Biology subjects

Zuckerman, D. M.

Publications and source records attributed to Zuckerman, D. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Multi-ion permeation and dynamic conductance modulation in connexin gap junction channels

Gap junction channels formed by connexins mediate direct intercellular communication and are essential for electrical signaling and tissue homeostasis. Despite their large, solvent-accessible pores, connexin channels exhibit distinct conductance, selectivity, and rectification properties, but the molecular mechanisms underlying these behaviors remain incompletely understood. Here, we performed [~]67 s of all-atom computational electrophysiology simulations of connexin-46 (Cx46), connexin-50 (Cx50), and heterotypic Cx46/50 gap junction channels based on high-resolution open-state structures, enabling characterization of both ion permeation and long-timescale channel dynamics. Simulations reveal a multi-ion, multi-pathway permeation mechanism governed by isoform-specific energetic barriers and transient ion coordination sites that shape conductance and selectivity. In heterotypic Cx46/50 channels, asymmetric energetic landscapes establish a mechanistic basis for rectification. Unexpectedly, the microsecond-timescale simulations further revealed dynamic interactions between the intracellular loop (ICL) region and N-terminal domain (NT) that transiently constrict the pore and attenuate ionic currents. These findings suggest that the open-state comprises an ensemble of rapidly interconverting conductance microstates rather than a single static conformation, providing structural information of potential mechanistic importance beyond what has been learned from cryo-EM studies. Together, our results provide a mechanistic basis for ion permeation and current modulation in gap junction channels and highlight the importance of long-timescale protein dynamics in shaping intercellular communication.

biophysics↗

bioRxiv2026

Comparison of AI protein structure ensemble prediction tools

Multiple AI prediction tools for protein structural ensembles have recently been released, building on the much heralded advances from AlphaFold, large language models, and other machine-learning approaches. Here we report on a comparison of several tools (BioEmu, AFSample2, ESMFlow) using a small test set of proteins, including three which exhibit well-studied structural transitions. Overall, while the AI platforms generate structurally diverse ensembles with overlapping regions, each tool produces clearly distinct conformational distributions. Thus, it is impossible that all the tools generate ensembles of high biophysical quality, analogous to a Boltzmann distribution. Experimental structures are often, but not always, covered by the ensembles in dimensionally reduced spaces. In cases where point mutations are known experimentally to cause large structural shifts, the AI tools exhibit either small or negligible shifts. Although our current analysis cannot evaluate the absolute quality of an ensemble, and hence cannot identify a best-performing AI tool, the results suggest users pursuing downstream applications such as protein engineering or drug design should interpret these ensembles with caution.

biophysics↗

bioRxiv2026

Rectifying AI-generated protein structure ensembles for equilibrium using physics-based computations

Recently, a number of tools have been released that generate ensembles of protein structures based on artificial intelligence (AI) approaches. Although ensembles generated by the tools differ significantly, we demonstrate a computational path to harmonizing the various outputs under a stationary condition using two complementary physics-based approaches. In the first stage, the AI ensemble is used to seed a weighted ensemble (WE) simulation, promoting relaxation toward the steady state. In the second stage, trajectory segments generated by WE are reweighted to steady state using the recently developed RiteWeight (RW) algorithm. We applied this approach to generate an atomically-detailed equilibrium ensemble of unliganded adenylate kinase conformations, starting from ensembles produced by three AI tools: AFSample2, ESMFlow-PDB (trained from PDB structures), and ESMFlow-MD (trained from molecular dynamics simulation data). Dramatic differences in the AI-generated ensembles are largely erased during the WE-RW process, yielding a consistent description of the equilibrium ensemble for a given force field.

biophysics↗

bioRxiv2025

Negative Cooperativity in a Polybivalent Complex Allows the Prevalence of a Partial Bound State.

The central region of the cytoplasmic dynein complex, comprising the intermediate chain (IC) and two light chains (LC8 and Tctex1), has eluded thorough quantitative characterization due to its participation in a highly coupled seven-state binding network. Although isothermal titration calorimetry (ITC) is the gold standard for measuring binding thermodynamics, conventional analyses are limited to simple interaction schemes because individual isotherms contain insufficient information to resolve complex reaction networks. Here, we overcome this limitation by combining extensive experimental sampling with hierarchical Bayesian inference. We collected 39 ITC isotherms spanning eight experiment types and developed a global Bayesian framework integrating multiple datasets while explicitly accounting for concentration uncertainty. Using this approach, we fit the complete dataset to a mechanistic seven-state model, estimating 190 parameters, including 12 thermodynamic parameters while marginalizing over 178 nuisance parameters. Remarkably, this strategy yields 95% confidence intervals for thermodynamic values as narrow as 0.05 kcal/mol and back-propagates to nanomolar precision in effective concentrations, even when experimental concentrations are in the hundreds of micromolar. The resulting thermodynamic landscape enables predictive modelling of assembly populations under different scenarios, including binding states inaccessible to standard ITC analyses. These results reveal previously unrecognized binding states that may play key roles in dynein cargo attachment and release. More broadly, this work reveals a form of "multi-cooperativity" governing dynein assembly and demonstrates how intensive experimentation coupled with modern statistical tools can resolve complex molecular systems beyond the reach of traditional biophysical techniques. Significance StatementLarge, complex mechanistic processes have remained difficult to fully characterize, which limits interpretability of the underlying biology. We utilize a large dataset of 39 complementary experiments to fully characterize a seven-state system using Bayesian inference. This process achieves impressively precise fits with 0.05 kcal/mol width confidence intervals. The high precision enables assessment of simultaneous positive and negative cooperativity in the assembly of the dynein intermediate chain with its light-chain partners. Simulation of state populations suggests that this balancing cooperativity is finely tuned to allow access to a half-bound state which has been previously inaccessible quantitatively. Our approach is broadly applicable and supports an emerging principle of molecular regulation--negative cooperativity as a strategy for tuning responsiveness and dynamic control.

biophysics↗

bioRxiv2025

Improving parameter inference by resolving Bayesian prior ambiguity via multi-dataset analysis: Application to isothermal titration calorimetry

Isothermal titration calorimetry (ITC) is a powerful technique for probing biomolecular interactions. However, accurate determination of binding parameters--such as enthalpy and free energy--as well as associated uncertainties can be hindered by noise and concentration variability. Notably, the mathematical ambiguity surrounding analyte concentrations in standard binding models intrinsically limits the precision with which binding parameters, particularly binding enthalpies, can be determined. Here, we present a Bayesian pipeline that resolves this ambiguity by combining two key strategies: simultaneous analysis of multiple ITC datasets and a hierarchical Bayesian treatment of analyte concentration priors. This dual approach not only lifts the degeneracy inherent in single-dataset studies but also removes an ambiguity typically present in Bayesian analysis by self-consistently refining concentration estimates, ensuring optimal joint inference of binding parameters and concentrations. Using modern Monte Carlo techniques enables our pipeline to provide robust posterior sampling for more than 10 datasets and 40 total parameters. We validate the approach with synthetic ITC datasets for single- and multi-site binding models and apply it to experimental data, including 14 datasets for 1:1 binding of Mg(II) to the chelator EDTA and multiple datasets of the hub protein LC8 with diverse binding partners. This work serves as a foundation for improving the precision of binding constants using multiple ITC datasets, while providing a systematic framework for assessing the reliability of experimental concentration estimates, paving the way for more accurate biomolecular interaction studies.

biophysics↗

bioRxiv2025

Inferring single-cell dynamics of a molecular reporter from unlabeled live-cell light microscopy data analyzed with delay embedding

Quantification of the temporal sequence of molecular behavior in live individual cells holds promise for improving causal and mechanistic models of cell biology. In recent years, different methods for inferring molecular labeling from microscopy data have been developed, especially in the context of "virtual pathology", but less effort has been directed to the context of single-cell dynamics and live-cell imaging. We demonstrate that phase-contrast live-cell imaging of MCF10A cells, without labeling data, is predictive of dynamical, single-cell behavior of the cell-cycle reporter Human DNA Helicase B (HDHB) - in particular, of the nuclear vs. cytoplasmic localization of this fluorescent reporter of cyclin-dependent kinase activity. Prediction quality improves substantially when temporal sequences of images are combined in a "delay embedding" framework. When different featurizations of the imaging data are examined, we find that features derived from a variational auto-encoder (VAE) outperform "classical" image features derived from shape and texture. We find the best performance, with Pearson R[~] 0.9 on test data, using VAE features augmented by categorical predictions, all within the delay-embedding framework - in comparison to R [~] 0.5 based on "ordinary" regression with VAE features.

cell biology↗

bioRxiv2024

Design of facilitated dissociation enables control over cytokine signaling duration

Protein design has focused primarily on the design of ground states, ensuring they are sufficiently low energy to be highly populated1. Designing the kinetics and dynamics of a system requires, in addition, the design of excited states that are traversed in transitions from one low-lying state to another2,3. This is a challenging task as such states must be sufficiently strained to be poorly populated, but not so strained that they are not populated at all, and because protein design methods have generally focused on creating near-ideal structures4-7. Here we describe a general approach for designing systems which use an induced-fit power stroke8 to generate a structurally frustrated9 and strained excited state, allosterically driving protein complex dissociation. X-ray crystallography, double electron-electron resonance spectroscopy, and kinetic binding measurements demonstrate that incorporating excited states enables design of effector-induced increases in dissociation rates as high as 6000-fold. We highlight the power of this approach by designing cytokine mimics which can be dissociated within seconds from their receptors.

biochemistry↗

bioRxiv2024

Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change

Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals change the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and predicts expression of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data. SummaryIn normal homeostatic tissues, extracellular signals induce changes in the behavior and state of epithelial cells, and aberrant responses to such signals are associated with diseases. To decode and potentially steer these responses, it is essential to link live-cell behavior to molecular programs; however, high-throughput molecular techniques are destructive or require fixation. Here we present a novel computational approach to connect single-cell measures of cell phenotype and behavior to bulk molecular readouts, enabling prediction of dynamic changes in gene expression programs. This reveals molecular programs associated with distinct cell states and identifies drivers that may be manipulated to control cell state change.

cell biology↗

bioRxiv2023

From average transient transporter currents to microscopic mechanism -- A Bayesian analysis

Electrophysiology studies of secondary active transporters have revealed quantitative, mechanistic insights over many decades of research. However, the emergence of new experimental and analysis approaches calls for investigation of the capabilities and limitations of the newer methods. We examine the ability of solid-supported membrane electrophysiology (SSME) to characterize discrete-state kinetic models with > 10 rate constants. We use a Bayesian framework applied to synthetic data for three tasks: to quantify and check (i) the precision of parameter estimates under different assumptions, (ii) the ability of computation to guide selection of experimental conditions, and (iii) the ability of SSME data to distinguish among mechanisms. When the general mechanism - event order - is known in advance, we show that a subset of kinetic parameters can be "practically identified" within[~] 1 order of magnitude, based on SSME current traces that visually appear to exhibit simple exponential behavior. This remains true even when accounting for systematic measurement bias and realistic uncertainties in experimental inputs (concentrations) are incorporated into the analysis. When experimental conditions are optimized or different experiments are combined, the number of practically identifiable parameters can be increased substantially. Some parameters remain intrinsically difficult to estimate through SSME data alone, suggesting additional experiments are required to fully characterize parameters. We additionally demonstrate the ability to perform model selection and determine the order of events when that is not known in advance, comparing Bayesian and maximum-likelihood approaches. Finally, our studies elucidate good practices for the increasingly popular, but subtly challenging, Bayesian calculations for structural and systems biology.

biophysics↗

bioRxiv2022

A Suite of Advanced Tutorials for the WESTPA 2.0 Rare-Events Sampling Software

We present six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for enabling applications of the weighted ensemble (WE) path sampling strategy to even larger systems and/or slower processes. The tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of "binless" schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling at the system biology level. Applications of the tutorials range from atomistic to residue-level to non-spatial models, and include complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics simulations and completed the previous suite of WESTPA tutorials.

biophysics↗

bioRxiv2022

Quantifying cooperative multisite binding through Bayesian inference

Multistep protein-protein interactions underlie most biological processes, but their characterization through methods such as isothermal titration calorimetry (ITC) is largely confined to simple models that provide little information on the intermediate, individual steps. In this study, we primarily examine the essential hub protein LC8, a small dimer that binds disordered regions of 100+ client proteins in two symmetrical grooves at the dimer interface. Mechanistic details of LC8 binding have remained elusive, hampered in part by ITC data analyses employing simple models that treat bivalent binding as a single event with a single binding affinity. We build on existing Bayesian ITC approaches to quantify thermodynamic parameters for multi-site binding interactions impacted by significant uncertainty in protein concentration. Using a two-site binding model, we model LC8 binding and identify positive cooperativity with high confidence for multiple client peptides. Application of an identical model to two-site binding between the coiled-coil dimer NudE and the intermediate chain of dynein reveals little evidence of cooperativity, in contrast to LC8. We propose that cooperativity in the LC8 system drives the formation of saturated 2:2 bound states, which play a functional role in many LC8 complexes. In addition to these system-specific findings, our work advances general ITC analysis in two ways. First, we describe a previously unrecognized mathematical ambiguity in concentrations in standard binding models and clarify how it impacts the precision with which binding parameters can be determined in cases of high uncertainty in analyte concentrations. Second, building on observations in the LC8 system, we develop a system-agnostic heat map of practical parameter identifiability calculated from synthetic data which demonstrates that certain binding parameters intrinsically inflate parameter uncertainty in ITC analysis, independent of experimental uncertainties. Author SummaryMulti-site protein-protein interactions govern many protein functions throughout the cell. Precise determination of thermodynamic constants of multi-site binding is a significant biophysical challenge, however. The application of complex models to multi-step interactions is difficult and hampered further by complications arising from uncertainty in analyte concentrations. To address these issues, we utilize Bayesian statistical techniques which calculate the likelihood of parameters giving rise to experimental observations to build probability density distributions for thermodynamic parameters of binding. To demonstrate the method and improve our understanding how the hub protein LC8 promotes dimerization of its 100+ binding partners, we test the pipeline on several of these partners and demonstrate that LC8 can bind clients cooperatively, driving interactions towards a fully bound functional state. We additionally examine an interaction between the dimer NudE and the intermediate chain of dynein, which does not appear to bind with cooperativity. Our work provides a solid foundation for future analysis of more complicated binding interactions, including oligomeric complexes formed between LC8 and clients with multiple LC8-binding sites.

biophysics↗

bioRxiv2022

Type two secretion systems secretins are necessary for exopolymeric slime secretion in cyanobacteria and myxobacteria

While protein translocation in Gram-negative bacteria is well understood, our knowledge about the translocation of other high-molecular-weight substances is limited. Nozzle-like structures that secrete exopolymeric substances during gliding motility have previously been observed in the outer membranes of cyanobacteria and myxobacteria. Here, we show that these nozzles are composed of the secretins PilQ/GspD, the outer membrane component of the type II and III secretion systems, the type IV pilus apparatus, and filamentous phage extrusion machinery. Our results show for the first time that secretins may be used for secretion of non-proteinaceous polymers in some bacteria, considerably expanding the repertoire of substrates of these multifunctional outer membrane gates. Moreover, we show that gspD is an essential gene in Myxococcus xanthus, which, when depleted, renders this bacterium defective in slime secretion and gliding motility. SignificanceMany bacteria exhibit gliding motility, movement across surfaces. This motility has been correlated with the deposit of slime trails in their wake. To date, the mechanism of slime secretion has not been understood, and no cell envelope-structures have been identified that are involved in slime secretion during gliding motility. Here, we show that cyanobacteria and myxobacteria use the secretins PilQ/GspD, the outer membrane channels of the T2SS, for slime secretion, which demonstrates a novel cargo transport capacity of these multifunctional outer membrane gates.

microbiology↗

bioRxiv2022

Ligand-mediated structural dynamics of a mammalian pancreatic KATP channel

Regulation of pancreatic KATP channels involves orchestrated interactions of channel subunits, Kir6.2 and SUR1, and their ligands. How ligand interactions affect channel conformations and activity is not well understood. To elucidate the structural correlates pertinent to ligand interactions and channel gating, we compared cryo-EM structures of channels in the presence and absence of pharmacological inhibitors and ATP, focusing on channel conformational dynamics. We found pharmacological inhibitors and ATP enrich a channel conformation in which the Kir6.2 cytoplasmic domain is closely associated with the transmembrane domain relative to one where the Kir6.2 cytoplasmic domain is extended away into the cytoplasm. This conformation change remodels a network of intra and inter-subunit interactions as well as both the ATP and PIP2 binding pockets. The structures resolved key contacts between the distal N-terminus of Kir6.2 and SUR1s ABC module involving residues implicated in channel function. A SUR1 residue, K134, is identified to directly contribute to the PIP2 binding pocket. Molecular dynamics simulations revealed two Kir6.2 residues, K39 and R54, that mediate both ATP and PIP2 binding, suggesting a mechanism for competitive gating by ATP and PIP2.

biochemistry↗

bioRxiv2021

WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications

The weighted ensemble (WE) family of methods is one of several statistical-mechanics based path sampling strategies that can provide estimates of key observables (rate constants, pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE. Key features of the new WESTPA 2.0 software enhance efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation datasets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and "binless" WE strategies. Table of Contents/Abstract ImageFor the manuscript "WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications" by Russo et al. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/471280v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@575710org.highwire.dtl.DTLVardef@151c3dorg.highwire.dtl.DTLVardef@1f0f525org.highwire.dtl.DTLVardef@6e5956_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Morphodynamical cell-state description via live-cell imaging trajectory embedding

Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories--that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

systems biology↗

bioRxiv2021

Vascular KATP channel structural dynamics reveal regulatory mechanism by Mg-nucleotides

Vascular tone is dependent on smooth muscle KATP channels comprising pore-forming Kir6.1 and regulatory SUR2B subunits, in which mutations cause Cantu syndrome. Unique among KATP isoforms, they lack spontaneous activity and require Mg-nucleotides for activation. Structural mechanisms underlying these properties are unknown. Here, we determined the first cryoEM structures of vascular KATP channels bound to inhibitory ATP and glibenclamide, which differ informatively from similarly determined pancreatic KATP channel isoform (Kir6.2/SUR1). Unlike SUR1, SUR2B subunits adopt distinct rotational "propeller" and "quatrefoil" geometries surrounding their Kir6.1 core. The previously unseen ED-rich linker connecting the two halves of the SUR-ABC core is observed in a quatrefoil-like conformation. MD simulations reveal MgADP-dependent dynamic tripartite interactions between this linker, SUR2B and Kir6.1. The structures captured implicate a progression of intermediate states between MgADP-free inactivated and MgADP-bound activated conformations wherein the ED-rich linker participates as mobile autoinhibitory domain, suggesting a conformational pathway toward KATP channel activation.

biochemistry↗

bioRxiv2020

What Markov state models can and cannot do: Correlation versus path-based observables in protein folding models

Markov state models (MSMs) have been widely applied to study the kinetics and pathways of protein conformational dynamics based on statistical analysis of molecular dynamics (MD) simulations. These MSMs coarse-grain both configuration space and time in ways that limit what kinds of observables they can reproduce with high fidelity over different spatial and temporal resolutions. Despite their popularity, there is still limited understanding of which biophysical observables can be computed from these MSMs in a robust and unbiased manner, and which suffer from the space-time coarse-graining intrinsic in the MSM model. Most theoretical arguments and practical validity tests for MSMs rely on long-time equilibrium kinetics, such as the slowest relaxation timescales and experimentally observable time-correlation functions. Here, we perform an extensive assessment of the ability of well-validated protein folding MSMs to accuractely reproduce path-based observable such as mean first-passage times (MFPTs) and transition path mechanisms compared to a direct trajectory analysis. We also assess a recently proposed class of history-augmented MSMs (haMSMs) that exploit additional information not accounted for in standard MSMs. We conclude with some practical guidance on the use of MSMs to study various problems in conformational dynamics of biomolecules. In brief, MSMs can accurately reproduce correlation functions slower than the lag time, but path-based observables can only be reliably reproduced if the lifetimes of states exceed the lag time, which is a much stricter requirement. Even in the presence of short-lived states, we find that haMSMs reproduce path-based observables more reliably.

biophysics↗

bioRxiv2020

Structure and function of a novel osmoregulated periplasmic fiber-forming high-molecular-weight carbohydrate of Myxococcus xanthus

Osmoregulation is of central importance for living cells. In Gram-negative bacteria, strategies for osmoregulation and turgor maintenance in hypotonic environments include the synthesis, accumulation, and modification of periplasmic oligosaccharides. These osmoregulated periplasmic glucans (OPGs, formerly known as membrane-derived oligosaccharides or MDOs) promote water uptake and retention, keeping the cells in an optimal state of hydration. While our understanding of OPG-dependent osmoregulation in a number of model organisms like Escherichia coli is quite detailed, less is known about these processes in bacteria that live in environments characterized by strongly fluctuating osmolarity, such as soil. Here we describe that the soil bacterium Myxococcus xanthus lacks a canonical low-molecular-weight OPG, but instead possesses a novel high-molecular-weight, fiber-forming polysaccharide. Chemical analysis reveals that this polysaccharide is several thousand kilodaltons in size, composed of a highly branched decasaccharide repeat unit containing mannose, glucose, N-acetylglucosamine, and rhamnose. Physiological experiments indicate that the polysaccharide is osmoregulated thereby functionally replacing the canonical OPG. Moreover, experiments indicate that this high-molecular-weight periplasmic polysaccharide forms a fibrillar meshwork that stabilizes the cell envelope during glycerol spore formation, a process during which the entire peptidoglycan of the cell is degraded and the rod-shaped vegetative cells convert into spherical spores. SignificanceOsmoprotection is a necessity for every living cell, particularly in an environment with fluctuating osmolarity. In Gram-negative bacteria, low-molecular-weight osmoregulated periplasmic glucans (OPGs) are an important component of the osmotic stress response in hypotonic environments. Here, we describe that the soil bacterium Myxococcus xanthus does not possess such an OPG but instead accumulates a novel high-molecular-weight fiber-forming polysaccharide in the periplasm in response to hypotonic conditions. This polymer is important for osmoprotection of the cells and plays a key role in the stabilization of the cell envelope during the conversion of rod-shaped vegetative cells into spherical spores. These results indicate that bacteria may use non-OPG carbohydrates for osmoprotection and cell wall stabilization during processes like cellular differentiation.

microbiology↗