Feline chronic enteropathy (CE) is a common gastrointestinal disorder in cats and mainly comprises inflammatory bowel disease (IBD) and small cell lymphoma (SCL). Both IBD and SCL in cats share features with chronic enteropathies such as IBD and monomorphic epitheliotropic intestinal T-cell lymphoma in humans. The aim of this study was to characterize the fecal microbiome of 38 healthy cats and 27 cats with CE (13 cats with IBD and 14 cats with SCL). Alpha diversity indices were significantly decreased in cats with CE (OTU p = 0.003, Shannon Index p = 0.008, Phylogenetic Diversity p = 0.019). ANOSIM showed a significant difference in bacterial communities, albeit with a small effect size (P = 0.023, R = 0.073). Univariate analysis and LEfSE showed a lower abundance of facultative anaerobic taxa of the phyla Firmicutes (families Ruminococcaceae and Turicibacteraceae), Actinobacteria (genus Bifidobacterium) and Bacteroidetes (i.a. Bacteroides plebeius) in cats with CE. The facultative anaerobic taxa Enterobacteriaceae and Streptococcaceae were increased in cats with CE. No significant difference between the microbiome of cats with IBD and those with SCL was found. Cats with CE showed patterns of dysbiosis similar to those in found people with IBD.

Feline chronic enteropathy (CE) is a common gastrointestinal disorder in cats and mainly comprises inflammatory bowel disease (IBD) and small cell lymphoma (SCL). Differentiation between IBD and SCL can be diagnostically challenging. We characterized the fecal metabolome of 14 healthy cats and 22 cats with naturally occurring CE (11 cats with IBD and 11 cats with SCL). Principal component analysis and heat map analysis showed distinct clustering between cats with CE and healthy controls. Random forest classification revealed good group prediction for healthy cats and cats with CE, with an overall out-of-bag error rate of 16.7%. Univariate analysis indicated that levels of 84 compounds in cats with CE differed from those in healthy cats. Polyunsaturated fatty acids held discriminatory power in differentiating IBD from SCL. Metabolomic profiles of cats with CE resembled those in people with CE with significant alterations of metabolites related to tryptophan, arachidonic acid, and glutathione pathways.

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Background The fecal microbiota from obese individuals can induce obesity in animal models. In addition, studies in humans, animal models and dogs have revealed that the fecal microbiota of subjects with obesity is different from that of lean subjects and changes after weight loss. However, the impact of weight loss on the fecal microbiota in dogs with obesity has not been fully characterized. Methods In this study, we used 16S rRNA gene sequencing to investigate the differences in the fecal microbiota of 20 pet dogs with obesity that underwent a weight loss program. The endpoint of the weight loss program was individually tailored to the ideal body weight of each dog. In addition, we evaluated the qPCR based Dysbiosis Index before and after weight loss. Results After weight loss, the fecal microbiota structure of dogs with obesity changed significantly (weightedANOSIM; p = 0.016, R = 0.073), showing an increase in bacterial richness (p = 0.007), evenness (p = 0.007) and the number of bacterial species (p = 0.007). The fecal microbiota composition of obese dogs after weight loss was characterized by a decrease in Firmicutes (92.3% to 78.2%, q = 0.001), and increase in Bacteroidetes (1.4% to 10.1%, q = 0.002) and Fusobacteria (1.6% to 6.2%, q = 0.040). The qPCR results revealed an overall decrease in the Dysbiosis Index, driven mostly due to a significant decrease in E. coli (p = 0.030), and increase in Fusobacterium spp. (p = 0.017). Conclusion The changes observed in the fecal microbiota of dogs with obesity after weight loss with a weight loss diet rich in fiber and protein were in agreement with previous studies in humans, that reported an increase of bacterial biodiversity and a decrease of the ratio Firmicutes/Bacteroidetes.

The ocular surface microbiome of veterinary species has not been thoroughly characterized using molecular-based techniques, such as next generation sequencing (NGS), as the vast majority of studies have utilized traditional culture-based techniques. To date, there is one pilot study evaluating the ocular surface of healthy dogs using NGS. Furthermore, alterations in the ocular surface microbiome over time and after topical antibiotic treatment are unknown. The objectives of this study were to describe the bacterial composition of the ocular surface microbiome in clinically normal dogs, and to determine if microbial community changes occur over time or following topical antibiotic therapy. Topical neomycin-polymyxin-bacitracin ophthalmic ointment was applied to one eye each of 13 adult dogs three times daily for seven days, while contralateral eyes served as untreated controls. The inferior conjunctival fornix of both eyes was sampled via swabbing at baseline prior to antibiotic therapy (day 0), after 1 week of treatment (day 7), and 4 weeks after discontinuing treatment (day 35). Genomic DNA was extracted from the conjunctival swabs and primers targeting the V4 region of bacterial 16S rRNA genes were used to generate amplicon libraries, which were then sequenced on an Illumina platform. Data were analyzed using Quantitative Insights Into Molecular Ecology (QIIME 2.0). At baseline, the most relatively abundant phyla sequenced were Proteobacteria (49.7%), Actinobacteria (25.5%), Firmicutes (12%), Bacteroidetes (7.5%), and Fusobacteria (1.4%). The most common families detected were Pseudomonadaceae (13.2%), Micrococcaceae (12%), Pasteurellaceae (6.9%), Microbacteriaceae (5.2%), Enterobacteriaceae (3.9%), Neisseriaceae (3.5%), and Corynebacteriaceae (3.3%). Alpha and beta diversity measurements did not differ in both control and treatment eyes over time. This report examines the temporal stability of the canine ocular surface microbiome. The major bacterial taxa on the canine ocular surface remained consistent over time and following topical antibiotic therapy.