Interplay between Neuroendocrine Biomarkers and Gut Microbiota in Dogs Supplemented with Grape Proanthocyanidins: Results of Dietary Intervention Study

Scarsella, Elisa; Cintio, Michela; Iacumin, Lucilla; Ginaldi, Federica; Stefanon, Bruno

doi:10.3390/ani10030531

Cited by 19 publications

(24 citation statements)

References 54 publications

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“…Briefly, dogs were recruited from different living environment for every DIS and they were undergone through diet modulation. The DIS 1 [ 12 ], 2 [ 13 ], 3 and 8 [ 14 ] were private kennels, DIS 4 [ 15 ] and 7 were shelters and DIS 5 and 6 were dogs privately owned. For every DIS, the first faecal sample (T0) was collected from dogs that were fed the usual diet they received since at least 6 months (control diet, CTR).…”

Section: Methodsmentioning

confidence: 99%

Learning machine approach reveals microbial signatures of diet and sex in dog

et al. 2020

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The characterization of the microbial population of many niches of the organism, as the gastrointestinal tract, is now possible thanks to the use of high-throughput DNA sequencing technique. Several studies in the companion animals field already investigated faecal microbiome in healthy or affected subjects, although the methodologies used in the different laboratories and the limited number of animals recruited in each experiment does not allow a straight comparison among published results. In the present study, we report data collected from several in house researches carried out in healthy dogs, with the aim to seek for a variability of microbial taxa in the faeces, caused by factors such as diet and sex. The database contains 340 samples from 132 dogs, collected serially during dietary intervention studies. The procedure of samples collection, storage, DNA extraction and sequencing, bioinformatic and statistical analysis followed a standardized pipeline. Microbial profiles of faecal samples have been analyzed applying dimensional reduction discriminant analysis followed by random forest analysis to the relative abundances of genera in the feces as variables. The results supported the responsiveness of microbiota at a genera taxonomic level to dietary factor and allowed to cluster dogs according this factor with high accuracy. Also sex factor clustered dogs, with castrated males and spayed females forming a separated group in comparison to intact dogs, strengthening the hypothesis of a bidirectional interaction between microbiota and endocrine status of the host. The findings of the present analysis are promising for a better comprehension of the mechanisms that regulate the connection of the microorganisms living the gastrointestinal tract with the diet and the host. This preliminary study deserves further investigation for the identification of the factors affecting faecal microbiome in dogs.

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Section: Methodsmentioning

confidence: 99%

Learning machine approach reveals microbial signatures of diet and sex in dog

et al. 2020

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“…The 16S rRNA annotated sequences were then normalized to ‰ abundance profiles for each sample and each taxonomic level, already known as relative abundance (RA). Taxa with RA lower than 10‰ were excluded from the statistical analysis [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Methodsmentioning

confidence: 99%

“…It has been also demonstrated, both in human and animal models, that the microorganism inhabitants of the gut and the compounds they produce, such as short-chain fatty acids, lactic acid, and metabolites of amino acids, affect host physiology [ 3 ]. Among the others, the microbial metabolism of tryptophan and threonine and the related interaction with the availability of neurotransmitters for the host has led to the development of the brain-gut-microbiome axis concept [ 4 , 5 ]. It has also been reported that the microbiome can influence distant organs by three principal effects, regulating the nutritional absorption and production of vitamins, regulating the immune system, and translocating bacteria through the endothelial barrier and into the bloodstream [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Gut Microbiome of Healthy and Arthritic Dogs

Cintio

Scarsella

Sgorlon

et al. 2020

Veterinary Sciences

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Several studies have underlined the interplay among host-microbiome and pathophysiological conditions of animals. Research has also focused specifically on whether and how changes in the gut microbiome have provoked the occurrence of pathological phenomena affecting cartilage and joints in humans and in laboratory animals. Here, we tried to evaluate the relationship between the gut microbiome and the hip and elbow arthritis in owned dogs. The study included 14 dogs suffering from chronic arthritis (AD) and 13 healthy dogs (HD). After the first visit and during the period of the study, the dogs, under the supervision of the owner, were fed a semi-moist complete diet supplemented with omega 3 fatty acids. Feces and blood samples were collected in the clinic at the first visit (T0) and after days (T45). The plasma C-reactive protein (CRP) was higher, and the serum vitamin B12 and folate concentrations were lower (p < 0.05) in the AD group in comparison to the HD group. Data of the fecal microbiome showed that the relative abundances of the genus Megamonas were higher in AD (p < 0.001), while the relative abundance of the families Paraprevotellaceae, Porphyromonadaceae, and Mogibacteriaceae was significantly lower in comparison to HD. The results of the study identified several bacterial groups that differed significantly in the fecal microbiome between healthy and diseased dogs. If the observed differences in fecal bacterial composition predispose dogs to hip and elbow arthritis or if these differences reflect a correlation with these conditions deserves further investigation.

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“…While a relationship between the gut microbiome and obesity has been observed, it remains unclear as to how the gut microbiome contributes to the development of obesity, but proposed mechanisms include the production of short chained fatty acids (SCFAs), monosaccharides, and other bioactive molecules. These bacterial products may lead to an increase in dietary energy harvest ( Turnbaugh et al, 2006 ), changes in lipid metabolism ( Ghazalpour et al, 2016 ), changes in fat storage regulation ( Bäckhed et al, 2004 ; Bäckhed et al, 2007 ), altered satiety ( Arora, Sharma & Frost, 2011 ), and an increase in systemic low-grade inflammation via the interaction with either the enteric nervous system ( Schwartz, 2000 ; Tehrani et al, 2012 ; De Lartigue, De La Serre & Raybould, 2011 ), the endocrine system ( Mondo et al, 2020 ; Kirchoff, Udell & Sharpton, 2019 ; Scarsella et al, 2020 ), or the immune system ( Cani et al, 2007 ; Cani et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Fecal microbiota in client-owned obese dogs changes after weight loss with a high-fiber-high-protein diet

Sanchez

Pilla

Sarawichitr

et al. 2020

PeerJ

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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.

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Interplay between Neuroendocrine Biomarkers and Gut Microbiota in Dogs Supplemented with Grape Proanthocyanidins: Results of Dietary Intervention Study

Cited by 19 publications

References 54 publications

Learning machine approach reveals microbial signatures of diet and sex in dog

Learning machine approach reveals microbial signatures of diet and sex in dog

Gut Microbiome of Healthy and Arthritic Dogs

Fecal microbiota in client-owned obese dogs changes after weight loss with a high-fiber-high-protein diet

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