AUTHOR=Zhai Yuting , Kim Miju , Fan Peixin , Rajeev Sharath , Kim Sun Ae , Driver J. Danny , Galvão Klibs N. , Boucher Christina , Jeong Kwangcheol C. 

TITLE=Machine learning-enhanced assessment of potential probiotics from healthy calves for the treatment of neonatal calf diarrhea

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2024.1507537

DOI=10.3389/fmicb.2024.1507537

ISSN=1664-302X

ABSTRACT=Neonatal calf diarrhea (NCD) remains a significant contributor to calf mortality within the first 3 weeks of life, prompting widespread antibiotic use with associated concerns about antimicrobial resistance and disruption of the calf gut microbiota. Recent research exploring NCD treatments targeting gut microbiota dysbiosis has highlighted probiotic supplementation as a promising and safe strategy for gut homeostasis. However, varying treatment outcomes across studies suggest the need for efficient treatment options. In this study, we evaluated the potential of probiotics Limosilactobacillus reuteri, formally known as Lactobacillus reuteri, isolated from healthy neonatal calves to treat NCD. Through in silico whole genome analysis and in vitro assays, we identified nine L. reuteri strains, which were then administered to calves with NCD. Calves treated with L. reuteri strains shed healthy feces and demonstrated restored gut microbiota and normal animal behavior. Leveraging a machine learning model, we evaluated microbiota profiles and identified bacterial taxa associated with calf gut health that were elevated by L. reuteri administration. These findings represent a crucial advancement towards sustainable antibiotic alternatives for managing NCD, contributing significantly to global efforts in mitigating antimicrobial resistance and promoting overall animal health and welfare.