterium were greater (P < 0.05) and the relative abundances of fecal Firmicutes and Turicibacter tended to be greater (P < 0.10) after 21 d compared with baseline. Based on qPCR data, fecal Turicibacter abundance was greater (P < 0.05) in dogs fed SCFP for 21 d than controls (Table 3). No other bacteria were impacted by SCFP, but fecal Streptococcus and Blautia abundances increased (P < 0.01) and fecal Bifidobacterium abundance tended to decrease (P = 0.07) after 21 d compared with baseline. Alpha diversity (observed OTUs; Faith’s Phylogenetic Diversity; Shannon diversity index; Simpson’s diversity index) measures were not impacted by SCFP feeding or period (baseline vs. day 21) (Supplementary Figure 1).

Table 2.

Fecal bacteria (% of sequences) of dogs before and after 21 d of SCFP consumption

Control SCFP1 P-value
Phyla Family Genus Day 0 Day 21 Day 0 Day 21 SEM Treatment Period Trt*P2
Actinobacteria 0.87 0.77 0.85 0.72 0.158 0.8504 0.8045 0.5426
Bacteroidetes 26.96 22.13 23.12 23.32 2.767 0.7125 0.1551 0.1235
Porphyromonadaceae Parabacteroides 0.46 0.29 0.53 0.34 0.106 0.6917 0.0837 0.1026
Prevotellaceae Prevotella 13.49 10.67 12.10 10.33 1.838 0.7193 0.0362 0.6180
Paraprevotellaceae Prevotella 3.50 3.05 2.47 3.01 0.491 0.4034 0.8642 0.0920
Firmicutes 46.86 55.04 53.32 54.31 4.197 0.4470 0.0547 0.1125
Lactobacillaceae Lactobacillus 7.51 2.22 7.99 6.33 2.239 0.1641 0.0182 0.0538
Streptococcaceae Streptococcus 0.56 3.65 1.48 2.24 1.016 0.6394 0.0032 0.2325
Turicibacteraceae Turicibacter 0.70 1.26 1.33 1.67 0.378 0.1010 0.0559 0.5418
Clostridiaceae Clostridium 10.76b 15.38a 13.22ab 12.16ab 1.739 0.8636 0.1229 0.0368
Lachnospiraceae Blautia 4.26 5.46 3.98 4.90 0.573 0.5403 0.0005 0.6891
Dorea 0.95 1.18 0.82 1.07 0.18 0.6677 0.0003 0.9591
Ruminococcaceae Butyricicoccus 0.31 0.22 0.37 0.24 0.033 0.6410 0.0076 0.8316
Faecalibacterium 4.64 6.98 5.83 6.94 0.737 0.7782 0.0019 0.2754
Erysipelotrichaceae Clostridium 0.27 0.46 0.32 0.40 0.07 0.9008 0.0205 0.0815
Eubacterium 1.42 1.90 1.28 1.95 0.289 0.9700 0.0002 0.8418
Fusobacteria 18.00 16.71 17.60 16.84 1.654 0.9400 0.5236 0.8677
Proteobacteria 7.29 5.32 5.08 4.79 0.879 0.4627 0.1357 0.1469
Alcaligenaceae Sutterella 4.07 2.57 3.59 3.38 0.45 0.7548 0.0252 0.0865
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; P, period.

a,bMeans with different superscripts within a row differ by Tukey’s test (P < 0.05).

Table 3.

Fecal bacteria (log DNA/gram of feces) and dysbiosis index of dogs before and after 21 d of SCFP consumption

Control SCFP1 P-value
Item Day 0 Day 21 Day 0 Day 21 SEM Treatment Period Trt*P2
Dysbiosis index −2.74 −2.37 −2.23 −2.05 0.336 0.3239 0.2406 0.6777
Universal bacteria 11.25 11.32 11.21 11.26 0.036 0.4036 0.0230 0.9414
Faecalibacterium 7.45 7.60 7.43 7.60 0.098 0.2880 0.1251 0.9414
Turicibacter 7.10 7.31 7.40 7.54 0.129 0.0133 0.2880 0.7030
Streptococcus 6.31 6.88 6.54 6.97 0.203 0.5095 0.0023 0.6386
Escherichia coli 4.50 4.45 4.94 4.81 0.306 0.3046 0.6344 0.8332
Blautia 10.27 10.44 10.23 10.37 0.056 0.3699 0.0030 0.7759
Fusobacterium 9.37 9.41 9.27 9.32 0.093 0.3358 0.6262 0.9170
Clostridium hiranonis 6.71 6.88 6.75 6.82 0.077 0.9171 0.1097 0.4572
Bifidobacterium 4.41 4.23 4.78 4.34 0.307 0.5459 0.0721 0.4422
Lactobacillus 7.10 6.56 7.03 7.22 0.229 0.2982 0.2926 0.1142
Enterococcus 3.99 4.10 4.18 4.19 0.175 0.4817 0.4361 0.9493
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; P, period.

In untrained dogs, exercise challenge reduced (P < 0.05) fecal pH and ammonia, indole, total phenols and indoles, isobutyrate, and isovalerate concentrations and increased (P < 0.05) fecal acetate and propionate concentrations (Table 4). Exercise challenge also tended to increase (P < 0.10) fecal total SCFA concentrations and tended to decrease (P < 0.10) total BCFA concentrations in untrained dogs. Consumption of SCFP did not affect fecal characteristics or metabolite concentrations during the exercise challenge in untrained dogs.

Table 4.

Fecal characteristics and metabolite concentrations of untrained dogs before and after exercise

Control SCFP1 P-value
Item Before After Before After SEM Treatment Exercise Trt*Ex2
Fecal score3 2.92 3.02 3.03 3.08 0.115 0.5083 0.3582 0.8657
pH 6.05 5.81 6.11 5.93 0.080 0.3708 0.0028 0.6148
Dry matter (%) 30.54 30.23 29.95 29.95 0.484 0.4754 0.6398 0.6481
µmol/g DM basis
Ammonia 87.49 72.17 87.00 77.77 4.893 0.6346 0.0087 0.4942
Phenols and indoles
 Phenol 0.63 0.61 0.61 0.62 0.058 0.9372 0.9917 0.7232
 Indole 1.50 1.00 1.49 1.29 0.135 0.3772 0.0034 0.1931
 Total phenols/indoles 2.13 1.62 2.1 1.91 0.160 0.4206 0.0208 0.2082
Short-chain fatty acids
 Acetate 479.02 493.87 465.3 520.39 18.513 0.7690 0.0238 0.1822
 Propionate 245.15 255.27 241.44 262.32 8.827 0.8730 0.0342 0.4485
 Butyrate 75.90 76.88 78.68 81.67 4.980 0.5135 0.6314 0.8080
 Total SCFA4 800.07 825.56 785.42 864.38 29.02 0.9148 0.0649 0.3226
Branched-chain fatty acids
 Isobutyrate 5.97 4.67 6.33 5.17 0.462 0.4637 0.0002 0.8097
 Isovalerate 9.09 7.33 10.07 8.00 0.823 0.4474 0.0002 0.7363
 Valerate 3.63 2.37 7.17 4.60 2.085 0.2808 0.1569 0.6231
 Total BCFA4 18.69 14.37 23.57 17.77 2.934 0.2745 0.0506 0.6867
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; Ex, exercise.

3Fecal samples were scored according to the following scale: 1, hard, dry pellets, small hard mass; 2, hard, formed, dry stool; remains firm and soft; 3, soft, formed, and moist stool, retains shape; 4, soft, unformed stool, assumes shape of container; and 5, watery, liquid that can be poured.

4SCFA, short-chain fatty acids; BCFA, branched-chain fatty acids.

Based on 16S rRNA gene amplicon data, exercise challenge affected several bacterial phyla and genera in untrained dogs (Table 5). At the phyla level, the relative abundances of fecal Actinobacteria and Firmicutes were higher (P < 0.05), while the relative abundances of fecal Bacteroidetes, Fusobacteria, and Proteobacteria were lower (P < 0.05) after exercise regardless of diet fed. At the genus level, the relative abundances of fecal Collinsella, Slackia, Clostridium (Clostridiaceae family), Blautia, Ruminococcus, Megamonas, and Catenibacterium were higher (P < 0.05) and the relative abundances of fecal Butyricicoccus, Dialister, Allobaculum, Clostridium (Erysipelotrichaceae family), and Eubacterium tended to be higher (P < 0.10) after exercise. In contrast, the relative abundances of fecal Bacteroides, Parabacteroides, Prevotella, Phascolarctobacterium, Fusobacterium, and Sutterella were lower (P < 0.05) after exercise. In the untrained state, dogs fed SCFP tended to have lower (P < 0.10) relative abundances of fecal Clostridium (Lachnospiraceae family) and Catenibacterium and tended to have higher (P < 0.10) relative abundance of fecal Anaerobiospirillum before and after exercise when compared with controls. Based on qPCR data, the abundance of fecal Turicibacter and Blautia was higher (P < 0.05), while the abundance of fecal Lactobacillus was lower (P < 0.05) in untrained dogs after exercise (Table 6). Alpha diversity (observed OTUs; Faith’s Phylogenetic Diversity; Shannon diversity index; Simpson’s diversity index) measures were not impacted by SCFP feeding or exercise in the untrained state (Supplementary Figure 2). Beta diversity analyses, as represented by principal coordinate (PCoA) plots of weighted UniFrac distances (P = 0.031, Figure 1), was altered by exercise challenge. In the untrained state, dogs in both treatment groups clustered together before exercise and shifted away and clustered together after exercise.

Table 5.

Fecal bacteria (% of sequences) of untrained dogs before and after exercise

Control SCFP1 P-value
Phyla Family Genus Before After Before After SEM Treatment Exercise Trt*Ex2
Actinobacteria 0.77 1.34 0.72 0.90 0.158 0.1501 0.0029 0.3318
Coriobacteriaceae Collinsella 0.58 1.07 0.52 0.68 0.129 0.1146 0.0019 0.4954
Slackia 0.13 0.18 0.11 0.13 0.027 0.2146 0.0082 0.5142
Bacteroidetes 22.01 16.74 23.31 17.93 2.609 0.7090 0.0028 0.9737
Bacteroidaceae Bacteroides 7.03 4.48 8.35 6.79 0.956 0.1422 0.0019 0.4247
Porphyromonadaceae Parabacteroides 0.29 0.17 0.34 0.22 0.051 0.4390 0.0075 0.9140
Prevotellaceae Prevotella 10.65 9.42 10.33 7.13 1.688 0.7112 0.0194 0.9105
Firmicutes 55.38 66.83 54.36 62.73 4.005 0.6061 0.0012 0.5869
Lactobacillaceae Lactobacillus 2.33 4.92 6.33 5.77 2.039 0.2650 0.1925 0.0844
Clostridiaceae Clostridium 15.30 17.45 12.15 15.90 1.717 0.1901 0.0130 0.8874
Lachnospiraceae Blautia 5.40 6.89 4.92 6.34 0.643 0.5469 0.0002 0.9256
Clostridium 0.05 0.07 0.03 0.04 0.013 0.0888 0.8576 0.2415
Ruminococcus 1.2 1.59 1.15 1.35 0.148 0.5646 0.0037 0.7070
Ruminococcaceae Butyricicoccus 0.22 0.26 0.24 0.26 0.019 0.8812 0.0787 0.4847
Veillonellaceae Dialister 0.04 0.08 0.06 0.08 0.023 0.5442 0.0927 0.2711
Megamonas 1.76 2.78 1.55 1.92 0.376 0.1763 0.0050 0.8486
Phascolarctobacterium 1.33 0.97 1.67 1.12 0.180 0.2549 0.0029 0.5099
Erysipelotrichaceae Allobaculum 1.37 1.71 1.14 1.47 0.177 0.4107 0.0918 0.9941
Catenibacterium 4.89 7.29 3.87 5.55 0.757 0.0885 0.0003 0.9184
Clostridium 0.45 0.44 0.4 0.52 0.088 0.7996 0.0830 0.1453
Eubacterium 1.85 2.3 1.95 2.17 0.329 0.9430 0.0834 0.8882
Fusobacteria 16.57 12.03 16.83 14.50 1.571 0.4571 0.0117 0.3984
Fusobacteriaceae Fusobacterium 6.03 3.27 5.97 4.58 0.767 0.5015 0.0009 0.2348
Proteobacteria 5.30 3.07 4.76 3.92 0.703 0.6686 0.0044 0.1690
Alcaligenaceae Sutterella 2.56 1.92 3.37 2.45 0.350 0.1309 0.0027 0.5561
Succinivibrionaceae Anaerobiospirillum 0.76 0.51 1.06 1.09 0.245 0.0575 0.6152 0.0960
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; Ex, exercise.

Table 6.

Fecal bacteria (log DNA/gram of feces) and dysbiosis index of dogs of untrained dogs before and after exercise

Control SCFP1 P-value
Item Before After Before After SEM Treatment Exercise Trt*Ex2
Dysbiosis index −2.37 −2.67 −2.05 −2.07 0.331 0.2854 0.4410 0.5153
Universal bacteria 11.32 11.32 11.26 11.28 0.036 0.6496 0.4043 0.7951
Faecalibacterium 7.60 7.45 7.60 7.51 0.119 0.5498 0.8540 0.7512
Turicibacter 7.31 7.57 7.54 7.68 0.130 0.3032 0.0290 0.4781
Streptococcus 6.88 6.78 6.97 6.94 0.224 0.6486 0.6970 0.8335
Escherichia coli 4.45 4.21 4.81 4.73 0.324 0.3037 0.3679 0.6635
Blautia 10.44 10.58 10.37 10.53 0.051 0.3314 0.0008 0.7616
Fusobacterium 9.41 9.24 9.32 9.27 0.093 0.6579 0.2477 0.4968
Clostridium hiranonis 6.88 6.86 6.82 6.75 0.067 0.2275 0.5416 0.7377
Bifidobacterium 4.23 4.28 4.34 4.13 0.301 0.7842 0.5496 0.5430
Lactobacillus 6.56 6.38 7.22 6.53 0.258 0.2102 0.0172 0.1525
Enterococcus 4.10 4.06 4.19 4.00 0.153 0.9279 0.3450 0.5391
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; Ex, exercise.

Figure 1.

Figure 1.

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Beta diversity of fecal samples collected from untrained dogs before and after an exercise challenge. The PCoA plot of unweighted UniFrac distances of fecal microbial communities (a) were not different, but the PCoA plot of weighted UniFrac distances of fecal microbial communities (b) demonstrated clustering before and after exercise.

In trained dogs, exercise challenge reduced (P < 0.05) fecal scores and butyrate concentrations and increased (P < 0.05) fecal pH and ammonia, isobutyrate, isovalerate, and total BCFA concentrations (Table 7). Exercise challenge also tended to increase (P < 0.10) fecal phenol concentrations and decrease (P < 0.10) fecal propionate and total SCFA concentrations in trained dogs. Consumption of SCFP did not affect fecal characteristics or metabolite concentrations during the exercise challenge in trained dogs.

Table 7.

Fecal characteristics and metabolite concentrations of trained dogs before and after exercise

Control SCFP1 P-value
Item Before After Before After SEM Treatment Exercise Trt*Ex2
Fecal Score3 3.5 3.28 3.64 3.25 0.101 0.5843 0.0054 0.4125
pH 5.85 6.32 5.88 6.43 0.086 0.4323 <0.0001 0.6096
Dry matter (%) 30.76 31.08 30.02 31.32 0.83 0.7675 0.3304 0.56
µmol/g DM basis
Ammonia 134.8 163.27 132.74 162.39 8.145 0.8278 <0.0001 0.9111
Phenols and indoles
 Phenol 0.00 0.19 0.00 0.08 0.079 0.4743 0.0898 0.4743
 Indole 0.15 0.28 0.07 0.29 0.126 0.7334 0.8649 0.6823
 Total phenols/indoles 0.15 0.47 0.07 0.37 0.178 0.4476 0.4841 0.4155
Short-chain fatty acids
 Acetate 416.24 401.8 432.08 390.91 16.83 0.8838 0.1032 0.4298
 Propionate 222.28 202.76 221.01 202.69 9.679 0.9449 0.0548 0.9511
 Butyrate 69.71 58.54 72.63 57.96 4.805 0.819 0.0074 0.7017
 Total SCFA4 708.11 663.11 725.72 651.55 28.259 0.9151 0.0562 0.6076
Branched-chain fatty acids
 Isobutyrate 4.96 6.29 4.72 7.03 0.384 0.5761 <0.0001 0.2555
 Isovalerate 7.29 8.94 7.02 10.4 0.667 0.4589 <0.0001 0.1450
 Valerate 2.24 3.81 2.03 1.99 0.611 0.1081 0.2148 0.1927
 Total BCFA4 14.46 19.04 13.76 19.42 1.244 0.9602 <0.0001 0.5575
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; Ex, exercise.

3Fecal samples were scored according to the following scale: 1, hard, dry pellets, small hard mass; 2,  hard, formed, dry stool; remains firm and soft; 3, soft, formed, and moist stool, retains shape; 4, soft, unformed stool, assumes shape of container; and 5, watery, liquid that can be poured.

4SCFA, short-chain fatty acids; BCFA, branched-chain fatty acids.

Based on 16S rRNA gene amplicon data, exercise challenge affected several bacterial phyla and genera in trained dogs (Table 8). At the phyla level, the relative abundances of fecal Actinobacteria and Firmicutes were higher (P < 0.05) and relative abundances of fecal Bacteroidetes, Fusobacteria, and Proteobacteria were lower (P < 0.05) after exercise. At the genus level, the relative abundances of fecal Collinsella, Slackia, Turicibacter, Blautia, Clostridium (Lachnospiraceae family), Dorea, Ruminococcus, Faecalibacterium, Catenibacterium, Clostridium (Erysipelotrichaceae family), and Eubacterium were higher (P < 0.05) and the relative abundance of fecal Dialister tended to be higher (P < 0.10) after exercise. In contrast, the relative abundances of fecal Bacteroides, Parabacteroides, Prevotella (Prevotellaceae family), Phascolarctobacterium, Fusobacterium, Sutterella, and Anaerobiospirillum were lower (P < 0.05) and the relative abundance of fecal Prevotella (Paraprevotellaceae family), Peptococcus, and Oscillospira tended to be lower (P < 0.10) after exercise. In the trained state, dogs fed SCFP had greater (P < 0.05) relative abundance of fecal Lactobacillus before and after exercise when compared with controls.

Table 8.

Fecal bacteria (% of sequences) of trained dogs before and after exercise

Control SCFP1 P-value
Phyla Family Genus Before After Before After SEM Treatment Exercise Trt*Ex2
Actinobacteria 1.08 1.57 0.90 1.66 0.224 0.8507 0.0047 0.8921
Coriobacteriaceae Collinsella 0.82 1.29 0.63 1.36 0.198 0.6241 0.0002 0.9523
Slackia 0.12 0.21 0.15 0.21 0.024 0.3635 0.0016 0.2588
Bacteroidetes 17.70 9.82 16.62 9.01 2.583 0.7699 <0.0001 0.9390
Bacteroidaceae Bacteroides 7.36 3.51 7.55 4.16 1.305 0.8052 <0.0001 0.7539
Porphyromonadaceae Parabacteroides 0.35 0.13 0.29 0.12 0.061 0.8312 0.0040 0.5332
Prevotellaceae Prevotella 6.22 3.85 5.63 2.70 1.132 0.3323 0.0103 0.8563
Paraprevotellaceae Prevotella 2.44 1.90 2.14 1.53 0.382 0.2524 0.0683 0.9107
Firmicutes 56.00 72.26 58.48 75.37 4.616 0.6262 <0.0001 0.9226
Lactobacillaceae Lactobacillus 3.23 4.1 5.88 7.72 1.938 0.0189 0.166 0.6017
Turicibacteraceae Turicibacter 1.46 2.89 1.13 2.8 0.615 0.1361 0.0006 0.5753
Lachnospiraceae Blautia 6.37 7.95 5.85 7.96 0.656 0.7710 <0.0001 0.4737
Clostridium 0.06 0.11 0.03 0.07 0.021 0.4189 0.0133 0.8787
Dorea 1.38 1.64 1.65 1.65 0.286 0.8675 0.0479 0.4475
Ruminococcus 1.56 2.05 1.65 1.85 0.208 0.9450 0.0026 0.3108
Peptococcaceae Peptococcus 0.66 0.55 0.63 0.45 0.086 0.6691 0.0747 0.2164
Ruminococcaceae Faecalibacterium 5.22 7.11 5.86 6.52 0.673 0.5981 0.0153 0.1325
Oscillospira 0.05 0.02 0.02 0.01 0.012 0.4328 0.0547 0.2148
Veillonellaceae Dialister 0.05 0.06 0.08 0.07 0.027 0.6115 0.0933 0.5472
Phascolarctobacterium 0.91 0.61 0.99 0.44 0.139 0.6537 0.0024 0.2980
Erysipelotrichaceae Catenibacterium 6.99 9.57 6.01 9.66 1.419 0.3554 0.0005 0.6779
Clostridium 0.59 0.65 0.70 0.84 0.109 0.3594 0.0486 0.6690
Eubacterium 2.45 3.47 2.37 3.92 0.431 0.7431 0.0026 0.3997
Fusobacteria 19.69 13.41 18.78 11.27 1.851 0.4703 0.0001 0.6958
Fusobacteriaceae Fusobacterium 6.62 2.81 6.02 3.01 0.968 0.8680 <0.0001 0.5536
Proteobacteria 5.47 2.93 5.16 2.67 0.885 0.8032 <0.0001 0.9606
Alcaligenaceae Sutterella 3.13 1.70 3.75 1.60 0.592 0.9945 <0.0001 0.1739
Succinivibrionaceae Anaerobiospirillum 0.83 0.55 0.80 0.61 0.176 0.7030 0.0101 0.7548
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; Ex, exercise.

Based on qPCR data, the abundance of fecal Turicibacter and Blautia were higher (P < 0.05) and the abundance of Faecalibacterium and Clostridium hiranonis tended to be higher (P < 0.10) in trained dogs after exercise (Table 9). In contrast, the abundance fecal Fusobacterium was lower (P < 0.05) in trained dogs after exercise. In the trained state, dogs fed SCFP tended to have higher (P < 0.10) abundances of fecal Lactobacillus and Enterococcus before and after exercise when compared with controls. Two measures of alpha diversity, namely observed OTUs or Faith’s Phylogenetic Diversity, were not affected by the exercise challenge in trained dogs (Figure 2). However, the other two measures of alpha diversity [Simpson’s diversity index (P = 0.0297) and Shannon’s diversity index (P = 0.0138)] were decreased after the exercise challenge in trained dogs. Beta diversity analyses, as represented by PCoA plots of unweighted (P = 0.034) and weighted UniFrac distances (P = 0.003), was altered by exercise challenge (Figure 3). In the trained state, dogs in both treatment groups clustered together before exercise and shifted away and clustered together after exercise. Beta diversity analyses, as represented by PCoA plots of unweighted and weighted UniFrac distances, did not measure differences due to SCFP consumption over the course of the study (Supplementary Figure 3 and Supplementary Figure 4).

Table 9.

Fecal bacteria (log DNA/gram of feces) and dysbiosis index of trained dogs before and after exercise

Control SCFP1 P-value
Item Before After Before After SEM Treatment Exercise Trt*Ex2
Dysbiosis index −2.26 −2.20 −1.76 −1.75 0.325 0.2132 0.8988 0.9252
Universal bacteria 11.37 11.42 11.31 11.44 0.043 0.6725 0.0248 0.2847
Faecalibacterium 7.68 7.79 7.62 7.72 0.063 0.3889 0.0549 0.9550
Turicibacter 7.37 7.83 7.50 8.00 0.138 0.3652 <0.0001 0.8851
Streptococcus 6.90 7.23 7.18 7.49 0.230 0.2272 0.1261 0.9552
Escherichia coli 4.75 4.86 5.08 5.19 0.313 0.4304 0.5304 0.9954
Blautia 10.66 10.83 10.57 10.84 0.081 0.7401 <0.0001 0.2630
Fusobacterium 9.55 9.44 9.55 9.34 0.076 0.5622 0.0151 0.4424
Clostridium hiranonis 6.79 6.99 6.78 6.99 0.116 0.9176 0.0931 0.9944
Bifidobacterium 3.73 3.90 3.98 3.98 0.268 0.2597 0.7171 0.6095
Lactobacillus 6.56 6.56 7.19 7.05 0.262 0.0855 0.7248 0.7137
Enterococcus 4.05 4.28 4.43 4.51 0.170 0.0963 0.3458 0.6557
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1SCFP, 1 tablet/day, 250 mg Saccharomyces cerevisiae fermentation product.

2Trt, treatment; Ex, exercise.

Figure 2.

Figure 2.

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Alpha diversity measures of fecal samples collected from trained dogs before and after an exercise challenge. SCFP, Saccharomyces cerevisiae fermentation product.

Figure 3.

Figure 3.

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Beta diversity of fecal samples collected from trained dogs before and after an exercise challenge. The PCoA plot of unweighted UniFrac distances of fecal microbial communities (a) and the PCoA plot of weighted UniFrac distances of fecal microbial communities (b) demonstrated clustering before and after exercise.

Discussion

SCFP has been shown to enhance performance and support health in broiler chickens, nursery pigs, dairy calves, and adult dogs (Gao et al., 2008; Magalhães et al., 2008; Shen et al., 2009; Lin et al., 2019; Varney et al., 2019), but its potential benefits during an exercise challenge were of interest in the current study. In dogs, supplementation of 125, 250, or 500 mg/dog/day of SCFP increased fecal scores and decreased fecal phenol concentrations (Lin et al., 2019). However, a similar dosage (250 mg/dog/day) had no effect on fecal characteristics or phenol concentrations in the present study. Similar to previous studies, SCFP had no effect on fecal characteristics or SCFA concentrations (Swanson et al., 2002; Pawar et al., 2017; Lin et al., 2019). This lack of effect on fecal samples may be due to the fact that SCFP is a fermentation product. Many of its components may not be fermented by bacteria as dietary fibers or prebiotics, but may be used directly by gastrointestinal bacteria or host cells without the need of fermentation. Another potential explanation may be due to the rapid absorption of SCFA by colonocytes, making it difficult to detect changes in fecal concentrations (Von Engelhardt et al., 1989).

SCFP supplementation had no influence on fecal bacterial alpha or beta diversity in this study or in an earlier study in healthy adult dogs (Lin et al., 2019). Fecal Turicibacter, however, was demonstrated to increase following SCFP administration in the current study. Turicibacter are included in the DI calculation and is a SCFA producer, which may support gastrointestinal health (AlShawaqfeh et al., 2017). Although no changes in fecal SCFA ­concentrations were identified, enrichment of Turicibacter may be beneficial to the animal. Furthermore, after 21 d, Clostridium increased in the control group, but not in the SCFP group. Clostridium are well-known gastrointestinal pathogens of several animal species (Songer, 1996). As a result, preventing the proliferation of this bacterial taxa may be beneficial to the animal.

Firmicutes, Bacteroidetes, and Fusobacteria predominate the microbiota of healthy adult dogs (Middelbos et al., 2010; Hand et al., 2013; Pilla and Suchodolski, 2020). Similarly, in the present study, the fecal microbial profiles were dominated by the phylum Firmicutes (47%–56%), Bacteroidetes (18%–27%), and Fusobacteria (17%–20%). After exercise challenge in untrained and trained dogs, the relative abundances of Bacteroidetes, Fusobacteria, and Proteobacteria decreased, whereas the relative abundances of Actinobacteria and Firmicutes increased. A reduction in the relative abundance of Proteobacteria was reported in a prior study following the exercise training of hunting dogs (Zannoni et al., 2020). In that study, relative abundances of fecal Streptococcus and Enterococcus increased, while the relative abundance of Prevotella decreased after training and after the hunting season compared with baseline (untrained state) (Zannoni et al., 2020). Furthermore, a trend for reduced relative abundances of fecal Faecalibacterium and Bacteroides was observed following physical activity (during training and hunting) compared with resting periods (before training; after hunting season) (Zannoni et al., 2020). In the present study, Prevotella and Bacteroides decreased after exercise in both untrained and trained dogs. Streptococcus and Enterococcus, on the other hand, were unaffected by the exercise challenge, whereas Faecalibacterium increased following activity in trained dogs.

Butyrate-producing bacteria include Roseburia spp., Eubacterium rectale, Eubacterium hallii, Faecalibacterium prausnitzii, Anaerostipes caccae, and Coprococcus eutactus. On the other hand, Bifidobacterium adolescentis, Lactobacillus spp., and Bacteroides thetaiotaomicron are among the bacteria that produce acetate, while propionate is produced by Roseburia insulinovorans, Veillonella spp., Ruminococcus obeum, Bacteroides spp., Dialister spp., and Phascolarctobacterium spp. (Fernández et al., 2016). Therefore, the increase in Eubacterium, Faecalibacterium, Ruminococcus, Dialister, and Lactobacillus after exercise may provide benefits to the animals and possibly explain the increase in fecal SCFA detected in animals after exercise. In contrast, the genera Bacteroides and Clostridium are primarily responsible for the fermentation of proteins to produce BCFA (Rios-Covian et al., 2020). The increase in Clostridium following exercise may explain the increase in BCFA in those animals.

In Zannoni et al. (2020), alpha diversity measures were not affected by training or hunting season. However, beta diversity assessment using unweighted and weighted UniFrac distances demonstrated significant separation of fecal microbiota populations of hunting dogs at different training states and/or times of the hunting season in that study. In particular, samples collected after training (but before hunting season) and samples collected after hunting season clustered separately from those collected from dogs prior to training when unweighted UniFrac distances were considered. Similar results were observed in the current study. Although alpha diversity was not impacted, significant separation was observed between fecal samples collected from dogs before and after exercise training. Most experts agree that a diverse gut microbiome is considered to be healthier to host health. The separation of microbial communities in regard to beta-diversity measures demonstrates a clear dissimilarity of the two communities (before and after exercise), indicating that exercise is an effective challenge to the gut microbiota.

Another study examined the effects of racing (400 km) on sled dog fecal microbiota populations (Tysnes et al., 2020). In that study, alpha diversity (i.e., species richness) was reduced after the race and beta diversity analyses revealed clustering of fecal microbiota of samples collected before and after the race. The race also led to increases in fecal Fusobacterium, C. hiranonis, Blautia, and Streptococcus, a decrease in Enterobacteriaceae, and tended to increase the fecal DI (Tysnes et al., 2020). The increases in fecal Blautia and C. hiranonis agreed with, but the increased Fusobacterium reported in that study contradicted what was observed in the current study. C. hiranonis is one of the primary bile acid-metabolizing microbes in the dog gastrointestinal tract (Pilla et al., 2020; Li et al., 2021). Because bile acids, including lithocholic acid, have an anti-bacterial effect, its change in abundance and/or activity may be related to changes in bacterial diversity (Kitahara et al., 2001; do Nascimento et al., 2015; Morville et al., 2018). In the current study, the exercise challenge had no effect on Enterobacteriaceae, Streptococcus, or the DI, which may have been due to the much shorter distance run, dogs tested, or some other factor.

In another study, the interactions between exercise (30 min on water treadmill and 30 min on land-based treadmill, three times a week), weight loss, and the composition of fecal microbiota and metabolites were investigated in dogs (Kieler et al., 2017). Those researchers did not observe any associations between fecal microbiota, fecal metabolites, and bouts of exercise. Because changes in the bacterial composition are likely related to the intensity and duration of exercise, the lack of changes observed by Kieler et al. (2017) suggest longer or more intense exercise was needed. In addition to the microbiota changes discussed above, the current study observed changes to fecal metabolites derived from both carbohydrates and proteins with training level. While increases in carbohydrate-based metabolites and decreases in protein-based metabolites were observed in untrained dogs, the opposite occurred with trained animals. Carbohydrate fermentation is thought to be beneficial to the host due to the production of SCFA, and protein fermentation produces a wide range of compounds, some of which may be harmful to gut health if present in high concentrations (Andriamihaja et al., 2015). Because these fermentative products were measured in the animals’ feces, one reason for the difference between trained and untrained animals could be a better adaptation of the host after training to the use of SCFA during exercise (carbohydrate-based metabolites), resulting in a decrease of these metabolites in their feces. Although the production of BCFA (protein-based metabolites) increases after training in trained animals, the values are comparable to untrained animals, implying that this increase may not be significant enough to be harmful to the host.

In conclusion, our findings suggest that both exercise and SCFP supplementation affect the populations and activity of the fecal microbiota of dogs. The major cause of fecal microbial shifts was due to exercise, with trained animals having more profound changes than untrained animals. Neither the intensity nor the type of exercise resulted in dysbiosis, however. Although dramatic SCFP-induced changes were not observed, changes to Clostridium (reduction), Turicibacter (increase), and Lactobacillus (increase) suggest that it may provide benefits to dogs undergoing training. Higher SCFP doses may result in greater changes and may be of interest in future research. More research is necessary to determine if and how different training and exercise interventions impact gastrointestinal microbiota and other indicators of health.

Supplementary Material

skac424_suppl_Supplementary_Material
Click here for additional data file. (2.9MB, docx)

Acknowledgments

The funding for this study was provided by Diamond V Mills, Inc. (Cedar Rapids, IA).

Glossary

Abbreviations

BCFA

branched-chain fatty acids

BW

body weight

DI

dysbiosis index

OTU

operational taxonomic unit

qPCR

quantitative polymerase chain reaction

PCoA

principal coordinate analysis

SCFA

short-chain fatty acids

SCFP

Saccharomyces cerevisiae fermentation product

Contributor Information

Patrícia M Oba, Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Meredith Q Carroll, Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Kelly M Sieja, Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Juliana P de Souza Nogueira, Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Xiaojing Yang, Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Tammi Y Epp, Cargill, Incorporated, Wayzata, MN 55391, USA.

Christine M Warzecha, Cargill, Incorporated, Wayzata, MN 55391, USA.

Jessica L Varney, Four Rivers Kennel, LLC, Walker, MO 64790, USA.

Jason W Fowler, Four Rivers Kennel, LLC, Walker, MO 64790, USA.

Craig N Coon, Four Rivers Kennel, LLC, Walker, MO 64790, USA.

Kelly S Swanson, Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Conflict of Interest Statement

T. Y. E. and C. M. W. were employed by Cargill, Incorporated at the time the study was conducted. All other authors have no conflicts of interest.

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