The necroptosis cell death pathway has been implicated in host defense and in the pathology of inflammatory diseases. While phosphorylation of the necroptotic effector pseudokinase Mixed Lineage Kinase Domain-Like (MLKL) by the upstream protein kinase RIPK3 is a hallmark of pathway activation, the precise checkpoints in necroptosis signaling are still unclear. Here we have developed monobodies, synthetic binding proteins, that bind the N-terminal four-helix bundle (4HB) “killer” domain and neighboring first brace helix of human MLKL with nanomolar affinity. When expressed as genetically encoded reagents in cells, these monobodies potently block necroptotic cell death. However, they did not prevent MLKL recruitment to the “necrosome” and phosphorylation by RIPK3, nor the assembly of MLKL into oligomers, but did block MLKL translocation to membranes where activated MLKL normally disrupts membranes to kill cells. An X-ray crystal structure revealed a monobody-binding site centered on the α4 helix of the MLKL 4HB domain, which mutational analyses showed was crucial for reconstitution of necroptosis signaling. These data implicate the α4 helix of its 4HB domain as a crucial site for recruitment of adaptor proteins that mediate membrane translocation, distinct from known phospholipid binding sites.

Epilepsy diagnosis can be costly, time-consuming and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-EEG monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially herald a more quantitative approach to therapeutic outcomes. There is substantial research into automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic; and, despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG.This study reports on a deep learning algorithm for computer-assisted EEG review. Deep, convolutional neural networks were trained to detect epileptic discharges using a pre-existing dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data was curated and confirmed independently by two epilepsy specialists . The resulting automated detection algorithm was then used to review diagnostic scalp-EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting.The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without .

Epilepsy diagnosis can be costly, time-consuming and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-EEG monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially herald a more quantitative approach to therapeutic outcomes. There is substantial research into automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic; and, despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG.This study reports on a deep learning algorithm for computer-assisted EEG review. Deep, convolutional neural networks were trained to detect epileptic discharges using a pre-existing dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data was curated and confirmed independently by two epilepsy specialists . The resulting automated detection algorithm was then used to review diagnostic scalp-EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting.The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes.

Structural maintenance of chromosomes flexible hinge domain containing 1 (SMCHD1) is an epigenetic regulator in which polymorphisms cause the human developmental disorder, Bosma arhinia micropthalmia syndrome, and the degenerative disease, facioscapulohumeral muscular dystrophy. SMCHD1 is considered a noncanonical SMC family member because its hinge domain is C-terminal, because it homodimerizes rather than heterodimerizes, and because SMCHD1 contains a GHKL-type, rather than an ABC-type ATPase domain at its N terminus. The hinge domain has been previously implicated in chromatin association; however, the underlying mechanism involved and the basis for SMCHD1 homodimerization are unclear. Here, we used x-ray crystallography to solve the three-dimensional structure of the Smchd1 hinge domain. Together with structure-guided mutagenesis, we defined structural features of the hinge domain that participated in homodimerization and nucleic acid binding, and we identified a functional hotspot required for chromatin localization in cells. This structure provides a template for interpreting the mechanism by which patient polymorphisms within the SMCHD1 hinge domain could compromise function and lead to facioscapulohumeral muscular dystrophy.

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