Maternal IL-33 critically regulates tissue remodeling and type 2 immune responses in the uterus during early pregnancy in mice.

The pregnant uterus is an immunologically rich organ, with dynamic changes in the inflammatory milieu and immune cell function underlying key stages of pregnancy. Recent studies have implicated dysregulated expression of the interleukin-1 (IL-1) family cytokine, IL-33, and its receptor, ST2, in poor pregnancy outcomes in women, including recurrent pregnancy loss, preeclampsia, and preterm labor. How IL-33 supports pregnancy progression in vivo is not well understood. Here, we demonstrate that maternal IL-33 signaling critically regulates uterine tissue remodeling and immune cell function during early pregnancy in mice. IL-33-deficient dams exhibit defects in implantation chamber formation and decidualization, and abnormal vascular remodeling during early pregnancy. These defects coincide with delays in early embryogenesis, increased resorptions, and impaired fetal and placental growth by late pregnancy. At a cellular level, myometrial fibroblasts, and decidual endothelial and stromal cells, are the main IL-33+ cell types in the uterus during decidualization and early placentation, whereas ST2 is expressed by uterine immune populations associated with type 2 immune responses, including ILC2s, Tregs, CD4+ T cells, M2- and cDC2-like myeloid cells, and mast cells. Early pregnancy defects in IL-33-deficient dams are associated with impaired type 2 cytokine responses by uterine lymphocytes and fewer Arginase-1+ macrophages in the uterine microenvironment. Collectively, our data highlight a regulatory network, involving crosstalk between IL-33-producing nonimmune cells and ST2+ immune cells at the maternal-fetal interface, that critically supports pregnancy progression in mice. This work has the potential to advance our understanding of how IL-33 signaling may support optimal pregnancy outcomes in women.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.