Lang, A. E. | bioRxiv Science

bioRxiv2020

Cryo-EM structure of the fully-loaded asymmetric anthrax lethal toxin in its heptameric pre-pore state

Anthrax toxin is the major virulence factor secreted by Bacillus anthracis, causing high mortality in humans and other mammals. It consists of a membrane translocase, known as protective antigen (PA), that catalyzes the unfolding of its cytotoxic substrates lethal factor (LF) and edema factor (EF), followed by translocation into the host cell. Substrate recruitment to the heptameric PA pre-pore and subsequent translocation, however, are not well understood. Here, we report three high-resolution cryo-EM structures of the fully-loaded anthrax lethal toxin in its heptameric pre-pore state, which differ in the position and conformation of LFs. The structures reveal that three LFs interact with the heptameric PA and upon binding change their conformation to form a continuous chain of head-to-tail interactions. As a result of the underlying symmetry mismatch, one LF binding site in PA remains unoccupied. Whereas one LF directly interacts with a part of PA called -clamp, the others do not interact with this region, indicating an intermediate state between toxin assembly and translocation. Interestingly, the interaction of the N-terminal domain with the -clamp correlates with a higher flexibility in the C-terminal domain of the protein. Based on our data, we propose a model for toxin assembly, in which the order of LF binding determines which factor is translocated first.

molecular biology↗

bioRxiv2020

Improved segmentation of the intracranial and ventricular volumes in populations with cerebrovascular lesions and atrophy using 3D CNNs

Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site studies (N=528 subjects for ICV, N=501 for ventricular segmentation) consisting of older adults with varying degrees of cerebrovascular lesions and atrophy, which pose significant challenges for most segmentation approaches. The models were tested on 238 participants, including subjects with vascular cognitive impairment and high white matter hyperintensity burden. Two of the three test sets came from studies not used in the training dataset. We assessed our algorithms relative to four state-of-the-art ICV extraction methods (MONSTR, BET, Deep Extraction, FreeSurfer), as well as a ventricular segmentation tool (FreeSurfer). Our multi-contrast models outperformed other methods across all evaluation metrics, with average Dice coefficients of 0.98 and 0.94 for ICV and ventricular segmentation respectively. Both models were also the most time efficient, segmenting the structures in orders of magnitude faster than some of the other available methods. Our networks showed an increased accuracy with the use of a conditional random field (CRF) as a post-processing step. We further validated both segmentation models, highlighting their robustness to images with lower resolution and signal-to-noise ratio, compared to tested techniques. The pipeline and models are available at: https://icvmapp3r.readthedocs.io and https://ventmapp3r.readthedocs.io to enable further investigation of the roles of ICV and ventricles in relation to normal aging and neurodegeneration in large multi-site studies.

neuroscience↗