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Biology subjects

Masters, S.

Publications and source records attributed to Masters, S..

2 recordsLinked to original sources

DPP9 is an endogenous and direct inhibitor of the NLRP1 inflammasome that guards against human auto-inflammatory diseases

The inflammasome is a critical immune complex that activates IL-1 driven inflammation in response to pathogen- and danger-associated signals. Nod-like receptor protein-1 (NLRP1) is a widely expressed inflammasome sensor. Inherited gain-of-function mutations in NLRP1 cause a spectrum of human Mendelian diseases, including systemic autoimmunity and skin cancer susceptibility. However, its endogenous regulation and its cognate ligands are still unknown. Here we apply a proteomics screen to identify dipeptidyl dipeptidase, DPP9 as a novel interacting partner and a specific endogenous inhibitor of NLRP1 inflammasome in diverse primary cell types from human and mice. DPP9 inhibition via small molecule drugs, targeted mutations in its catalytic site and CRISPR/Cas9-mediated genetic deletion potently and specifically activate the NLRP1 inflammasome leading to pyroptosis and IL-1 processing via ASC and caspase-1. Mechanistically, DPP9 maintains NLRP1 in its monomeric, inactive state by binding to the auto-cleaving FIIND domain. NLRP1-FIIND is a self-sufficient DPP9 binding module and its disruption by a single missense mutation abrogates DPP9 binding and explains the aberrant inflammasome activation in NAIAD patients with arthritis and dyskeratosis. These findings uncover a unique peptidase enzyme-based mechanism of inflammasome regulation, and suggest that the DPP9-NLRP1 complex could be broadly involved in human inflammatory disorders.

immunology

Ximmer: A System for Improving Accuracy and Consistency of CNV Calling from Exome Data

Detection of copy number variation (CNVs) is a challenging but highly valuable application of exome and targeted high throughput sequencing (HTS) data. While there are dozens of CNV detection methods available, using these methods remains challenging due to variable accuracy both across different data sets and within the same data set with different methods. We propose that extracting good results from CNV detection on HTS data requires a systematic approach involving rigorous quality control, adjustment of method parameters and calibration of confidence measures for filtering results. We present Ximmer, a tool which supports an end to end process for applying these procedures including a simulation framework, CNV detection analysis pipeline, and a visualisation and curation tool which enables interactive exploration of CNV results. We apply Ximmer to perform a comprehensive evaluation of CNV detection on four data sets using four different detection methods, representing one of the most comprehensive evaluations to date. Ximmer is open source and freely available at http://ximmer.org (example results are viewable at http://example.ximmer.org).

bioinformatics