Biology subjects

Waxham, N.

Publications and source records attributed to Waxham, N..

7 recordsLinked to original sources

bioRxiv2026

PP2A and CDK16 antagonistically regulate WIPI2B phosphorylation and neuronal autophagosome biogenesis

Autophagy is a recycling pathway that clears cellular constituents, supporting homeostasis. In primary murine neurons, autophagosome biogenesis declines during aging. Importantly, this decline can be restored by the ectopic expression of key autophagy component WIPI2B. The phosphorylation state of WIPI2B serine 395 is critical for this restoration, suggesting that WIPI2B S395 phosphorylation regulates autophagosome biogenesis. Here, we identified protein phosphatase 2A (PP2A) and CDK16 as regulators of WIPI2B S395 phosphorylation and neuronal autophagy. Using Caenorhabditis elegans, we showed that PP2A and CDK16 regulate neuronal autophagy through the same genetic pathway as WIPI2B in vivo. Further, purified mammalian PP2A and CDK16 directly modified WIPI2B S395 phosphorylation in vitro. In primary murine neurons, PP2A and CDK16 colocalized with WIPI2B at autophagosomes, and manipulation of PP2A and CDK16 expression altered WIPI2B puncta formation and rates of autophagosome biogenesis. Altogether, our data support the conclusion that PP2A and CDK16 regulate WIPI2B S395 phosphorylation, modulating autophagosome biogenesis in neurons.

cell biology↗

bioRxiv2025

Training Generalized Segmentation Networks with Real and Synthetic Cryo-ET data.

Deep learning excels at segmenting objects within noisy cryo-electron tomograms, but the approach is typically bottlenecked by access to ground truth training data. To address this issue we have developed CryoTomoSim (CTS), an open-source software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. Using CTS outputs, we demonstrate the effects of key microscope parameters (dose, defocus, and pixel size) on deep learning-based segmentation, and show that including both molecular crowding and diversity within synthetic datasets is key to training cellular segmentation networks from purely synthetic inputs. While very effective as initial models, the accuracy of these networks is currently limited, and real cellular data is necessary to train the most accurate and generalizable U-Nets. Using a co-training approach, we first segment over 100 tomograms from neuronal growth cones to quantify their cytoskeletal distributions and then we build a generalized cellular cryo-ET segmentation network called NeuralSeg that can segment a subset of cellular features in tomograms from all domains of life.

cell biology↗

bioRxiv2024

Halogenated cholesterol alters the phase behavior of ternary lipid membranes

Eukaryotic plasma membranes exhibit nanoscale lateral lipid heterogeneity, a feature that is thought to be central to their function. Studying these heterogeneities is challenging since few biophysical methods are capable of detecting domains at sub-micron length scales. We recently showed that cryogenic electron microscopy (cryo-EM) can directly image nanoscale liquid-liquid phase separation in extruded liposomes due to its ability to resolve the intrinsic thickness and electron density differences of ordered and disordered phases. However, the intensity contrast between these phases is poor compared to conventional fluorescence microscopy and is thus both a limiting factor and a focal point for optimization. Because the fundamental source of intensity contrast is the spatial variation in electron density within the bilayer, lipid modifications aimed at selectively increasing the electron density of one phase might enhance the ability to resolve coexisting phases. To this end, we investigated model membrane mixtures of DPPC/DOPC/cholesterol in which one hydrogen of cholesterols C19 methyl group was replaced by an electron-rich halogen atom (either bromine or iodine). We characterized the phase behavior as a function of composition and temperature using fluorescence microscopy, FRET, and cryo-EM. Our data suggest that halogenated cholesterol variants distribute approximately evenly between liquid-ordered and liquid-disordered phases and are thus ineffective at enhancing the intensity difference between them. Furthermore, replacing more than half of the native cholesterol with halogenated cholesterol variants dramatically reduces the size of membrane domains. Our results reinforce how small changes in sterol structure can have a large impact on the lateral organization of membrane lipids.

biophysics↗

bioRxiv2024

Cryo-EM images of phase separated lipid bilayer vesicles analyzed with a machine learning approach

Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-melting lipids plus cholesterol offer a simplified experimental model for raft domains in which a liquid-disordered (Ld) phase coexists with a liquid-ordered (Lo) phase. Using such models, we recently showed that cryogenic electron microscopy (cryo-EM) can detect phase separation in lipid vesicles based on differences in bilayer thickness. However, the considerable noise within cryo-EM data poses a significant challenge for accurately determining the membrane phase state at high spatial resolution. To this end, we have developed an image processing pipeline that utilizes machine learning (ML) to predict the bilayer phase in projection images of lipid vesicles. Importantly, the ML method exploits differences in both the thickness and molecular density of Lo compared to Ld, which leads to improved phase identification. To assess accuracy, we used artificial images of phase-separated lipid vesicles generated from all-atom molecular dynamics simulations of Lo and Ld phases. Synthetic ground truth datasets mimicking a series of compositions along a tieline of Ld+Lo coexistence were created and then analyzed with various ML models. For all tieline compositions, we find that the ML approach can correctly identify the bilayer phase at 5 nm lateral resolution with > 90% accuracy, thus providing a means to isolate the intensity profiles of coexisting Ld and Lo phases, as well as accurately determine domain size distributions, number of domains, and phase area fractions. The method described here provides a framework for characterizing nanoscopic lateral heterogeneities in membranes and paves the way for a more detailed understanding of raft properties in biological contexts. SignificanceLipid rafts are important for cell function, but in most cases cannot be detected with conventional optical microscopy because of their extremely small size. Cryogenic electron microscopy (cryo-EM), because of its much greater spatial resolution, is capable of imaging domains as small as 5-10 nm. In this report, we show how machine learning techniques can be used to automatically and accurately identify raft-like domains in simulated cryo-EM images, a powerful approach that could ultimately lead to a better understanding of raft properties.

biophysics↗

bioRxiv2023

Rapid Synthesis of Cryo-ET Data for Training Deep Learning Models

Deep learning excels at cryo-tomographic image restoration and segmentation tasks but is hindered by a lack of training data. Here we introduce cryo-TomoSim (CTS), a MATLAB-based software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. We then demonstrate the effectiveness of these simulated datasets in training different deep learning models for use on real cryotomographic reconstructions. Computer-generated ground truth datasets provide the means for training models with voxel-level precision, allowing for unprecedented denoising and precise molecular segmentation of datasets. By modeling phenomena such as a three-dimensional contrast transfer function, probabilistic detection events, and radiation-induced damage, the simulated cryo-electron tomograms can cover a large range of imaging content and conditions to optimize training sets. When paired with small amounts of training data from real tomograms, networks become incredibly accurate at segmenting in situ macromolecular assemblies across a wide range of biological contexts. SummaryBy pairing rapidly synthesized Cryo-ET data with computed ground truths, deep learning models can be trained to accurately restore and segment real tomograms of biological structures both in vitro and in situ.

cell biology↗

bioRxiv2022

Serinc5 restricts HIV membrane fusion by altering lipid order and heterogeneity in the viral membrane

The host restriction factor, Serinc5, incorporates into budding HIV particles and inhibits their infection by an incompletely understood mechanism. We have previously reported that Serinc5 but not its paralogue, Serinc2, blocks HIV cell entry by membrane fusion, specifically by inhibiting fusion pore formation and dilation. A compelling body of work also suggests Serinc5 may alter the conformation and clustering of the HIV fusion protein, Env. To contribute an additional perspective to the developing model of Serinc5 restriction, we assessed Serinc2 and Serinc5s effects on HIV pseudoviral membranes. Using fluorescence lifetime imaging with an order sensitive dye, FLIPPER-TR, and by measuring pseudoviral membrane thickness via cryo electron microscopy (cryoEM), Serinc5 was found to increase membrane heterogeneity, skewing the distribution towards a larger fraction of the viral membrane in an ordered phase. We also directly observed for the first time the coexistence of membrane domains within individual viral membrane envelopes. Using a TIRF-based single particle fusion assay, we found that incorporation of exogenous phosphatidylethanolamine (PE) into the viral membrane rescued HIV pseudovirus fusion from restriction by Serinc5, which was accompanied by decreased membrane heterogeneity and order. This effect was specific for PE and did not depend on acyl chain length or saturation. Together, these data suggest that Serinc5 alters multiple interrelated properties of the viral membrane--lipid chain order, rigidity, line tension, and lateral pressure--which decrease accessibility of fusion intermediates and disfavor completion of fusion. These biophysical insights into Serinc5 restriction of HIV infectivity could contribute to the development of novel antivirals that exploit the same weaknesses of HIV and potentially other enveloped viruses.

biophysics↗

bioRxiv2020

Direct label-free imaging of nanodomains in biomimetic and biological membranes by cryogenic electron microscopy

The nanoscale organization of biological membranes into structurally and compositionally distinct lateral domains is believed to be central to membrane function. The nature of this organization has remained elusive due to a lack of methods to directly probe nanoscopic membrane features. We show here that cryogenic electron microscopy (cryoEM) can be used to directly image coexisting nanoscopic domains in synthetic and bio-derived membranes without extrinsic probes. Analyzing a series of single-component liposomes composed of synthetic lipids of varying lengths, we demonstrate that cryoEM can distinguish bilayer thickness differences as small as 0.5 [A], comparable to the resolution of small-angle scattering methods. Simulated images from computational models reveal that features in cryoEM images result from a complex interplay between the atomic distribution normal to the plane of the bilayer and imaging parameters. Simulations of phase separated bilayers were used to predict two sources of contrast between coexisting ordered and disordered phases within a single liposome, namely differences in membrane thickness and molecular density. We observe both sources of contrast in biomimetic membranes composed of saturated lipids, unsaturated lipids, and cholesterol. When extended to isolated mammalian plasma membranes, these methods reveal similar nanoscale lateral heterogeneities. The methods reported here for direct, probe-free imaging of nanodomains in unperturbed membranes open new avenues for investigation of nanoscopic membrane organization. SIGNIFICANCEWe have used cryoEM to achieve direct, probe-free imaging of lateral domains in biomimetic lipid membranes under native conditions and to characterize differences in their structures. First, measurements of membrane thickness in laterally uniform single-component membranes show that cryoEM is capable of sub-angstrom resolution of interleaflet membrane thickness. All-atom simulations are used to predict the cryo-EM appearance of submicron domains in vesicles with coexisting liquid domains and these are quantitatively validated by direct imaging of phase separated membranes. We then extend this approach to observe nanoscopic domains in isolated cellular membranes, comprising the first direct imaging of nanodomains in biomembranes.

biophysics↗