Applying Microbiome Data to Improve Food-Producing Animal Health

Applying microbiota data in clinical practice represents a growing frontier in veterinary and human medicine. While most of these studies have relied on DNA sequencing to profile the microbiota, the effectiveness of these methods has been called into question. Issues such as the lack of absolute quantification, methodological bias, and the significant time and effort required have hindered the reproducibility and clinical applicability of these studies. Despite these limitations, microbiome data has been employed in humans, as well as in cats and dogs, to develop diagnostic tests for detecting intestinal dysbiosis. These tests support early preventive measures and targeted treatments, ultimately helping to improve patient outcomes. Our preliminary results indicate that uterine dysbiosis in cows can be detected up to three weeks before the onset of metritis and shortly before the onset of diarrhea in calves. Other researchers have demonstrated bacterial shifts in cows with ruminal acidosis prior to clinical signs onset. These findings suggest that early diagnosis through bacterial markers could facilitate preventive interventions. Consequently, microbiome data holds significant potential for identifying dysbiosis biomarkers and developing rapid, reliable, and cost-effective diagnostic tests. The objectives of this lecture are to explain the limitations of studies characterizing microbiomes using DNA sequencing methodologies, to explain the importance of early detection of dysbiosis across various body systems highlighting the discovery of biomarkers to develop rapid, reliable, and cost-effective diagnostic tests and present future perspectives in the microbiome field, including developing next-generation probiotics and targeted therapeutic approaches for improving health and productivity in cattle.