Invited review: Multiomics insights into the molecular and regulatory mechanisms underlying bovine mastitis

Recent developments and application of cutting-edge technologies and digital information is enabling the dairy industry to generate substantial data across different spectra. These data and, in particular, research-driven omics data are becoming increasingly accessible for understanding the factors underlying production and health traits. Mastitis, an inflammation of the mammary gland caused by a wide range of organisms, represents a pivotal concern due to its substantial impact on milk quality and production, animal welfare, environment and public health concerns, and economic losses. Among the strategies employed to control mastitis is preventive and therapeutic administration of antibiotics, which unfortunately has association with the development of antimicrobial resistance (AMR). Given the intricate nature and paramount importance of dairy production and the urgency to control the development of AMR in livestock production, exploration of the various factors and molecular mechanisms implicated in the development of mastitis will advance the development of strategies to manage and reduce mastitis. In recent times, advancements in omics technologies have enhanced our understanding of bovine mammary gland health. The single omics datasets that are mostly applied are limited in their ability to capture the interactions and relationships between different layers of biological processes that underlie a complex disease condition such as mastitis. Meanwhile integration of multiomics datasets is a promising approach for gaining deeper understanding and elucidation of the complex biological systems and identification of the potential causative molecular mechanisms underlying mastitis. This review presents the multiomics insights into disease processes, host-pathogen interactions, the immune response, and impaired mammary gland productivity during mastitis. It also discusses how multiomics integration enhances biomarker discovery, what needs to be considered for the application of multiomics, and what are the research gaps.