LAUSR

Simple Summary Fecal microbiota transplantation (FMT) represents a very promising approach to decrease disease activity in chronic enteropathies (CE). Although CE and dysbiosis are undoubtedly connected, the relationship between remission mechanisms and microbiome changes has not been elucidated yet. Indeed, CE is a heterogeneous disease consisting of many different subtypes, and the dynamic of the microbial community is very complex. The aim of this study was to report the clinical effects of oral freeze-dried FMT in dogs affected by CE, analyzing the fecal microbiome before and after FMT, and comparing the microbial composition with a healthy population. Artificial intelligence algorithms were applied to address the high complexity of microbiomes. Clinical signs of improvement were observed in three-quarters of receivers, proving the effectiveness of the treatment in the freeze-dried form. Machine learning algorithms successfully predicted healthy and diseased animal categories, using microbial compositions. Every receiver showed microbiome variation after the transplant, but there was high heterogeneity in the response. These findings are the first step for further research on a larger dataset that could identify different healing patterns of microbiome changes. Abstract Fecal microbiota transplantation (FMT) represents a very promising approach to decreasing disease activity in canine chronic enteropathies (CE). However, the relationship between remission mechanisms and microbiome changes has not been elucidated yet. The main objective of this study was to report the clinical effects of oral freeze-dried FMT in CE dogs, comparing the fecal microbiomes of three groups: pre-FMT CE-affected dogs, post-FMT dogs, and healthy dogs. Diversity analysis, differential abundance analysis, and machine learning algorithms were applied to investigate the differences in microbiome composition between healthy and pre-FMT samples, while Canine Chronic Enteropathy Clinical Activity Index (CCECAI) changes and microbial diversity metrics were used to evaluate FMT effects. In the healthy/pre-FMT comparison, significant differences were noted in alpha and beta diversity and a list of differentially abundant taxa was identified, while machine learning algorithms predicted sample categories with 0.97 (random forest) and 0.87 (sPLS-DA) accuracy. Clinical signs of improvement were observed in 74% (20/27) of CE-affected dogs, together with a statistically significant decrease in CCECAI (median value from 5 to 2 median). Alpha and beta diversity variations between pre- and post-FMT were observed for each receiver, with a high heterogeneity in the response. This highlighted the necessity for further research on a larger dataset that could identify different healing patterns of microbiome changes.