Biology subjects

Forest, M. G.

Publications and source records attributed to Forest, M. G..

8 recordsLinked to original sources

bioRxiv2025

Study of Protein-Protein Interactions in Septin Assembly: Multiple amphipathic helix domains cooperate in binding to the lipid membrane

Septins are a conserved family of cytoskeletal proteins known for sensing micron-scale membrane curvature via amphipathic helix (AH) domains. While cooperative interactions in septin assembly have been suggested, the molecular mechanisms governing membrane binding and assembly remain unclear. Building on prior findings, we use all-atom molecular dynamics simulations to examine how single and paired extended AH domains, derived from Cdc12, interact with lipid bilayers. A single membrane-bound AH adopts a curved conformation. In solution, a second AH peptide preferentially interacts with the bound peptide through conserved salt bridges, favoring an antiparallel arrangement. Simulations of covalently linked AH tandems confirm this configuration. Dual membrane-bound peptides induce lipid packing defects, reduce tail order, and exhibit slight membrane displacement, suggesting curved membranes may better accommodate multiple AH domains. Our findings advance the mechanistic understanding of septin-membrane interactions and highlight the role of cooperative AH domain binding in stabilizing higher-order structures.

biophysics↗

bioRxiv2024

Charge distribution and helical content tune the binding of septins amphipathic helix domain to lipid membranes

Septins are a class of cytoskeletal proteins that preferentially bind to domains of micron-scale curvature on the cell membrane. Studies have shown that amphipathic helix (AH) domains in septin oligomers are essential for septin curvature sensing. Yet, the underlying mechanochemical interactions that modulate this curvature sensing remain ambiguous. Here we use all-atom molecular dynamics alongside a metadynamics enhanced sampling approach to bridge the gap between time and length scales required to optimize and validate experimental design of amphipathic helices. Simulations revealed that the local charge on the termini of an 18-amino-acid AH peptide impacts its helical content and positioning within lipid membranes. These computational observations are confirmed with experiments measuring the binding of synthetic AH constructs with variable helical content and charged termini to lipid vesicles. Taken together, these results identify the helical content of amphipathic helices as a regulator of septin binding affinity to lipid membranes. Additionally, we examined an extended AH sequence including 8 amino acids upstream and downstream of the minimal 18-amino-acid-long AH domain to more closely mimic the native protein in simulations and experiments. Simulations and experiments show that the extended peptide sequence adopts a strong alpha-helical conformation when free in solution, giving rise to a higher affinity to lipid membranes than that of the shorter AH sequence. Together, these results provide insight into how the native septin proteins interact with membranes, and establish general design principles that can guide the interaction of future synthetic materials with lipid membranes in a programmable manner. STATEMENT OF SIGNIFICANCEUnderstanding how cells sense and react to their shape is necessary for numerous biological processes. Here we explore the interactions between amphipathic helices, a curvature sensing protein motif, and lipid membranes. Using molecular dynamics simulations, enhanced simulation sampling techniques, and experiments, we find that increasing the helical content of the amphipathic helix or adding charged capping sequences yields higher membrane binding affinity. Understanding these parameters for membrane-binding could enable us to interface and regulate native protein functions, as well as guide the design of synthetic curvature-sensing materials that can interact with and deform lipid membranes.

biophysics↗

bioRxiv2023

Statistical Methods for Microrheology of Airway Mucus with Extreme Heterogeneity

The mucus lining of the human airway epithelium contains two gel-forming mucins, MUC5B and MUC5AC. During progression of cystic fibrosis (CF), mucus hyper-concentrates as its mucin ratio changes, coinciding with formation of insoluble, dense mucus flakes. We explore rheological heterogeneity of this pathology with reconstituted mucus matching three stages of CF progression and particle-tracking of 200 nm and 1 micron diameter beads. We introduce statistical data analysis methods specific to low signal-to-noise data within flakes. Each bead time series is decomposed into: (i) a fractional Brownian motion (fBm) classifier of the pure time-series signal; (ii) high-frequency static and dynamic noise; and (iii) low-frequency deterministic drift. Subsequent analysis focuses on the denoised fBm classifier ensemble from each mucus sample and bead diameter. Every ensemble fails a homogeneity test, compelling clustering methods to assess levels of heterogeneity. The first binary level detects beads within vs. outside flakes. A second binary level detects within-flake bead signals that can vs. cannot be disentangled from the experimental noise floor. We show all denoised ensembles, within- and outside-flakes, fail a homogeneity test, compelling additional clustering; next, all clusters with sufficient data fail a homogeneity test. These levels of heterogeneity are consistent with outcomes from a stochastic phase-separation process, and dictate applying the generalized Stokes-Einstein relation to each bead per cluster per sample, then frequency-domain averaging to assess rheological heterogeneity. Flakes exhibit a spectrum of gel-like and sol-like domains, outside-flake solutions a spectrum of sol-like domains, painting a rheological signature of the phase-separation process underlying flake-burdened mucus.

microbiology↗

bioRxiv2023

Global Sensitivity Analysis of the Onset of Nasal Passage Infection by SARS-CoV-2 With Respect to Heterogeneity in Host Physiology and Host Cell-Virus Kinetic Interactions

Throughout the COVID-19 pandemic, positive nasal swab tests have revealed dramatic population heterogeneity in viral titers spanning 6 orders-of-magnitude. Our goal here is to probe potential drivers of infection outcome sensitivity arising from (i) physiological heterogeneity between hosts and (ii) host-variant heterogeneity in the detailed kinetics of cell infection and viral replication. Toward this goal, we apply global sensitivity methods (Partial Rank Correlation Coefficient analysis and Latin Hypercube Sampling) to a physiologically faithful, stochastic, spatial model of inhaled SARS-CoV-2 exposure and infection in the human respiratory tract. We focus on the nasal passage as the primary origin of respiratory infection and site of clinical testing, and we simulate the spatial and dynamic progression of shed viral load and infected cells in the immediate 48 hours post infection. We impose immune evasion, i.e., suppressed immune protection, based on the preponderance of clinical evidence that nasal infections occur rapidly post exposure, largely independent of immune status. Global sensitivity methods provide the de-correlated outcome sensitivities to each source of within-host heterogeneity, including the dynamic progression of sensitivities at 12, 24, 36, and 48 hours post infection. The results reveal a dynamic rank-ordering of the drivers of outcome sensitivity in early infection, providing insights into the dramatic population-scale outcome diversity during the COVID-19 pandemic. While we focus on SARS-CoV-2, the model and methods are applicable to any inhaled virus in the immediate 48 hours post infection.

immunology↗

bioRxiv2022

Modeling predicts mechanisms altered by mutations of the SARS-CoV-2 delta and omicron variants

We apply our mechanistic, within-host, pre-immunity, respiratory tract infection model for unvaccinated, previously uninfected, and immune-compromised individuals. Starting from published cell infection and viral replication data for the SARS-CoV-2 alpha variant, we explore variability in outcomes of viral load and cell infection due to three plausible mechanisms altered by SARS-CoV-2 mutations of delta and omicron. We seek a mechanistic explanation of clinical test results: delta nasal infections express [~]3 orders-of-magnitude higher viral load than alpha, while omicron infections express an additional 1 to 2 orders-of-magnitude rise over delta. Model simulations reveal shortening of the eclipse phase (the time between cellular uptake of the virus and onset of infectious viral replication and shedding) alone can generate 3-5 orders-of-magnitude higher viral load within 2 days post initial infection. Higher viral replication rates by an infected cell can generate at most one order-of-magnitude rise in viral load, whereas higher cell infectability has minimal impact and lowers the viral load.

immunology↗

bioRxiv2019

3D neural network-based particle tracking reveals spatial heterogeneity of the cytosol

The spatial structure and physical properties of the cytosol are not well understood. Measurements of the material state of the cytosol are challenging due to its spatial and temporal heterogeneity, the lack of truly passive probes, and the need for probes of many sizes to accurately describe the state across scales. Recent development of genetically encoded multimeric nanoparticles (GEMs) has opened up study of the cytosol at the length scales of multiprotein complexes (20-60 nm). Using these probes to spatially resolve diffusivity of a cytoplasmic volume within a cell requires accurate and automated 3D tracking methods. We developed an image analysis pipeline for whole-cell imaging of GEMs in the context of large, multinucleate fungi where there is evidence of functional compartmentalization of the cytosol for both the nuclear division cycle and branching. We apply a neural network to track particles in 3D, generate surface meshes to project data on representations of the cell, and create dynamic visualizations of local diffusivities. Using this pipeline, we have found that there is remarkable variability in the properties of the cytosol both within a single cell and between cells. By analyzing the spatial diffusivity patterns, we saw an enrichment of low diffusivity zones at hyphal tips and near some nuclei. These results show that the physical state of the cytosol varies spatially within a single cell and exhibits significant cell-to-cell variability. Thus, molecular crowding contributes to heterogeneity within individual cells and across populations.

cell biology↗

bioRxiv2019

Transient crosslinking kinetics optimize gene cluster interactions

Our understanding of how chromosomes structurally organize and dynamically interact has been revolutionized through the lens of long-chain polymer physics. Major protein contributors to chromosome structure and dynamics are condensin and cohesin that stochastically generate loops within and between chains, and entrap proximal strands of sister chromatids. In this paper, we explore the ability of transient, protein-mediated, gene-gene crosslinks to induce clusters of genes, thereby dynamic architecture, within the highly repeated ribosomal DNA that comprises the nucleolus of budding yeast. We implement three approaches: live cell microscopy; computational modeling of the full genome during G1 in budding yeast, exploring four decades of timescales for transient crosslinks between 5kbp domains (genes) in the nucleolus on Chromosome XII; and, temporal network models with automated community (cluster) detection algorithms applied to the full range of 4D modeling datasets. The data analysis tools detect and track gene clusters, their size, number, persistence time, and their plasticity (deformation). Of biological significance, our analysis reveals an optimal mean crosslink lifetime that promotes pairwise and cluster gene interactions through \"flexible\" clustering. In this state, large gene clusters self-assemble yet frequently interact (merge and separate), marked by gene exchanges between clusters, which in turn maximizes global gene interactions in the nucleolus. This regime stands between two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, \"rigid\" clustering emerges with clusters that interact infrequently; with longer crosslink lifetimes, there is a dissolution of clusters. These observations are compared with imaging experiments on a normal yeast strain and two condensin-modified mutant cell strains. We apply the same image analysis pipeline to the experimental and simulated datasets, providing support for the modeling predictions.\n\nAuthor SummaryThe spatiotemporal organization of the genome plays an important role in cellular processes involving DNA, but remains poorly understood, especially in the nucleolus, which does not facilitate conventional techniques. Polymer chain models have shown ability in recent years to make accurate predictions of the dynamics of the genome. We consider a polymer bead-chain model of the full yeast genome during the interphase portion of the cell cycle, featuring special dynamic crosslinking to model the effects of structural maintenance proteins in the nucleolus, and investigate how the kinetic timescale on which the crosslinks bind and unbind affects the resulting dynamics inside the nucleolus. It was previously known that when this timescale is sufficiently short, large, stable clusters appear, but when it is long, there is no resulting structure. We find that there additionally exists a range of timescales for which flexible clusters appear, in which beads frequently enter and leave clusters. Furthermore, we demonstrate that these flexible clusters maximize the cross-communication between beads in the nucleolus. Finally, we apply network temporal community detection algorithms to identify what beads are in what communities at what times, in a way that is more robust and objective than conventional visual-based methods.

genomics↗

bioRxiv2018

Antibody-mediated immobilization of virions in mucus

Antibodies have been shown to hinder the movement of Herpes Simplex Virus (HSV) virions in cervicovaginal mucus (CVM), as well as other viruses in other mucus secretions. However, it has not been possible to directly observe the mechanisms underlying this phenomenon, so the nature of virion-antibody-mucin interactions remain poorly understood. In this work, we analyzed thousands of virion traces from single particle tracking experiments to explicate how antibodies must cooperate to immobilize virions for relatively long time periods. First, using a clustering analysis, we observed a clear separation between two classes of virion behavior: Freely Diffusing and Immobilized. While the proportion of Freely Diffusing virions decreased with antibody concentration, the magnitude of their diffusivity did not, implying an all-or-nothing dichotomy in the pathwise effect of the antibodies. Proceeding under the assumption that all binding events are reversible, we used a novel switch-point detection method to conclude that there are very few, if any, state-switches on the experimental time scale of twenty seconds. To understand this slow state-switching, we analyzed a recently proposed continuous-time Markov chain model for binding kinetics and virion movement. Model analysis implied that virion immobilization requires cooperation by multiple antibodies that are simultaneously bound to the virion and mucin matrix, and that there is an entanglement phenomenon that accelerates antibody-mucin binding when a virion is immobilized. In addition to developing a widely-applicable framework for analyzing multi-state particle behavior, this work substantially enhances our mechanistic understanding of how antibodies can reinforce a mucus barrier against passive invasive species.

immunology↗