Insulin receptor loss impairs mammary tumorigenesis in mice.

Breast cancer (BC) prognosis and outcome are adversely affected by obesity. Hyperinsulinemia, common in the obese state, is associated with higher risk of death and recurrence in BC. Up to 80% of BCs overexpress the insulin receptor (INSR), which correlates with worse prognosis. INSR's role in mammary tumorigenesis was tested by generating MMTV-driven polyoma middle T (PyMT) and ErbB2/Her2 BC mouse models, respectively, with coordinate mammary epithelium-restricted deletion of INSR. In both models, deletion of either one or both copies of INSR leads to a marked delay in tumor onset and burden. Longitudinal phenotypic characterization of mouse tumors and cells reveals that INSR deletion affects tumor initiation, not progression and metastasis. INSR upholds a bioenergetic phenotype in non-transformed mammary epithelial cells, independent of its kinase activity. Similarity of phenotypes elicited by deletion of one or both copies of INSR suggest a dose-dependent threshold for INSR impact on mammary tumorigenesis.

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions.

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .