Transcriptional accessibility of chromatin is central to guiding CD4+ T cell function through regulation of lineage specific gene expression. Myst1 is a histone acetyltransferase responsible for acetylation of the protein tail of histone 4 at lysine residue 16 (H416ac), resulting in increased transcriptional accessibility and activation of gene transcription. Previous studies have described a role for Myst1 in governing lymphocyte development in the thymus, however the role of Myst1 and H4K16ac in guiding activation of peripheral CD4+ T cells has not been studied. Activation of human and murine CD4+ T cells resulted in upregulation of Myst1 expression, and deletion of Myst1 resulted in changes in proliferative responses to both polyclonal stimulus and exogenous cytokines. Myst1-deficient T cells also exhibited modulations in lineage commitment, with decreased function in TH1/TH2 skewing conditions and increased function in response to TH17-promoting conditions. Regulation of Myst1 function in CD4+ T cells appears governed at least in part by STAT5, as Myst1 expression is regulated by STAT5 expression and DNA binding, and modulations in H4K16ac in Myst1-deficient CD4+ T cells is observable at sites in the promoter regions of lineage specific genes following skewing to the TH1 or TH2 lineage in vitro. Taken together, these results indicate an important role for the STAT5-Myst1 epigenetic axis in governing the activation and effector function of CD4+ T cells.
Many data sets come as point patterns of the form (longitude, latitude, time, magnitude). The examples of data sets in this format includes tornado events, origins/destination of internet flows, earthquakes, terrorist attacks and etc. It is difficult to visualize the data with simple plotting. This research project studies and implements non-parametric kernel smoothing in Python as a way of visualizing the intensity of point patterns in space and time. A two-dimensional grid M with size mx, my is used to store the calculation result for the kernel smoothing of each grid points. The heat-map in Python then uses the grid to plot the resulting images on a map where the resolution is determined by mx and my. The resulting images also depend on a spatial and a temporal smoothing parameters, which control the resolution (smoothness) of the figure. The Python code is applied to visualize over 56,000 tornado landings in the continental U.S. from the period 1950 - 2014. The magnitudes of the tornado are based on Fujita scale.