Panigrahi Manjit, Rajawat Divya, Nayak Sonali Sonejita, Jain Karan, Nayak Ambika, Rajput Atul Singh, Sharma Anurodh, Dutt Triveni
Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, UP, India.
Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, UP, India.
Microb Pathog. 2025 Feb;199:107233. doi: 10.1016/j.micpath.2024.107233. Epub 2024 Dec 16.
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.
乳腺炎是一种多病因疾病，会对产奶量和繁殖效率产生重大影响。在经过高强度选育以提高产奶量且近亲繁殖普遍的奶牛群体中，乳腺炎非常普遍。气候变化和卫生管理不善加剧了这一问题，使得疾病控制颇具挑战性。关键障碍包括抗生素耐药性、最大残留限量、水平基因转移以及抗病育种成效有限。预测基因组学通过识别与乳腺炎易感性相关的遗传特征，为乳腺炎预防提供了一个有前景的解决方案。本综述汇编了关于基因组学及其相关方法（如泛基因组学、表观遗传学、蛋白质组学和转录组学）在乳腺炎诊断、理解和治疗方面的研究及发现。在奶牛生产中，人工智能（AI），特别是卷积神经网络（CNN）等深度学习（DL）技术，已显示出提高产奶量和提升农场盈利能力的巨大潜力。它强调了机器学习（ML）、CRISPR和泛基因组学等先进技术的整合，以增进我们对乳腺炎流行病学、病原体进化的了解，以及为奶牛群制定更有效的诊断、预防和治疗策略。基因组学的进展为乳腺炎的复杂性提供了关键见解，为理解其动态变化提供了新途径。将这些发现与关键诱发因素相结合，可以推动有针对性的预防和更有效的疾病管理。
