BackgroundDogs suffer from many of the same maladies as humans that may be affected by the gut microbiome, but knowledge of the canine microbiome is incomplete. This work aimed to use 16S rDNA tag pyrosequencing to phylogenetically characterize hindgut microbiome in dogs and determine how consumption of dietary fiber affects community structure.Principal FindingsSix healthy adult dogs were used in a crossover design. A control diet without supplemental fiber and a beet pulp-supplemented (7.5%) diet were fed. Fecal DNA was extracted and the V3 hypervariable region of the microbial 16S rDNA gene amplified using primers suitable for 454-pyrosequencing. Microbial diversity was assessed on random 2000-sequence subsamples of individual and pooled DNA samples by diet. Our dataset comprised 77,771 reads with an average length of 141 nt. Individual samples contained approximately 129 OTU, with Fusobacteria (23 – 40% of reads), Firmicutes (14 – 28% of reads) and Bacteroidetes (31 – 34% of reads) being co-dominant phyla. Feeding dietary fiber generally decreased Fusobacteria and increased Firmicutes, but these changes were not equally apparent in all dogs. UniFrac analysis revealed that structure of the gut microbiome was affected by diet and Firmicutes appeared to play a strong role in by-diet clustering.ConclusionsOur data suggest three co-dominant bacterial phyla in the canine hindgut. Furthermore, a relatively small amount of dietary fiber changed the structure of the gut microbiome detectably. Our data are among the first to characterize the healthy canine gut microbiome using pyrosequencing and provide a basis for studies focused on devising dietary interventions for microbiome-associated diseases.

The relative contribution of novel fibers such as polydextrose and soluble corn fiber (SCF) to the human gut microbiome and its association with host physiology has not been well studied. This study was conducted to test the impact of polydextrose and SCF on the composition of the human gut microbiota using 454 pyrosequencing and to identify associations among fecal microbiota and fermentative end-products. Healthy adult men (n = 20) with a mean dietary fiber (DF) intake of 14 g/d were enrolled in a randomized, double-blind, placebo-controlled crossover study. Participants consumed 3 treatment snack bars/d during each 21-d period that contained no supplemental fiber (NFC), polydextrose (PDX; 21 g/d), or SCF (21 g/d) for 21 d. There were no washout periods. Fecal samples were collected on d 16-21 of each period; DNA was extracted, followed by amplification of the V4-V6 region of the 16S rRNA gene using barcoded primers. PDX and SCF significantly affected the relative abundance of bacteria at the class, genus, and species level. The consumption of PDX and SCF led to greater fecal Clostridiaceae and Veillonellaceae and lower Eubacteriaceae compared with a NFC. The abundance of Faecalibacterium, Phascolarctobacterium, and Dialister was greater (P < 0.05) in response to PDX and SCF intake, whereas Lactobacillus was greater (P < 0.05) only after SCF intake. Faecalibacterium prausnitzii, well known for its antiinflammatory properties, was greater (P < 0.05) after fiber consumption. Principal component analysis clearly indicated a distinct clustering of individuals consuming supplemental fibers. Our data demonstrate a beneficial shift in the gut microbiome of adults consuming PDX and SCF, with potential application as prebiotics.

High-protein, low-carbohydrate (HPLC) diets are common in cats, but their effect on the gut microbiome has been ignored. The present study was conducted to test the effects of dietary protein:carbohydrate ratio on the gut microbiota of growing kittens. Male domestic shorthair kittens were raised by mothers fed moderate-protein, moderate-carbohydrate (MPMC; n 7) or HPLC (n 7) diets, and then weaned at 8 weeks onto the same diet. Fresh faeces were collected at 8, 12 and 16 weeks; DNA was extracted, followed by amplification of the V4 -V6 region of the 16S rRNA gene using 454 pyrosequencing. A total of 384 588 sequences (average of 9374 per sample) were generated. Dual hierarchical clustering indicated distinct clustering based on the protein:carbohydrate ratio regardless of age. The protein:carbohydrate ratio affected faecal bacteria. Faecal Actinobacteria were greater (P, 0·05) and Fusobacteria were lower (P, 0·05) in MPMC-fed kittens. Faecal Clostridium, Faecalibacterium, Ruminococcus, Blautia and Eubacterium were greater (P, 0·05) in HPLC-fed kittens, while Dialister, Acidaminococcus, Bifidobacterium, Megasphaera and Mitsuokella were greater (P,0·05) in MPMC-fed kittens. Principal component analysis of faecal bacteria and blood metabolites and hormones resulted in distinct clusters. Of particular interest was the clustering of blood TAG with faecal Clostridiaceae, Eubacteriaceae, Ruminococcaceae, Fusobacteriaceae and Lachnospiraceae; blood ghrelin with faecal Coriobacteriaceae, Bifidobacteriaceae and Veillonellaceae; and blood glucose, cholesterol and leptin with faecal Lactobacillaceae. The present results demonstrate that the protein:carbohydrate ratio affects the faecal microbiome, and highlight the associations between faecal microbes and circulating hormones and metabolites that may be important in terms of satiety and host metabolism.

Four healthy adult cats were used in a crossover design to determine phylogeny and metabolic functional capacity of the cat's gastrointestinal microbiota using a metagenomic approach. Healthy adult cats (1.7 years old) were fed diets containing 4% cellulose, fructooligosaccharides (FOS), or pectin for 30 d, at which time fresh fecal samples were collected. Fecal DNA samples from each cat consuming each diet were subjected to 454 pyrosequencing. Dominant phyla determined using two independent databases (MG-RAST and IMG/M) included Firmicutes (mean=36.3 and 49.8%, respectively), Bacteroidetes (mean=36.1 and 24.1%, respectively), and Proteobacteria (mean=12.4 and 11.1%, respectively). Primary functional categories as determined by KEGG were associated with carbohydrates, clustering-based subsystems, protein metabolism, and amino acids and derivatives. Primary functional categories as determined by COG were associated with amino acid metabolism and transport, general function prediction only, and carbohydrate transport and metabolism. Analysis of carbohydrate-active enzymes revealed modifications in several glycoside hydrolases, glycosyl transferases, and carbohydrate-binding molecules with FOS and pectin consumption. While the cat is an obligate carnivore, its gut microbiome is similar regarding microbial phylogeny and gene content to omnivores.