Skip to main content
NCBI home page
The Journal of Veterinary Medical Science logoLink to The Journal of Veterinary Medical Science
. 2026 Jul 28;88(10):1471–1478. doi: 10.1292/jvms.24-0494

Comparison of the effects of four commercially available prescription diet regimens on the fecal microbiome in healthy cats

Takumi KOMIYA 1, Kaoru KOYAMA 1, Ran AKIYAMA 1, Hitomi ODA 1, Kureha GOKITA 1, Toshinori SAKO 1, Akihiro MORI 1,*
PMCID: PMC13680700  PMID: 42528237

Abstract

Diet significantly influences the gut microbiota, with variations in macronutrient content (proteins, fats, and carbohydrates) and the presence of non-digestible carbohydrates, such as fiber, playing crucial roles in shaping microbial composition. This study investigated the effects of four commercial prescription diets (low-carbohydrate, weight-loss, high soluble fiber, and renal) on the fecal microbiome of five healthy domestic cats using a 4 × 4 Latin square design. Fecal samples were analyzed using Illumina sequencing of the bacterial 16S rRNA gene to assess bacterial community structure and diversity. Results showed that cats fed the renal diet (low protein, high carbohydrate, high fat) were associated with higher relative abundances of the class Actinobacteria, particularly Bifidobacterium. The low-carbohydrate diet was associated with higher relative abundances of Adlercreutzia, Collinsella, and Slackia. The high-soluble fiber diet was associated with a higher relative proportion of Streptococcaceae. In contrast, the renal diet was associated with lower relative abundances of Blautia and Erysipelotrichales. No significant differences were observed in alpha-diversity indices among diets, whereas beta-diversity analysis showed significant differences in microbial community composition among dietary groups. These findings suggest that dietary composition may influence the fecal microbiome in healthy cats. However, given the exploratory design and compositional nature of the sequencing data, these findings should be interpreted cautiously and considered hypothesis-generating.

Keywords: feces, feline, prescription diet

INTRODUCTION

Diet is a critical factor influencing the gut microbiota. Specifically, the quantity and quality of the three major nutrients (proteins, fats, and carbohydrates) have been shown to significantly impact the gut microbiota in various studies involving humans, dogs, and cats [5, 18, 25, 27, 35, 44]. Non-digestible carbohydrates, such as fiber, are fermented by colonic bacteria, which alters the composition of the gut microbiota and leads to the production of short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate. These SCFAs are vital for maintaining the host’s gut health [31, 41, 45].

In our previous study, we fed four types of therapeutic prescription diets to healthy dogs and revealed diverse changes in the gut microbiota depending on the diet, thereby elucidating the effects of therapeutic diets on the gut microbiota [26]. Commercial prescription diets are widely used to manage various medical conditions in pet cats, with their composition (including carbohydrate, protein, fat, vitamin, and mineral content) tailored to address specific conditions. However, despite their prevalence, the effects of these diets on the intestinal microbiota of cats remain poorly understood. This study aims to assess the impact of four distinct commercial prescription diets, each characterized by unique nutritional profiles, on the fecal microbiomes of healthy cats.

MATERIALS AND METHODS

Animals

Five healthy domestic cats aged 1–3 years were maintained in our laboratory for this study. The group included one castrated male, two intact males, and two females, with body weights ranging from 2.9–5.8 kg. All cats had a body condition score (BCS) of 3 on a five-point scale (1: thin, 2: lean, 3: optimal, 4: overweight, 5: obese). Prior to the experiment, all cats were fed a commercial diet (WU or HF, Blue Buffalo Japan, K.K., Tokyo, Japan) twice daily (8 am and 6 pm) with caloric intake set at 1.0–1.4 × RER (BW0.75 × 70) to maintain ideal body weight, where RER is the resting energy requirement and BW is body weight. Although baseline microbiota may have been influenced by the prior diet, all cats were maintained under identical feeding conditions before experimental allocation. This potential baseline influence is acknowledged as a limitation. The coefficient (1.0–1.4) was adjusted according to individual metabolic variation and activity levels to maintain stable body weight throughout the study period. Complete blood counts and comprehensive serum biochemistry analyses were performed, including glucose, total protein, albumin, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, blood urea nitrogen, creatinine, lipase activity, triglycerides, total cholesterol, and electrolytes (Na, K, Cl). Urinalysis was also conducted in all cats. All laboratory values were within normal reference ranges. In addition, fecal examinations and imaging diagnostics were performed to exclude underlying diseases. Based on these comprehensive assessments, all cats were confirmed to be clinically healthy prior to enrollment. The study protocol was approved by the Animal Research Committee of Nippon Veterinary and Life Science University (approval number: 30S-27). The care and use of experimental animals complied with local animal welfare laws, guidelines, and policies.

Diets

The prescription diets compared in this study were as follows: low-carbohydrate diet (diabetic; high protein, medium fat, low carbohydrate, and medium fiber) weight-loss diet (satiety-support; high protein, low fat, low carbohydrate, and high fiber), high soluble fiber diet (Fibre response; medium protein, medium fat, medium carbohydrate, and medium fiber), renal diet (Renal; low protein, high fat, high carbohydrate, and medium fiber). All diets were from Royal Canin, Japon. Each diet has a particular aim suggested by the manufacturer. The chemical composition of each diet is summarized in Table 1.

Table 1. The chemical composition of prescription diets.

Units Low-carbohydrate diet Weight-loss diet High soluble fiber diet Renal diet
Protein % 46 34 31 23
Fat % 12 9 15 17
Carbohydrate % 26.7 28.8 37.7 44.1
Fiber % 11.5 23.6 11.7 10.7
Ash % 6.2 8.8 7.9 5.7
Water % 5.5 5.5 5.5 5.5
Calcium % 0.9 1.3 1.1 0.6
Phosphorus % 0.9 1.2 1 0.3
Sodium % 0.4 0.6 0.5 0.4
Potassium % 0.9 1.1 1 0.9
Chloride % 0.6 1 1 1
Magnesium % 0.07 0.06 0.07 0.07
Vitamin A IU/kg 27,000 29,000 25,000 22,000
Vitamin E mg/kg 500 700 600 600
Vitamin C mg/kg 300 300 200 300
Vitamin B1 mg/kg 16.9 17.1 14.3 13.5
Vitamin B2 mg/kg 61 61.6 51.3 48.6
Vitamin B6 mg/kg 46.8 47.3 42.5 40.3
Vitamin B12 mg/kg 0.17 0.18 0.15 0.14
Biotin mg/kg 3.51 3.54 3.24 3.07
Folate mg/kg 12.9 13 12.2 11.6
Open in a new tab

Each prescription diet was fed twice daily (9 am and 5 pm). Caloric intake was adjusted individually according to body weight and condition to maintain ideal body weight throughout the study period.

Experimental design

Each of the four diets was offered to the five healthy cats for 21 days in a replicated 4 × 4 Latin square design. Two cats followed one diet sequence, and the remaining three cats followed the reverse sequence, ensuring that each cat received all four diets across four experimental periods while helping to minimize potential period effects. Following a 19-day adaptation period, one fresh fecal sample was collected within 15 min of defecation on either day 19–21 of each period. Samples were collected within 15 min of defecation to minimize environmental exposure and oxidative changes while ensuring practical feasibility in a laboratory setting [6]. The feces were stored at −80°C until further analysis. Fecal samples were sent to Anicom Advanced Medical Research Institute, Inc. (Tokyo, Japan) for gut microbiota analysis.

Fecal DNA extraction

Frozen fecal samples were thawed prior to DNA extraction. For each sample, 810 µL of a pre-prepared mixture of lysis buffer and Proteinase K was added to 200 µL of fecal material. Mechanical disruption was performed using a bead-beating method at 6,000 rpm for 20 sec, followed by a 30-sec interval and an additional 20-sec disruption.

The samples were then incubated at 70°C for 10 min and subsequently heated at 95°C for 5 min to terminate enzymatic reactions. The resulting lysates were transferred to a 96-well Deep Well Plate and subjected to automated DNA purification using the CMG-1076 kit (chemagic DNA Stool Kit, PerkinElmer Japan G.K., Yokohama, Japan) and the chemagic 360 (PerkinElmer Japan G.K.) automated nucleic acid extraction system, following the manufacturer’s dedicated protocol (chemagic DNA Stool_CMG-1076). During the elution step, elution buffer was added and DNA was finally recovered in a volume of 100 µL.

PCR amplification and library preparation

Amplification of bacterial DNA targeting the 16S rRNA gene was performed according to our in-house standard operating procedure (SOP, version 2.9.2).

The primers used for amplicon PCR were as follows: Illumina_16S_341F 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGA CAGCCTACGGGNGGCWGCAG-3′ Illumina_16S_805R 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACH VGGGTWTCTAAT-3′ Prior to amplicon PCR, genomic DNA from each sample was normalized to a concentration of 1 ng/µL. The first PCR (1st PCR; total reaction volume: 25 µL) was prepared by mixing 12.5 µL of 2× KAPA amplification kit (F. Hoffmann-La Roche, Ltd., Basel, Switzerland), 9.9 µL of PCR-grade water, 0.05 µL of forward amplicon primer, 0.05 µL of reverse amplicon primer, and 2.5 µL of template DNA (1 ng/µL). PCR amplification was performed with an initial denaturation at 95°C for 3 min, followed by 30 cycles consisting of denaturation at 95°C for 30 sec, annealing at 55°C for 30 sec, and extension at 72°C for 30 sec. A final extension was carried out at 72°C for 5 min. The PCR products were purified using AMPure XP beads (Beckman Coulter, Brea, CA, USA) and subsequently quantified. The purified amplicon PCR products were then normalized to 10 ng/µL. The second PCR (2nd PCR; index PCR, total reaction volume: 25 µL) was prepared by mixing 12.5 µL of 2× KAPA amplification kit, 5.0 µL of PCR-grade water, 2.5 µL of forward index primer, 2.5 µL of reverse index primer, and 2.5 µL of template DNA (10 ng/µL). PCR was performed with an initial denaturation at 95°C for 3 min, followed by 12 cycles consisting of denaturation at 95°C for 30 sec, annealing at 55°C for 30 sec, and extension at 72°C for 30 sec, with a final extension at 72°C for 5 min. The index PCR products were purified using AMPure XP beads and quantified. Libraries were adjusted to a final concentration of 9 pM. Subsequently, 850 µL of the prepared library was mixed with 150 µL of PhiX control at the same concentration and subjected to sequencing using the Illumina MiSeq platform with the MiSeq Reagent Kit v3 (600-cycle) (Illumina Inc., San Diego, CA, USA).

Sequencing analysis

The sequence data were processed using QIIME 2 version 2018.8.0. The DADA2 software package version 2018.8.0 incorporated into QIIME 2 was used to correct the amplicon sequence errors and yield high quality reads. To rarefy the data, we used 1,000 high quality reads from each sample. Unique ASVs of the 16S rRNA gene and its abundances were summarized in an ASV abundance table, and the alpha and beta diversities were calculated using QIIME2. Microbial taxonomy was assigned using a Naïve Bayes classifier trained on the greengenes 13_8 database.

Statistical analysis

Data are presented as medians (minimum–maximum range) for relative abundances and as means ± standard deviation where appropriate. Taxonomic distributions at various levels (phylum, class, order, family, and genus) were compared across diet periods using the Kruskal–Wallis test (GraphPad Prism 6, GraphPad Software, Inc., San Diego, CA, USA) as an exploratory non-parametric approach. Because amplicon-based 16S rRNA gene sequencing generates compositional data expressed as relative abundances, results from these exploratory analyses should be interpreted with caution. Alpha diversity indices, including Shannon index, Observed OTUs (Operational Taxonomic Units), Pielou’s evenness, and Faith’s phylogenetic diversity (Faith_PD), were compared among dietary groups using the Kruskal–Wallis test. Beta diversity was assessed using Bray–Curtis distance matrices generated from the microbial abundance table. Differences in overall microbial community composition among dietary groups were evaluated using permutational multivariate analysis of variance (PERMANOVA; 999 permutations) and analysis of similarities (ANOSIM; 999 permutations) implemented in the vegan package in R (version 4.5.2). Individual cat identity was included in the PERMANOVA model to account for the repeated-measures study design. Principal coordinate analysis (PCoA) and hierarchical clustering were performed using QIIME2 for visualization of microbial community structure.

RESULTS

No cats exhibited clinical signs such as vomiting or diarrhea during the study period, and no significant changes in body weight were observed. The proportions of bacterial taxa in the feces of five healthy cats fed four different diets are shown in Table 2.

Table 2. Relative proportions of the bacterial phyla, classes, orders, families, and genera in the feces of the cats fed four different diets.

Phylum Class Order Family Genus Median% (min-max %) Kruskul-Wallis test
Low-carbohydrate diet Weight-loss diet High soluble fiber diet Renal diet
Actinobacteria 8.4 (4.9–12.5) 3.3 (2.0–7.0) 4.4 (2.3–8.3) 8.0 (4.0–35.6) 0.0296*
Actinobacteria 0.1 (0.0–0.4) 0.0 (0.0–0.0) 0.1 (0.1–3.4) 1.2 (1.0–6.8) 0.0274*
Actinomycetales 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–3.0) 0.0 (0.0–0.0) 0.097
Actinomycetaceae 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.1) 0.0 (0.0–0.0) 0.097
Actinomyces 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.1) 0.0 (0.0–0.0) 0.097
Bifidobacteriales 0.0 (0.0–0.2) 0.0 (0.0–0.1) 0.1 (0.1–0.4) 1.2 (1.0–6.8) 0.0048*
Bifidobacteriaceae 0.0 (0.0–0.2) 0.0 (0.0–0.1) 0.1 (0.1–0.4) 1.2 (1.0–6.8) 0.0048*
Bifidobacterium 0.0 (0.0–0.2) 0.0 (0.0–0.1) 0.1 (0.1–0.4) 1.2 (1.0–6.8) 0.0048*
Coriobacteriia 8.3 (4.9–12.4) 3.3 (1.7–6.8) 4.0 (1.0–8.1) 7.1 (0.4–32.1) 0.184
Coriobacteriales 8.3 (4.9–12.4) 3.3 (1.7–6.8) 4.0 (1.0–8.1) 7.1 (0.4–32.1) 0.190
Coriobacteriaceae 8.3 (4.9–12.4) 3.3 (1.7–6.8) 4.0 (1.0–8.1) 7.1 (0.4–32.1) 0.194
Adlercreutzia 0.2 (0.1–0.3) 0.0 (0.0–0.1) 0.0 (0.0–0.2) 0.0 (0.0–0.1) 0.0192*
Collinsella 7.1 (4.3–10.3) 3.1 (1.5–6.4) 3.5 (0.8–7.7) 1.5 (0.0–4.0) 0.0267*
Eggerthella 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.1) 0.392
Slackia 0.2 (0.1–0.3) 0.1 (0.1–0.2) 0.1 (0.0–0.2) 0.0 (0.0–0.1) 0.0231*
Bacteroidetes 34.5 (18.7–48.0) 38.4 (36.8–52.9) 26.5 (18.6–39.0) 22.2 (4.4–44.4) 0.138
Bacteroidia 34.5 (18.7–48.0) 38.4 (36.8–52.9) 26.5 (18.6–39.0) 22.2 (4.4–44.3) 0.138
Bacteroidales 34.5 (18.7–48.0) 38.4 (36.8–52.9) 26.5 (18.6–39.0) 22.2 (4.4–44.3) 0.138
Bacteroidaceae 6.7 (3.8–46.8) 13.8 (4.9–49.8) 13.2 (1.4–18.6) 0.0 (0.0–36.4) 0.612
Bacteroides 6.7 (3.8–46.8) 13.8 (4.9–49.8) 13.2 (1.4–18.6) 0.0 (0.0–36.4) 0.611
Porphyromonadaceae 0.3 (0.2–1.1) 0.5 (0.1–3.1) 0.0 (0.0–3.0) 0.0 (0.0–0.8) 0.186
Parabacteroides 0.3 (0.2–1.1) 0.5 (0.1–3.1) 0.0 (0.0–3.0) 0.0 (0.0–0.8) 0.114
Prevotellaceae 11.7 (0.1–40.0) 22.9 (0.0–32.0) 6.8 (0.0–37.6) 3.7 (0.0–30.4) 0.773
Prevotella 11.7 (0.1–40.0) 22.9 (0.0–32.0) 6.8 (0.0–37.6) 3.7 (0.0–30.4) 0.773
[Paraprevotellaceae] 0.0 (0.0–0.0) 0.0 (0.0–0.2) 0.1 (0.0–1.7) 0.0 (0.0–0.1) 0.238
Firmicutes 41.8 (23.8–51.3) 31.6 (23.3–47.3) 47.2 (23.6–52.6) 46.6 (36.7–73.8) 0.322
Bacilli 5.3 (0.4–33.1) 0.0 (0.0–0.1) 6.1 (0.5–23.4) 9.4 (0.2–60.6) 0.0126*
Lactobacillales 0.7 (0.2–33.1) 0.0 (0.0–0.1) 5.6 (0.5–23.4) 5.9 (0.0–60.6) 0.0154*
Enterococcaceae 0.0 (0.0–0.7) 0.0 (0.0–0.0) 0.0 (0.0–2.3) 0.0 (0.0–0.1) 0.746
Enterococcus 0.0 (0.0–0.7) 0.0 (0.0–0.0) 0.0 (0.0–2.3) 0.0 (0.0–0.1) 0.746
Lactobacillaceae 0.4 (0.0–8.6) 0.0 (0.0–0.0) 0.0 (0.0–1.9) 5.1 (0.0–37.3) 0.460
Lactobacillus 0.4 (0.0–8.6) 0.0 (0.0–0.0) 0.0 (0.0–1.9) 5.1 (0.0–37.3) 0.460
Streptococcaceae 0.2 (0.0–24.5) 0.0 (0.0–0.1) 1.7 (0.2–20.9) 0.1 (0.0–23.3) 0.0487*
Streptococcus 0.2 (0.0–24.5) 0.0 (0.0–0.0) 1.5 (0.0–20.9) 0.1 (0.0–23.3) 0.068
Turicibacterales 0.0 (0.0–4.9) 0.0 (0.0–0.1) 0.0 (0.0–0.3) 0.0 (0.0–3.5) 0.925
Turicibacteraceae 0.0 (0.0–4.9) 0.0 (0.0–0.1) 0.0 (0.0–0.3) 0.0 (0.0–3.5) 0.773
Turicibacter 0.0 (0.0–4.9) 0.0 (0.0–0.1) 0.0 (0.0–0.3) 0.0 (0.0–3.5) 0.993
Clostridia 23.4 (14.4–31.6) 26.2 (19.5–42.6) 28.6 (16.1–31.7) 35.9 (11.6–45.8) 0.669
Clostridiales 23.4 (14.4–31.6) 26.2 (19.5–42.6) 28.6 (16.1–31.7) 35.9 (11.6–45.8) 0.669
Clostridiaceae 2.7 (0.8–3.7) 5.4 (0.6–8.9) 2.7 (0.9–14.3) 1.4 (0.2–11.0) 0.588
Clostridium 2.5 (0.8–3.7) 3.7 (0.6–4.8) 1.8 (0.5–2.4) 0.7 (0.2–11.0) 0.480
Lachnospiraceae 4.4 (2.7–11.1) 7.2 (4.1–14.6) 3.7 (0.1–14.5) 1.4 (0.4–10.0) 0.142
Anaerostipes 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.1) 0.0 (0.0–2.0) 0.549
Blautia 3.2 (2.2–10.6) 1.8 (1.3–4.2) 1.5 (0.6–8.2) 0.2 (0.1–0.4) 0.0068*
Coprococcus 0.0 (0.0–0.4) 0.0 (0.0–0.4) 0.0 (0.0–0.1) 0.0 (0.0–0.0) 0.412
Dorea 0.6 (0.1–1.9) 0.7 (0.3–3.7) 0.4 (0.1–1.0) 0.0 (0.0–0.6) 0.051
Epulopiscium 0.0 (0.0–0.1) 0.0 (0.0–0.6) 0.0 (0.0–0.1) 0.0 (0.0–0.0) 0.768
Roseburia 0.0 (0.0–0.1) 0.1 (0.0–0.2) 0.0 (0.0–1.1) 0.0 (0.0–2.1) 0.726
[Ruminococcus] 0.7 (0.4–1.6) 1.4 (0.8–1.7) 2.8 (0.6–3.4) 0.2 (0.0–2.6) 0.166
Peptococcaceae 1.3 (0.4–16.1) 0.1 (0.0–0.5) 0.3 (0.0–6.8) 0.0 (0.0–0.4) 0.0259*
Peptococcus 0.6 (0.2–1.9) 0.1 (0.0–0.5) 0.2 (0.0–0.6) 0.0 (0.0–0.1) 0.0105*
Peptostreptococcaceae 0.2 (0.0–3.6) 0.1 (0.0–0.2) 0.7 (0.2–1.4) 0.5 (0.0–2.0) 0.192
Ruminococcaceae 3.2 (2.3–7.9) 7.4 (3.6–13.0) 2.0 (0.5–7.0) 0.6 (0.0–21.6) 0.119
Faecalibacterium 0.4 (0.0–1.2) 3.2 (2.2–7.1) 0.2 (0.2–3.1) 0.0 (0.0–7.1) 0.058
Oscillospira 0.5 (0.3–0.7) 0.7 (0.1–2.2) 0.2 (0.0–0.7) 0.0 (0.0–1.0) 0.136
Ruminococcus 0.0 (0.0–0.0) 0.0 (0.0–0.1) 0.0 (0.0–0.1) 0.1 (0.0–10.5) 0.139
Veillonellaceae 3.4 (2.1–11.7) 5.1 (3.1–6.1) 4.2 (0.0–21.0) 12.5 (0.1–30.1) 0.406
Dialister 0.2 (0.1–2.8) 0.2 (0.1–1.8) 0.1 (0.0–0.3) 0.1 (0.0–0.3) 0.237
Megamonas 0.3 (0.0–2.5) 2.6 (0.7–3.4) 1.5 (0.0–5.6) 1.7 (0.2–9.5) 0.266
Erysipelotrichi 7.9 (3.8–14.5) 3.9 (1.6–12.5) 6.0 (1.4–19.6) 1.1 (0.2–14.5) 0.031*
Erysipelotrichales 7.9 (3.8–14.5) 3.9 (1.6–12.5) 6.0 (1.4–19.6) 1.1 (0.2–2.1) 0.031*
Erysipelotrichaceae 5.5 (0.0–14.5) 3.9 (1.6–12.5) 1.5 (0.0–11.8) 0.6 (0.0–1.6) 0.098
Fusobacteria 0.1 (0.0–5.1) 3.3 (0.0–23.4) 0.0 (0.0–5.2) 0.0 (0.0–0.0) 0.178
Fusobacteriia 0.1 (0.0–1.2) 3.3 (0.0–23.4) 0.0 (0.0–5.2) 0.0 (0.0–0.0) 0.176
Fusobacteriales 0.1 (0.0–1.2) 3.3 (0.0–23.4) 0.0 (0.0–5.2) 0.0 (0.0–0.0) 0.176
Fusobacteriaceae 0.1 (0.0–1.2) 3.3 (0.0–23.4) 0.0 (0.0–5.2) 0.0 (0.0–0.0) 0.176
Fusobacterium 0.1 (0.0–1.2) 3.3 (0.0–23.4) 0.0 (0.0–5.2) 0.0 (0.0–0.0) 0.176
Proteobacteria 5.4 (0.5–47.8) 11.8 (5.5–17.0) 14.7 (7.7–42.9) 3.1 (0.0–32.0) 0.193
Betaproteobacteria 0.7 (0.2–2.4) 5.3 (3.6–12.5) 0.7 (0.5–3.1) 0.1 (0.0–0.6) 0.0015*
Burkholderiales 0.7 (0.2–2.4) 5.3 (3.6–12.5) 0.7 (0.5–3.1) 0.1 (0.0–0.5) 0.0016*
Alcaligenaceae 0.7 (0.2–4.2) 5.3 (3.6–12.5) 0.7 (0.5–3.1) 0.1 (0.0–0.5) 0.0016*
Sutterella 0.7 (0.2–2.4) 5.3 (3.6–12.5) 0.5 (0.3–3.1) 0.1 (0.0–0.5) 0.0019*
Epsilonproteobacteria 0.1 (0.0–0.7) 0.4 (0.0–0.7) 0.2 (0.0–1.0) 0.0 (0.0–4.7) 0.552
Campylobacterales 0.1 (0.0–0.7) 0.4 (0.0–0.7) 0.2 (0.0–1.0) 0.0 (0.0–4.7) 0.552
Campylobacteraceae 0.1 (0.0–0.7) 0.4 (0.0–0.7) 0.1 (0.0–1.0) 0.0 (0.0–4.7) 0.679
Campylobacter 0.1 (0.0–0.7) 0.3 (0.0–0.7) 0.1 (0.0–1.0) 0.0 (0.0–4.7) 0.679
Helicobacteraceae 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.1) 0.0 (0.0–0.0) 0.097
Gammaproteobacteria 4.2 (1.2–47.6) 0.9 (0.3–11.6) 11.0 (6.2–23.9) 1.2 (0.0–31.9) 0.146
Aeromonadales 0.1 (0.0–0.2) 0.6 (0.0–0.9) 0.0 (0.0–0.9) 0.0 (0.0–0.0) 0.056
Succinivibrionaceae 0.1 (0.0–0.5) 0.6 (0.0–0.9) 0.0 (0.0–0.9) 0.0 (0.0–0.0) 0.056
Enterobacteriales 3.7 (1.0–47.5) 0.3 (0.0–11.2) 10.6 (4.2–23.9) 1.0 (0.0–31.9) 0.142
Enterobacteriaceae 3.7 (1.0–47.5) 0.3 (0.0–11.2) 10.6 (4.2–23.9) 1.0 (0.0–31.9) 0.142
Open in a new tab

* Indicates significant difference (P<0.05, Kruskal-Wallis test).

The phylum Actinobacteria was higher in relative abundance in cats fed the low-carbohydrate and renal diets compared to the other diets (exploratory Kruskal–Wallis P<0.05). Within Actinobacteria, the order Bifidobacteriales, family Bifidobacteriaceae, and genus Bifidobacterium were higher in relative abundance in the renal diet group compared to the other groups (P<0.05). The genera Adlercreutzia, Collinsella, and Slackia, belonging to the family Coriobacteriaceae, were elevated in the low-carbohydrate diet group compared to other diets (P<0.05).

No significant differences were observed in the phylum Bacteroidetes and its downstream taxa across the four diets.

Within the phylum Firmicutes, the proportion of Bacilli-Lactobacillales showed lower relative abundance (exploratory Kruskal–Wallis P<0.05) in the weight-loss diet group compared to the other three groups. In contrast, the proportion of the family Streptococcaceae showed higher relative abundance (exploratory Kruskal–Wallis P<0.05) in the high soluble fiber diet group compared to the other groups. The genus Blautia, belonging to the family Lachnospiraceae, showed lower relative abundance in the renal diet group compared to the other groups (P<0.05). Additionally, the proportion of Peptococcaceae-Peptococcus was increased in the low-carbohydrate diet group compared to the other groups, while the proportion of Erysipelotrichi-Erysipelotrichales showed lower relative abundance (exploratory Kruskal–Wallis P<0.05) in the renal diet group compared to the other groups.

No significant differences were found in the phylum Fusobacteria and its downstream taxa across the four diets.

Within the phylum Proteobacteria, the proportion of Betaproteobacteria-Burkholderiales-Alcaligenaceae-Sutterella showed higher relative abundance (exploratory Kruskal–Wallis P<0.05) in the weight-loss diet group compared to the other groups.

No significant differences were observed in alpha-diversity indices among the four dietary groups. The Shannon index (P=0.088), observed OTUs (P=0.074), Pielou’s evenness (P=0.234), and Faith_PD index (P=0.273) did not differ significantly among diets (Kruskal–Wallis test; Table 3).

Table 3. Alpha diversity indices of fecal microbiota in cats fed four different diets.

Shannon index Observed OTUs Pielou’s evenness Faith PD
Low-carbohydrate diet 4.7 ± 0.7 75.4 ± 8.5 0.76 ± 0.01 7.9 ± 2.5
Weight-loss diet 5.3 ± 0.6 88.6 ± 14.6 0.83 ± 0.06 7.4 ± 1.2
High soluble fiber diet 5.2 ± 0.8 92.6 ± 41.9 0.81 ± 0.05 8.2 ± 3.1
Renal diet 4.2 ± 0.7 49.4 ± 21.5 0.75 ± 0.07 5.6 ± 1.6
P-values 0.088 0.074 0.234 0.273
Open in a new tab

Values are presented as mean ± SD. P-values were calculated using the Kruskal–Wallis test. OTUs; operational taxonomic units. PD; phylogenetic diversity.

Beta-diversity analysis based on Bray–Curtis distances demonstrated significant differences in microbial community composition among dietary groups (PERMANOVA, pseudo-F=1.726, R2=0.245, P=0.027). This effect remained significant after accounting for individual cat identity in the repeated-measures design (PERMANOVA, pseudo-F=2.285, R2=0.245, P=0.003). ANOSIM analysis also supported significant differences among dietary groups (R=0.169, P=0.032). Although the PCoA plot based on unweighted UniFrac distances showed only modest separation among groups, the renal and weight-loss diets tended to cluster apart from the other dietary treatments (Fig. 1).

Fig. 1.

Fig. 1.

Open in a new tab

Principal coordinates analysis of unweighted UniFrac distances based on 16S rRNA gene sequences obtained from five healthy cats fed four different diets: low-carbohydrate (blue), weight-loss (orange), high soluble fiber (red), and renal (green). Modest visual separation among dietary groups was observed. Permutational multivariate analysis of variance (PERMANOVA) based on Bray–Curtis distances indicated significant differences among dietary groups (R2=0.245, P=0.027). Analysis of similarities (ANOSIM) also detected significant differences among groups (R=0.169, P=0.032).

DISCUSSION

This exploratory study suggests that different commercial prescription diets may be associated with variation in the fecal microbiome of healthy cats. In this study, members of the phylum Actinobacteria, particularly Bifidobacterium, showed higher relative abundance in cats fed the renal diet (low protein, high carbohydrate and high fat). Previous studies have demonstrated that high-carbohydrate diets promote the growth of Bifidobacterium, as carbohydrate fermentation by gut bacteria supports its proliferation [31]. In contrast, diets high in protein are often associated with a reduced proportion of Bifidobacterium [36]. Thus, the higher levels of Bifidobacterium observed in cats on the renal diet may result from its high carbohydrate and low protein content.

In the group fed a low-carbohydrate diet, there was an increase in the genera Adlercreutzia, Collinsella, and Slackia, all belonging to the family Coriobacteriaceae. Adlercreutzia and Slackia are known as equol-producing bacteria [33], and equol (a metabolite produced by gut flora) has been associated with various health benefits, particularly for women’s health [13, 16]. However, the specific effects of these bacteria in cats are not well understood and warrant further investigation. Additionally, Collinsella is thought to influence cholesterol and lipid metabolism, and its association with type 2 diabetes and rheumatoid arthritis has also been reported in humans [10]. There are studies reporting a decrease in Collinsella abundance with high-protein or high-fiber diets, which contradicts the findings of this study [4, 17], as the low-carbohydrate diet used in this study is high in protein and moderate in fiber. Meanwhile, studies have linked the presence of Collinsella and Slackia to improved fecal scores in cats with spontaneous chronic diarrhea, suggesting potential benefits of these genera for gut health in cats [30]. However, given the compositional nature of relative abundance data, the observed increase may also reflect proportional shifts rather than absolute expansion [9]. Therefore, the biological relevance of these observations in healthy cats remains unclear, and the present findings should be considered exploratory.

No statistically significant differences were detected in the phyla Bacteroidetes and Firmicutes among the four diets. However, the Firmicutes/Bacteroidetes (F/B) ratio numerically varied across diets: 1.270 for the low-carbohydrate diet, 0.638 for the weight-loss diet, 1.315 for the high-soluble fiber diet, and 1.658 for the renal diet (exploratory Kruskal–Wallis P=0.0414; data were not shown in results). An increased F/B ratio has been reported in genetically obese mice (ob/ob) and obese humans [19, 20, 40]. Conversely, plant-rich, high-fiber diets have been shown to induce a lower F/B ratio in human children [7]. Thus, the low F/B ratio observed for the weight-loss diet in this study may be related to its high fiber content.

The proportion of the family Streptococcaceae belonging to the Lactobacillales-Bacilli-Firmicutes showed higher relative abundance (exploratory Kruskal–Wallis P<0.05) in the high soluble fiber diet group compared to other groups (P<0.05; Kruskal-Wallis test). Previous research has shown that Streptococcus abundance is elevated in dogs with inflammatory bowel disease (IBD) [24], and quantitative analysis of Streptococcus is included in the dysbiosis index assessment [1]. Similarly, changes in Streptococcus levels have been observed in cats with IBD compared to healthy cats, suggesting a potential role in the pathogenesis of IBD in felines [14]. Furthermore, Streptococcus is included in the feline fecal dysbiosis index, indicating its relevance in dysbiosis-associated microbial shifts in cats [38]. Given that the high-soluble fiber diet is often used for managing constipation, the observed increase in fecal streptococcus warrants further investigation.

Blautia (genus) belonging to the Lachnospiraceae-Clostridiales-Clostridia-Firmicutes was decreased in the renal diet group. Previous studies have shown that Blautia is influenced by dietary protein-to-carbohydrate ratios. Blautia increased in kittens fed a high-protein, low-carbohydrate diet, which is consistent with our research findings [12]. Blautia is involved in glucose metabolism and produces SCFAs like acetic and butyric acids, which are crucial for maintaining gut health [22, 23]. These SCFAs enhance intestinal barrier function and have anti-inflammatory effects by inducing regulatory T cells [2].

Clostridium hiranonis is an important bacterial species incorporated into the feline dysbiosis index [38]. In the present study, the relative abundance of reads classified as C. hiranonis based on 16S rRNA gene sequencing differed among groups (medians, min–max): low-carbohydrate diet (1.05, 0.52–3.33), weight-loss diet (0.86, 0.31–3.40), high soluble fiber diet (1.15, 0.10–1.52), and renal diet (0.00, 0.00–0.05), although these data are not shown in the Results section. However, species-level resolution using the V3–V4 region of the 16S rRNA gene is limited, and accurate identification and quantification of C. hiranonis remain constrained. This bacterium is known to play a critical role in bile acid metabolism; nevertheless, the findings of this study are based on 16S-derived taxonomic assignment and may therefore be subject to underestimation or misclassification. Further validation using species-specific quantitative PCR or shotgun metagenomic approaches is warranted.

The proportion of Erysipelotrichales (order) within Erysipelotrichi (class) belonging to the Firmicutes was reduced in the renal diet group. The proportion of Erysipelotrichaceae has been positively correlated with protein digestibility in dogs [3]. The renal diet used in this study contained only 23% protein (Table 1), which is lower than in the other diets, and featured highly digestible protein content. Enhanced protein digestibility may reduce the risk of food hypersensitivity [28, 42], potentially contributing to the observed reduction in Erysipelotrichales in the renal diet group.

Sutterella, an anaerobic or microaerophilic gram-negative bacterium, is thought to regulate inflammatory responses in the gut [15]. It has been demonstrated that the number of Sutterella spp. is increased in gastrointestinal biopsies obtained from children with autism spectrum disorder [43]. Meanwhile, Sutterella is less abundant in the microbiomes of dogs with inflammatory bowel disease [37] or steroid-responsive enteropathy [29] compared to healthy controls. Therefore, the present finding should not be interpreted as evidence of a beneficial or detrimental effect of this taxon.

An important methodological limitation of this study is that amplicon-based 16S rRNA gene sequencing generates compositional data. Because taxa are expressed as relative abundances and are not statistically independent, standard non-parametric tests do not inherently account for compositional constraints [9]. Accordingly, our findings should be interpreted cautiously and regarded as exploratory. The small sample size (n=5) and the inclusion of healthy cats only further limit generalizability. The absence of diseased or antibiotic-treated comparison groups restricts contextual interpretation of microbiome variation. In addition, inter-individual variability may have influenced the observed relative abundance shifts. Next-generation sequencing also lacks complete analytical standardization across laboratories; differences in DNA extraction methods, primer sets, sequencing platforms, and bioinformatic pipelines may contribute to variability between studies and limit direct comparability [8, 11, 32]. Furthermore, analytical validation and cross-platform reproducibility remain incomplete, which constrains its immediate translational applicability. Therefore, integration of sequencing results with metabolomic profiling and validated quantitative assays will be necessary to strengthen biological interpretation [21, 34, 39]. Finally, baseline fecal microbiota may have been influenced by the pre-study diet (Blue Buffalo WU or HF). Although all cats were maintained under identical conditions before experimental allocation, this potential carryover effect should be considered a limitation.

In conclusion, the four commercially available prescription diets were associated with differing fecal microbial profiles in healthy cats. However, because of the exploratory design, very small sample size, repeated-measures structure, and compositional limitations of 16S relative abundance data, these observations should be interpreted cautiously and regarded as hypothesis-generating.

CONFLICTS OF INTEREST

The authors have nothing to disclose.

REFERENCES

  • 1.AlShawaqfeh MK, Wajid B, Minamoto Y, Markel M, Lidbury JA, Steiner JM, Serpedin E, Suchodolski JS. 2017. A dysbiosis index to assess microbial changes in fecal samples of dogs with chronic inflammatory enteropathy. FEMS Microbiol Ecol 93: 11. doi: 10.1093/femsec/fix136 [DOI] [PubMed] [Google Scholar]
  • 2.Atarashi K, Tanoue T, Oshima K, Suda W, Nagano Y, Nishikawa H, Fukuda S, Saito T, Narushima S, Hase K, Kim S, Fritz JV, Wilmes P, Ueha S, Matsushima K, Ohno H, Olle B, Sakaguchi S, Taniguchi T, Morita H, Hattori M, Honda K. 2013. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature 500: 232–236. doi: 10.1038/nature12331 [DOI] [PubMed] [Google Scholar]
  • 3.Bermingham EN, Maclean P, Thomas DG, Cave NJ, Young W. 2017. Key bacterial families (Clostridiaceae, Erysipelotrichaceae and Bacteroidaceae) are related to the digestion of protein and energy in dogs. PeerJ 5: e3019. doi: 10.7717/peerj.3019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Chen J, Wright K, Davis JM, Jeraldo P, Marietta EV, Murray J, Nelson H, Matteson EL, Taneja V. 2016. An expansion of rare lineage intestinal microbes characterizes rheumatoid arthritis. Genome Med 8: 43. doi: 10.1186/s13073-016-0299-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Coelho LP, Kultima JR, Costea PI, Fournier C, Pan Y, Czarnecki-Maulden G, Hayward MR, Forslund SK, Schmidt TSB, Descombes P, Jackson JR, Li Q, Bork P. 2018. Similarity of the dog and human gut microbiomes in gene content and response to diet. Microbiome 6: 72. doi: 10.1186/s40168-018-0450-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Cunningham JL, Bramstång L, Singh A, Jayarathna S, Rasmusson AJ, Moazzami A, Müller B. 2020. Impact of time and temperature on gut microbiota and SCFA composition in stool samples. PLoS One 15: e0236944. doi: 10.1371/journal.pone.0236944 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, Collini S, Pieraccini G, Lionetti P. 2010. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci USA 107: 14691–14696. doi: 10.1073/pnas.1005963107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Forry SP, Servetas SL, Kralj JG, Soh K, Hadjithomas M, Cano R, Carlin M, Amorim MG, Auch B, Bakker MG, Bartelli TF, Bustamante JP, Cassol I, Chalita M, Dias-Neto E, Duca AD, Gohl DM, Kazantseva J, Haruna MT, Menzel P, Moda BS, Neuberger-Castillo L, Nunes DN, Patel IR, Peralta RD, Saliou A, Schwarzer R, Sevilla S, Takenaka IKTM, Wang JR, Knight R, Gevers D, Jackson SA. 2024. Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols. Sci Rep 14: 9785. doi: 10.1038/s41598-024-57981-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. 2017. Microbiome datasets are compositional: and this is not optional. Front Microbiol 8: 2224. doi: 10.3389/fmicb.2017.02224 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Gomez-Arango LF, Barrett HL, Wilkinson SA, Callaway LK, McIntyre HD, Morrison M, Dekker Nitert M. 2018. Low dietary fiber intake increases Collinsella abundance in the gut microbiota of overweight and obese pregnant women. Gut Microbes 9: 189–201. doi: 10.1080/19490976.2017.1406584 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Hoisington AJ, Stamper CE, Ellis JC, Lowry CA, Brenner LA. 2024. Quantifying variation across 16S rRNA gene sequencing runs in human microbiome studies. Appl Microbiol Biotechnol 108: 367. doi: 10.1007/s00253-024-13198-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Hooda S, Vester Boler BM, Kerr KR, Dowd SE, Swanson KS. 2013. The gut microbiome of kittens is affected by dietary protein:carbohydrate ratio and associated with blood metabolite and hormone concentrations. Br J Nutr 109: 1637–1646. doi: 10.1017/S0007114512003479 [DOI] [PubMed] [Google Scholar]
  • 13.Iino C, Shimoyama T, Iino K, Yokoyama Y, Chinda D, Sakuraba H, Fukuda S, Nakaji S. 2019. Daidzein intake is associated with equol-producing status through an increase in the intestinal bacteria responsible for equol production. Nutrients 11: 433. doi: 10.3390/nu11020433 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Janeczko S, Atwater D, Bogel E, Greiter-Wilke A, Gerold A, Baumgart M, Bender H, McDonough PL, McDonough SP, Goldstein RE, Simpson KW. 2008. The relationship of mucosal bacteria to duodenal histopathology, cytokine mRNA, and clinical disease activity in cats with inflammatory bowel disease. Vet Microbiol 128: 178–193. doi: 10.1016/j.vetmic.2007.10.014 [DOI] [PubMed] [Google Scholar]
  • 15.Kaakoush NO. 2020. Sutterella species, IgA-degrading bacteria in ulcerative colitis. Trends Microbiol 28: 519–522. doi: 10.1016/j.tim.2020.02.018 [DOI] [PubMed] [Google Scholar]
  • 16.Kodera M, Nakamura K, Yokoyama SI. 2024. Chitin promotes equol production via N-acetylglucosamine in human fecal cultures. Anaerobe 91: 102923. [DOI] [PubMed] [Google Scholar]
  • 17.Lambeth SM, Carson T, Lowe J, Ramaraj T, Leff JW, Luo L, Bell CJ, Shah VO. 2015. Composition, diversity and abundance of gut microbiome in prediabetes and type 2 diabetes. J Diabetes Obes 2: 1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Lee AH, Jha AR, Do S, Scarsella E, Shmalberg J, Schauwecker A, Steelman AJ, Honaker RW, Swanson KS. 2022. Dietary enrichment of resistant starches or fibers differentially alter the feline fecal microbiome and metabolite profile. Anim Microbiome 4: 61. doi: 10.1186/s42523-022-00213-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. 2005. Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 102: 11070–11075. doi: 10.1073/pnas.0504978102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Ley RE, Turnbaugh PJ, Klein S, Gordon JI. 2006. Microbial ecology: human gut microbes associated with obesity. Nature 444: 1022–1023. doi: 10.1038/4441022a [DOI] [PubMed] [Google Scholar]
  • 21.Li F, Liu J, Maldonado-Gómez MX, Frese SA, Gänzle MG, Walter J. 2024. Highly accurate and sensitive absolute quantification of bacterial strains in human fecal samples. Microbiome 12: 168. doi: 10.1186/s40168-024-01881-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Liu C, Li J, Zhang Y, Philip A, Shi E, Chi X, Meng J. 2015. Influence of glucose fermentation on CO2 assimilation to acetate in homoacetogen Blautia coccoides GA-1. J Ind Microbiol Biotechnol 42: 1217–1224. doi: 10.1007/s10295-015-1646-1 [DOI] [PubMed] [Google Scholar]
  • 23.Liu C, Finegold SM, Song Y, Lawson PA. 2008. Reclassification of Clostridium coccoides, Ruminococcus hansenii, Ruminococcus hydrogenotrophicus, Ruminococcus luti, Ruminococcus productus and Ruminococcus schinkii as Blautia coccoides gen. nov., comb. nov., Blautia hansenii comb. nov., Blautia hydrogenotrophica comb. nov., Blautia luti comb. nov., Blautia producta comb. nov., Blautia schinkii comb. nov. and description of Blautia wexlerae sp. nov., isolated from human faeces. Int J Syst Evol Microbiol 58: 1896–1902. doi: 10.1099/ijs.0.65208-0 [DOI] [PubMed] [Google Scholar]
  • 24.Minamoto Y, Otoni CC, Steelman SM, Büyükleblebici O, Steiner JM, Jergens AE, Suchodolski JS. 2015. Alteration of the fecal microbiota and serum metabolite profiles in dogs with idiopathic inflammatory bowel disease. Gut Microbes 6: 33–47. doi: 10.1080/19490976.2014.997612 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Mo R, Zhang M, Wang H, Liu T, Zhang G, Wu Y. 2023. Short-term changes in dietary fat levels and starch sources affect weight management, glucose and lipid metabolism, and gut microbiota in adult cats. J Anim Sci 101: skad276. doi: 10.1093/jas/skad276 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Mori A, Goto A, Kibe R, Oda H, Kataoka Y, Sako T. 2019. Comparison of the effects of four commercially available prescription diet regimens on the fecal microbiome in healthy dogs. J Vet Med Sci 81: 1783–1790. doi: 10.1292/jvms.19-0055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Murphy EA, Velazquez KT, Herbert KM. 2015. Influence of high-fat diet on gut microbiota: a driving force for chronic disease risk. Curr Opin Clin Nutr Metab Care 18: 515–520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Onozawa E, Goto A, Oda H, Seki S, Sako T, Mori A. 2022. Comparison of the effects of two commercially available prescription diet regimens on the fecal microbiomes of client-owned healthy pet dogs. Pol J Vet Sci 25: 93–101. doi: 10.24425/pjvs.2022.140845 [DOI] [PubMed] [Google Scholar]
  • 29.Pilla R, Guard BC, Blake AB, Ackermann M, Webb C, Hill S, Lidbury JA, Steiner JM, Jergens AE, Suchodolski JS. 2021. Long-term recovery of the fecal microbiome and metabolome of dogs with steroid-responsive enteropathy. Animals (Basel) 11: 2498. doi: 10.3390/ani11092498 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Ramadan Z, Xu H, Laflamme D, Czarnecki-Maulden G, Li QJ, Labuda J, Bourqui B. 2014. Fecal microbiota of cats with naturally occurring chronic diarrhea assessed using 16S rRNA gene 454-pyrosequencing before and after dietary treatment. J Vet Intern Med 28: 59–65. doi: 10.1111/jvim.12261 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Rivière A, Selak M, Lantin D, Leroy F, De Vuyst L. 2016. Bifidobacteria and butyrate-producing colon bacteria: Importance and strategies for their stimulation in the human gut. Front Microbiol 7: 979. doi: 10.3389/fmicb.2016.00979 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Roume H, Mondot S, Saliou A, Le Fresne-Languille S, Doré J. 2023. Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach. Sci Rep 13: 22593. doi: 10.1038/s41598-023-46062-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Setchell KD, Clerici C. 2010. Equol: history, chemistry, and formation. J Nutr 140: 1355S–1362S. doi: 10.3945/jn.109.119776 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Sezgin E, Terlemez G, Bozkurt B, Bengi G, Akpinar H, Büyüktorun İ. 2022. Quantitative real-time PCR analysis of bacterial biomarkers enable fast and accurate monitoring in inflammatory bowel disease. PeerJ 10: e14217. doi: 10.7717/peerj.14217 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Simpson JM, Martineau B, Jones WE, Ballam JM, Mackie RI. 2002. Characterization of fecal bacterial populations in canines: effects of age, breed and dietary fiber. Microb Ecol 44: 186–197. doi: 10.1007/s00248-002-0001-z [DOI] [PubMed] [Google Scholar]
  • 36.Singh RK, Chang HW, Yan D, Lee KM, Ucmak D, Wong K, Abrouk M, Farahnik B, Nakamura M, Zhu TH, Bhutani T, Liao W. 2017. Influence of diet on the gut microbiome and implications for human health. J Transl Med 15: 73. doi: 10.1186/s12967-017-1175-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Suchodolski JS, Markel ME, Garcia-Mazcorro JF, Unterer S, Heilmann RM, Dowd SE, Kachroo P, Ivanov I, Minamoto Y, Dillman EM, Steiner JM, Cook AK, Toresson L. 2012. The fecal microbiome in dogs with acute diarrhea and idiopathic inflammatory bowel disease. PLoS One 7: e51907. doi: 10.1371/journal.pone.0051907 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Sung CH, Marsilio S, Chow B, Zornow KA, Slovak JE, Pilla R, Lidbury JA, Steiner JM, Park SY, Hong MP, Hill SL, Suchodolski JS. 2022. Dysbiosis index to evaluate the fecal microbiota in healthy cats and cats with chronic enteropathies. J Feline Med Surg 24: e1–e12. doi: 10.1177/1098612X221077876 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Sung CH, Pilla R, Chen CC, Ishii PE, Toresson L, Allenspach-Jorn K, Jergens AE, Summers S, Swanson KS, Volk H, Schmidt T, Stuebing H, Rieder J, Busch K, Werner M, Lisjak A, Gaschen FP, Belchik SE, Tolbert MK, Lidbury JA, Steiner JM, Suchodolski JS. 2023. Correlation between targeted qPCR assays and untargeted DNA shotgun metagenomic sequencing for assessing the fecal microbiota in dogs. Animals (Basel) 13: 2597. doi: 10.3390/ani13162597 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP, Egholm M, Henrissat B, Heath AC, Knight R, Gordon JI. 2009. A core gut microbiome in obese and lean twins. Nature 457: 480–484. doi: 10.1038/nature07540 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Venkataraman A, Sieber JR, Schmidt AW, Waldron C, Theis KR, Schmidt TM. 2016. Variable responses of human microbiomes to dietary supplementation with resistant starch. Microbiome 4: 33. doi: 10.1186/s40168-016-0178-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Wang K, Crevel RWR, Mills ENC. 2022. Assessing protein digestibility in allergenicity risk assessment: a comparison of in silico and high throughput in vitro gastric digestion assays. Food Chem Toxicol 167: 113273. doi: 10.1016/j.fct.2022.113273 [DOI] [PubMed] [Google Scholar]
  • 43.Wang L, Christophersen CT, Sorich MJ, Gerber JP, Angley MT, Conlon MA. 2013. Increased abundance of Sutterella spp. and Ruminococcus torques in feces of children with autism spectrum disorder. Mol Autism 4: 42. doi: 10.1186/2040-2392-4-42 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R, Sinha R, Gilroy E, Gupta K, Baldassano R, Nessel L, Li H, Bushman FD, Lewis JD. 2011. Linking long-term dietary patterns with gut microbial enterotypes. Science 334: 105–108. doi: 10.1126/science.1208344 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Zimmer J, Lange B, Frick JS, Sauer H, Zimmermann K, Schwiertz A, Rusch K, Klosterhalfen S, Enck P. 2012. A vegan or vegetarian diet substantially alters the human colonic faecal microbiota. Eur J Clin Nutr 66: 53–60. doi: 10.1038/ejcn.2011.141 [DOI] [PubMed] [Google Scholar]

Articles from The Journal of Veterinary Medical Science are provided here courtesy of Japanese Society of Veterinary Science

RESOURCES