Set1 and MLL1/2 Target Distinct Sets of Functionally Different Genomic Loci In Vivo

SummaryHistone H3 lysine 4 trimethylation (H3K4me3) is known to correlate with both active and poised genomic loci, yet many questions remain regarding its functional roles in vivo. We identify functional genomic targets of two H3K4 methyltransferases, Set1 and MLL1/2, in both the stem cells and differentiated tissue of the planarian flatworm Schmidtea mediterranea. We show that, despite their common substrate, these enzymes target distinct genomic loci in vivo, which are distinguishable by the pattern each enzyme leaves on the chromatin template, i.e., the breadth of the H3K4me3 peak. Whereas Set1 targets are largely associated with the maintenance of the stem cell population, MLL1/2 targets are specifically enriched for genes involved in ciliogenesis. These data not only confirm that chromatin regulation is fundamental to planarian stem cell function but also provide evidence for post-embryonic functional specificity of H3K4me3 methyltransferases in vivo

Spatial Prediction of COVID-19 Pandemic Dynamics in the United States

The impact of COVID-19 across the United States (US) has been heterogeneous, with rapid spread and greater mortality in some areas compared with others. We used geographically-linked data to test the hypothesis that the risk for COVID-19 was defined by location and sought to define which demographic features were most closely associated with elevated COVID-19 spread and mortality. We leveraged geographically-restricted social, economic, political, and demographic information from US counties to develop a computational framework using structured Gaussian process to predict county-level case and death counts during the pandemic’s initial and nationwide phases. After identifying the most predictive information sources by location, we applied an unsupervised clustering algorithm and topic modeling to identify groups of features most closely associated with COVID-19 spread. Our model successfully predicted COVID-19 case counts of unseen locations after examining case counts and demographic information of neighboring locations, with overall Pearson’s correlation coefficient and the proportion of variance explained as 0.96 and 0.84 during the initial phase and 0.95 and 0.87 during the nationwide phase, respectively. Aside from population metrics, presidential vote margin was the most consistently selected spatial feature in our COVID-19 prediction models. Urbanicity and 2020 presidential vote margins were more predictive than other demographic features. Models trained using death counts showed similar performance metrics. Topic modeling showed that counties with similar socioeconomic and demographic features tended to group together, and some of these feature sets were associated with COVID-19 dynamics. Clustering of counties based on these feature groups found by topic modeling revealed groups of counties that experienced markedly different COVID-19 spread. We conclude that topic modeling can be used to group similar features and identify counties with similar features in epidemiologic research

Novel cell types and developmental lineages revealed by single-cell RNA-seq analysis of the mouse crista ampullaris

This study provides transcriptomic characterization of the cells of the crista ampullaris, sensory structures at the base of the semicircular canals that are critical for vestibular function. We performed single-cell RNA-seq on ampullae microdissected from E16, E18, P3, and P7 mice. Cluster analysis identified the hair cells, support cells and glia of the crista as well as dark cells and other nonsensory epithelial cells of the ampulla, mesenchymal cells, vascular cells, macrophages, and melanocytes. Cluster-specific expression of genes predicted their spatially restricted domains of gene expression in the crista and ampulla. Analysis of cellular proportions across developmental time showed dynamics in cellular composition. The new cell types revealed by single-cell RNA-seq could be important for understanding crista function and the markers identified in this study will enable the examination of their dynamics during development and disease