A comprehensive review on genomic insights and advanced technologies for mastitis prevention in dairy animals

• Published in: Microbial pathogenesis 2024-12, Vol.199, p.107233, Article 107233
• Main Authors: Panigrahi, Manjit, Rajawat, Divya, Nayak, Sonali Sonejita, Jain, Karan, Nayak, Ambika, Rajput, Atul Singh, Sharma, Anurodh, Dutt, Triveni
• Format: Article
• Language: English
• Subjects: Bovine; Genomics; Machine learning; Mastitis; Predictive genomics
• ISSN: 0882-4010; 1096-1208; 1096-1208
• DOI: 10.1016/j.micpath.2024.107233

Mastitis is a multi-etiological disease that significantly impacts milk production and reproductive efficiency. It is highly prevalent in dairy populations subjected to intensive selection for higher milk yield and where inbreeding is common. The issue is amplified by climate change and poor hygiene management, making disease control challenging. Key obstacles include antibiotic resistance, maximum residue levels, horizontal gene transfer, and limited success in breeding for resistance. Predictive genomics offers a promising solution for mastitis prevention by identifying genetic traits linked with susceptibility to mastitis. This review compiles the research and findings on genomics and its allied approaches, such as pan-genomics, epigenetics, proteomics, and transcriptomics, for diagnosing, understanding, and treating mastitis. In dairy production, artificial intelligence (AI), particularly deep learning (DL) techniques like convolutional neural networks (CNNs), has demonstrated significant potential to enhance milk production and improve farm profitability. It highlights the integration of advanced technologies like machine learning (ML), CRISPR, and pan-genomics to improve our knowledge of mastitis epidemiology, pathogen evolution, and the development of more effective diagnostic, preventive and therapeutic strategies for dairy herds. Genomic advancements provide critical insights into the complexities of mastitis, offering new avenues for understanding its dynamics. Integrating these findings with key predisposing factors can drive targeted prevention and more effective disease management. •Advanced Approaches for SCM Regulation: Genomic and epigenomic techniques are utilized to elucidate the regulatory mechanisms of subclinical mastitis (SCM).•Comprehensive Molecular Insights: These integrative methods provide a detailed understanding of the molecular basis of SCM and therapeutic strategies.•Enhanced Detectionand Resistance: Predictive genomics, combined with other advanced techniques facilitating more precise and efficient control measures for SCM.