Augmenting subnetwork inference with information extracted from the scientific literature.

Many biological studies involve either (i) manipulating some aspect of a cell or its environment and then simultaneously measuring the effect on thousands of genes, or (ii) systematically manipulating each gene and then measuring the effect on some response of interest. A common challenge that arises in these studies is to explain how genes identified as relevant in the given experiment are organized into a subnetwork that accounts for the response of interest. The task of inferring a subnetwork is typically dependent on the information available in publicly available, structured databases, which suffer from incompleteness. However, a wealth of potentially relevant information resides in the scientific literature, such as information about genes associated with certain concepts of interest, as well as interactions that occur among various biological entities. We contend that by exploiting this information, we can improve the explanatory power and accuracy of subnetwork inference in multiple applications. Here we propose and investigate several ways in which information extracted from the scientific literature can be used to augment subnetwork inference. We show that we can use literature-extracted information to (i) augment the set of entities identified as being relevant in a subnetwork inference task, (ii) augment the set of interactions used in the process, and (iii) support targeted browsing of a large inferred subnetwork by identifying entities and interactions that are closely related to concepts of interest. We use this approach to uncover the pathways involved in interactions between a virus and a host cell, and the pathways that are regulated by a transcription factor associated with breast cancer. Our experimental results demonstrate that these approaches can provide more accurate and more interpretable subnetworks. Integer program code, background network data, and pathfinding code are available at https://github.com/Craven-Biostat-Lab/subnetwork_inference.

Enhancer transcribed RNAs arise from hypomethylated, Tet-occupied genomic regions.

Enhancers are cis-acting elements capable of regulating transcription in a distance and orientation-independent manner. A subset of enhancers are occupied by RNA polymerase II (RNAP II) and transcribed to produce long non-coding RNAs termed eRNAs. We thoroughly investigated the association between eRNA productivity and various chromatin marks and transcriptional regulators in mouse embryonic stem cells (ESCs) through an integrative approach. We found that eRNA-producing enhancers exhibited elevated levels of the active mark H3K27Ac, decreased DNA methylation, and enrichment for the DNA hydroxylase Tet1. Many eRNA-producing enhancers have recently been characterized as "super-enhancers," suggesting an important role in the maintenance of pluripotency. Using experimental methods, we focally investigated a well-characterized enhancer linked to the Nanog locus and confirmed its exclusive eRNA productivity in ESCs. We further demonstrate that the binding of Sall4 and Tet family proteins were required for eRNA productivity at this locus. Collectively, we demonstrate that Tet1 binding and DNA hypomethylation are hallmarks of eRNA production.