PLoS PathogensMicrobiologyPeer-reviewed journal article
Genome-wide analysis of fitness determinants of Staphylococcus aureus during growth in milk
Marita Torrissen MårliiD, Anne Olsdatter Ohren NordraakiD, Vincent de BakkeriD, Anja Ruud WintheriD, Xue LiuiD, Jan‐Willem VeeningiD, Davide PorcellatoiD, Morten KjosiD
Abstract
Staphylococcus aureus is a major concern in the dairy industry due to its significance as a pathogen causing bovine mastitis as well as a source of food poisoning. The nutrient-rich milk environment supports bacterial growth, but the specific genetic determinants that facilitate S. aureus proliferation and persistence in milk are poorly understood. In this study, we conducted a genome-wide CRISPR interference sequencing (CRISPRi-seq) screen to identify fitness determinants essential for S. aureus growth and survival in milk. We identified 282 milk-essential genes, including those with key roles in DNA replication, protein synthesis, and metabolism. Comparative analysis with brain heart infusion (BHI) as growth medium, revealed 79 genes with differential fitness, highlighting specific adaptations required for growth in milk. Notably, we found that purine biosynthesis, folate cycle pathways, and metal acquisition were particularly important in this environment. Based on this, we further demonstrate that S. aureus is more sensitive to the folate inhibitors trimethoprim-sulfamethoxazole (TMP-SMX) in milk and identify several genes whose knockdown results in hypersensitivity to TMP-SMX in milk. Additionally, our analysis showed a relatively reduced importance of cell wall components, such as teichoic acids, for S. aureus fitness in milk, which is also reflected in reduced efficiency of antimicrobials targeting teichoic acids. Together, these findings provide new insights into the genetic basis of S. aureus fitness and antibiotic susceptibility in milk, offering directions for novel treatment strategies against bovine mastitis.
- research focus: drug response / drug discovery
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: genetic perturbation / CRISPR
- Citations
- 8
- FWCI
- 5.32
- Field percentile
- top 4%
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