An essential role for Cmtr2 in mammalian embryonic development.

CMTR2 is an mRNA cap methyltransferase with poorly understood physiological functions. It catalyzes 2'-O-ribose methylation of the second transcribed nucleotide of mRNAs, potentially serving to mark RNAs as "self" to evade the cellular innate immune response. Here we analyze the consequences of Cmtr2 deficiency in mice. We discover that constitutive deletion of Cmtr2 results in mouse embryos that die during mid-gestation, exhibiting defects in embryo size, placental malformation and yolk sac vascularization. Endothelial cell deletion of Cmtr2 in mice results in vascular and hematopoietic defects, and perinatal lethality. Detailed characterization of the constitutive Cmtr2 KO phenotype shows an activation of the p53 pathway and decreased proliferation, but no evidence of interferon pathway activation. In summary, our study reveals the essential roles of Cmtr2 in mammalian cells beyond its immunoregulatory function.

Synthetic Health Data Can Augment Community Research Efforts to Better Inform the Public During Emerging Pandemics.

The COVID-19 pandemic had disproportionate effects on the Veteran population due to the increased prevalence of medical and environmental risk factors. Synthetic electronic health record (EHR) data can help meet the acute need for Veteran population-specific predictive modeling efforts by avoiding the strict barriers to access, currently present within Veteran Health Administration (VHA) datasets. The U.S. Food and Drug Administration (FDA) and the VHA launched the precisionFDA COVID-19 Risk Factor Modeling Challenge to develop COVID-19 diagnostic and prognostic models; identify Veteran population-specific risk factors; and test the usefulness of synthetic data as a substitute for real data. The use of synthetic data boosted challenge participation by providing a dataset that was accessible to all competitors. Models trained on synthetic data showed similar but systematically inflated model performance metrics to those trained on real data. The important risk factors identified in the synthetic data largely overlapped with those identified from the real data, and both sets of risk factors were validated in the literature. Tradeoffs exist between synthetic data generation approaches based on whether a real EHR dataset is required as input. Synthetic data generated directly from real EHR input will more closely align with the characteristics of the relevant cohort. This work shows that synthetic EHR data will have practical value to the Veterans' health research community for the foreseeable future.