Self-guarding of MORC3 enables virulence factor-triggered immunity.

Pathogens use virulence factors to inhibit the immune system1. The guard hypothesis2,3 postulates that hosts monitor (or 'guard') critical innate immune pathways such that their disruption by virulence factors provokes a secondary immune response1. Here we describe a 'self-guarded' immune pathway in human monocytes, in which guarding and guarded functions are combined in one protein. We find that this pathway is triggered by ICP0, a key virulence factor of herpes simplex virus type 1, resulting in robust induction of anti-viral type I interferon (IFN). Notably, induction of IFN by ICP0 is independent of canonical immune pathways and the IRF3 and IRF7 transcription factors. A CRISPR screen identified the ICP0 target MORC34 as an essential negative regulator of IFN. Loss of MORC3 recapitulates the IRF3- and IRF7-independent IFN response induced by ICP0. Mechanistically, ICP0 degrades MORC3, which leads to de-repression of a MORC3-regulated DNA element (MRE) adjacent to the IFNB1 locus. The MRE is required in cis for IFNB1 induction by the MORC3 pathway, but is not required for canonical IFN-inducing pathways. As well as repressing the MRE to regulate IFNB1, MORC3 is also a direct restriction factor of HSV-15. Our results thus suggest a model in which the primary anti-viral function of MORC3 is self-guarded by its secondary IFN-repressing function-thus, a virus that degrades MORC3 to avoid its primary anti-viral function will unleash the secondary anti-viral IFN response.

Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.

The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility between well characterized reference cell types and a query cellular context, and copies over signal of transcription factor binding and modification of histones from reference cell types when chromatin profiles are similar to the query. Epitome achieves state-of-the-art accuracy when predicting transcription factor binding sites on novel cellular contexts and can further improve predictions as more epigenetic signals are collected from both reference cell types and the query cellular context of interest.

Mango: Exploratory Data Analysis for Large-Scale Sequencing Datasets.

The decreasing cost of DNA sequencing over the past decade has led to an explosion of sequencing datasets, leaving us with petabytes of data to analyze. However, current sequencing visualization tools are designed to run on single machines, which limits their scalability and interactivity on modern genomic datasets. Here, we leverage the scalability of Apache Spark to provide Mango, consisting of a Jupyter notebook and genome browser, which removes scalability and interactivity constraints by leveraging multi-node compute clusters to allow interactive analysis over terabytes of sequencing data. We demonstrate scalability of the Mango tools by performing quality control analyses on 10 terabytes of 100 high-coverage sequencing samples from the Simons Genome Diversity Project, enabling capability for interactive genomic exploration of multi-sample datasets that surpass the computational limitations of single-node visualization tools. Mango is freely available for download with full documentation at https://bdg-mango.readthedocs.io/en/latest/.