Dayakar V. Badri

Low-carbohydrate (LC) foods have been associated with weight loss, insulin resistance, and reduced inflammation. Gut microbes may drive these effects by changing the host’s immune and metabolic state, yet the optimal replacement strategies (e.g., protein vs. fat) and the biochemical impacts are still uncertain. Thus, we conducted a randomized cross-over study in canines (n = 35) using a high-carbohydrate food for 4 wk, followed by two 5-wk intervals of LC foods replacing carbohydrates with fat or protein. Feces and blood samples collected after each interval underwent metabolome profiling, as well as shotgun metagenomic and metatranscriptomic sequencing from feces. We observed strong associations between food and overall fecal and serum chemical and gut microbial composition. Bifidobacterium spp. were significantly reduced during consumption of either LC food, whereas many species from Firmicutes increased. In addition to broad taxonomic changes, we also found that several microbially associated metabolites (tryptophan derivatives, secondary bile acids, and short-chain fatty acid fermentation) shifted in abundance in response to differing LC foods. Broadly, we also identified a strong trend toward decreased amino acids in the feces of dogs fed LC foods, particularly the high fat food. Coupled with decreased fecal dipeptide levels and increased fecal ammonia, these findings indicate a broad metabolic shift within the gut microbiome, potentially toward protein catabolism. Together, the data suggest that diverse carbohydrate replacement strategies induce extensive gut reprogramming, with some changes dependent on whether carbohydrate energy was replaced with fat or protein.

Introduction: Periodontitis affects over 80% of dogs over 3 years of age, progressing irreversibly from gingivitis due to an imbalance in the subgingival microbial community that triggers an immune response. Early diagnosis of gingivitis is challenging, often relying on visible redness or bleeding noticed by pet owners or professionals. Therefore, an easy-to-interpret, clinically relevant, and responsive measurement tool based on the subgingival microbiome is needed to facilitate early diagnosis of oral health issues. We developed the Canine Tooth Microbiome Gingival Index (CTMGI), a single-score metric derived from subgingival plaque microbiome data and machine learning models, and validated its responsiveness via nutritional intervention. Methods: We collected subgingival plaque microbiome profiles from 692 tooth samples of 347 dogs, generated through 16S amplicon sequencing. For the machine learning models, the tooth gingivitis scores were dichotomized into healthy (gingivitis score <3) and unhealthy (gingivitis score ≥3), along with other clinical scores such as tooth recession, pocket depth, and attachment loss. The raw data were split into training and test sets, and five distinct machine learning models were employed to identify features that distinguish healthy from gingivitis sites. Results: The two top-performing models—random forest and logistic regression—yielded 22 unique features. These 22 features included the sum of early and late colonizers, the phyla actinobacteria and proteobacteria, and other bacterial species. The CTMGI was derived from the 22 features, categorized as “positive” or “negative” based on their influence on gingivitis. The CTMGI classification cutoff score was set at −0.12 with a Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) of 0.761, a sensitivity of 0.701, and a specificity of 0.752. A score greater than −0.12 was found to indicate a “healthy” gingival condition; otherwise, it indicated “unhealthy.” Furthermore, we conducted a nutritional intervention study to validate the responsiveness of the CTMGI, in which the test food, which has documented oral health benefits, resulted in a significantly higher CTMGI score (1.32) compared to the control food, which offers no oral health benefits (0.66). Discussion: Overall, this study developed and validated a quantitative, single-score, microbiome-based metric that is clinically translatable for the assessment of early-stage canine gingival health. Furthermore, its demonstrated responsiveness to nutritional intervention suggests that this index can serve as a prognostic measure.

Protein production through precision fermentation is an attractive option to reduce animal use in food production. This study focused on the safety and digestibility of food containing a test ingredient, brewed lamb protein (Saccharomyces cerevisiae expressing a lamb protein), at inclusion levels of 0% (control), 15%, 30%, or 40% were evaluated over 182 days in 40 dogs. No significant differences in body weight, body condition score, body composition, physical examination, food intake, or urinary parameters were observed among groups. Hematological and serum biochemical parameters were within reference ranges and were largely similar among groups. Levels of chloride, cholesterol, and homocysteine were significantly lower at some time points in dogs fed the test ingredient foods compared with the control food, but all remained within reference ranges. Whole blood taurine levels indicated no deficiency. Alanine aminotransferase levels were above the normal range at Day 177 in dogs in the control and 15% test ingredient groups. Fecal score was slightly decreased in dogs fed the 30% or 40% test ingredient foods, and fecal pH was decreased in dogs fed any of the test ingredient foods. Digestibility of most parameters was similar among foods, but apparent fat digestibility was significantly lower in foods with the test ingredient. Overall, these results indicate that the test ingredient is safe for use in dog food.

Dietary fiber confers multiple health benefits originating from the expansion of beneficial gut microbial activity. However, very few studies have established the metabolic consequences of interactions among specific fibers, microbiome composition, and function in either human or representative animal models. In a study design reflective of realistic population dietary variation, fecal metagenomic and metabolomic profiles were analyzed from healthy dogs fed 12 test foods containing different fiber sources and quantities (5–13% as-fed basis). Taxa and functions were identified whose abundances were associated either with overall fiber intake or with specific fiber compositions. Fourteen microbial species were significantly enriched in response to ≥1 specific fiber source; enrichment of fiber-derived metabolites was more pronounced in response to these fiber sources. Positively associated fecal metabolites, including short-chain fatty acids, acylglycerols, fiber bound sugars, and polyphenols, co-occurred with microbes enriched in specific food groups. Critically, the specific metabolite pools responsive to differential fiber intake were dependent on differences both in individual microbial community membership and in overall ecological configuration. This helps to explain, for the first time, differences in microbiome-diet associations observed in companion animal epidemiology. Thus, our study corroborates findings in human cohorts and reinforces the role of personalized microbiomes even in seemingly phenotypically homogeneous subjects. IMPORTANCE: Consumption of dietary fiber changes the composition of the gut microbiome and, to a larger extent, the associated metabolites. Production of health-relevant metabolites such as short-chain fatty acids from fiber depends both on the consumption of a specific fiber and on the enrichment of beneficial metabolite-producing species in response to it. Even in a seemingly homogeneous population, the benefit received from fiber consumption is personalized and emphasizes specific fiber-microbe-host interactions. These observations are relevant for both population-wide and personalized nutrition applications.

The gastrointestinal microbiome comprises trillions of bacteria, and is a vitally important organ with wide ranging effects on pet health: Food impacts the composition and metabolism of the microbiome, as well as the production of postbiotics, which are bioactive end products of microbial consumption of prebiotics. Postbiotics include metabolites of nutrients like fatty acids, amino acids, vitamins and polysaccharides. Also gut bacteria can produce postbiotics from plant antioxidants like polyphenols. A growing body of evidence has shown that influencing postbiotic production may have beneficial effects on pet health. Soluble and insoluble fibers are commonly used sources of prebiotics in the pet food industry, some of these fibers have polyphenols bound to them. Understanding the impact of each fiber type on the microbiome composition, metabolism and postbiotic production provides insights into formulating foods that are optimal for pet health.

Consumption of fiber in its different forms can result in positive health effects. Prior studies in dogs found that addition of a fiber bundle (composed of pecan shells, flax seed, and powders of cranberry, citrus, and beet) to food resulted in a shift in fecal bacterial metabolism from proteolysis to saccharolysis. The present study evaluated the changes in fecal metabolites and microbiota in healthy cats following the consumption of this fiber bundle. Following a 28-day pre-feed period, 56 healthy adult cats received food with none or one of three concentrations (0%, 1%, 2%, and 4%) of the fiber bundle for a 31-day period. In cats that consumed the 4% fiber bundle, levels of ammonium and fecal branched-chain fatty acids (BCFAs) decreased from baseline and compared with the other groups. Addition of any level of the fiber bundle resulted in increases in beneficial metabolites: polyphenols hesperidin, hesperetin, ponciretin, secoisolariciresinol diglucoside, secoisolariciresinol, and enterodiol. Little change in fecal microbiota was observed. Since higher levels of ammonia and BCFAs indicate putrefactive metabolism, the decreases in these with the 4% fiber bundle indicate a shift toward saccharolytic metabolism despite little change in the microbiota composition.

This study assessed changes in canine fecal metabolites and microbiota with the consumption of foods with increasing concentrations of a fiber bundle including pecan shells, flax seed, and powders of cranberry, citrus, and beet that was previously shown (at 14% w/w) to improve stool quality, shift fecal bacterial metabolism from proteolysis to saccharolysis, increase abundance of saccharolytic bacteria, and decrease abundance of proteolytic bacteria. In this study, 48 healthy adult dogs were split evenly to consume different inclusion levels (0%, 1%, 2%, and 4%) of the fiber bundle for a 31-day period following a 28-day pre-feed period. Increases from baseline in the fecal short-chain fatty acids butyric acid, valeric acid, and hexanoic acid were observed only in the dogs that consumed the food with the 4% fiber bundle. With addition of any level of the fiber bundle, increases were seen in the polyphenols hesperidin, hesperetin, ponciretin, secoisolariciresinol diglucoside, secoisolariciresinol, and enterodiol. However, fecal microbiota and their metabolism, and stool scores were largely unaffected by the fiber bundle. Overall, addition of the fiber bundle appeared to increase bioactive metabolites of increased antioxidant and anti-inflammatory potency for beneficial to health and, at levels ≥4%, shifted gut bacterial metabolism toward saccharolysis.

Relative levels of dietary protein and carbohydrate intake influence microbiota and their functional capabilities, but the effect has not been well documented in cats. The impact of 3 foods with different protein:carbohydrate ratios on the gut microbiota and functional attributes in healthy adult cats was evaluated. Male and female cats (n = 30; mean age: 5.1 y; mean body weight: 5.26 kg) were fed 1 of 3 foods [P28 (28.3% protein, dry matter basis), P35 (35.1%), and P55 (54.8%)] for 90 d in a Williams Latin Square design. Each food had a 1:1 ratio of animal (dried chicken) to plant (pea) protein; protein replaced carbohydrate as protein level increased. Fecal microbiota and their functional capability were assessed with 16S sequencing and the Kyoto Encyclopedia of Genes and Genomes database, respectively. Fecal pH, ammonia, and branched-chain fatty acids (BCFAs) were higher when cats consumed P55 food than when they consumed P28 and P35. Clear separation of samples between P28 and P55 based on bacterial genera was observed, with partitioning into saccharolytic and proteolytic functions, respectively. Significantly higher α diversity was seen with P55 than with P28 and P35. Amino acid metabolism, mucin foraging pathways, and urea metabolism were higher with P55 than with P28, whereas feces from cats fed P28 had higher concentrations of carbohydrate-active enzymes and enzymes involved in SCFA pathways than with P55. Bacterial genera that showed positive associations with amino acid catabolism also showed positive associations with mucin degradation. Despite higher protein digestibility and less protein arriving to the colon, when healthy adult cats consumed the highest level of protein (P55), their gut microbiota exhibited higher mucin glycan foraging and amino acid metabolism, leading to higher fecal pH, ammonia, and BCFAs. This is likely due to lower availability of carbohydrate substrates and dietary fiber as protein replaced carbohydrate in the food.