bioRxiv Science⌕ Search

Biology subjects

McEwen, K.

Publications and source records attributed to McEwen, K..

2 recordsLinked to original sources

Sex differences in gene expression and proliferation are dependent on the epigenetic modifier HP1γ

Sex differences in growth rate in very early embryos have been recognized in a variety of mammals and attributed to sex-chromosome complement effects as they occur before overt sexual differentiation. We previously found that sex-chromosome complement, rather than sex hormones regulates heterochromatin-mediated silencing of a transgene and autosomal gene expression in mice. Here, sex dimorphism in proliferation was investigated. We confirm that male embryonic fibroblasts proliferate faster than female fibroblasts and show that this proliferation advantage is completely dependent upon heterochromatin protein 1 gamma (HP1{gamma}). To determine whether this sex-regulatory effect of HP1{gamma} was a more general phenomenon, we performed RNA sequencing on MEFs derived from males and females, with or without HP1{gamma}. Strikingly, HP1{gamma} was found to be crucial for regulating nearly all sexually dimorphic autosomal gene expression because deletion of the HP1{gamma} gene in males abolished sex differences in autosomal gene expression. The identification of a key epigenetic modifier as central in defining gene expression differences between males and females has important implications for understanding physiological sex differences and sex bias in disease.

developmental biology↗

Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

Gene expression is controlled by networks of transcription factors and regulators, but the structure of these networks is as yet poorly understood and is thus inferred from data. Recent work has shown the efficacy of information theoretical approaches for network reconstruction from single cell transcriptomic data. Such methods use information to estimate dependence between every pair of genes in the dataset, then edges are inferred between top-scoring pairs. Dependence, however, does not indicate significance, and the definition of \"top-scoring\" is often arbitrary and a priori related to expected network size. This makes comparing networks across datasets difficult, because networks of a similar size are not necessarily similarly accurate. We present a method for performing formal hypothesis tests on putative network edges derived from information theory, bringing together empirical Bayes and work on theoretical null distributions for information measures. Thresholding based on empirical Bayes allows us to control network accuracy according to how we intend to use the network. Using single cell data from mouse pluripotent stem cells, we recover known interactions and suggest several new interactions for experimental validation (using a stringent threshold) and discover high-level interactions between sub-networks (using a more relaxed threshold). Furthermore, our method allows for the inclusion of prior information. We use in-silico data to show that even relatively poor quality prior information can increase the accuracy of a network, and demonstrate that the accuracy of networks inferred from single cell data can sometimes be improved by priors from population-level ChIP-Seq and qPCR data.

systems biology↗