Dietary supplementation with fiber, “biotics,” and spray-dried plasma affects apparent total tract macronutrient digestibility and the fecal characteristics, fecal microbiota, and immune function of adult dogs

Abstract

A variety of functional ingredients, including fibers, prebiotics, probiotics, and postbiotics may be added to pet foods to support gastrointestinal and immune health. While many of these ingredients have been tested individually, commercial foods often include blends that also require testing. This study was conducted to evaluate the effects of diets containing blends of fibers, “biotics,” and/or spray-dried plasma on apparent total tract digestibility (ATTD), stool quality, fecal microbiota and metabolites, and immune health outcomes of adult dogs. A total of 12 healthy adult intact English pointer dogs (6 M, 6 F; age = 6.4 ± 2.0 yr; BW = 25.8 ± 2.6 kg) were used in a replicated 3 × 3 Latin square design to test diets formulated to: 1) contain a low concentration of fermentative substances (control diet, CT); 2) be enriched with a fiber–prebiotic–probiotic blend (FPPB); and 3) be enriched with a fiber–prebiotic–probiotic blend + immune-modulating ingredients (iFFPB). In each 28-d period, 22 d of diet adaptation was followed by a 5-d fecal collection phase and 1 d for blood sample collection. All data were analyzed using SAS 9.4, with significance being P < 0.05 and trends being P < 0.10. FPPB and iFPPB diets led to shifts in numerous outcome measures. Dry matter (DM), organic matter, fat, fiber, and energy ATTD were lower (P < 0.01), fecal scores were lower (P < 0.01; firmer stools), and fecal DM% was higher (P < 0.0001) in dogs fed FPPB or iFPPB than those fed CT. Serum triglycerides and cholesterol were lower (P < 0.01) in dogs fed FPPB or iFPPB than those fed CT. Fecal protein catabolites (isobutyrate, isovalerate, indole, and ammonia) and butyrate were lower (P < 0.05), while fecal immunoglobulin A (IgA) was higher (P < 0.01) in dogs fed FPPB and iFPPB than those fed CT. Fecal microbiota populations were affected by diet, with alpha-diversity being lower (P < 0.05) in dogs fed iFPPB and the relative abundance of 20 bacterial genera being altered in dogs fed FPPB or iFPPB compared with CT. The circulating helper T cell:cytotoxic T cell ratio was higher (P < 0.05) in dogs fed iFPPB than those fed CT. Circulating B cells were lower (P < 0.05) in dogs fed FPPB than those fed iFPPB, and lower (P < 0.05) in dogs fed iFPPB than those fed CT. Our results demonstrate that feeding a fiber–prebiotic–probiotic blend may provide many benefits to canine health, including improved stool quality, beneficial shifts to fecal microbiota and metabolite profiles, reduced blood lipids, and increased fecal IgA.

Lay Summary

A variety of functional ingredients—those that provide benefits beyond their nutritional value—may be added to pet foods to support gastrointestinal and immune health. While many of these ingredients have been tested individually, commercial foods often include blends that also require testing. This study was conducted to evaluate the effects of diets containing blends of dietary fibers and other functional ingredients on nutrient digestibility and the stool characteristics and immune health outcomes of adult dogs consuming them. Treatments included a control diet containing low amounts of dietary fiber, a diet containing a fiber–prebiotic–probiotic blend, and a diet containing the fiber–prebiotic–probiotic blend as well as immune-modulating ingredients. The test diets were shown to shift many outcome measures. First, they were shown to reduce nutrient digestibility and decrease fecal scores (more firm stool). Second, test diets reduced blood lipids and beneficially altered fecal metabolite concentrations. Third, test diets increased fecal immunoglobulin A concentrations, suggesting enhanced gut immunity. Lastly, the test diets shifted fecal bacterial populations. Our results demonstrate that feeding a fiber–prebiotic–probiotic blend may provide many benefits to canine health, including improved stool quality, beneficial shifts to fecal bacteria and metabolite profiles, reduced blood lipids, and enhanced gut immunity.

This study demonstrates that feeding a diet containing a fiber–prebiotic–probiotic blend to adult dogs may provide many health benefits, including improved stool quality, beneficial shifts to fecal bacteria and metabolite profiles, reduced blood lipids, and enhanced gut immunity.

Introduction

Dogs are considered to be family members by most pet owners today and addressing their health and longevity has become a priority. The inclusion of functional ingredients such as dietary fibers, prebiotics, probiotics, and postbiotics in commercial pet foods has become a widespread practice. The addition of these ingredients aims to provide functional benefits beyond the basic nutritional needs of the animal and to support gastrointestinal (GI) and immune health. The GI microbiota and metabolite profiles are a common target, with many experiments in recent years demonstrating the impacts of diet on canine fecal microbiota populations (Barko et al., 2018; Mondo et al., 2019; Alessandri et al., 2020; Pilla and Suchodolski, 2020; Wernimont et al., 2020). Bacteria may utilize nondigestible nutrients that reach the colon, converting carbohydrate-based substrates into compounds such as short-chain fatty acids (SCFA) that serve as an important energy source for colonocytes and provide many other benefits to host health (Schmitz and Suchodolski, 2016). Undigested proteins that reach the colon may be fermented by bacteria to yield compounds such as branched-chain fatty acids (BCFA), phenols, indoles, and ammonia that are typically thought to have a negative impact on intestinal health and contribute to fecal odor.

Many of the functional ingredients used to support GI health in pet foods are designed to manipulate the activity and composition of the resident microbiota populations, leading to increased SCFA, modulation of GI immune cell response, pathogen inhibition, or other responses leading to health benefits. Providing health benefits may be done by providing ingredients that serve as substrates for beneficial GI microbiota (i.e., fibers or prebiotics; Gibson et al., 2017) or those that contain live microbes (i.e., probiotics; Hill et al., 2014), a mixture of live microbes and substrates (i.e., synbiotics; Swanson et al., 2020), or a mixture of inanimate microorganisms and/or their components (i.e., postbiotics; Salminen et al., 2021). While much of the focus is on SCFA production, the modulation of the commensal bacteria may also prevent gut colonization of pathogens via other mechanisms, including the diversification of B cells and increasing immunoglobulin A (IgA) production (Kamada et al., 2013; Butler et al., 2016; Tizard and Jones, 2018).

In the past, studies have tested many different types of functional ingredients and described the beneficial health effects in dogs as well as livestock species. Dietary supplementation of fibers and/or prebiotics may aid in glucose control and homeostasis, improve stool quality, increase production of SCFA, lower caloric density of food to aid in weight loss, and improve GI immune function (Banta et al., 1979; Fahey et al., 1992; Massimino et al., 1998; Swanson et al., 2002a). Common sources of dietary fiber in pet foods include beet pulp, cellulose, wheat bran, soybean hulls, Miscanthus grass, pectin, and others (Elliott et al., 2006; Middelbos et al., 2007; Detweiler et al., 2019; Donadelli and Aldrich, 2019). The most common prebiotics in pet foods are fructan-based ingredients that are present as short-chain fructooligosaccharides (scFOS; Swanson et al., 2002a, 2002b), inulin (Alexander et al., 2018), or natural sources such as chicory (Grieshop et al., 2004). Probiotics may contain a variety of bacterial taxa alone or in mixtures, but most tend to be lactic acid-producing bacteria. Probiotics provide health benefits and function via several mechanisms, including increased gut barrier function (Collado et al., 2007), maintenance of epithelial tight junction integrity, enhanced immune response (Pagnini et al., 2010), increased secretory IgA production, prevention and displacement of pathogenic bacterial growth (Lee et al., 2003), and increased production of SCFA (Sakata et al., 2003; Oelschlaeger, 2010; Thomas and Versalovic, 2010). Dietary fibers, prebiotics, and probiotics may impact immune function, but other ingredients have this capability as well. Yeast-based products, including yeast cell wall extracts and yeast fermentation products, have been studied and shown to modulate GI immune cell response (Swanson et al., 2002a), blood cell populations and activity (Grieshop et al., 2004; Lin et al., 2019), and fecal microbiota populations (Lin et al., 2019). Spray-dried animal plasma (SDAP) has been shown to promote immune health in pigs and rodent models (Pérez-Bosque et al., 2006; Peace et al., 2011; Miró et al., 2020). It has potential application to pet health but has only been tested for its impact on diet characteristics in pet foods thus far (Rodríguez et al., 2016).

Although studies have demonstrated efficacy and identified effective dosages for many of the ingredients listed above, most have been tested individually and prior to the advent of high-throughput molecular tools to measure GI microbiota and canine-specific assays to measure immune responses. Moreover, because many commercial pet foods contain blends of these functional ingredients, research is necessary to determine whether they provide the same benefits. Therefore, the objective of this study was to evaluate the effects of diets containing blends of fibers, “biotics,” and/or spray-dried plasma on apparent total tract digestibility (ATTD), stool quality, fecal microbiota and metabolites, and immune health outcomes of adult dogs. The control diet was formulated to be a premium diet that provided a low level of substrate for microbial fermentation. The second diet contained a blend of fibrous ingredients (i.e., oat groats, beet pulp, and pea fiber), a probiotic, and a prebiotic (i.e., inulin), which was expected to impact GI and immune health primarily through fermentation and SCFA production. The third diet contained the fiber–probiotic–prebiotic blend plus ingredients thought to support immune function (i.e., SDAP and yeast fermentation product) via different mechanisms. We hypothesized that the dietary blends would positively shift fecal microbiota populations (greater Bifidobacterium, Lactobacillus, and Faecalibacterium; lower Proteobacteria, Clostridium, and Fusobacterium), improve fecal metabolite concentrations (greater SCFA and lower protein catabolites), and enhance immune responses of dogs without negatively impacting stool quality.

Materials and Methods

The animal study was conducted at Kennelwood Inc. (Champaign, IL), with all procedures being approved by the Kennelwood, Inc. IACUC prior to experimentation.

Experimental design, animals, and diets

A total of 12 healthy adult intact English pointer dogs (6 males, 6 females; age = 6.4 ± 2.0 yr; BW = 25.8 ± 2.6 kg) were used in a replicated 3 × 3 Latin square study design. Each 28-d experimental period consisted of a diet adaptation phase (days 1 to 22), fecal collection phase (days 23 to 27), and a blood collection phase (day 28). Dogs were housed individually in pens in a room on a 10 h light:14 h dark cycle, with gates providing access to individual outdoor pens.

Dogs were fed dry, extruded diets formulated to meet all Association of American Feed Control Officials (AAFCO, 2018) nutrient recommendations for adult dogs at maintenance. Dietary treatments consisted of diets formulated to: 1) contain a low concentration of fermentative substances (control diet; CT); 2) enriched with a fiber–prebiotic–probiotic blend (FPPB); and 3) enriched with a fiber–prebiotic–probiotic blend + immune-modulating ingredients (iFFPB). Dogs were fed once a day (access from 10:00 a.m. to 12:00 p.m.) to maintain BW throughout the study, with ad libitum access to water. Weekly BW and body condition score (BCS) measurements were recorded using a nine-point scale (Laflamme, 1997). Food intake was adjusted on a weekly basis as needed to maintain BW. Ingredients and analyzed chemical composition of the experimental diets are listed in Table 1.

Table 1.

Ingredient and analyzed chemical composition of experimental diets fed to dogs

	Dietary treatments1
	CT	FPPB	iFPPB
Ingredient	%, as-is basis
Chicken meal, regular ash	20.00	–	–
Deboned chicken slurry	20.00	20.00	20.55
Brewer’s rice	20.00	–	–
Potato flour	20.00	–	–
Oat groats	–	17.40	14.60
Dried peas	–	13.00	13.00
Menhaden fish meal	–	11.40	11.40
Chicken meal, low ash	–	11.40	11.40
Chicken fat with Naturox2	9.54	–	–
Chicken fat	–	5.96	5.96
Dried chicken	5.00	5.00	5.00
Dried plain beet pulp	–	4.00	4.00
Flaxseed meal	–	3.00	3.00
Pea fiber	–	3.00	3.00
Liquid digest3	3.00	3.00	3.00
Spray-dried plasma4	–	–	2.00
Dried dog digest with Lacto-sacc probiotic5	–	1.20	1.20
Digest6	1.00	–	–
Dried brewer’s yeast	0.92	0.92	0.92
Yeast fermentation product7	–	–	0.25
Potassium chloride	0.19	0.19	0.19
Inulin	–	0.10	0.10
Chelated mineral premix	0.10	0.10	0.10
Choline chloride, 70% dry	0.10	0.10	0.10
Vitamin premix	0.10	0.10	0.10
Dried Naturox	0.05	0.05	0.05
Vitamin E, 50 % dry	–	0.03	0.03
Turmeric powder	–	0.03	0.03
Ascorbic acid8	–	0.03	0.03
Chemical composition	–	–	–
Dry matter (DM), %	93.38	93.24	93.22
	%, dry matter
Crude protein	26.7	33.0	31.8
Acid-hydrolyzed fat	15.7	15.4	14.8
Ash	7.6	9.3	10.2
Total dietary fiber	9.5	17.5	16.5
Insoluble fiber	6.8	12.8	11.1
Soluble fiber	2.7	4.7	5.5
ME9, kcal/g	3.7	3.3	3.3

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

Naturox (Kemin Industries, Des Moines, IA).

AFB C15065 (AFB International, St. Charles, MO).

Spray-dried plasma (APC Inc, Ankeny, IA).

AFB C28131 (AFB International, St. Charles, MO).

AFB C26017 (AFB International, St. Charles, MO).

Diamond V (Diamond V, Cedar Rapids, IA).

Stay C-35 (DSM Nutritional Products Inc., Parsippany, NJ).

Metabolizable energy (ME) = 3.5 kcal/g × crude protein (%) + 8.5 kcal/g × acid-hydrolyzed fat (%) + 3.5 kcal/g × nitrogen free extract (%); nitrogen free extract (%) = 100% − (crude protein % + acid-hydrolyzed fat % + total dietary fiber % + ash %).

Sample collection

Following a 22-d diet adaptation phase, fecal samples were collected for 5 d. Feces were collected from the pen floor, weighed, and scored using a 5-point scale scoring system: 1 = very 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 the shape of the container; 5 = watery, liquid that can be poured. On the first day of fecal collections, a fresh fecal sample was collected (within 15 min of defecation) from each dog and processed. Fecal pH was measured immediately upon fresh sample collection using a pH meter (Denver Instrument, Bohemia, NY) with an attached electrode probe (Beckman Instruments Inc., Fullerton, CA). The remaining samples were aliquoted accordingly for determination of fecal dry matter (DM), metabolite concentrations (SCFA, BCFA, ammonia, phenol, and indole), microbiota populations, IgA, and calprotectin.

Fecal samples were placed on aluminum pans in duplicate and dried at 105 °C for 2 d for DM determination. For SCFA and BCFA analysis, samples were mixed with 2N hydrochloric acid in a 1:1 ratio and stored at −20 °C until further analysis. Fecal samples were aliquoted in duplicate and stored at −20 °C until phenol and indole analysis. For microbial analyses, fecal samples were aliquoted into four cryovials and stored at −80 °C until analyses. Following fresh fecal sampling, all fecal samples were weighed, scored, and stored at −20 °C until chemical analysis and ATTD calculations.

On the last day of each period (day 28), blood samples were collected from each dog via cephalic venipuncture. Blood was drawn and immediately transferred to appropriate tubes: 1) heparin-treated plasma tubes (#366480, Becton Dickinson, Franklin Lakes, NJ) for peripheral blood mononuclear cell (PBMC) collection; 2) K₂ EDTA microtainer tubes (#365974, Becton Dickinson) for complete blood count; and 3) serum tubes with clot activator and gel for serum separation (#368016, Becton Dickinson) for serum chemistry and oxidative stress marker (superoxide dismutase [SOD] and malondialdehyde [MDA]) analyses.

Dietary chemical composition analysis and ATTD calculations

Diets were subsampled from several bags for chemical composition analysis. Total fecal samples collected from days 23 to 27 were composited and dried at 57 °C for 7 d. Fecal and diet samples were ground using a Wiley Mill (model 4, Thomas Scientific, Swedesboro, NJ). DM and organic matter (OM) concentrations were measured according to the Association of Official Analytical Chemists (AOAC, 2006; method 934.01 for DM and method 942.05 for OM). Crude protein content was calculated from total nitrogen values measured by LECO (TruMac N, Leco Corp., St. Joseph, MI; AOAC; method 922.15, 2006). Acid-hydrolyzed fat concentration was determined using methods according to the American Association of Cereal Chemists (AACC, 1983; method 30-14) and Budde (1952). Total dietary fiber (TDF) was determined for diet and fecal samples according to Prosky et al. (1992). Gross energy was measured using a bomb calorimeter (Model 6200, Parr Instruments, Moline, IL). ATTD of nutrients and energy was calculated using the following equation:

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{\%}\mathrm{D}\mathrm{i}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}=\frac{\mathrm{N}\mathrm{u}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}(\mathrm{g}/\mathrm{d})-\mathrm{F}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}(\mathrm{g}/\mathrm{d})}{\mathrm{N}\mathrm{u}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}(\mathrm{g}/\mathrm{d})}\times 100\mathrm{\%}}\end{array}}$$

Fecal metabolite analysis

Fecal SCFA (acetate, propionate, and butyrate) and BCFA (valerate, isovalerate, and isobutyrate) concentrations were determined according to Erwin et al. (1961) using a gas chromatography (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/min. Oven, detector, and injector temperatures were 125 °C, 175 °C, and 180 °C, respectively. Fecal concentrations of phenols and indoles were evaluated using gas chromatography according to Flickinger et al. (2003) and fecal ammonia concentration was measured according to the method of Chaney and Marbach (1962).

Fecal microbiota analysis

Total fecal DNA was extracted using DNeasy PowerLyzer PowerSoil kits (Qiagen Inc., Carlsbad, CA). The concentration of extracted DNA was quantified using a Qubit 3.0 Fluorometer (Life Technologies, Carlsbad, CA). The quality of extracted DNA was assessed by electrophoresis using agarose gels (E-Gel EX Gel 1%; Invitrogen, Carlsbad, CA). 16S rRNA gene amplicons were generated using a Fluidigm Access Array (Fluidigm Corporation, South San Francisco, CA) in combination with Roche High Fidelity Fast Start Kit (Roche Diagnostics, Indianapolis, IN). The primers 515F (5ʹ-GTGCCAGCMGCCGCGGTAA-3ʹ) and 806R (5ʹ-GGACTACHVGGGTWTCTAAT-3ʹ) that target a 252 bp-fragment of the V4 region of the 16S rRNA gene were used for amplification (primers synthesized by IDT Corp., Coralville, IA) (Caporaso et al., 2010). CS1 forward tag and CS2 reverse tag were added according to the Fluidigm protocol. The quality of the amplicons was assessed using a Fragment Analyzer (Advanced Analytics, Ames, IA) to confirm amplicon regions and sizes. A DNA pool was generated by combining equimolar amounts of the amplicons from each sample. The pooled samples were then size selected on a 2% agarose E-gel (Life Technologies) and extracted using a Qiagen gel purification kit (Qiagen Inc.). Cleaned size-selected pooled products were run on an Agilent Bioanalyzer to confirm appropriate profile and average size. Illumina sequencing was performed on a MiSeq using v3 reagents (Illumina Inc., San Diego, CA) at the W. M. Keck Center for Biotechnology at the University of Illinois.

Bioinformatics

Sequence data were processed using Quantitative Insights Into Microbial Ecology 2 (QIIME 2.2020.8; Bolyen et al., 2019). Sequences with a quality value ≥20 were demultiplexed. Sequences were denoised and assembled into amplicon sequence variants using DADA2 (Callahan et al., 2016). Taxonomy was assigned utilizing SILVA_138 reference database with a 99% similarity threshold (Quast et al., 2013). A total of 5,511,994 16S rRNA-based amplicon sequences were obtained, with an average of 83,515 reads per sample. Alpha- and beta-diversity measurements were presented using an even sampling depth of 44,111 sequences per sample. Beta-diversity was calculated using weighted and unweighted UniFrac (Lozupone and Knight, 2005) distance measures and represented using principal coordinates analysis (PCoA) plots.

Fecal IgA and calprotectin analysis

Fecal protein was extracted according to Vilson et al. (2016). Fecal samples (500 mg) were vortexed with 1.5 mL of extraction buffer containing 50 mM-EDTA (ThermoFisher, Waltham, MA) and 100 μg/L soybean trypsin inhibitor (Sigma, St. Louis, MO) in PBS/L percent bovine serum albumin (Tocris Bioscience, Bristol, UK). Phenylmethanesulphonyl fluoride (12.5 μL, 350 mg/L; Sigma) was added to each tube and centrifuged for 10 min. Supernatants were collected for measurements of fecal IgA (E-40A; Immunology Consultants Laboratory, Portland, OR) and calprotectin (MBS030023; MyBiosource Inc., San Diego, CA) using commercial enzyme-linked immunosorbent assay (ELISA) kits.

Serum chemistry, serum oxidative stress marker, and complete blood count analysis

Serum was isolated by centrifugation at 1,300 × g at 4 °C for 10 min (Beckman CS-6R centrifuge; Beckman Coulter Inc., Brea, CA). Blood and serum samples were sent to the University of Illinois Veterinary Medicine Diagnostic Laboratory for a complete blood count and serum chemistry profile using a Hitachi 911 clinical chemistry analyzer (Roche Diagnostics). Serum concentrations of lipopolysaccharide-binding protein (LBP; MBS093112; MyBioSource Inc.), SOD (MBS004921; MyBioSource Inc.), and MDA (MBS2700234; MyBioSource Inc.) were measured using commercial ELISA kits.

Immune cell populations

Blood was layered over Ficoll Histopaque (Sigma) in a 1:1 volume ratio and centrifuged at 300 × g at 4 °C for 30 min to isolate PBMC from the blood. The percentage of antigen-presenting cells (APC), natural killer (NK) cells, and T cells were evaluated using a BD LSR flow cytometer (Becton Dickinson). To determine T cell populations, PBMC were divided into two tubes (1 × 10⁶ cells per tube) with one tube as control and another stimulated with cell stimulation cocktail (phorbol 12-myristate 13-acetate, ionomycin, brefeldin A and monensin; eBioscience, San Diego, CA). Both tubes were incubated for 4 h at 37 °C in 5% CO₂. Following incubation, cells were labeled using surface marker antibodies: anti-CD3-fluorescein isothiocyanate (FITC; MCA1774F; BioRad, Hercules, CA), anti-CD4-allophycocyanin (APC; MCA1038APC; BioRad, Hercules, CA), and anti-CD8-pacific blue (MCA1039PB; BioRad, Hercules, CA). Once labeled, cells were fixed and permeabilized using a buffer solution (00-8222-49; eBioscience, San Diego, CA). Intracellular marker staining was then performed with anti-IFN-γ-phycoerythrin (PE; MCA1783PE; BioRad, Hercules, CA). For NK cell populations, one aliquot of cells (1 × 10⁶ cells per tube) was labeled with an anti-CD5-APC antibody (MCA1037APC; BioRad, Hercules, CA). For APC, the cells of interest include B cells and monocytes presenting major histocompatibility complex class II (MHC-II; MCA1044F; BioRad, Hercules, CA) on the cell surface. One aliquot of PBMC (1 × 10⁶ cells per tube) was stained with anti-CD14-pacific blue (MCA1568PB; BioRad, Hercules, CA), anti-CD21-PE (MCA1781PE; BioRad, Hercules, CA), and anti-MHC-II-FITC antibodies. Flow cytometry data were analyzed using FCS Express 6 Flow Cytometry Software (De Novo Software, Glendale, CA). Gating strategies used to determine immune cell populations are shown in Supplementary Figure S1. For NK cells, the population was determined according to Huang et al. (2008).

Immune cell responsiveness to toll-like receptor (TLR) agonists

In a 96-well plate, PBMC (1 × 10⁶ cells per tube) were seeded. Agonists of TLR2 (100 µg/mL zymosan; tlrl-zyn; Invivogen, San Diego, CA), TLR3 (50 µg/mL polyinosinic–polycytidylic acid sodium salt, poly[I:C]; P9582-5MG; Sigma), TLR4 (100 ng/mL LPS; L3024; Sigma), and TLR7/8 (5 µg/mL resiquimod; tlrl-r848; Invivogen, San Diego, CA) were added into assigned wells separately in triplicate. Following 24 h of incubation at 37 °C in 5% CO₂, supernatants were collected for measurement of TNF-α concentrations using a commercial ELISA kit (CATA00; R&D systems, Minneapolis, MN).

Statistical analyses

All data were analyzed using a Mixed Models procedure of SAS (version 9.4; SAS Institute, Cary, NC) with treatment as a fixed effect and dog as a random effect. Data were reported as means ± pooled standard error of the mean (SEM) and statistical significance was set as P < 0.05 and a trend set as P < 0.10.

Results

Food intake, body weight, BCS, and apparent total tract energy and macronutrient digestibility

Of the 12 dogs that were initially enrolled in the study, only 11 were considered for statistical analysis. One dog was removed from the experiment due to an external injury suffered that required antibiotic treatment so it was excluded from the analysis. On average, daily food and caloric intake was 459.6 g/d and 1,580 kcal/d, respectively, across all treatment groups. Food intake and caloric intake were lower (P < 0.05) in dogs fed FPPB and iFPPB compared with those fed CT (Table 2). ATTD of DM, OM, fat, TDF, and energy were lower (P < 0.01) in dogs fed FPPB or iFPPB than those fed CT. ATTD of CP was not affected by treatment.

Table 2.

BW, BCS, daily food and energy intake, and apparent total tract macronutrient and energy digestibility of dogs fed experimental diets

	Dietary treatments1			SEM	P-value
	CT	FPPB	iFPPB		Treatment	CT vs. FPPB + iFPPB
Body weight, kg	25.5b	24.9a	25.5b	0.958	0.05	0.20
Body condition score2	5.5	5.4	5.5	0.136	0.44	0.52
Daily intake, g/day	475.2	454.3	449.2	25.915	0.11	0.04
Energy intake, kcal/day	1758.37b	1499.53a	1482.44a	88.216	<0.0001	<0.0001
Digestibility, %
Dry matter	85.2b	74.9a	74.9a	1.204	<0.0001	<0.0001
Organic matter	89.5b	81.3a	80.8a	0.945	<0.0001	<0.0001
Crude protein	81.2	80.5	81.5	1.504	0.85	0.92
Acid-hydrolyzed fat	97.7b	94.6a	95.0a	0.460	<0.0001	<0.0001
Total dietary fiber	54.0b	43.8a	39.7a	3.090	<0.01	<0.01
Energy	89.1b	82.0a	81.9a	0.890	<0.0001	<0.0001

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

Nine-point BCS system used (Laflamme, 1997).

Mean values within the same row with unlike superscript letters differ significantly (P < 0.05).

Fecal characteristics and fecal fermentative end-products

Fecal pH was not affected by dietary treatments (Table 3). However, dogs fed FPPB or iFPPB had lower (P < 0.0001) fecal scores (firmer stools) and a higher (P < 0.0001) fecal DM% than those fed CT. Many of the fecal metabolites were altered by dietary treatment. Fecal butyrate, isobutyrate, isovalerate, total BCFA, indole, and ammonia concentrations were lower (P < 0.01) in dogs fed FPPB or iFPPB than those fed CT. Fecal 7-methylindole and calprotectin concentrations were higher (P < 0.05) in dogs fed FPPB than those fed CT. Fecal IgA concentrations were higher (P < 0.01) in dogs fed FPPB or iFPPB than those fed CT.

Table 3.

Fecal characteristics and fecal metabolite concentrations of dogs fed experimental diets

	Dietary treatments1			SEM	P-value
	CT	FPPB	iFPPB		Treatment	CT vs. FPPB and iFPPB
Fecal characteristics
pH	6.41	6.33	6.08	0.127	0.13	0.15
Fecal score2	4.14b	3.05a	3.14a	0.133	<0.0001	<0.0001
Fecal DM (%)	27.32a	31.18b	30.62b	0.601	0.001	<0.0001
Fecal metabolites	µmol/g DM
Total SCFA3	594.16	606.91	621.29	22.676	0.68	0.46
Acetate	347.35	353.90	375.61	14.237	0.33	0.31
Propionate	172.35	191.75	186.03	9.103	0.29	0.14
Butyrate	74.46b	61.26a	59.65a	3.891	0.02	0.01
Total BCFA4	34.93b	20.51a	20.12a	2.940	<0.01	<0.01
Isobutyrate	10.98b	6.87a	6.78a	0.770	<0.0001	<0.0001
Isovalerate	17.14b	9.94a	10.14a	1.133	<0.0001	<0.0001
Valerate	6.81	3.70	3.21	1.986	0.38	0.17
Total P/I5	4.84	5.38	3.78	0.647	0.22	0.75
Phenol	0.47	0.28	0.20	0.175	0.55	0.29
Indole	2.64b	1.16a	0.79a	0.294	<0.01	<0.0001
7-Methylindole	1.72a	3.94b	2.79ab	0.432	<0.01	<0.01
Ammonia	181.81b	143.80a	140.65a	10.728	<0.01	<0.01
Fecal IgA, mg/g	2.53a	4.34b	4.59b	0.622	0.01	<0.01
Fecal calprotectin, μg/g	0.12a	0.15b	0.14ab	0.010	0.05	0.02

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

Fecal score: 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.

SCFA = acetate + propionate + butyrate.

BCFA = valerate + isovalerate + isobutyrate.

P/I = phenols + indoles.

Mean values within the same row with unlike superscript letters differ significantly (P < 0.05).

Fecal microbiota

Alpha-diversity, which represents species richness and evenness of the microbial community within a sample, was found to be lower (P < 0.05) in dogs fed iFPPB than those fed CT (Figure 1A). Beta-diversity, which represents species richness among samples, did not differ across dietary treatments when based on both weighted and unweighted UniFrac distance measures represented as PCoA plots (Figure 1B and C).

Figure 1.

Alpha- and beta-diversity measures of fecal samples collected from dogs fed the control diet (CT), a diet containing a fiber-prebiotic-probiotic blend (FPPB), and a diet containing a fiber-prebiotic-probiotic blend + immune-modulating ingredients (iFPPB). Alpha-diversity, represented by the Shannon diversity index, suggested that species richness was higher (P < 0.05) in dogs fed CT than those fed iFPPB (A). PCoA plots for unweighted (B) and weighted (C) UniFrac distances of fecal microbial communities were not altered by dietary treatments.

Although alpha- and beta-diversity indexes were not greatly affected by diet, 20 bacterial genera were different among treatments. The predominant bacterial phyla present in all dogs of this study were Firmicutes (57.6% to 59.8%), Fusobacteria (17.5% to 18.6%), Bacteroidetes (15.9% to 17.0%), Proteobacteria (4.9% to 5.9%), and Actinobacteria (0.9% to 1.9%) (Table 4). At the phyla level, the relative abundance of Actinobacteria was lower (P < 0.01) in dogs fed iFPPB than those fed CT. The other bacterial phyla were not affected by diet. At the genus level, the relative abundances of Catenibacterium, Streptococcus, undefined Lachnospiraceae, Ruminococcus, and Megamonas were higher (P < 0.05), while the relative abundances of Collinsella and Alloprevotella were lower (P < 0.05) in dogs fed FPPB or iFPPB than those fed CT. The relative abundances of Peptoclostridium, Peptococcus, and Sutterella were lower (P < 0.05) in dogs fed iFPPB than those fed CT, while the relative abundance of Lactobacillus was lower (P < 0.05) in dogs fed FPPB than those fed CT. The relative abundance of undefined Prevotellaceae was lower (P < 0.05) in dogs fed FPPB than those fed CT or iFPPB. The relative abundances of Turicibacter and uncultured Lachnospiraceae tended to be lower (P < 0.10), while the relative abundance of Faecalibacterium tended to be higher (P < 0.10) in dogs fed FPPB or iFPPB than those fed CT. The relative abundance of Prevotella tended to be higher (P < 0.10), while the relative abundances of Blautia, Megasphaera, and Parasutterella tended to be lower (P < 0.10) in dogs fed FPPB or iFPPB than dogs fed CT.

Table 4.

Predominant bacterial phyla and genera (expressed as percent of total sequences) in feces of dogs fed experimental diets

Phylum	Genus2	Dietary treatments1			SEM	P-value
		CT	FPPB	iFPPB		Treatment	CT vs. FPPB and iFPPB
Actinobacteria		1.96b	1.49ab	0.87a	0.216	0.01	0.01
	Collinsella	1.56b	0.93a	0.60a	0.171	<0.01	<0.01
Bacteroidetes		16.95	15.98	17.01	1.575	0.82	0.78
	Bacteroides	8.78	7.70	9.20	0.986	0.49	0.76
	Alloprevotella	2.25b	1.42a	1.14a	0.299	<0.01	<0.01
	Prevotella	3.52	6.01	5.33	1.006	0.15	0.06
	Undefined Prevotellaceae	1.31b	0.66a	1.19b	0.211	0.03	0.07
Firmicutes		57.60	59.79	58.55	2.942	0.84	0.63
	Catenibacterium	0.90	2.61	1.73	0.527	0.06	0.04
	Allobaculum	1.71	1.59	1.38	0.254	0.46	0.35
	Holdemanella	1.58	1.65	1.53	0.294	0.95	0.98
	Turicibacter	0.96	0.29	0.26	0.240	0.08	0.03
	Lactobacillus	16.64b	7.79a	10.73ab	2.426	0.04	0.02
	Streptococcus	2.45a	11.30b	10.66b	1.711	<0.01	<0.01
	Undefined Lachnospiraceae	1.85a	2.42b	2.79b	0.237	0.01	<0.01
	Blautia	4.29	3.63	3.22	0.363	0.12	0.06
	Ruminococcus Gnavus	1.21a	1.92b	1.86b	0.219	0.04	0.01
	Ruminococcus Torques	0.66a	1.17b	1.13b	0.130	0.02	0.01
	Uncultured Lachnospiraceae	1.12	0.82	0.70	0.133	0.07	0.03
	Faecalibacterium	3.29	4.02	5.51	0.745	0.07	0.08
	Peptococcus	0.99b	0.68ab	0.59a	0.137	0.05	0.02
	Peptoclostridium	9.08b	7.13ab	5.89a	0.681	0.01	<0.01
	Phascolarctobacterium	0.75	0.94	0.83	0.100	0.31	0.21
	Megamonas	3.10a	5.10b	5.31b	0.795	0.05	0.02
	Megasphaera	1.10	0.44	0.23	0.395	0.21	0.09
Fusobacteria		17.51	17.78	18.59	1.706	0.87	0.72
	Fusobacterium	17.51	17.78	18.59	1.706	0.87	0.72
Proteobacteria		5.90	4.92	4.94	0.576	0.40	0.18
	Anaerobiospirillum	1.29	1.00	1.27	0.276	0.71	0.65
	Parasutterella	0.69	0.37	0.49	0.219	0.12	0.06
	Sutterella	3.43b	2.38ab	2.24a	0.315	0.02	0.01

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

All genera with relative abundance >0.5% of total sequences are presented.

Mean values within the same row with unlike superscript letters differ significantly (P < 0.05).

Blood metabolites, blood cell count, oxidative stress markers, and LBP

Serum metabolites were within reference ranges for all dogs, except for alanine aminotransferase (ALT) and triglycerides (Table 5). Serum ALT exceeded the upper reference range in dogs fed FPPB or iFPPB, but was highly variable and not different among treatments. Serum triglyceride concentrations were slightly below the reference range in dogs fed FPPB or iFPPB and lower (P < 0.05) in dogs fed FPPB or iFPPB than those fed CT. Serum cholesterol concentrations were also lower (P < 0.05) in dogs fed FPPB or iFPPB than those fed CT. Serum albumin was higher (P < 0.05) in dogs fed iFPPB than those fed CT. Serum sodium was higher (P < 0.05) in dogs fed FPPB or iFPPB than those fed CT. Serum albumin:globulin ratio tended to be higher (P < 0.10) in dogs fed FPPB or iFPPB than those fed CT.

Table 5.

Serum chemistry profiles of dogs fed experimental diets

Item	Reference ranges2	Dietary treatments1			SEM	P-value
		CT	FPPB	iFPPB		Treatment	CT vs. FPPB and iFPPB
Creatinine, mg/dL	0.5–1.5	0.80	0.79	0.80	0.037	0.93	0.85
BUN2, mg/dL	6–30	16.18	16.27	16.45	1.065	0.89	0.72
Total protein, g/dL	5.1–7.0	6.28	6.29	6.37	0.132	0.48	0.49
Albumin, g/dL	2.5–3.8	3.05a	3.14ab	3.18b	0.093	0.02	0.01
Globulin, g/dL	2.7–4.4	3.23	3.15	3.19	0.197	0.66	0.44
Albumin:globulin	0.6–1.1	0.98	1.03	1.05	0.068	0.19	0.08
Calcium, mg/dL	7.6–11.4	9.36	9.39	9.43	0.077	0.80	0.59
Phosphorus, mg/dL	2.7–5.2	3.29	3.44	3.39	0.139	0.27	0.13
Sodium, mmol/L	141–152	143.00a	144.00b	144.09b	0.377	0.01	<0.01
Potassium, mmol/L	3.9–5.5	4.39	4.40	4.33	0.056	0.23	0.49
Sodium:potassium	28–36	32.64	32.91	33.27	0.439	0.25	0.17
Chloride, mmol/L	107–118	109.82	110.00	110.09	0.488	0.79	0.53
Glucose, mg/dL	68–126	92.27	91.09	91.09	2.957	0.89	0.63
ALP3, U/L	7–92	46.55	44.82	46.91	4.261	0.68	0.76
CALP3, U/L	0–40	25.64	26.27	28.00	4.082	0.47	0.39
ALT3, U/L	8–65	54.18	87.82	79.64	27.626	0.63	0.36
GGT3, U/L	0–7	4.09	4.91	4.09	0.685	0.46	0.53
Total bilirubin, mg/dL	0.1–0.3	0.22	0.17	0.21	0.020	0.24	0.27
CPK3, U/L	26–310	119.18	123.36	118.82	12.572	0.95	0.89
Cholesterol, mg/dL	129–297	197.45b	150.00a	168.09a	8.540	<0.0001	<0.0001
Triglyceride, mg/dL	32–154	43.45b	29.64a	31.36a	4.160	0.02	<0.01

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

Reference ranges were provided from the University of Illinois Veterinary Diagnostic Laboratory.

BUN, blood urea nitrogen; ALP, total alkaline phosphatase; CALP, corticosteroid isoenzyme of ALP; ALT, alanine aminotransferase; GGT, gamma-glutamyltransferase; CPK, creatinine phosphokinase.

Mean values within the same row with unlike superscript letters differ significantly (P < 0.05).

Blood cell counts were all within the reference ranges (Table 6), but platelet volume was higher (P < 0.05) in dogs fed FPPB or iFPPB than those fed CT. A contrast comparing CT vs. FPPB and iFPPB showed that platelet count tended to be lower (P < 0.10) in dogs fed FPPB or iFPPB than those fed CT. A contrast comparing CT vs. FPPB and iFPPB also showed that serum SOD concentrations tended to be lower (P < 0.10) in dogs fed FPPB or iFPPB than those fed CT (Table 7). Serum LPS-binding protein concentrations were not altered due to treatment (Table 7). Serum MDA concentrations were below the detectable range (15.6 to 500 umol/L) indicated by the ELISA kit and are not reported.

Table 6.

Complete blood count of dogs fed experimental diets

Item	Reference ranges2	Dietary treatments1			SEM	P-value
		CT	FPPB	iFPPB		Treatment	CT vs. FPPB and iFPPB
Red blood cell, 106/µL	5.5–8.5	7.28	7.31	7.26	0.182	0.95	0.94
Hemoglobin, g/dL	12.0–18.0	16.50	16.60	16.53	0.434	0.96	0.84
Hematocrit, %	35.0–52.0	48.26	48.45	48.19	1.105	0.95	0.94
Mean cell volume, fl	58.0–76.0	66.32	66.32	66.42	0.545	0.96	0.89
White blood cell, 103/µL	6.00–17.00	8.07	8.58	8.60	0.588	0.48	0.23
Neutrophil, 103/µL	NA	64.98	73.27	70.97	3.607	0.44	0.41
Lymphocyte, 103/µL	NA	16.69	17.87	20.48	1.672	0.16	0.15
Monocyte, 103/µL	NA	6.15	5.28	4.58	0.860	0.45	0.26
Eosinophil, 103/µL	NA	3.82	3.52	3.80	0.659	0.91	0.82
MCH3, pg	20.0–25.0	21.62	22.73	22.80	0.707	0.35	0.15
MCHC4, g/dL	33.0–38.6	34.18	34.24	34.26	0.212	0.93	0.72
Platelets, 103/µL	200–700	351.68	302.55	307.53	32.907	0.12	0.04
Mean platelet volume, fl	NA	11.34a	11.99b	12.23b	0.542	0.01	<0.01

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

Reference ranges were provided by the University of Illinois Veterinary Diagnostic Laboratory.

MCH: mean corpuscular hemoglobin.

MCHC: mean corpuscular hemoglobin concentration.

Mean values within the same row with unlike superscript letters differ significantly (P < 0.05).

Table 7.

Serum superoxide dismutase and LBP concentrations of dogs fed experimental diets

	Dietary treatments1			SEM	P-value
	CT	FPPB	iFPPB		Treatment	CT vs. FPPB and iFPPB
Superoxide dismutase, U/mL	323.82	309.19	313.08	11.854	0.14	0.06
LPS binding protein, μmol/L	1.89	1.9373	1.9036	0.120	0.81	0.64

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

Immune cell populations and responsiveness to TLR agonists

The helper T cell:cytotoxic T cell ratio in unstimulated (control) cells was higher (P < 0.05) in dogs fed iFPPB than those fed FPPB or CT (Table 8). Cytotoxic T cells (% of lymphocytes) in unstimulated (control) cells tended to be higher (P < 0.10) in dogs fed FPPB than those fed CT or iFPPB. All other T cell populations were unaffected by dietary treatment. NK cell populations were not altered by dietary treatment (Table 9). Of the APC populations measured, B cells (% of lymphocytes) were lower (P < 0.01) in dogs fed FPPB than those fed iFPPB. Dogs fed iFPPB also had lower (P < 0.01) B cells (% of lymphocytes) than dogs fed CT. Monocyte counts were unaffected by dietary treatment. Cell responsiveness to TLR agonists was unaffected by dietary treatment (Table 10).

Table 8.

T-cell populations of dogs fed experimental diets

	Dietary treatments1			SEM	P-value
	CT	FPPB	iFPPB		Treatment	CT vs. FPPB and iFPPB
Control
Lymphocyte, % of PBMC2	12.43	24.67	19.39	5.552	0.34	0.19
T cell, % of lymphocyte	59.83	60.27	63.23	7.387	0.94	0.84
Helper T cell, % of lymphocyte	25.56	30.35	33.62	3.852	0.28	0.16
Cytotoxic T cell, % of lymphocyte	26.56	32.67	23.89	3.777	0.09	0.60
Helper:cytotoxic T-cell ratio	1.27a	1.29a	1.75b	0.408	0.03	0.13
IFN-γ secreting T cell, % of lymphocyte	0.01	0.01	0.02	0.007	0.22	0.33
IFN-γ secreting helper cell, % of lymphocyte	0.01	0.01	0.01	0.004	0.99	0.96
IFN-γ secreting cytotoxic cell, % of lymphocyte	0.00	0.00	0.01	0.003	0.27	0.43
Stimulation3
Lymphocyte, % of PBMC2	46.75	49.55	41.08	4.555	0.41	0.80
T cell, % of lymphocyte	60.85	61.35	59.88	4.137	0.97	0.96
Helper T cell, % of lymphocyte	36.84	38.87	39.23	2.556	0.73	0.45
Cytotoxic T cell, % of lymphocyte	29.18	29.86	29.38	3.336	0.93	0.78
Helper:cytotoxic T-cell ratio	1.68	1.68	1.49	0.344	0.65	0.65
IFN-γ secreting T cell, % of lymphocyte	6.49	7.40	4.61	1.328	0.31	0.77
IFN-γ secreting helper cell, % of lymphocyte	2.97	4.26	2.73	0.756	0.33	0.58
IFN-γ secreting cytotoxic cell, % of lymphocyte	2.89	2.62	1.66	0.526	0.15	0.21

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

PBMC, peripheral blood mononuclear cells.

Cells were stimulated with cell stimulation cocktail (phorbol 12-myristate 13-acetate, ionomycin, brefeldin A, and monensin) for 4 h.

Mean values within the same row with unlike superscript letters differ significantly (P < 0.05).

Table 9.

NK cell and APC populations of dogs fed experimental diets

	Dietary treatments1			SEM	P-value
	CT	FPPB	iFPPB		Treatment	CT vs. FPPB and iFPPB
Natural killer cell, % of lymphocyte	15.23	13.87	14.91	1.809	0.83	0.68
Antigen-presenting cells
B cell, % of lymphocyte	10.56c	6.72a	8.55b	1.183	<0.01	<0.01
B cell, MHC II+, % of B cell2	98.66	97.33	98.32	0.977	0.61	0.49
Monocyte, % of white blood cell	4.16	5.17	3.93	1.210	0.75	0.80
Monocyte, MHC II+, % of monocyte2	64.15	59.99	58.71	3.821	0.56	0.31

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

B cells or monocytes that present major histocompatibility complex class II (MHC II).

Mean values within the same row with unlike superscript letters differ significantly (P < 0.05).

Table 10.

TNF-α concentrations (pg/mL) in cell culture supernatants of dogs fed experimental diets

	Dietary treatments1			SEM	P-value
	CT	FPPB	iFPPB		Treatment	CT vs. FPPB and iFPPB
Control	225.56	196.45	108.43	70.736	0.63	0.35
Zymosan	813.54	746.53	966.68	178.350	0.15	0.65
Poly (I:C)2	1829.64	1423.21	1204.45	356.930	0.28	0.14
Lipopolysaccharide	875.66	778.81	819.54	198.400	0.82	0.57
R848 (requisimod)	3687.21	3996.15	4622.72	677.640	0.57	0.43

CT, control diet; FPPB, a diet containing a fiber–prebiotic–probiotic blend; iFPPB, a diet containing a fiber–prebiotic–probiotic blend + immune-modulating ingredients.

Poly (I:C), polyinosinic:polycytidylic acid.

Discussion

The growing interest in the health and well-being of pets by pet owners has fueled the growth of the functional ingredient market. Many of these ingredients are added for their beneficial health effects, but also to increase tag appeal by adding structure-function claims on product labels. When it comes to targeting GI health, the inclusion of dietary fibers, prebiotics, probiotics, or postbiotics are common. Those ingredients and others including yeast-based products and SDAP may also support immune health. While functional ingredients are often included and tested on their own, it may be useful to evaluate the effects of these ingredients as a blend because that is commonly how they are used in commercial diets. Therefore, research studies evaluating the physiological effects of these functional ingredient blends may provide useful information beyond what is already known about each individual ingredient.

The objective of the current study was to evaluate the effects of functional blends composed of dietary fibers, prebiotics, probiotics, yeast products, and SDAP on ATTD, stool quality, fecal fermentative end-products, fecal microbiota, and immune indices in healthy adult dogs. So that the functional blends could be tested effectively, the control diet was formulated to be a premium diet that provided a low level of substrate for microbial fermentation. The FPPB diet was formulated to contain a blend of fibrous ingredients (i.e., oat groats, beet pulp, and pea fiber), a probiotic, and a prebiotic (i.e., inulin) at effective dosages. The iFPPB diet was formulated to contain the same fiber–probiotic–prebiotic blend plus ingredients thought to support immune function (i.e., SDAP and yeast fermentation product).

Though no differences were observed in the overall food intake (g/d) across treatments, dogs consuming FPPB or iFPPB diets had a reduced caloric intake. This was likely due to the higher fiber content of these diets, which decreased caloric density and may have provided greater satiety (Jackson et al., 1997). ATTD of DM, OM, fat, and TDF was also reduced by FPPB and iFPPB diets, which was also likely due to the higher fiber inclusion level. Previous research has shown a linear decrease (P < 0.05) in DM, OM, and fat digestibility with increasing dietary beet pulp (0%, 2.5%, 5.0%, 7.5%, 10%, and 12.5% inclusion levels) as a source of fiber (Fahey et al., 1990). In that study, a cubic response for nitrogen digestibility was reported, with intermediate to lower digestibility being present at 7.5%, 10%, and 12.5% beet pulp treatment levels (Fahey et al., 1990). Fat digestion differed among treatments, but all values were above 90% digestibility, a value that is usually observed in commercially extruded diets (Orr, 1965; Ahlstrom and Skrede, 1998). Even though many others have reported reduced protein ATTD with increasing dietary fiber (Weber et al., 2007; Kröger et al., 2017), that response was not observed in the current study. Protein digestion may be impacted by fiber content, but also may be affected by its quality, as has been reported in ileal-cannulated animals (Cramer et al., 2007).

All dogs remained healthy throughout the study and most of the serum chemistry and hematology values were within reference ranges for healthy dogs. Serum ALT and triglycerides were slightly out of the reference ranges, but no signs of clinical abnormality were observed during the study. For ALT, the increase above the reference range was primarily driven by one dog that had elevated ALT throughout the entire study. Because the dog showed no clinical signs of disease, had a good appetite, and did not show abnormal behaviors, it was retained in the study. The increased serum albumin in dogs fed iFPPB may be attributed to the SDAP supplementation. A similar effect was reported recently in dogs fed 0% or 12% spray-dried porcine plasma (Andrade et al., 2019). The decreased serum cholesterol and triglyceride concentrations observed in dogs fed FPPB and iFPPB agree with previous research demonstrating a blood lipid-lowering effect with increased dietary fiber and/or dietary inclusion of β-glucans and scFOS (Diez et al., 1997; Beylot, 2005; Surampudi et al., 2016; Phungviwatnikul et al., 2020).

Stool quality was improved in dogs fed FPPB or iFPPB diets, with fecal scores being closer to the ideal score (2.5 to 3.0 on a 5-point scale) than dogs fed CT that had higher (looser) fecal scores. Many of the fecal fermentative end-products were altered by dietary treatments in the current study too. In contrast to our hypothesis, fecal butyrate concentrations were lower in feces of dogs fed the FPPB and iFPPB diets, while the other SCFA were not altered by treatment. This was an unexpected outcome, as many of the fibers, prebiotics, and yeast products included in the diet often yield higher SCFA concentrations due to the increased fermentation of indigestible carbohydrates in the colon (Silvio et al., 2000; Strompfová et al., 2012; Panasevich et al., 2013). This surprising outcome may be related to the sample type (i.e., feces) analyzed, which must be considered when interpreting fermentative-end product data. Because SCFA are rapidly absorbed by colonocytes and used for energy, fecal concentrations often do not reflect the amount produced in the gut. The drastic decrease in fecal BCFA, isovalerate, isobutyrate, phenol, and ammonia concentrations in dogs fed the FPPB and iFPPB diets were expected. Those metabolites are often decreased in the feces of animals consuming increased dietary nondigestible carbohydrates because they promote saccharolytic vs. proteolytic fermentation by gut microbiota (Jackson and Jewell, 2019).

In the current study, fecal IgA concentrations were observed in the dogs fed FPPB or iFPPB diets compared with those fed CT. This suggests a beneficial immune response to the functional blend, as secretory IgA plays an important role in maintaining gut immune homeostasis. Secretory IgA protects by inhibiting pathogen binding to the intestinal mucosal surface and preventing inappropriate inflammatory responses in the GI tract (Rogier et al., 2014; Zhu et al., 2017). This response is in agreement with several studies that have reported elevated IgA in response to yeast, mannan oligosaccharides, or probiotic treatment in dogs (Swanson et al., 2002a; Middelbos et al., 2007; Delucchi et al., 2014; Lin et al., 2020). Fecal calprotectin concentrations were statistically higher in dogs fed the FPPB diet compared with those fed CT. However, the numerical difference was very small (0.03 ug/g) and the concentrations reported in all three treatments in this study were within the values observed for healthy dogs (Heilmann et al., 2018) so it does not appear to be physiologically relevant.

Many studies have shown that increased intake of dietary fiber, prebiotics, and/or probiotics alter the GI microbiota. In agreement with other studies, the predominant bacterial phyla in this study were Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria (Handl et al., 2011; Deng and Swanson, 2015). In this study, we hypothesized that the functional fiber and biotic blends would positively shift the gut microbiota by increasing Bifidobacterium, Lactobacillus, and Faecalibacterium, while decreasing Proteobacteria, Clostridium, and Fusobacterium. Even though those specific bacterial taxa were not altered, the relative abundance of Catenibacterium, Streptococcus, undefined Lachnospiraceae, Ruminococcus, and Megamonas were higher in dogs fed iFPPB or FPPB diets than those fed CT. Catenibacterium, Lachnospiraceae, Megamonas, and Ruminococcus are core SCFA-producing bacteria that have been shown to be decreased in dogs with inflammatory bowel disease (Hidaka et al., 2008; Beloshapka et al., 2013; Pilla and Suchodolski, 2020; Do et al., 2021; Lee et al., 2021). Unexpectedly, bacterial diversity was lower in dogs fed iFPPB than those fed CT. Although reduced bacterial diversity is often associated with GI disease (Xenoulis et al., 2008; Guard et al., 2015), clinical signs of disease were not observed during this study.

Based on the literature, SDAP and yeast fermentation products were added to the iFPPB diet as immune-modulating agents (Swanson et al., 2002a; Grieshop et al., 2004; Middelbos et al., 2007; Peace et al., 2011; Pawar et al., 2017; Andrade et al., 2019; Lin et al., 2019; Moreto et al., 2020). In the current study, blood WBC and TNF-α responses were not altered, as has been reported in other studies (Middelbos et al., 2007; Peace et al., 2011; Lin et al., 2019). The ratio between T helper cells and cytotoxic T cells, however, was greater in dogs fed the iFPPB diet. Arguably this change may have been driven by the numerical increase observed in the number of T helper cells of the control (unstimulated) lymphocytes. A similar observation was made by Echeverry et al. (2021), where yeast cell wall products upregulated the response of T helper-1 and 2 cells in both unchallenged and LPS-challenged B lymphocytes of chickens. B lymphocyte populations were decreased in dogs fed iFPPB or FPPB diets compared with those fed CT in the current study. Supplementation of 6% SDAP in pigs has been shown to reduce the percentage of B lymphocytes as well as macrophages and T cells in ileocolic lymph nodes (Nofrarías et al., 2006). The opposite effect has been noted in the literature, however, where fructooligosaccharide supplementation increased B lymphocyte populations in the Peyer’s patches of mice (Manhart et al., 2003). Because our current study only measured circulating lymphocytes, future studies should consider measuring cell populations of the GI mucosa, if possible, as it is the direct immune barrier.

In conclusion, dietary inclusion of functional blends composed of dietary fibers, “biotics,” and/or spray-dried plasma have positive impacts on stool quality, fecal metabolites, and immune function in dogs. These functional blends also help to beneficially modulate gut microbiota by increasing the relative abundance of some of the core SCFA-producing bacterial genera, including Catenibacterium, Lachnospiraceae, Megamonas, and Ruminococcus. Although these results may only be attributed to the blend in question—not any single ingredients alone—and were tested in healthy dogs, they appear to support GI and immune health of dogs. While the microbial shifts did not correlate with increased fecal SCFA, that may have been due to the sample type (i.e., feces) that is not a good measure of SCFA. Future studies should explore other tools that may provide a more direct and meaningful measurement of SCFA production and/or explore the functional capacity of the microbiota by utilizing shotgun sequencing. Investigating the effects of these fiber, biotic, and immune-modulating blends on geriatric or obese dog populations that are more susceptible to inflammation and gut microbial dysbiosis may also be of interest in future studies.

Glossary

Abbreviations

ATTD

apparent total tract digestibility

BCFA

branched-chain fatty acids

BCS

body condition score

BW

body weight

CT

control diet

FPPB

fiber–prebiotic–probiotic blend containing diet

GI

gastrointestinal

iFPPB

fiber–prebiotic–probiotic blend + immune-modulating ingredient containing diet

IgA

immunoglobulin A

SCFA

short-chain fatty acids

SDAP

spray-dried animal plasma

Conflict of interest statement

A.S. is employed by PetSmart. All other authors have no conflicts of interest.