EP3 (E-Prostanoid 3) Receptor Mediates Impaired Vasodilation in a Mouse Model of Salt-Sensitive Hypertension

We previously showed that impaired vasodilation in systemic and renal vessels contributes to salt-sensitive hypertension in a mouse model of impaired PPARγ function. We determined the mechanisms mediating impaired salt-induced vasodilation and whether improved vasodilation attenuates augmented hypertension in response to salt. Mice selectively expressing a PPARγ dominant negative mutation in vascular smooth muscle (S-P467L) exhibited salt-sensitive hypertension and severely impaired vasodilation in systemic and renal vessels. High salt diet (HSD) fed S-P467L and control mice displayed comparable levels of renal oxidative stress markers. Pre-incubation with Tempol, a superoxide dismutase mimetic, or Calphostin C, a protein kinase C (PKC) inhibitor failed to improve salt-induced impairment of vasodilation in S-P467L mice, arguing against a role of oxidative stress or PKC activity. Inhibition of Rho kinase partially rescued impaired vasodilation in HSD-fed S-P467L mice suggesting a contribution of the RhoA/Rho Kinase pathway. HSD selectively increased synthesis of prostaglandin E2 (PGE2) in S-P467L aorta. Expression of E-prostanoid 3 (EP3) receptor mRNA was increased in aorta from chow- and high salt-fed S-P467L mice. Pharmacological inhibition of cyclooxygenase 2 or blockade of EP3 completely normalized the impaired vasodilation and EP3 antagonism induced larger decreases in systolic blood pressure in HSD-fed S-P467L mice. In conclusion, interference with PPARγ in vascular smooth muscle causes activation of the PGE2/EP3 signaling pathway in systemic and renal vasculature resulting in salt-induced impairment of vasodilation and salt-sensitive hypertension. PGE2/EP3 axis maybe a druggable target to prevent salt-sensitive hypertension in chronic conditions associated with decreased PPARγ activity

Machine learning reveals genetic modifiers of the immune microenvironment of cancer

Summary: Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets