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. 2024 Sep 25;102:skae291. doi: 10.1093/jas/skae291

Effects of supplementation of live and heat-treated Bifidobacterium animalis subspecies lactis CECT 8145 on glycemic and insulinemic response, fecal microbiota, systemic biomarkers of inflammation, and white blood cell gene expression of adult dogs

Emanuela Kayser 1, Fei He 2, Sophie Nixon 3, Adrian Howard-Varona 4, Araceli Lamelas 5, Juan Martinez-Blanch 6, Empar Chenoll 7, Gary M Davenport 8, Maria R C de Godoy 9,10,✉
PMCID: PMC11525486  PMID: 39320367

Abstract

The popularity of functional ingredients such as probiotics and postbiotics has increased as pet owners seek ways to improve the health quality and longevity of their pets. Limited research has been conducted regarding the use of probiotics and postbiotics and their effects on canine health. The objective of this study was to evaluate the effects of daily supplementation of Bifidobacterium animalis subsp. lactis CECT 8145, in both live probiotic (PRO) and heat-treated postbiotic (POST) forms, on fecal fermentative end-products and microbiome, insulin sensitivity, serum gut hormones, oxidative stress, inflammatory biomarkers, and white blood cell gene expression of adult dogs. Eighteen adult beagles and 18 adult English pointers were used in a double-blinded placebo-controlled parallel group design, with 12 animals per group (6 English pointers and 6 beagles). The study began with a 60 d adaptation period followed by a 90 d period of daily supplementation with either PRO, POST, or placebo (maltodextrin; CON). Longitudinal assessment of body weight, body condition score, and pelvic circumference did not differ among dietary supplements (P > 0.05). Throughout the experimental period, fecal scores did not differ (P > 0.05); however, fecal pH was lower (P = 0.0049) in the dogs fed POST compared with CON. A higher fecal concentration of propionate (P = 0.043) was observed in dogs fed PRO and POST when compared with CON. While PRO and POST supplementation were associated with changes in bacterial composition at the family and genus level, the overall richness and diversity of the microbiome were not significantly affected. Functional analysis of the metagenome also suggests that PRO and POST supplementation induced potentially beneficial changes in the abundance of pathways involved in pathogenicity, amino acid biosynthesis, and DNA repair. No differences in glycemic or insulinemic responses were observed among the groups (P > 0.05). Dogs supplemented with PRO had a higher (P < 0.05) mean white blood cell leptin relative fold gene expression compared with groups POST and CON. Serum metabolites and complete blood cell counts were within normal ranges and all dogs remained healthy throughout the study. Together, these data suggest that the PRO and POST can safely be supplemented for dogs. Moreover, the results of this study support further investigation of the role of PRO and POST in supporting parameters related to gut health and hormonal regulation.

Keywords: canine, glycemic response, gut health, metagenome, microbiome, postbiotics

This study is the first to examine the effects of probiotic and postbiotic forms of Bifidobacterium animalis subsp. lactis CECT 8145 supplementation in dogs. The results suggest it is safe for use and can potentially improve gut health. The results support further exploratory studies to determine the effects of this microorganism on the gut health of overweight/obese dogs.

Introduction

The global canine pet population continues to grow along with its owners’ interest in increasing their pets’ life span (Deng and Swanson, 2015). With this, scientists in the pet industry and many academic groups worldwide have been working to identify novel functional ingredients to help improve canine health and longevity. Recently, attention has focused on supplementation with probiotics (live microorganisms) and postbiotics (inanimate microorganisms and/or their components), both of which confer health benefits to the host (Salminen et al., 2021).

There is increasing evidence from several studies to suggest that the probiotic Bifidobacterium animalis subsp. lactis CECT 8145, may positively affect fat metabolism, insulin sensitivity, and inflammation in other species such as humans and mice (Martorell et al., 2016; Caimari et al., 2017; Carreras et al., 2018; Pedret et al., 2019). For example, investigation in Caenorhabditis elegans demonstrated significantly reduced total lipids, triglycerides, and antioxidant effects in nematodes treated with the probiotic compared with the control group (Martorell et al., 2016). In addition, Bifidobacterium animalis subsp. lactis CECT 8145 was shown to decrease plasma ghrelin, tumor necrosis factor-α (a proinflammatory cytokine), and malondialdehyde (a biomarker of oxidative stress) in Zücker fatty rats (Carreras et al., 2018). Moreover, the ratio of plasma total cholesterol/plasma cholesterol transported by high-density lipoproteins also significantly decreased. A study in Wistar rats showed intake of postbiotic heat-treated Bifidobacterium animalis subsp. lactis CECT 8145 ameliorated mesenteric white adipose tissue, increased energy expenditure, lean mass, and improved insulin sensitivity (Caimari et al., 2017). Furthermore, a randomized, placebo-controlled clinical trial in obese humans investigated the effects of probiotic and postbiotic Bifidobacterium animalis subsp. lactis CECT 8145 with results demonstrating consumption of both forms improved anthropometric adiposity biomarkers (Pedret et al., 2019).

We hypothesized that the effects demonstrated in other species could also be extrapolated to dogs. Therefore, the objectives of this study were to evaluate the effects of daily supplementation of this specific strain in both live probiotic and heat-treated postbiotic forms, on fecal fermentative end-product concentrations and microbiome, serum oxidative stress, inflammatory biomarkers, gut hormones, and postprandial glycemic and insulinemic responses in adult dogs. We tested this using a randomized, parallel, double-blind, placebo-controlled trial which administered live probiotic Bifidobacterium animalis subsp. lactis CECT 8145 (PRO) and heat-treated postbiotic Bifidobacterium animalis subsp. lactis CECT 8145 (POST) to adult dogs.

Materials and Methods

All animal care procedures were approved by the Kennelwood, Inc. Animal Care and Use Committee prior to animal experimentation. All methods were performed in accordance with the United States Public Health Service Policy on Humane Care and Use of Laboratory Animals.

Animals and dietary treatments

A total of 36 adult dogs either overweight or on ideal body weight (BW) were used in this study; 18 adult beagles (mean age: 9.0 ± 1.97 yr; mean BW: 16.7 ± 3.66 kg; mean body condition score [BCS]: 6.5 ± 1.47), and 18 adult English pointers (mean age: 6.0 ± 2.95 yr; mean BW: 27.6 ± 4.75 kg; mean BCS: 5.5 ± 0.85) were used in this randomized, parallel, double-blind, placebo-controlled trial. Full details for housing and feeding routines are detailed in Supplementary Material.

The dogs were randomized into 3 treatment groups with 12 dogs per treatment:

  1. CON: Control diet* + placebo (maltodextrin carrier).

  2. PRO: Control diet* + probiotic source: Bifidobacterium animalis subsp. lactis CECT 8145 (daily dosage: 1010 CFU; however, plate counts conducted at the end of the study revealed that overtime dosage decreased to 6 × 109). The original dose was based on previous studies in humans (Pedret et al., 2019) and rats (Carreras et al., 2018).

  3. POST: Control diet* + postbiotic source: Heat-treated Bifidobacterium animalis subsp. lactis CECT 8145 (daily dosage 1010 heat-treated cells). The heat-treatment protocol has been described previously by Caimari et al. (2017), with confirmatory assays to determine whether the bacteria are inactivated.

Treatments were given via visually identical gelatin capsules daily before morning feeding to ensure consumption of the total dose of either PRO, POST, or CON. Beagles and English pointers received the same daily dosage.

*Control diet: Pedigree Adult Complete Nutrition Roasted Chicken, Rice & Vegetable Flavor Dry Dog Food, Mars, Franklin, TN. Daily food intake for dogs was originally determined according to the NRC daily metabolizable energy requirements recommendation for adult laboratory kennel dogs. However, during the adaptation phase, daily food intake had to be adjusted for a few dogs in order to maintain their current BWs. Once the supplementation phase started, daily food intake was kept constant for each dog throughout the experimental period.

Experimental design and sample collection

The study design, timeline, as well as the analyses performed are summarized in Figure 1. After the adaption period, dogs were allocated to one of the 3 experimental groups based on adaptation period measurements (i.e., age, breed, BCS, serum chemistry, complete blood count [CBC], and serum oxidative and inflammatory cytokines) to minimize individual variation among groups. Dogs received their assigned treatment for a total of 90 d, with researchers and animal care staff blinded to the assigned treatments. Only a designated researcher out of the animal or university settings had access to the identification of the treatments.

Figure 1.

Figure 1.

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Timeline of study procedures. (A) Biometrical measurements included: Body condition score (BCS) accessed on a 9-point scale, with ‘1’ being severely malnourished, ‘5’ being an ideal body weight, and ‘9’ being severely obese (Laflamme, 1997); Body weight (BW); Pelvic circumference (PC). (B) Markers of oxidative stress analyzed included superoxide dismutase (SOD) and malondialdehyde (MDA). (C) Biomarkers of inflammation measured included serum cytokine levels of IL-7, 1L-8, keratinocyte chemoattract-like (KC-like), IP-10, monocyte chemoattractant protein (MCP), and leukocytes. (D) Fecal analysis included: pH; Dry matter (DM) was analyzed in 2 g duplicates and dried for 48 h in a 105 °C forced-air oven; Short-chain fatty acids (SCFA); Branched-chain fatty acids (BCFA); For analysis of BCFA, SCFA, and ammonia, 5 g of feces were collected in a 30 mL Nalgene bottle containing 5 mL of 2N hydrochloric acid. A separate fecal aliquot (2 g) was used to determine phenol/indole concentrations. SCFA and phenol/indole samples were stored at −20 °C until analyses (Chaney and Marbach, 1962; Sunvold et al., 1965); Fecal phenol and indole concentrations were analyzed through gas chromatography (Flickinger et al., 2003); Fecal scores assessments were performed using a 5-step scoring system: 1 = hard, dry pellets; 2 = hard formed, remains firm and soft, 3 = soft, formed and moist stool; 4 = soft, unformed stool; or 5 = watery, liquid that can be poured.

At predetermined time points, a 21 mL fasted blood sample was collected via jugular venipuncture from each dog for analysis, as detailed in Figure 1andSupplementary Material. Serum chemistry and CBC were used to assess the health of the dogs throughout the study and were analyzed by the University of Illinois Veterinary School Diagnostics laboratory using Hitachi 911 clinical chemistry analyzer (Roche Diagnostics Indianapolis, IN). Markers of oxidative stress were measured using commercial bioassay and enzyme-linked immunosorbent assay (ELISA) kits, respectively (SOD and MDA: MyBioSource, San Diego, CA); serum cytokine concentration was determined using a commercial canine-specific multiplex bead-based assay (Milliplex Canine Cytokine/Chemokine Magnetic Bead Panel; Millipore, Billerica, MA).

A fresh fecal sample was collected from each dog within 15 min of defecation for analysis on days 0, 30, 60, and 90, according to Figure 1. Fecal samples allocated for microbiome analyses were stored in collection tubes provided by ADM Biopolis (Valencia, Spain), which included REAL stock (Durviz SL, Paterna, Valencia, Spain) as a sample-stabilizing buffer to ensure its stability. All fresh fecal samples were stored at −20 °C until further analysis (i.e., Short-chain fatty acids [SCFA] and branched-chain fatty acids) detailed in Figure 1.

Fecal DNA extraction, amplification, sequencing, and bioinformatic analysis

Total DNA from fecal samples was extracted using Mo-Bio PowerSoil kits (MO BIO Laboratories, Inc., Carlsbad, CA). Shotgun libraries were prepared from Xng of purified genomic DNA with the Nextera XT Library Preparation kit (Illumina) following the manufacturer’s protocol. Once libraries were prepared, an equimolar mixture was used for sequencing in a NovaSeq 6000 of Illumina with the following configuration: 150 × 2 Paired End. The sequences were obtained from the sequencing platform using the software bcl2fastq v2.20.0.422 (Illumina) for demultiplexing. Sequence filtering was performed using the program BBMap v38.36 with the following parameters: Using a minimum value of Q20 of Phred quality score (Ewing et al., 1998); trimming bases at the edges with a lower quality value than Q20; trimming full sequences with a lower mean average value than Q20; and filtering those sequences with a lower length than 70 nucleotides which increase complexity in downstream analysis. After the reads were filtered, the presence of host genomes in the samples was filtered using genome reference Canis lupus familiaris (CanFam3.1). Sequences with high homology to this genome were eliminated using the program NGLess v1.0.0-Linux64 (Coelho et al., 2019). For taxonomic analysis, the filtered reads were aligned with single-copy marker genes. The relative abundances of the taxa identified were calculated with the MetaPhlAn v2.0 pipeline (Segata et al., 2012) by means of which the readings of each sample were aligned against single-copy genetic markers present in almost all bacteria. From these alignments, the relative abundances of the identified taxa were obtained computationally. A Gene Set Enrichment Analysis method (GSEA) was used for functional analysis with the GAGE library of R v2.46. Pathogenic factors were identified with PathoFact software using default parameters.

Glucose tolerance test

An intravenous (i.v.) glucose tolerance test was performed on day 0 to establish baseline values and repeated on day 90. The full methodology is detailed in Supplementary Material but briefly, dogs were infused with a 500 g/L solution (Hospira, Lake Forest, IL) of d-glucose (2 g/kg BW; Larson et al., 2003) before blood collection (3 mL). Glucose was immediately analyzed using the glucose oxidase method via an AlphaTRAK 2 blood glucose meter (Zoetis, Parsippany, NJ). The remaining blood was transferred to an EDTA vacutainer tube (Becton, Dickinson and Company, Franklin Lakes, NJ), which was centrifuged at 1,000 g at 4 °C for 15 minutes for the measurement of plasma (stored in −80 °C freezer until analyses were performed) gut related hormones, including insulin, glucagon, leptin, pancreatic polypeptide (PP), ghrelin, peptide tyrosine–tyrosine (PYY), and gastric inhibitory polypeptide (GIP) using a commercially available kit (Milliplex Canine Gut Hormone Magnetic Bead Panel Assay Kit, EMD Millipore, Billerica, MA). The EDTA tubes were primed with 20 μL of dipeptidyl transferase IV inhibitor (EMD Millipore, Billerica, MA) prior to blood collection and centrifugation to avoid degradation of the gut-related hormones. Measurements of gut-related hormones within plasma samples were performed using a commercially available kit (Milliplex Canine Gut Hormone Magnetic Bead Panel Assay Kit, EMD Millipore, Billerica, MA).

Leukocyte gene expression

Total RNA from blood cells was isolated using PAXgene Blood Kit (Qiagen, Valencia, CA). RNA concentration was determined using a Qubit 2.0 Fluorometer (Life Technologies, Grand Island, CA). cDNA was synthesized using Superscript IV reverse transcriptase (Invitrogen, Carlsbad, CA, USA). Gene expression was quantified using the Fluidigm qPCR Biomark HD high throughput amplification system 48.48 qPCR amplification (Fluidigm Corporation, San Francisco, CA, USA) and EVA Control fluorescent dye (Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign). Ribosomal protein 19 (RPS19), ribosomal protein S5 (RPS5), and Hypoxanthine-guanine phosphoribosyl transferase 1 (HPRT), were measured as reference housekeeping genes within each sample (Brinkhof et al., 2006). The quantification of relative mRNA abundance was determined using the 2−ΔΔCt method and represented as gene expression relative to the average abundance of the 3 internal control reference housekeeping genes measured within each sample (Livak and Schmitten, 2001). The genes analyzed and their respective validated primer’s unique assay identification (Bio-Rad Laboratories, Hercules, CA) are shown in Table 1.

Table 1.

Canine leukocyte genes analyzed

Gene name Gene symbol Unique SYBR green assay ID
Peroxisome proliferator-activated receptor gamma coactivator 1-alpha PPARGC1 qCfaCID0022062
Sterol regulatory element binding protein 1 SREBF-1 qCfaCID0020511
Insulin-like growth factor 1 IGF1 qCfaCID0035607
Mitochondrial uncoupling protein UCP qCfaCID0021392
Hormone-sensitive lipase HSL qCfaCED0033647
Adiponectin receptor 1 ADIPOR1 qCfaCED0026903
Superoxide dismutase SOD qCfaCED0038911
Mammalian target to rapamycin MTOR qCfaCID0024417
Toll-like receptor 4 TLR4 qCfaCED0026117
Interleukin 6 IL6 qCfaCID0020842
Glutathione peroxidase 1 GSR qCfaCED0031064
Cyclooxygenase 2 COX2 qCfaCED0024663
Matrix metallopeptidase 3 MMP3 qCfaCED0026432
Ribosomal protein S6 kinase A5 RSP6KA5 qCfaCID0020846
Small heat shock protein beta 1 HSP1 qCfaCID0021845
Insulin receptor substrate 1 IRS1 qCfaCED0033081
Insulin receptor substrate 2 IRS2 qCfaCED0033658
Lipoprotein lipase LPL qCfaCID0037071
Carnitine palmitoyltransferase 1 CPT1 qCfaCID0022062
Peroxisome proliferator-activated receptor gamma PPARG qCfaCID0022062
Leptin N/A qCfaCID0029861
Catalase N/A qCfaCED0028561
Resistin N/A qCfaCED0038240
Adiponectin N/A qCfaCED0026903
Ribosomal protein 5 RPS5 qCfaCEP0009200
Ribosomal protein 19 RPS19 qCfaCEP0020224
Hypoxanthine-guanine phosphoribosyltransferase HPRT qCfaCEP0015713
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Statistical analysis

Except for fecal microbiota, all data were analyzed using SAS (SAS Institute INC., version 9.4, Cary, NC) through PROC MIXED with a dog as the random effect, and treatment was used as the fixed effect. Breed was analyzed as a covariate, when significant it was added to the statistical model. However, no significant interaction of breed by treatment or breed by treatment by day was noted. The day was used as a repeated measure, when appropriate, to determine the interaction of treatment by d. The covariance structure used for the repeated measure was based on the lowest AIC values using Kenward-Roger correction. The normality of residuals was analyzed using PROC UNIVARIATE. Differences among treatments were determined using Least Squares Means with a Tukey adjustment to control for type-1 experiment-wise error. A probability of P < 0.05 was accepted as statistically significant and the reported pooled SEM was determined according to the Mixed Models procedure of SAS. Additionally, a probability of 0.05 < P < 0.10 was designated as a trend.

Regarding bacterial taxonomical analyses, data were normalized using the rarefaction technique from Phyloseq R package (Weiss et al., 2017) in order to perform alpha diversity analysis. Shannon, Simpson, and Richness indexes were calculated using a vegan R package (Oksanen et al., 2019), the Kruskal–Wallis rank sum test was used to determine significance in alpha diversity among groups, and the Wilcoxon test was used to find significant differences between groups by paisd. Bray‐Curtis dissimilarity matrix and PERMANOVA analysis for beta diversity were performed using a vegan R package, after normalization by relative frequency for each sample.

For biomarkers identification, the feature table was first normalized using the calcNormFactors function, with the trimmed mean of M‐values with singleton pairing (TMMwsp) option. After normalization, the ka R package (version 3.48.3v) function voom (Law et al., 2014) was used to convert normalized counts to log2‐counts‐per‐million and assign precision weights to each observation based on the mean‐variance trend. The functions lmFit, eBayes, and topTable in the limma R package were used to fit weighted linear regression models, perform tests based on an empirical Bayes moderated t‐statistic and obtain Benjamin-Hochberg FDR‐corrected P‐values. A taxon or gene was considered differentially abundant if the corrected P‐value > 0.05 and if it was present in at least 50% of the samples of one of the compared groups.

Results

During the 90 d of supplementation, there were no adverse events (e.g., vomiting, diarrhea, lethargy, and any clinical symptoms of an animal feeling unwell) or treatment-related adverse events observed. Analyses of serum chemistry and CBC demonstrated dogs remained within reference ranges during the PRO and POST supplementation period (Supplementary Tables 1 and 2).

Fecal metabolites and fecal score

PRO and POST supplements did not affect (P > 0.050) fecal consistency scores during adaptation or supplementation periods (Supplementary Table 3). During 90 d of supplementation with PRO and POST, the fecal concentration of propionate increased (P = 0.043) in the PRO (170.5 μmol/g) and POST (170.7 μmol/g) groups compared with the CON (150.5 μmol/g) group (Figure 2A). Fecal pH was lower (P = 0.0049) in the POST group (5.8) when compared with the CON group (6.0) on day 90 (Figure 2A). There were no changes (P > 0.050) in the concentration of fecal phenols, indoles, acetate, butyrate, isobutyrate, and valerate (Figure 2B and C).

Figure 2.

Figure 2.

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Average fecal concentrations of fermentative-end products (A: SCFA and pH; B: BCFA; and C: phenol and indole) of dogs supplemented with control (CON, 12 dogs), probiotic (PRO, 12 dogs) or postbiotic (POST, 12 dogs). No significant time and treatment (P > 0.05). The main effect of treatment was observed for fecal pH, which was lower in the POST group compared to CON (P = 0.0049). Fecal propionate concentration was higher in PRO and POST compared with CON (P = 0.0427). *P < 0.05.

Gut microbiota

Bifidobacterium animalis subsp. lactis CECT 8145 supplementation outcomes on the fecal microbiome

Supplementation with POST increased the abundance of Burkholderiales_noname (P = 0.027, log2FC = 10.24), Eubacteriaceae (P = 0.009, log2FC = 2.23), Micrococcaceae (P = 0.027, log2FC = 1.23) and Propionibacteriaceae (P = 0.023, log2FC = 1.57) families, with significant increases observed for dogs in the POST group between days 0 and 90 (Figure 3A). In contrast to this, these family groups, Burkholderiales_noname (P = 0.024, log2FC = −6.39), Eubacteriaceae (P = 0.003, log2FC = −2.06), Micrococcaceae (P = 0.014, log2FC = −1.06) and Propionibacteriaceae (P = 0.009, log2FC = −1.40) families, were found to decrease in the CON group, while the abundance of Enterobacteriaceae (P = 0.001, log2FC = 4.94) significantly increased in CON group during this time (Figure 3A).

Figure 3.

Figure 3.

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Heatmap of the log2 fold change of fecal microbiota taxa abundance at the family level between days 0 and 90 (A) and treatments vs treatments (B) of adult dogs fed CON (12 dogs), PRO (12 dogs), and POST (12 dogs). Statistical analysis was performed using a moderated t-test and adjusted FDR from limma-voom. Significance is shown as *P < 0.05. CPM, counts per million.

Longitudinal abundance analysis at the family level directly comparing changes in PRO and POST to those in CON groups, revealed an abundance of Propionibacteriaceae (CON vs POST: P = 0.006, log2FC = −2.96; CON vs PRO: P = 0.015, log2FC = −2.91) and Micrococcaceae (CON vs POST: P = 0.011, log2FC = −2.30; CON vs PRO: P = 0.022, log2FC = −2.24) increased in the treatments PRO and POST over time in comparison with the CON group (Figure 3B). Likewise, supplementation of POST resulted in a higher relative abundance of Eubacteriaceae (CON vs POST: P = 0.002, log2FC = −4.29) and Burkhoderiales_noname (CON vs POST: P = 0.012, log2FC = −16.63) from days 0 to 90 compared with changes observed with CON-treated group (Figure 3B).

At the genus level, a total of 14 genera showed a time-dependent increase (P < 0.050) in the groups PRO and POST when compared with the CON group (Figure 4), with the highest level of change being presented in Adlercreutzia (CON vs POST: P = 6.826E-09, log2FC = −15.74; CON vs PRO: P = 6.227E-05, log2FC = −17.70), Erysipelotrichaceae_noname (CON vs POST: P = 0.032, log2FC = −7.94; CON vs PRO: P = 0.017, log2FC = −8.55) and Klebsiella (CON vs POST: P = 0.0003, log2FC = −4.90; CON vs PRO: P = 1E-05, log2FC = −5.18) genera (Table 2). Moreover SCFA-producing bacteria like Butyrivibrio (CON vs POST: P = 0.01, log2FC = −2.34; CON vs PRO: P = 0.0009, log2FC = −3.19) and Turicibacter spp. (CON vs POST: P = 0.0003, log2FC = −3.45; CON vs PRO: P = 7.59E-05, log2FC = −3.74) increased in PRO and POST in comparison with CON. Also, an increase in the genera Bilophia (CON vs PRO: P = 0.005, log2FC = −2.63), Brevundimonas (CON vs PRO: P = 0.013, log2FC = −2.63), Dermathophilaceae unclassified (CON vs PRO: P = 0.005, log2FC = −2.63), Dietzia (CON vs PRO: P = 0.004, log2FC = −2.63), Fusobacterium (CON vs PRO: P = 0.007, log2FC = −2.63), and Leucobacter (CON vs PRO: P = 0.007, log2FC = −2.63) was observed only in the dogs supplemented with PRO (Figure 4), whereas genus Escherichia (CON vs PRO: P = 0.036, log2FC = 8.29) showed a time-dependent decrease in the group PRO when compared with the CON group.

Figure 4.

Figure 4.

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Heatmap of the log2 fold change of the differential increase (fold change from days 0 to 90) intestinal bacterial genus between feeding groups in adult dogs fed CON (12 dogs), PRO (12 dogs), and POST (12 dogs). Log2 fold change of above 0 for a specific taxa in the “CON vs PRO/POST comparison” = taxa abundance is increased in CON relative to PRO/POST. Log2 fold change of below 0 means taxa abundance is increased in PRO/POST relative to CON. Statistical analysis was performed using a moderated t-test and adjusted FDR from lima-voom. Significance is shown as *P < 0.05. CPM: Counts per million.

Table 2.

Increases in abundance of genera in the gut microbiome between days 0 and 90 of adult dogs supplemented either with PRO or POST

Genus Treatment group Log2 fold change
(days 0 to 90)
Aldercreutzia PRO 17.7
POST 17.7
Erysipelotrichaceae_noname PRO 8.5
POST 7.9
Klebsiella PRO 5.2
POST 4.9
Proteus PRO 5.1
POST 4.8
Propionibactericeae_unclassified PRO 4.0
POST 3.7
Cronobacter PRO 4.0
POST 3.7
Turibacter PRO 3.7
POST 3.4
Kocuria PRO 3.7
POST 3.4
Butyrivibrio PRO 3.9
POST 2.3
Weisella PRO 2.6
POST 2.7
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Genus level time-dependent increases (P < 0.05) in log2 fold changes included. Statistical analysis was performed using a moderated t-test FDR adjusted from limma-voom.

Changes in the α-diversity of the fecal microbial community before (day 0) and after supplementation (day 90) were evaluated using Richness, Simpson, Shannon, and Pielou’s evenness indexes (Supplementary Figure 1). Kruskal–Wallis rank sum test was significant (P = 0.011) in richness values. Simpson and Pielou’s evenness indexes indicated an increase (Simpson: P = 0.036; Pielou’s evenness: P = 0.049) in richness values for dogs in the CON group at day 90 compared with day 0, and increased richness (P = 0.002) of dogs on POST supplementation at day 90 in relation to dogs on CON at day 90 (Supplementary Figure 1).

Bifidobacterium animalis subsp. lactis CECT 8145 supplementation outcomes on the functional microbiome profile

A metagenomics functional analysis study was run to assess changes in the metabolic pathways in the gut microbiome after 90 d of supplementation with PRO and POST compared with the CON group (Figure 5). The abundance of pathways involved in pathogenicity (such as cationic antimicrobial peptide resistance, bacterial secretion system), biofilm formation from pathogenic bacteria, bacterial mobility (such as chemotaxis, and flagellar assembly), and lipopolysaccharide biosynthesis decreased over time in PRO and POST groups when compared with CON group.

Figure 5.

Figure 5.

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Heatmap of the magnitude of gene-set level changes (Stat.mean) of metabolism pathways abundance at L3 level between dog groups depending on the time (CON vs PRO, CON vs POST, PRO vs POST) and between times (days 90 vs 0) in dogs fed CON (12 dogs), PRO (12 dogs), and POST (12 dogs). Pathways are shown as over-represented (red color) or under-represented (blue color) in the first group of the comparison. Significance is shown as *P < 0.05. L1: high-level functions and utilities; L2: General categories; L3: Pathway level. Stat.mean: is the magnitude of gene-set level changes calculated using the non-parametric Kolmogorov–Smirnov tests.

Pertaining to amino acid metabolism, phenylalanine metabolism and nitrotoluene degradation decreased in the PRO and POST groups in comparison with the CON group over time. In contrast, pathways related to amino acid biosynthesis (such as cysteine and methionine metabolism, biosynthesis of valine, leucine and isoleucine, lysine, and biosynthesis of phenylalanine, tyrosine and tryptophan) and DNA repair (such as nucleotide excision repair and mismatch repair) increased (P < 0.050) over time in the PRO and the POST groups when compared with CON group. Likewise, gene abundance in pathways involved in carbon metabolism (such as the pentose phosphate pathway and starch and sucrose metabolism) also increased (P < 0.050) over time in the PRO and the POST groups in contrast to the CON group (Figure 5).

Serum chemistry and CBC, inflammatory biomarkers, leukocyte gene expression

Serum chemistry and CBC parameters, between days 0 and 90, remained within the reference range for all dogs (reference ranges from the Veterinary Diagnostic Laboratory from the University of Illinois at Urbana-Champaign), and there were no significant changes identified when the groups were compared (Supplementary Tables 1 and 2). There were also no changes (P > 0.050) in serum cytokines or chemokines in dogs supplemented with CON, PRO, or POST (Supplementary Table 4).

Twenty-four genes were analyzed in canine leukocytes (Supplementary Table 5), the majority of which showed no differences in expression among the CON, PRO, and POST groups, except for leptin, HSP1, and CPT1A (Figure 6, Supplementary Table 5). An increase (P < 0.050) in the relative fold change of HSP1 and leptin gene expression in the PRO (HSP1 = 1.3; Leptin = 0.8) group compared with the CON (HSP1 = 0.9; Leptin = 0.6) group, whereas lower expression in the POST (HSP1 = 0.8; Leptin = 0.5) group when compared with the PRO (HSP1 = 1.3; Leptin = 0.8) group were observed. Supplementation with POST (0.5) resulted in lower (P < 0.005) gene fold expression of CPT1A compared with the CON group (Figure 6.).

Figure 6.

Figure 6.

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Mean relative fold change in leukocyte gene expression from dogs fed CON (12 dogs), PRO (12 dogs), and POST (12 dogs). * P < 0.05.CPT1A, carnitine palmitoyltransferase; HSP1, small heat shock protein.

Biometrics, glucose homeostasis, and gut hormones

BW, BCS, and pelvic circumference (PC) remained stable throughout the adaptation and supplementary periods in PRO, POST, and CON groups of dogs (Supplementary Table 6). Results of the intravenous glucose tolerance test, including half-life, fractional clearance rate, and IAUC, did not differ (P > 0.050) among treatment groups (Supplementary Table 7). Among the 6 gut hormones that were analyzed (insulin, glucagon, leptin, PP, ghrelin, and GIP), PP decreased (P = 0.017) in the PRO (7.85 pmol/L) group when compared with the CON (12.6 pmol/L) group (Table 3).

Table 3.

Longitudinal assessment of plasma levels of gut hormones from adult dogs fed CON (12 dogs), PRO (12 dogs), and POST (12 dogs)

Treatments Statistics
CON PRO POST SEM1 Type 3 fixed effects
Item; pmol/L d 0 d 90 d 0 d 90 d 0 d 90 T × D Trt Day T × D
Insulin 32.4 43.5 26.1 24.3 28.4 31.7 8.73 0.5349 0.3299 0.4409
Glucagon 19.8 15.6 17.3 11.9 18.4 16.6 2.60 0.6132 0.0001 0.1955
Leptin 1302.2 662.2 730.8 541.2 825.9 667.8 266.23 0.6023 0.0054 0.1618
PP2,* 12.6 12.6 9.3 6.4 11.3 9.17 1.45 0.0169 0.1758 0.5949
Ghrelin 30.2 46.6 56.1 50.3 35.9 49.3 9.43 0.4494 0.1667 0.2393
GIP3 1.3 1.0 1.2 1.3 1.6 1.5 0.27 0.5089 0.5828 0.5895
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1Pooled standard error of the mean.

2Pancreatic polypeptide.

3Gastric inhibitory polypeptide.

*Treatment CON differs from treatment PRO (P = 0.0169).

Discussion

Probiotics and postbiotics are a growing area of interest for researchers and industry alike who seek to understand the benefits of use in companion animals (Deng and Swanson, 2015). To the authors’ knowledge, this study was the first to investigate the effects of daily supplementation of both pro- and postbiotic Bifidobacterium animalis subsp. lactis CECT 8145 in adult dogs, highlighting the novel aspects of this research trial.

Based on our results no adverse events were observed during 90 d of supplementation with PRO and POST forms of Bifidobacterium animalis subsp. lactis CECT 8145. Examination of serum chemistry and CBC confirmed PRO and POST were well tolerated in dogs, and no health concerns were reported during the study.

Supplementation with PRO and POST over 90 d did not affect fecal consistency scores or fecal concentrations of phenols, indoles, acetate, butyrate, isobutyrate, or valerate. However, fecal concentrations of propionate significantly increased in the PRO and POST groups compared with the CON group. These results highlight that Bifidobacterium animalis subsp. lactis CECT 8145 could be advantageous for dogs as propionate is known to have immunomodulatory effects, and there is evidence in humans that propionate is associated with improved glucose tolerance and insulin sensitivity (Venter et al., 1990; Hoyles et al., 2018). Contrary to our data, Strompfová et al. (2014) did not observe increases in fecal concentrations of propionate with the supplementation of a Bifidobacterium probiotic strain in healthy adult dogs. However, in the previous study, the supplementation period was 2 wk, followed by 3 wk of post-supplementation. It is important to note that the probiotic effect is rapidly cleared from the intestine after supplementation is discontinued, thus colonization is dependent on continual bacterial supplementation (Manninen et al., 2006). Previous studies have indicated that fecal metabolites, such as propionate, can stimulate leptin expression in the adipocytes, which regulates feeding behavior and metabolic rate (Xiong et al., 2003; Gabriel and Fantuzzi, 2019). Further work is needed to assess whether increases in propionate could improve glucose tolerance and insulin sensitivity in obese or insulin-resistant dogs.

Age, diet, and environmental factors play a role in the gut microbiome in dogs (Sanchez et al., 2020). In the present experiment, the abundance of family Eubacteriaceae decreased in the group supplemented with POST compared with the CON group. Eubacteriaceae is a gram-positive bacteria reported to be present at higher abundance (0.01% to 0.02%) in dogs with inflammatory bowel disease and intestinal lymphoma (Omori et al., 2017). Dogs supplemented with PRO had a greater relative abundance of Fusobacterium than CON dogs. Fusobacterium genus increase has been noted after weight loss on a high fiber and protein diet (Sanchez et al., 2020). An intriguing difference in the core microbiota between dogs and humans is the association of Fusobacterium with colon cancer, whereas in dogs appears to play an important role in the maintenance of health and has been reported to be decreased in dogs with gastrointestinal diseases (AlShawaqfeh et al., 2017; Sanchez et al., 2020). The changes in abundance of the Eubacteriaceae family and Fusobacterium genus suggest supplementation with PRO or POST may benefit overall gut health in dogs.

PRO and POST supplementation may be associated with an increase in the described genus Adlercreutzia (Maruo et al., 2008), which is present in healthy gut microbiomes in companion animals (Jha et al., 2020). Interestingly, members of Adlercreutzia, Propionibactericeae, Butyrivibrio, and Turicibacter, SCFA-producing bacteria, increased in both PRO and POST groups. A decrease in their abundance has been associated with acute diarrhea in dogs (Suchodolski et al., 2012). In our study, PRO and POST groups increased SCFA-producing bacteria and fecal propionate concentration. Furthermore, the genus Turicibacter has displayed its potential to increase lipid metabolism in murine models, resulting in significant reductions in systemic triglyceride levels and the size of inguinal adipocytes (Fan and Pedersen, 2021). In a recent examination of fecal microbiomes using 16S rRNA gene sequencing, a distinct trend emerged: Turicibacter abundances exhibited noticeable depletion at the genus level in dogs suffering from canine cardiac disease, in comparison to their healthy counterparts (Li, 2022).

While supplementation with PRO and POST over 90 d resulted in a few significant changes in bacterial composition at both family and genus levels, the overall richness and diversity of the gut microbiome were not significantly affected. Previous studies based on the metagenome analysis approach have reported that probiotics containing Lactobacillus casei Zhang, Lactobacillus plantarum P-8, and Bifidobacterium animalis subsp. lactis V9 could improve gut health, particularly in dogs with diarrhea, with probiotic supplementation over 60 d resulting in the upregulation of pathways involved in the metabolism of amino acids and downregulation of pathways associated with virulence of pathogenic bacteria (Xu et al., 2019). Analysis of the gut microbiota gene content reveals an abundance increase in genes related to amino acid biosynthesis and DNA repair in PRO and POST supplemented groups, accompanied by a reduction in pathogenicity factors gene content compared with the CON group. Increased abundance was observed in the pathways involved in essential amino acid biosynthesis (such as Trp, Lys, Val, Leu, Iso, and Met), which are indispensable for mammals as they cannot be synthesized endogenously, mainly tryptophan (an amino acid crucial for dogs), which is a precursor for critical compounds like kynurenine, serotonin, melatonin, and indole (Templeman et al., 2019). Levels of tryptophan in plasma were reported to be inversely correlated with disease severity in dogs and cats (Sakai et al., 2018; Tamura et al., 2019). Diminished tryptophan levels have the effect of limiting the production of serotonin—a neurotransmitter essential for functions such as gastrointestinal secretion, motility, and pain perception in all species (Foster et al., 2017).

As for pathogenicity, a noticeable reduction in gene abundance associated with lipopolysaccharide (LPS) biosynthesis was observed in both the PRO and POST groups compared with the CON group. Gram-negative bacteria, such as E. coli, which is depleted in these groups, can potentially impact intestinal lipopolysaccharide (LPS) levels. The intestinal LPS levels are intricately linked to chronic inflammation in dogs (Park et al., 2015). In consideration of these outcomes, there emerges a suggestive indication that Bifidobacterium animalis subsp. lactis CECT 8145 could potentially be influential in the broader landscape of canine gut health.

Modulation of the immune system is one plausible mechanism underlying the beneficial effects of probiotics and postbiotics (Yan and Polk, 2011). Probiotic strains that have been extensively studied in humans and mice (i.e., L. acidophilus, L. casei, L. salivarius, L. lactis, B. bifidum, and B. infantis) have been reported to enhance innate immunity and modulate pathogen-induced inflammation via toll-like receptor-regulated signaling pathways (Vanderpool et al., 2008). Most of the dogs used in this study were overweight adult dogs (the overall BCS average of the 36 dogs was 6, on a scale [Laflamme, 1997] from 1, extremely thin, to 9, extremely obese, being 4 and 5 the ideal) but otherwise healthy animals, detecting a beneficial effect on immune health after Bifidobacterium animalis subsp. lactis CECT 8145 supplementation in this cohort was less likely considering that no strategies were used to force an immunological response. However, there was evidence suggesting that both PRO and POST supplements influenced leukocyte HSP1 and CPT1A gene expression, with significantly higher HSP1 expression in the PRO group than in the CON group. The HSP1 peptide is reported to be involved in many cellular processes, including the activation of macrophages to produce proinflammatory cytokines and chemokines, as demonstrated in mice (Carrillo et al., 2008). CPT1A catalyzes the transport of fatty acids to the mitochondria for oxidation initiation, and therefore upregulation would be considered beneficial (Respondek et al., 2008). A positive correlation between increased fecal metabolites and higher CPT1A gene expression in adipocytes has been reported (Respondek et al., 2008); however, in this study, CPT1A expression in WBC decreased in the POST group compared with the CON group.

No differences were observed in the longitudinal assessment of BW, BCS, and PC among groups throughout the study. This was expected as the dogs were fed on a maintenance diet optimized to maintain current BW during the adaptation period. This protocol was adopted as this would allow to determination of potential anorexigenic effects of PRO and POST supplementation by modulation of gut hormones. However, a more aggressive protocol, in which animals are fed ad libitum, might be necessary, even though this is not an encouraged feeding practice in veterinary medicine. In addition, no significant differences in glycemic responses were observed. In a previous study, using a murine obesity model were demonstrated significant changes in glycemic responses, potentially similar effects could be observed in obese dogs supplemented with Bifidobacterium animalis subsp. lactis CECT 8145 (Stenman et al., 2014).

Analysis of gut hormones showed PP significantly decreased in the PRO group compared with the CON group. PP is involved in glucose regulation and sensitizes the liver to the action of insulin by increasing the number of insulin receptors in the hepatocytes and therefore aids glucose metabolism (Seymour et al., 1995, 1996). It was intriguing to discover that serum PP levels were decreased in the PRO group and these findings should be explored in further studies to help determine the cause and mechanism of this result.

There were no significant changes in serum leptin levels when treatment groups were compared. However, there was a significant increase in leukocyte leptin gene expression in the PRO and POST groups compared with the CON group. Leptin is a mediator of long-term regulation of energy balance, suppressing food intake and thereby inducing weight loss (Klok et al., 2007). Although increased leptin gene expression was observed in the dogs supplemented with PRO and POST, it did not result in increased leptin concentration in the serum.

A few differences existed between the live and heat-treated forms of Bifidobacterium animalis subsp. lactis CECT 8145. Firstly, PRO significantly affected HSP1 and leptin gene expression, indicating that modulation of the expression of these 2 genes required Bifidobacterium animalis subsp. lactis CECT 8145 to be in its live form. This may be because heat-treating the probiotic to generate the postbiotic destroyed the molecule (s) responsible for modulating gene expression, or because the effect required generation of metabolite(s) in situ in the gut. Secondly, analysis of changes in taxa abundance at the family level revealed that supplementation with POST resulted in significant increases in Eubacteriaceae compared with PRO supplementation. However, significant changes in taxa abundance at the genus level occurred in a higher proportion of genera after supplementation with PRO (vs CON) compared with POST (vs CON). Dogs supplemented with PRO also showed a significant decrease in the genus Escherichia when compared with results in the CON group. The analysis performed did not allow for differentiation of pathogenic and nonpathogenic strains, such as Escherichia coli (E. coli). However, there is an association of E. coli with diarrhea and vomiting in dogs (Beutin, 1999), thus it could be beneficial to reduce the abundance of Escherichia genus in the canine gut microbiome.

There were a few limitations associated with this study. Firstly, gene expression of important markers was measured in canine leukocytes, demonstrating significant changes. However, downstream analysis of protein levels was not performed to evaluate whether changes in gene expression resulted in an important functional outcome whereby the cellular protein levels significantly changed. This would have helped to determine whether PRO and POST supplements were able to influence protein expression in the canine leukocytes. Ideally, the determination of leptin concentration in adipose tissue would have been beneficial as this hormone is primarily produced in white adipose tissue (Macdougald et al., 1995), and is important in regulating food intake and lipolysis (Kelesidis et al., 2010). Due to the noninvasive nature of research studies in companion animals, only gene expression of white blood cells could be analyzed. This is an intrinsic constraint of studies performed in research- or client-owned pet animals due to concerns about animal welfare and humane practices. Previous studies, however, have reported strong associations between serum leptin concentrations and obesity, with leptin resistance being characterized as nutrient over-consumption and increased total body mass in humans and rodents (Myers et al., 2012; Izquierdo et al., 2019; Obradovic et al., 2021).

Further investigations are needed to better understand the effects of Bifidobacterium animalis subsp. lactis CECT 8145 supplementation to dogs, including 1) Establish if the changes in the leukocyte gene expression impact HSP1 peptide levels and subsequent inflammatory processes; 2) Analyze the decreased CPT1A expression in WBC results in overall canine physiology; 3) Compare leptin gene expression in lean and obese or insulin/ leptin resistant dogs; 4) Characterize the effect of the decrease in the genus Escherichia in the fecal microbiota.

Conclusion

Supplementation of Bifidobacterium animalis subsp. lactis CECT 8145 in PRO and POST forms was well tolerated and resulted in no adverse effects in dogs over 90 d. Furthermore, results from fecal analysis showed significantly increased propionate concentrations in PRO and POST groups, in addition to changes in the abundance of genes involved in energy metabolism and immune function, through the modulation of the gut microbiota, all of which are indicators of improvements in gut health. Therefore, the results of this research support further exploratory studies to determine the effects of Bifidobacterium animalis subsp. lactis CECT 8145 in the modulation of energy homeostasis in obese or insulin-resistant dogs.

Supplementary Material

skae291_suppl_Supplementary_Material
skae291_suppl_supplementary_material.docx (819.2KB, docx)

Acknowledgments

We would like to acknowledge ADM for funding this study.

Glossary

Abbreviations

ADIPOR1

adiponectin receptor

BCFA

branched-chain fatty acids

BCS

body condition score

BW

body weight

CBC

complete blood count

COX2

cyclooxigenase 2

CON

placebo control;

CPT1A

carnitine palmitoyltransferase 1

GIP

gastric inhibitory polypeptide

GSR

glutathione peroxidase one

HSL

hormone sensitive-lipase

HSP1

small heat shock protein beta 1

IAUC

incremental area under the curve

IGF1

insulin-like growth factor one

IL

interleukin

IRS1

insulin receptor 1

IRS2

insulin receptor 2

LPL

lipoprotein lipase

MDA

malondialdehyde

MMP3

matrix metallopeptidase 3

MTOR

mammalian target rapamycin

PC

pelvic circumference

POST

heat-treated Bifidobacterium animalis subsp. lactis CECT 8145

PP

pancreatic polypeptide

PPARG

peroxisome proliferator activated receptor gamma

PPARGC1

peroxisome proliferator-activated receptor gamma coactivator 1-alpha

PRO

live bifidobacterium animalis subsp. lactis CECT 8145

SCFA

Short-chain fatty acids

SEM

standard error of the mean

SOD

superoxide dismutase

SREBF-1

sterol regulatory element binding protein one

TLR4

toll-like receptor 4

UCP

mitochondrial uncoupling protein

Contributor Information

Emanuela Kayser, Division of Nutritional Sciences University of Illinois, Urbana, IL, 61801, USA.

Fei He, Department of Animal Sciences University of Illinois, Urbana, IL, 61801, USA.

Sophie Nixon, ADM Health & Wellness, Lopen Head, TA13 5JH, UK.

Adrian Howard-Varona, ADM Biopolis, University of Valencia Science Park (Parc Científic de la Universitat de València), Valencia, 46980, Spain.

Araceli Lamelas, ADM Biopolis, University of Valencia Science Park (Parc Científic de la Universitat de València), Valencia, 46980, Spain.

Juan Martinez-Blanch, ADM Biopolis, University of Valencia Science Park (Parc Científic de la Universitat de València), Valencia, 46980, Spain.

Empar Chenoll, ADM Biopolis, University of Valencia Science Park (Parc Científic de la Universitat de València), Valencia, 46980, Spain.

Gary M Davenport, ADM, Decatur, IL, 62526, USA.

Maria R C de Godoy, Division of Nutritional Sciences University of Illinois, Urbana, IL, 61801, USA; Department of Animal Sciences University of Illinois, Urbana, IL, 61801, USA.

Conflict of interest statement

EK, FH, and MRCG have no conflict of interest to declare. SN, AH-V, AL, JM-B, and EC are employees of ADM.

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