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. 2025 Jul 18;103:skaf231. doi: 10.1093/jas/skaf231

Effects of live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic on apparent total tract nutrient digestibility and the immune function and fecal characteristics, metabolites, and microbiota of healthy adult dogs

Jocelyn F Wren 1, Sofia M Wilson 2, Yifei Kang 3, Patrícia M Oba 4, John F Menton 5, Elena Vinay 6, Mathieu Millette 7, Melissa R Kelly 8, Kelly S Swanson 9,10,11,✉
PMCID: PMC12415552  PMID: 40685326

Abstract

Probiotics and postbiotics have the potential to shift the gut microbiota, support gastrointestinal health, and enhance immune function, but must be tested for safety and efficacy in the target host. The Bacillus and Lacticaseibacillus genera have been shown to positively influence microbial balance and enhance immune response and immune function in humans and livestock. The objective of this study was to determine the effects of live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic on dietary apparent total tract digestibility and the hematology, serum metabolites, fecal characteristics, metabolites and microbiota, and skin and nasal microbiota of adult dogs. Twelve healthy adult English pointer dogs (age = 6.38 ± 2.75 yr; body weight = 23.98 ± 4.61 kg) were used in a replicated 3 × 3 Latin square design to test the following treatments administered via gelatin capsules: 1) placebo (control; 250 mg maltodextrin/day); 2) live B. pumilus [5 × 109 colony-forming units (CFU)/day]; 3) L. paracasei postbiotic (100 mg; derived from 2 × 109 CFU). Each experimental period was 28 d in length, including a 22-d adaptation phase, 5-d fecal collection phase, and 1 d for blood collection, nasal swabs, and skin swabs. Data were analyzed using the Mixed Model procedure of SAS, with P < 0.05 being significant and P < 0.10 being trends. Neither B. pumilus nor L. paracasei influenced nutrient digestibility, food intake, fecal output, or fecal characteristics. Based on 16S rRNA gene sequencing, the relative abundance of fecal Actinobacteriota tended to be higher (P < 0.10) and the relative abundance of fecal Collinsella was higher (P < 0.05) in dogs fed B. pumilus than those fed L. paracasei and controls. Treatments appeared to alter skin bacteria as well, with the relative abundance of skin Erysipelotrichaceae UCG-003 being higher (P < 0.05) in dogs fed L. paracasei than in dogs fed B. pumilus. Skin Ligilactobacillus relative abundance was lower (P < 0.05) in dogs fed B. pumilus than in controls. The relative abundance of skin Peptoclostridium was higher (P < 0.05) in dogs fed L. paracasei than in controls. Most hematology measures were within the reference ranges for adult dogs and unaffected by treatment. Overall, our results demonstrate that consumption of the B. pumilus SG154 and L. paracasei 327 tested are well-tolerated and does not influence nutrient digestibility or fecal characteristics.

Keywords: canine health, canine nutrition, companion animal, probiotic

This study was conducted to determine the effects of live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic on dietary nutrient digestibility and the hematology, serum metabolites, fecal characteristics, metabolites, and microbiota, and skin and nasal microbiota of healthy adult dogs. Some fecal and skin microbiota were altered by treatments. Overall, the data demonstrate that the treatments were well-tolerated and did not influence nutrient digestibility or fecal characteristics.

Introduction

The gastrointestinal (GI) microbiome plays an important role in the overall health and well-being of its host. The microbiota inhabiting the gut can positively affect a multitude of processes in the body, such as metabolism, immunity, and GI protection; however, inflammation or disease may occur as a negative side effect (Flint et al., 2012; Bull and Plummer, 2014; Jandhyala et al., 2015). To discover new ways to support the gut microbiome and host health, probiotics and postbiotics have gained recognition in recent years. Probiotics are defined as “live microorganisms which when administered in adequate amounts confer a health benefit on the host” (Hill et al., 2014). These microorganisms are widely available and sought after due to their health-maintaining properties; however, their implementation can be limited by manufacturing processes that affect viability (Gueimonde and Sánchez, 2012). Postbiotics, defined as a “preparation of inanimate microorganisms and/or their components that confers a health benefit on the host” (Salminen et al., 2021), may present a solution to this issue. These non-viable microbial cells are more stable and may provide many of the same benefits to the host as probiotics, such as improving gut health and modulating the immune system. Using probiotics alongside postbiotics may enhance their benefits and further advance the field of health and nutrition (Ma et al., 2023).

Whether they are in the form of a probiotic or postbiotic, the bacterial genera Bacillus and Lacticaseibacillus (formerly Lactobacillus) may provide benefits to the host. Both genera belong to the class Bacilli within the Bacillota (formerly Firmicutes) phylum (Elshaghabee et al., 2017). The Bacillus genus is spore-forming, which allows these strains to be more resilient under stressful conditions. This property sets Bacillus species apart from non-spore-forming probiotic species, as they can survive harsh processing and remain dormant for long durations until proper nutrients or environmental parameters, such as those in the intestines, are present (Cho and Chung, 2020; Łubkowska et al., 2023). Lacticaseibacillus species are not spore-forming. However, as inanimate postbiotics, they have better stability and can be considered safer than live bacteria due to their inability to proliferate in the gut or transfer antibiotic-resistant genes (Ma et al., 2023). Postbiotics contain a variety of bioactive components (proteins, peptides, lipids, carbohydrates, organic acids) that may facilitate benefits within the body once consumed (Teame et al., 2020).

The current study focused specifically on live Bacillus pumilus SG154 and a Lacticaseibacillus paracasei 327 postbiotic. Previous in vitro and chicken studies on B. pumilus highlight its ability to support immune function and enhance gut health through the inhibition of pathogenic bacteria and promotion of a balanced microbiome (van Heel et al., 2017; Bilal et al., 2021; Łubkowska et al., 2023). Research on L. paracasei as a postbiotic is minimal; however, studies suggest that supplementation with inanimate L. paracasei can increase defecation frequency in humans with low defecation frequency and improve skin conditions (Saito et al., 2017, 2018; Xie et al., 2023; Senda-Sugimoto et al., 2024).

Both live B. pumilus SG154 and L. paracasei 327 postbiotics lack research that demonstrates their effects on canines. In the current study, we determined the effects of a live B. pumilus SG154 or a L. paracasei 327 postbiotic on dietary apparent total tract digestibility (ATTD) of nutrients and energy and its effects on the hematology, serum metabolites, fecal characteristics, metabolites, and microbiota, and skin and nasal microbiota of healthy adult dogs. We hypothesized that consumption of live B. pumilus SG154 or L. paracasei 327 postbiotic would beneficially shift the fecal, skin, and nasal microbiota, beneficially shift fecal metabolites, and increase fecal IgA concentrations of adult dogs compared with controls and without negatively affecting macronutrient digestibility, fecal characteristics, serum chemistry, or hematology.

Materials and Methods

All animal care procedures were approved by the Kennelwood, Inc. (Champaign, IL) Institutional Animal Care and Use Committee before initiation of the experiment (protocol #UI2405D).

Animals, diets, and experimental timeline

Twelve healthy adult English pointer dogs (6 intact males, 6 intact females; mean age: 6.38 ± 2.75 yr old; mean body weight: 23.98 ± 4.61 kg) were used. All dogs were housed individually (inside run = 1.17 m × 1.42 m; outside run = 1.08 m × 3.05 m) at Kennelwood, Inc. Fresh water was available ad libitum. Based on the maintenance energy requirement for adult dogs and information from previous feeding records, food measured to maintain body weight was offered once a day, and intake was recorded. Body weight and body condition scores of dogs were assessed weekly using a 9-point scale (Laflamme, 1997) prior to feeding. A commercial diet containing no probiotics and little fermentable fiber (ShowTime 21/12; Mid-South Feeds Inc., Alma, GA) was fed to all dogs as the basal diet. The analyzed chemical composition of this diet is listed in Supplementary Table 1. The extruded kibble diet was formulated to meet all AAFCO (2023) nutrient recommendations for adult dogs at maintenance. Using a replicated 3 × 3 Latin square design, the following treatments were tested: 1) placebo (control; 250 mg maltodextrin/day); 2) live B. pumilus SG154 [5 × 109 colony-forming units (CFU)/day]; 3) L. paracasei 327 postbiotic (100 mg; derived from 2 × 109 CFU). The capsules of placebo and active ingredients were graciously provided by Kerry USA (Beloit, WI). Treatments were administered orally in gelatin capsules before each daily feeding. The experiment consisted of 3 28-d experimental periods. Each experiment included a 22-d transition phase, 5-d fecal collection phase, and 1 d for blood collection, nare and pinnae swabbing for microbial analysis, and skin and coat scoring.

Fecal collection, scoring, and handling

During the fecal collection phase, total feces excreted were collected from each dog, weighed, and frozen at −20 °C until analyses. All fecal scores were determined according to the Waltham feces scoring system using the following scale: 1 = hard dry and crumbly; 1.5 = hard and dry; 2 = well formed; does not leave a mark when picked up; “kickable”; 2.5 = well formed, with a slightly moist surface, which leaves a mark when picked up; 3 = moist beginning to lose form, leaving a definite mark when picked up; 3.5 = very moist, but still has some definite form; 4 = the majority, if not all the form is lost; poor consistency; viscous; 4.5 = diarrhea, with some areas of consistency; 5 = watery diarrhea.

During the fecal collection phase, one fresh fecal sample (within 15 min of defecation) was collected, and pH was measured using an AP10 pH meter (Denver Instrument, Bohemia, NY) equipped with a Beckman Electrode (Beckman Instruments Inc., Fullerton, CA). Aliquots were collected for dry matter (DM) content, microbiota populations, metabolite concentrations, and IgA concentrations. DM content was assessed using a 105 °C oven in accordance with AOAC (2006). Fecal aliquots for analysis of phenols and indoles were frozen at −20 °C immediately after collection. One aliquot was collected and placed in 2N hydrochloric acid for ammonia, short-chain fatty acid (SCFA), and branched-chain fatty acid analyses and frozen at −20 °C. Four aliquots of fresh feces were immediately transferred to sterile cryogenic vials (Nalgene, Rochester, NY), snap-frozen on dry ice, and stored at −80 °C for microbiota and IgA analysis.

Fecal and dietary chemical analyses

Diet subsamples were collected from each bag of dry food and stored at −20 °C until completion of the study. Fecal samples for chemical analysis and ATTD calculations were dried at 55 °C in a forced-air oven and then diet and fecal samples were ground using a Wiley mill (model 4, Thomas Scientific, Swedesboro, NJ) through a 2-mm screen. Diet and fecal samples were analyzed for DM and ash according to AOAC (2006; methods 934.01 and 942.05), with organic matter calculated. Crude protein was calculated from Leco (TruMac N, Leco Corporation, St. Joseph, MI) total nitrogen values according to AOAC (2006; method 992.15). Total lipid content (acid-hydrolyzed fat) was determined according to the methods of the American Association of Cereal Chemists (AACC, 2000) and Budde (1952). Total dietary fiber content of the basal diet was determined according to Prosky et al. (1992). Gross energy was measured using an oxygen bomb calorimeter (model 6200, Parr Instruments, Moline, IL).

Fecal metabolite analysis

Fecal SCFA and branched-chain fatty acid 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 was the carrier with a flow rate of 75 mL/minute. 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 IgA concentrations

Fecal proteins were extracted according to Vilson et al. (2016). Fecal samples (250 mg) were vortexed with 750 µL extraction buffer containing 50 mM-EDTA (ThermoFisher, Waltham, MA) and 100 µg/L soybean trypsin inhibitor (Sigma, St. Louis, MO) in PBS/1 percent bovine serum albumin (Tocris Bioscience, Bristol, U.K.). Phenylmethanesulphonyl fluoride (12.5 µL, 350 mg/L; Sigma, St. Louis, MO) was added into each tube, followed by centrifugation for 10 minutes. The supernatants were collected for measurement of IgA using a commercial ELISA kit (#E-40A; Immunology Consultants Laboratory, Portland, OR).

Blood collection and analysis

Blood samples were immediately transferred to appropriate vacutainer tubes for hematology (#367841 BD Vacutainer Plus plastic whole blood tube—Lavender with K2EDTA additive) and serum collection (#367974 BD Vacutainer Plus plastic serum tube—red/gray with clot activator and gel for serum separation; BD, Franklin Lakes, NJ). The blood tube for serum isolation was centrifuged at 1,300 × g at 4 °C for 10 minutes (Beckman CS-6R centrifuge; Beckman Coulter Inc., Brea, CA). Once serum was collected, one aliquot was transported to the University of Illinois Veterinary Medicine Diagnostics Laboratory for serum chemistry analysis. Other aliquots were collected for serum Ig analysis. The K2EDTA tubes were cooled (but not frozen) and then transported to the University of Illinois Veterinary Medicine Diagnostics Laboratory for hematology analyses.

Serum immunoglobulin concentrations

The concentration of serum immunoglobulins, including IgA, IgG, IgM, and IgE, were measured using commercial ELISA kits (#E40-A; #E40-G; #E40-M; #E40-E; Immunology Consultants Laboratory, Inc., Portland, OR).

Skin and nasal swabs

Skin and nasal swabs were collected on day 28 prior to blood collection. Skin swab samples were collected from the right and left pinnae using sterile culture swabs (Puritan HydraFlock Sterile Flocked Collection Device, Puritan Medical Products Company LLC, Guilford, ME). One sterile culture swab applicator was rubbed on the right or left pinna 40 times, while rotating each swab by one quarter for every 10 strokes. The swabs were combined in the same properly labeled tube and stored at −80 °C until analysis. Nasal swabs were obtained from both anterior nares using sterile swabs (BBL CultureSwab EZ Collection & Transport Systems, BD, Franklin Lakes, NJ) and stored at −80 °C until analysis.

Skin and hair coat assessment

Three blinded evaluators scored the skin and hair coat of dogs on day 28. Each dog was assessed in the same room with consistent lighting. Scoring for both evaluations followed a 1 to 5 scale (Rees et al., 2001): Skin: 1 = dry; 2 = slightly dry; 3 = normal; 4 = slightly greasy; 5 = greasy; Hair: 1 = dull, coarse, dry; 2 = poorly reflective, non-soft; 3 = medium reflective, medium soft; 4 = highly reflective, very soft; 5 = greasy.

DNA extraction and PacBio sequencing of 16S rRNA gene amplicons

Bacterial DNA was extracted from fresh fecal, nare, and pinnae samples using the DNeasy PowerLyzer PowerSoil Kit (MoBio Laboratories, Carlsbad, CA). Concentrations of extracted DNA were quantified using a Qubit 3.0 Fluorometer (Life Technologies, Grand Island, NY). The quality of extracted DNA was assessed by electrophoresis using agarose gels (E-Gel EX Gel 1%; Invitrogen, Carlsbad, CA). The Roy J. Carver Biotechnology Center at the University of Illinois performed PacBio sequencing. The 16S rRNA gene amplicons were generated with the barcoded full-length 16S rRNA gene primers from PacBio and the 2× Roche KAPA HiFi Hot Start Ready Mix (Roche, Wilmington, MA). Full-length 16S rRNA gene PacBio (Pacific Biology, Menlo Park, CA) primers (forward: AGRGTTYGATYMTGGCTCAG; reverse: RGYTACCTTGTTACGACTT) were added in accordance with the PacBio protocol. The amplicons were pooled and converted to a library with the SMRT Bell Express Template Prep kit 3.0. (Pacific Biology, Menlo Park, CA). The library was sequenced on a SMRT cell 8M in the PacBio Sequel Iie using the CCS sequencing mode and a 15-h movie time. Analysis of CCS was done using SMRT Link V11.1.0 using the following parameters: minimum passes 3, and minimum rq 0.999; HiFi presets (minimum score of 80; minimum end score of 50, minimum reference (read) span of 0.75); asymmetric (different, minimum number of scoring barcode regions 2).

Sequence data processing

PacBio-based FASTQ reads were processed using a Nextflow-based workflow, TADA ( Ras et al., 2021). TADA automates using DADA2 v1.22 (Callahan et al., 2019)) for trimming and denoising reads based on the protocols used for PacBio data to generate amplicon sequence variants. Fecal, skin, and nasal samples were rarefied to 10,688, 32,190, and 9,085 reads, respectively. The specific run here used Github checkout 738affa. The input sample sheet was a simple comma-separated file with sample IDs and the path for the relevant sample FASTQ. The DADA2 implementation of the RDP classifier ( Lan et al., 2012 ) was used to classify reads using the SILVA 138.1 release, with a database formatted for PacBio HiFi read data (https://zenodo.org/record/4587955). Multiple sequence alignment and maximum likelihood phylogenetic analysis were performed using DECIPHER v2.22 ( Wright, 2015 ) and FastTree v2.1.10 (Price et al., 2010).

Quantitative polymerase chain reaction and dysbiosis index

DNA of fecal samples was extracted from an aliquot of 100 to 120 mg using a bead-beating method with a MO BIO PowerSoil DNA isolation kit. The qPCR assays were applied to quantify total bacteria, Blautia spp., Clostridium (Peptacetobacter) hiranonis, Escherichia coli, Faecalibacterium spp., Fusobacterium spp., Streptococcus spp., and Turicibacter spp. as described in (AlShawaqfeh et al., 2017). In addition to the bacterial groups included in the dysbiosis index calculation, Bacteroides, Bifidobacterium, Collinsella, Prevotella copri, and Ruminococcus gnavus were also quantified by qPCR as described before (Sung et al., 2023). Both positive and negative controls were included for all qPCR assays to ensure the accuracy and reliability of the results. The dysbiosis index was calculated based on the results of the qPCR assays using a previously described algorithm (AlShawaqfeh et al., 2017). A dysbiosis index <0 and with all targeted taxa within the reference interval, was considered normal. A dysbiosis index <0 but with any of the targeted taxa outside the reference interval was defined as a minor shift in the microbiome. A dysbiosis index between 0 and 2 was defined as a mild to moderate microbiome shift. A dysbiosis index >2 was classified as significant dysbiosis.

Statistical analysis

Data were analyzed using the Mixed Models procedure of SAS (SAS Institute, Inc., Cary, NC). The fixed effect of treatment was tested. Dog was considered a random effect for all analyses. Data were tested for normality using the UNIVARIATE procedure of SAS. Differences between treatments were determined using a Fisher-protected least significant difference, with a Tukey adjustment to control for experiment-wise error. However, no false discovery rate adjustment was applied for 16S rRNA sequencing data. If normality was not met, a logarithmic transformation was applied. If transformation failed, data were analyzed using Kruskal-Wallis tests to determine significance. A probability of P < 0.05 was accepted as being statistically significant, and P < 0.10 was considered a trend.

Results

Live B. pumilus or L. paracasei postbiotic supplementation had no effect on the body weight or body condition score of dogs (Table 1). Food intake, fecal output, and ATTD of nutrients and energy were also unaffected by treatments (Table 1). B. pumilus or L. paracasei treatments had no effect on fresh fecal pH, score, DM percentage, or fecal metabolite and IgA concentrations (Table 2).

Table 1.

Dietary apparent total tract macronutrient and energy digestibility and body weight, body condition score, food intake, energy intake, and fecal output of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic

Item Control Probiotic Postbiotic SEM P-value
Body weight, kg 23.6 23.7 23.7 1.10 0.9055
Body condition score1 4.8 4.8 4.8 0.07 0.8058
Food intake
g food/d (as-is) 676.4 693.5 662.2 42.96 0.8478
g DM/d 622.3 638.1 609.3 39.53 0.8478
g OM/d 559.8 574.0 548.0 35.56 0.8478
g CP/d 163.3 166.3 159.8 10.46 0.8877
g fat/d 106.8 108.8 104.5 6.84 0.8877
kcal/d 3253 3335 3185 206.7 0.8478
Fecal output
Fecal output, as-is, g/d 558.7 613.1 572.5 45.48 0.4664
Fecal output, DM, g/d 149.2 161.8 153.1 10.33 0.5620
As-is fecal output, g/d/DMI, g/d 0.90 0.98 0.93 0.04 0.2531
Nutrient and energy digestibility, %
Dry matter 75.9 74.1 74.8 0.95 0.3333
Organic matter 80.4 78.8 79.4 0.81 0.2956
Protein 77.8 76.1 76.8 1.07 0.3683
Fat 91.2 90.7 91.2 0.49 0.6521
Energy 81.5 80.1 80.7 0.76 0.3359
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1Nine-point body condition scoring system used (Laflamme, 1997).

SEM, pooled standard errors of the means.

Table 2.

Fresh fecal characteristics and metabolite concentrations of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic

Item Control Probiotic Postbiotic SEM P-value
Fecal characteristics
Fecal pH 6.31 5.96 6.09 0.16 0.2314
Fecal score1 3.66 3.48 3.50 0.16 0.5599
Fecal DM, % 26.35 26.62 27.42 0.81 0.4360
Fecal metabolites, µmol/g DM
Total SCFA2 617.03 609.09 630.29 30.98 0.8312
 Acetate 388.59 371.33 379.19 16.70 0.7289
 Propionate 158.25 172.69 183.02 13.08 0.2607
 Butyrate 70.06 64.08 68.08 7.57 0.8487
Total BCFA2 13.14 15.09 15.45 2.65 0.5846
 Isobutyrate 4.86 4.61 5.02 0.51 0.7900
 Isovalerate 7.26 6.36 7.44 0.63 0.4418
 Valerate 1.02 4.18 2.98 2.14 0.6732
Total phenols and indoles 5.16 4.82 4.56 0.61 0.7826
 Total phenols 4.09 4.03 3.62 0.46 0.7332
 Total indoles 1.07 0.80 0.93 0.18 0.3919
Ammonia 175.41 187.98 179.84 10.26 0.6825
Fecal IgA, mg/g DM 6.77 12.56 10.67 2.43 0.1796
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1Fecal scores: 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; 5 = watery, liquid that can be poured.

2Total SCFA = acetate + propionate + butyrate; Total BCFA = valerate + isovalerate + isobutyrate.

SEM, pooled standard errors of the means.

When using 16S rRNA gene sequencing, alpha diversity measures (Shannon Index, Faith’s PD, Evenness, Observed Features), which provide a measure of biodiversity within samples and indicate microbial richness (number of species present) and evenness (how the population is distributed), were not different due to treatment (Supplementary Figure 1). Similarly, fecal bacterial beta diversity measures, which assess species richness using unweighted and weighted UniFrac distances, were not affected by treatment (Supplementary Figure 2). When using qPCR to assess microbiota, fecal bacterial abundances and dysbiosis index of dogs were shown to be within the reference ranges and not affected by treatment (Supplementary Table 2). Of the predominant bacterial phyla measured by 16S rRNA sequencing, the relative abundance of fecal Actinobacteriota tended to be greater (P < 0.10) in dogs supplemented with live B. pumilus. Of the predominant bacterial phyla measured by 16S rRNA sequencing, the relative abundance of fecal Collinsella spp. was higher (P < 0.05) in dogs supplemented with live B. pumilus (1.31%) than in controls (0.71%) or dogs supplemented with the L. paracasei postbiotic (0.68%; Table 3; Figure 1). The relative abundances of all other bacterial phyla and other bacterial genera in fecal samples were unaffected by treatment.

Table 3.

Predominant fecal bacterial relative abundances (% of sequences) of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic

Phyla Genus Control Probiotic Postbiotic SEM P-value
Actinobacteriota 1.28 1.83 1.19 0.20 0.0613
Bifidobacterium 0.47 0.34 0.39 0.17 0.4123
Collinsella 0.71b 1.31a 0.68b 0.17 0.0182
Slackia 0.09 0.17 0.11 0.03 0.4481
Bacteroidota 8.50 7.14 9.03 3.08 0.4694
Alloprevotella 2.00 1.12 1.67 0.63 0.4923
Bacteroides 2.97 3.57 4.45 1.50 0.6926
Muribaculaceae 0.21 0.07 0.39 0.21 0.7231
Prevotella 2.68 1.89 1.80 1.15 0.4518
Prevotellaceae_Ga6A1_group 0.59 0.50 0.59 0.22 0.3201
Bacillota 81.24 84.33 78.14 4.92 0.5049
[Eubacterium]_brachy_group 0.32 0.60 0.35 0.20 0.9111
[Ruminococcus]_gauvreauii_group 0.66 0.68 0.50 0.10 0.2803
[Ruminococcus]_gnavus_group 0.72 0.52 0.60 0.20 0.3965
[Ruminococcus]_torques_group 2.33 2.23 1.84 0.41 0.4859
Allobaculum 2.21 1.66 1.23 0.52 0.5520
Blautia 17.54 16.57 16.89 2.28 0.9456
Butyricicoccus 0.17 0.15 0.14 0.03 0.4077
Catenibacterium 1.21 1.78 1.70 0.48 0.7668
Clostridium_sensu_stricto_1 0.59 0.45 0.24 0.28 0.4556
Dubosiella 0.14 0.02 0.23 0.14 0.3519
Enterococcus 1.53 2.08 0.63 0.78 0.5112
Erysipelatoclostridium 0.19 0.38 0.25 0.08 0.4144
Erysipelotrichaceae_UCG-003 0.53 0.46 0.40 0.22 0.7394
Faecalibacterium 1.23 0.94 1.10 0.30 0.4674
Fournierella 0.12 0.04 0.07 0.03 0.2592
Holdemanella 1.44 1.70 1.31 0.39 0.7817
Lachnoclostridium 0.81 0.58 0.52 0.19 0.8650
Lachnospiraceae_NK4A136_group 0.31 0.25 0.29 0.06 0.6313
Lactobacillus 21.69 23.06 21.10 5.48 0.9572
Megamonas 1.01 0.76 1.56 0.30 0.3071
Negativibacillus 0.21 0.16 0.12 0.05 0.5029
Peptoclostridium 13.19 14.42 14.14 2.28 0.9131
Peptococcus 1.07 0.56 0.81 0.21 0.1220
Phascolarctobacterium 1.40 1.04 1.76 0.49 0.5608
Romboutsia 0.43 0.47 0.38 0.21 0.3861
Sellimonas 0.56 0.39 0.47 0.09 0.3673
Streptococcus 4.89 9.14 5.20 2.09 0.2696
Turicibacter 0.70 0.84 0.60 0.28 0.8743
Tyzzerella 0.11 0.11 0.10 0.04 0.9405
UCG-005 0.37 0.21 0.30 0.07 0.2870
uncultured 1.40 1.16 1.19 0.28 0.9228
uncultured 0.70 0.21 0.90 0.51 0.5002
Fusobacteriota 7.69 5.59 8.78 1.88 0.4350
Fusobacterium 7.69 5.59 8.78 1.88 0.4350
Pseudomonadota 1.24 0.87 2.85 1.06 0.4593
Escherichia-Shigella 0.43 0.12 1.70 0.99 0.7661
Parasutterella 0.24 0.06 0.38 0.24 0.5241
Sutterella 0.54 0.65 0.64 0.26 0.6814
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SEM, pooled standard errors of the means.

Figure 1.

Figure 1.

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Bacterial relative abundances (% of sequences) from the feces (A) or pinnae (B, C, D) of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic.

Treatment had no effect on the skin and hair coat scores (Supplementary Table 3). Based on 16S rRNA gene sequencing of pinnae swab samples, bacterial alpha diversity measures (Figure 2) and beta diversity measures (Figure 3) were not affected by treatment. However, treatment did appear to alter a few of the bacterial taxa present on the skin (Table 4; Figure 1). Relative abundance of skin Bacillota tended to be lower (P < 0.10), while relative abundances of skin Patescibacteria and Pasteurella tended to be higher (P < 0.10) in dogs supplemented with live B. pumilus. The relative abundance of skin Erysipelotrichaceae UCG-003 was higher (P < 0.05) in dogs supplemented with the L. paracasei postbiotic (0.27%) than in dogs supplemented with live B. pumilus (0.09%). Skin Ligilactobacillus spp. relative abundance was lower (P < 0.05) in dogs supplemented with the live B. pumilus (10.50%) than in controls (17.30%). Dogs supplemented with the L. paracasei postbiotic had a higher (P < 0.05) relative abundance of skin Peptoclostridium spp. (3.45%) than controls (2.04%). The relative abundance of skin Streptococcus also tended to be greater (P < 0.10) in dogs supplemented with live B. pumilus and the L. paracasei postbiotic.

Figure 2.

Figure 2.

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Bacterial alpha diversity measures [Observed features (Observed), Chao1, Shannon Index (Shannon), Simpson Index (Simpson), Inverse Simpson Index (InvSimpson), Faith’s PD index (PD)] of samples from the pinnae of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic.

Figure 3.

Figure 3.

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Principal coordinates analysis plots of bacterial beta diversity indices, represented by unweighted and weighted UniFrac distance, of the pinnae of dogs supplemented with Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic.

Table 4.

Predominant bacterial relative abundances (% of sequences) on the pinnae of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic

Phylum Genus Control Probiotic Postbiotic SEM P-value
Actinobacteriota 2.84 2.90 2.88 0.24 0.9842
Actinomyces 0.43 0.63 0.39 0.11 0.7307
Bifidobacterium 0.96 0.50 0.58 0.19 0.1310
Collinsella 0.98 1.14 1.27 0.15 0.3957
Corynebacterium 0.19 0.31 0.34 0.15 0.4017
Bacteroidota 14.14 17.91 14.96 1.82 0.3282
Alloprevotella 1.42 2.25 1.34 0.31 0.1280
Bacteroides 2.86 3.73 5.05 1.06 0.3033
Bergeyella 0.67 0.86 0.61 0.14 0.1834
Capnocytophaga 0.23 0.27 0.18 0.04 0.2209
Chryseobacterium 0.05 0.16 0.16 0.10 0.6865
Flavobacterium 0.69 2.03 0.80 0.57 0.7220
Porphyromonas 2.17 2.47 1.77 0.45 0.3109
Prevotella_9 4.80 4.76 3.10 1.07 0.4977
Prevotellaceae Ga6A1 group 0.81 0.96 1.62 0.42 0.4750
Unclassified 0.09 0.13 0.08 0.03 0.4988
Unclassified 0.12 0.09 0.08 0.06 0.6104
Campylobacterota 0.28 0.26 0.20 0.09 0.3345
Arcobacter 0.17 0.12 0.09 0.06 0.9834
Deinococcota 0.03 0.02 0.03 0.01 0.3780
Deinococcus 0.03 0.02 0.03 0.01 0.3780
Bacillota 57.74 47.61 56.04 3.33 0.0913
[Ruminococcus] gauvreauii group 0.12 0.11 0.12 0.02 0.7047
[Ruminococcus] torques group 0.39 0.37 0.44 0.04 0.5190
Abiotrophia 0.06 0.08 0.16 0.07 0.5431
Allobaculum 1.05 1.14 0.69 0.41 0.6594
Blautia 3.60 3.61 4.07 0.39 0.6151
Catenibacterium 0.67 0.84 0.95 0.17 0.5449
Clostridium sensu stricto 1 1.97 1.54 1.57 0.34 0.4756
Dubosiella 0.21 0.11 0.17 0.09 0.9423
Erysipelotrichaceae UCG-003 0.11ab 0.09a 0.27b 0.06 0.0183
Faecalibacterium 0.25 0.28 0.28 0.06 0.9045
Helcococcus 0.25 0.19 0.12 0.08 0.2606
Holdemanella 0.89 0.84 1.09 0.22 0.3617
Lachnoclostridium 0.06 0.07 0.11 0.02 0.1868
Lactobacillus 4.65 2.36 3.02 1.05 0.2806
Ligilactobacillus 17.30b 10.50a 13.19ab 2.07 0.0448
Limosilactobacillus 4.24 2.53 2.54 0.67 0.2615
Megamonas 0.53 0.64 0.74 0.15 0.2901
Mycoplasma 0.04 0.30 0.20 0.16 0.8657
Paeniclostridium 0.11 0.08 0.10 0.03 0.9614
Peptoclostridium 2.04b 2.66ab 3.45a 0.41 0.0493
Peptococcus 0.15 0.16 0.16 0.03 0.9778
Phascolarctobacterium 0.41 0.41 0.40 0.06 0.9664
Romboutsia 2.96 2.09 2.59 0.63 0.5820
Streptococcus 8.23 10.39 12.47 1.62 0.0914
Turicibacter 4.97 3.55 4.63 1.37 0.4038
Unclassified 0.12 0.17 0.09 0.06 0.6502
Unclassified 0.28 0.33 0.38 0.16 0.9861
Weissella 0.02 0.11 0.05 0.03 0.4102
Fusobacteriota 2.33 4.17 5.72 1.24 0.1197
Fusobacterium 2.30 4.12 5.71 1.24 0.1099
Patescibacteria 0.10 0.22 0.15 0.05 0.0931
Unclassified 0.07 0.15 0.09 0.03 0.0728
Pseudomonadota 9.09 10.39 7.12 1.90 0.4674
Aeromonas 0.14 0.06 0.05 0.05 0.9233
Alcaligenes 0.22 0.23 0.05 0.13 0.5131
Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium 0.06 0.53 0.07 0.27 0.2596
Comamonas 0.39 0.28 0.15 0.14 0.4043
Conchiformibius 0.54 0.96 0.60 0.21 0.5331
Escherichia-Shigella 0.06 0.41 0.25 0.21 0.4307
Frederiksenia 0.20 0.35 0.26 0.07 0.2409
Haemophilus 0.13 0.22 0.30 0.13 0.8844
Lautropia 0.14 0.18 0.12 0.03 0.2510
Luteimonas 0.07 0.13 0.08 0.03 0.5612
Moraxella 1.22 1.70 1.62 0.56 0.2567
Neisseria 0.36 0.46 0.36 0.10 0.6030
Parasutterella 0.08 0.14 0.10 0.04 0.9915
Pasteurella 0.11 0.19 0.11 0.03 0.0529
Pseudochrobactrum 0.63 0.39 0.10 0.22 0.8762
Pseudomonas 2.20 1.59 0.53 0.83 0.9987
Psychrobacter 0.34 0.29 0.28 0.12 0.5066
Simplicispira 0.01 0.03 0.13 0.07 0.5561
Stenotrophomonas 0.25 0.19 0.05 0.10 0.4602
Unclassified 0.47 0.52 0.57 0.17 0.6446
Spirochaetota 0.01 0.01 0.01 0.01 0.3168
Treponema 0.01 0.01 0.01 0.01 0.3168
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SEM, pooled standard errors of the means.

Based on 16S rRNA gene sequencing of nasal swab samples, alpha diversity measures (Figure 4) and beta diversity measures (Figure 5) were not affected by treatment. Treatment did not appear to shift any specific bacterial taxa in the nasal cavity (Table 5).

Figure 4.

Figure 4.

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Bacterial alpha diversity measures [Observed features (Observed), Chao1, Shannon Index (Shannon), Simpson Index (Simpson), Inverse Simpson Index (InvSimpson), Faith’s PD index (PD)] of samples from the nares of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic.

Figure 5.

Figure 5.

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Principal coordinates analysis plots of bacterial beta diversity indices, represented by unweighted and weighted UniFrac distance, of the nares of dogs supplemented with Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic.

Table 5.

Predominant bacterial relative abundances (% of sequences) on the nares of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic

Phylum Genus Control Probiotic Postbiotic SEM P-value
Actinobacteriota 2.31 0.83 1.61 0.47 0.1075
Actinomyces 0.41 0.28 0.24 0.11 0.2663
Bifidobacterium 0.31 0.15 0.48 0.17 0.7244
Collinsella 0.50 0.25 0.65 0.24 0.1155
Leucobacter 0.85 0.05 0.01 0.40 0.2358
Bacteroidota 1.18 0.88 0.95 0.42 0.3545
Alloprevotella 0.08 0.18 0.07 0.11 0.2557
Bergeyella 0.29 0.17 0.45 0.19 0.5778
Capnocytophaga 0.10 0.04 0.04 0.03 0.5235
Flavobacterium 0.26 0.17 0.07 0.10 0.8292
Porphyromonas 0.37 0.30 0.29 0.15 0.4193
Desulfobacterota 0.01 0.01 0.01 0.01 0.6465
Desulfobulbus 0.01 0.01 0.01 0.01 0.6465
Bacillota 45.32 44.12 40.91 8.71 0.8925
[Ruminococcus] gauvreauii group 0.04 0.05 0.10 0.04 0.3994
[Ruminococcus] torques group 0.23 0.32 0.39 0.14 0.6290
Abiotrophia 0.25 0.06 0.23 0.15 0.5103
Allobaculum 0.37 0.18 0.42 0.11 0.4492
Blautia 2.22 1.93 3.20 1.17 0.4653
Catenibacterium 0.56 0.32 0.35 0.11 0.2520
Clostridium sensu stricto 1 0.35 0.40 0.33 0.13 0.4026
Dubosiella 0.14 0.05 0.11 0.06 0.3841
Enterococcus 1.18 0.42 1.15 0.65 0.5958
Erysipelotrichaceae UCG-003 0.10 0.04 0.16 0.05 0.6004
Helcococcus 2.80 3.69 3.30 1.27 0.4843
Holdemanella 0.76 0.28 0.47 0.14 0.2294
Lachnoclostridium 0.02 0.05 0.13 0.07 0.6690
Lactobacillus 2.73 1.56 1.19 0.70 0.5290
Ligilactobacillus 15.07 6.90 7.81 2.80 0.1237
Limosilactobacillus 2.70 1.62 1.38 0.66 0.6397
Mycoplasma 0.16 0.43 0.40 0.21 0.1915
Peptoclostridium 1.82 1.54 2.42 0.95 0.1966
Peptococcus 0.15 0.07 0.13 0.04 0.5359
Romboutsia 0.45 0.34 0.66 0.23 0.5396
Staphylococcus 3.58 8.05 0.84 3.40 0.8012
Streptococcus 9.41 15.13 11.52 4.93 0.6553
Turicibacter 2.18 1.25 3.02 0.84 0.3746
Unclassified 0.19 0.38 0.10 0.13 0.8785
Ureaplasma 0.02 0.05 0.33 0.13 0.9875
Weissella 0.06 0.12 0.04 0.07 0.9695
Fusobacteriota 0.05 0.04 0.09 0.03 0.1831
Fusobacterium 0.05 0.04 0.09 0.03 0.1831
Patescibacteria 0.16 0.15 0.02 0.08 0.1709
Unclassified 0.16 0.15 0.02 0.08 0.1709
Pseudomonadota 42.87 47.23 48.42 9.95 0.8769
Conchiformibius 1.08 0.49 0.56 0.29 0.1659
Frederiksenia 0.86 0.03 0.12 0.32 0.2047
Moraxella 38.52 45.67 46.35 10.22 0.7693
Neisseria 0.19 0.12 0.20 0.08 0.3593
Suttonella 1.39 0.48 0.17 0.35 0.3288
Unclassified 0.56 0.34 0.73 0.38 0.1749
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SEM, pooled standard errors of the means.

Serum IgA, IgG, IgM, and IgE concentrations were not affected by treatment (Supplementary Table 4). Most serum metabolite concentrations were within the reference ranges for adult dogs and unaffected by treatment (Table 6). However, blood glucose concentrations were lower (P < 0.05) in dogs supplemented with live B. pumilus than in controls. Serum alkaline phosphatase and corticosteroid-induced alkaline phosphatase concentrations were above the reference ranges for adult dogs but were unaffected by treatment. Nearly all hematology measures were within the reference ranges for adult dogs and unaffected by treatment (Supplementary Table 5). Nevertheless, mean corpuscular hemoglobin was slightly below the reference range for dogs supplemented with the L. paracasei postbiotic.

Table 6.

Serum chemistry of dogs supplemented with live Bacillus pumilus SG154 or a Lacticaseibacillus paracasei 327 postbiotic

Item Reference range1 Control Probiotic Postbiotic SEM P-value
Creatinine, mg/dL 0.5 to 1.5 0.7 0.7 0.7 0.03 0.4808
Blood urea nitrogen, mg/dL 6 to 30 18.0 17.9 18.1 0.84 0.9879
Total protein, g/dL 5.1 to 7.0 5.7 5.8 5.8 0.13 0.4895
Albumin, g/dL 2.5 to 3.8 3.0 3.0 3.1 0.10 0.8670
Globulin, g/dL 2.7 to 4.4 2.7 2.7 2.7 0.13 0.7617
Albumin:globulin ratio 0.6 to 1.1 1.2 1.1 1.1 0.07 0.8675
Ca, mg/dL 7.6 to 11.4 9.3 9.3 9.3 0.10 0.8853
P, mg/dL 2.7 to 5.2 4.0 3.7 3.8 0.13 0.1548
Na, mmol/L 141 to 152 146.7 145.9 145.9 0.50 0.9688
K, mmol/L 3.9 to 5.5 4.6 4.6 4.6 0.07 0.8298
Na:K ratio 28 to 36 32.0 31.8 32.0 0.54 0.8885
Cl, mmol/L 107 to 118 112.2 110.5 111.2 0.74 0.5028
Glucose, mg/dL 68 to 126 91.7b 85.5a 90.7ab 1.77 0.0421
Alkaline phosphatase, ALP, U/L 7 to 92 104.1 102.2 102.4 24.40 0.9093
Corticosteroid-induced ALP, U/L 0 to 40 73.3 68.2 72.1 22.51 0.3718
Alanine transaminase, U/L 8 to 65 50.1 51.0 48.3 3.74 0.5213
Gamma glutamyltransferase, U/L 0 to 7 4.9 4.6 4.8 0.42 0.2664
Total bilirubin, mg/dL 0.1 to 0.3 0.2 0.2 0.2 0.02 0.8444
Creatine phosphokinase, U/L 26 to 310 123.2 113.2 115.5 7.03 0.5876
Cholesterol, mg/dL 129 to 297 175.7 173.4 171.8 10.41 0.9435
Triglycerides, mg/dL 32 to 154 50.0 44.3 41.0 4.16 0.1286
Bicarbonate, mmol/L 16 to 24 23.3 23.4 23.0 0.54 0.7942
Anion gap 8 to 25 16.1 16.6 16.5 0.38 0.5841
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1Reference ranges were provided from the University of Illinois Veterinary Diagnostic Laboratory.

SEM, pooled standard errors of the means.

Discussion

The results of this study suggest that supplementation of live B. pumilus SG154 or a L. paracasei 327 postbiotic was well tolerated in healthy adult dogs, with no adverse effects on body weight, nutrient digestibility, or hematology. These findings align with expectations for postbiotics, as they are considered low-risk additives (Ma et al., 2023). One dog was excluded during the first period, and another during the third period, due to antibiotic treatments unrelated to this experiment. Removal from statistical analysis was necessary to ensure the accuracy of data, as antibiotics can significantly alter the gut microbiome.

While some blood parameters fell outside the reference range, the deviations were minimal and did not indicate any physiological concern. Blood glucose concentrations remained within range; however, a reduction was observed in dogs supplemented with B. pumilus compared to controls. This observation may reflect a metabolic interaction between the probiotic and the host. Additional research would be necessary to determine and validate the potential benefits of B. pumilus on this subject, such as weight loss or diabetes management.

Fecal characteristics, metabolites, and IgA concentrations were not affected by treatment in this study. Fecal microbial concentrations were influenced slightly, with the abundance of Collinsella spp. being higher in dogs supplemented with B. pumilus than in those receiving the L. paracasei postbiotic. The genus Collinesella is regularly detected in fecal samples of healthy dogs and is generally not associated with adverse effects at low levels (Doulidis et al., 2023). It is, however, considered a pathobiont, so it can become harmful in high amounts and under certain conditions. Previous studies have linked high Collinsella spp. abundance to a reduction in tight junction proteins in humans and increased proinflammatory cytokines in mice (Chen et al., 2016; Gomez-Arango et al., 2018; Kim et al., 2023). In the present study, no adverse effects linked to elevated Collinsella spp. levels were observed in the dogs, and the B. pumilus treatment group did not differ from the control group, suggesting that the increased abundance in this case may not be clinically relevant.

Although no noticeable changes were observed in the skin’s appearance, supplementation of the L. paracasei 327 postbiotic did alter skin (pinnae) microbial composition in the phylum Bacillota. Dogs receiving the L. paracasei postbiotic exhibited increased relative abundances of Erysipelotrichaceae UCG-003 compared with dogs supplemented with live B. pumilus. The role of this genus in the GI tract appears to be inconclusive, with studies showing conflicting effects on inflammation. Higher abundances of this taxon have been linked to metabolic disorders such as obesity and colorectal cancer in humans (Chen et al., 2012; Zhou et al., 2024). In contrast, a study reported increased levels of Erysipelotrichaceae UCG-003 in healthy elderly (70-82 years old) humans compared with those suffering from one or more major diseases (Singh et al., 2019). The abundance was also notably reduced in patients with inflammatory bowel disease (Gevers et al., 2014). The effects of Erysipelotrichaceae UCG-003 on the skin are unknown, however. It has been theorized to indirectly promote skin health by modulating inflammation via SCFA production (Wang et al., 2022).

Dogs receiving the L. paracasei 327 postbiotic also had an increased relative abundance of skin Peptoclostridium spp. compared with controls. Peptoclostridium spp. is commonly found as a commensal member of the GI tract of healthy dogs, but it is associated with exacerbating symptoms of dogs with diseases (Zheng et al., 2018). Nevertheless, research on Peptoclostridium spp. and its effects on the skin is limited. Notably, a study in adult beagles reported that Peptoclostridium spp. levels were higher in healthy dogs compared with those diagnosed with atopic dermatitis (Rostaher et al., 2022). While its role in the gut is well understood, the function of Peptoclostridium spp. in skin health requires further investigation.

Skin microbial composition was also altered by supplementation of live B. pumilus, with a lower relative abundance of Ligilactobacillus spp. present in treated dogs compared with controls. Ligilactobacillus spp. contains species commonly used as probiotics due to their ability to support immunity, inhibit pathogens, and secrete proteins that aid in digestion (Yang et al., 2023, 2024). Despite the observed reduction in Ligilactobacillus spp., the dogs showed no signs of adverse effects, suggesting that this change did not compromise skin health. Examining the interaction between B. pumilus SG154 and Ligilactobacillus spp. may provide a deeper understanding of their roles within the microbiome.

Previous studies have demonstrated that both live and inanimate L. paracasei positively impact skin health in humans and dogs (Ohshima-Terada et al., 2015; Kawano et al., 2023; Xie et al., 2023; Pimazzoni et al., 2024). However, in the present study, supplementation had no effect on skin or hair coat scores. One limitation of this experiment was the focus on subjective scoring methods; future research should incorporate quantitative skin measures to provide a more comprehensive assessment. Studying animals with existing skin and coat disease may also be of interest. Another limitation was that each test ingredient was only tested at one dosage. In future studies, multiple dosages may be considered. Another point of consideration is the low bacterial abundance of skin and nasal samples, which may have increased the risk of background contamination, reduced reproducibility, and increased overall variability of our results. All 3 sample types (i.e., fecal, skin, and nasal) had an acceptable sequencing depth, but the low DNA abundance creates challenges for 16S rRNA sequencing. These limitations must be considered when interpreting the data generated by this study. Analytical validation is suggested for future studies.

A statistical limitation was the lack of a false discovery rate adjustment for microbial analysis, which increases the potential for type 1 errors. Furthermore, it is possible that primer bias influenced our results, as the same primer sets were used across different sample types. Because the primers used were designed to amplify the full-length 16S rRNA gene, the impact of primer bias should have been lower than those only targeting a specific variable region, however. Because of these limitations, replication of these findings in an independent study is necessary before any strong conclusions can be drawn about the effects of live B. pumilus SG154 or a L. paracasei 327 postbiotic on the canine fecal, nasal, and skin microbiota. Doing so may also reveal potential mechanisms by which oral supplements can influence the skin or nasal microbiome.

In conclusion, the data from this study demonstrate that the supplementation of live B. pumilus SG154 or a L. paracasei 327 postbiotic was well-tolerated in healthy dogs, with no negative impacts on body weight, nutrient digestibility, or general health. Minor alterations in microbial composition were observed in the skin and feces from both treatments, but diversity remained stable overall. Given the health status of the dogs, the minimal effects of either treatment were not entirely unexpected. Future studies should explore the use of live B. pumilus SG154 or L. paracasei 327 postbiotic in dogs experiencing microbial perturbations, such as following antibiotic treatment or a significant dietary change.

Supplementary Material

skaf231_suppl_Supplementary_Materials_1
skaf231_suppl_supplementary_materials_1.docx (687.6KB, docx)

Acknowledgment

Funding for this study was provided by Kerry Group (Beloit, WI).

Glossary

Abbreviations

ATTD

apparent total tract digestibility

BCFA

branched-chain fatty acids

CFU

colony-forming units

DM

dry matter

Ig

immunoglobulin

SCFA

short-chain fatty acids

Contributor Information

Jocelyn F Wren, Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

Sofia M Wilson, Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

Yifei Kang, The Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

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

John F Menton, Kerry Group, Beloit, WI 53511, USA.

Elena Vinay, Kerry Group, Beloit, WI 53511, USA.

Mathieu Millette, Kerry (Canada), Laval, Quebec H7V 4B3, Canada.

Melissa R Kelly, Science Made Simple, LLC, Winston Salem, NC 27101, USA.

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

Conflict of Interest Statement

J. F. Menton, E. Vinay, and M. Millette are employees of Kerry Group. M. R. Kelly is a private consultant for Kerry Group. All other authors have no conflicts of interest.

Author Contributions

Jocelyn Wren (Data curation, Formal analysis, Writing - original draft, Writing - review & editing), Sofia Wilson (Data curation, Formal analysis), Yifei Kang (Formal analysis, Writing - review & editing), Patrícia Oba (Data curation, Formal analysis), John Menton (Resources, Writing - review & editing), Elena Vinay (Resources, Writing - review & editing), Mathieu Millette (Resources, Writing - review & editing), Melissa Kelly (Resources, Writing - review & editing), and Kelly Swanson (Conceptualization, Funding acquisition, Project administration, Supervision, Writing - review & editing)

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