Publications

Endurance exercise induces multisystem adaptations that improve performance and benefit health. Gene regulatory circuit responses within individual skeletal muscle cell types, which are key mediators of exercise effects, have not been studied. Here, we map transcriptome, chromatin, and regulatory circuit responses to acute endurance exercise in muscle using same-cell RNA-seq/ATAC-seq multiome assays. High-quality data were obtained from 37,154 nuclei comprising 14 cell types in vastus lateralis samples collected before and 3.5 h after either 40 min cycling exercise at 70% VO2max or 40 min supine rest. Both shared and cell-type-specific regulatory programs were identified. Differential gene expression and accessibility sites are largely distinct within nuclei for each cell type and muscle fiber, with the largest numbers of regulatory events observed in the three muscle fiber types (slow, fast, and intermediate) and lumican (LUM)-expressing fibro-adipogenic progenitor cells. Single-cell regulatory circuit triad reconstruction (transcription factor, chromatin interaction site, regulated gene) also identifies largely distinct gene regulatory circuits modulated by exercise in the three muscle fiber types and LUM-expressing fibro-adipogenic progenitor cells, involving a total of 328 transcription factors acting at chromatin sites regulating 2025 genes. This web-accessible single-cell data set and regulatory circuitry map serve as a resource for understanding the molecular underpinnings of the metabolic and physiological effects of exercise and for guiding interpretation of the exercise response literature in bulk tissue.

An isolated charge-neutral drop suspended in an unbounded medium does not migrate in a uniform dc electric field. A nearby wall breaks the symmetry and causes the drop to drift towards or away from the boundary, depending on the electric properties of the fluids and the wall. In the case of an electrically insulating wall and an electric field applied tangentially to the wall, the interaction of the drop with its electrostatic image gives rise to repulsion by the wall. However, the electrohydrodynamic flow causes either repulsion for a drop with R/P1. We experimentally measure droplet trajectories and quantify the wall-induced electrohydrodynamic lift in the case R/P1 case. The results show that the lateral migration of a drop in a uniform electric field applied parallel to an insulating wall is dominated by the long-range flow due to the image stresslet.

Viruses exploit host cell reliance on compartmentalization to facilitate their replication. Herpes simplex virus type 1 (HSV-1) modulates the subcellular localization of host proteins to suppress immune activation, license viral gene expression, and achieve translational shutoff. To spatially resolve dynamic protein-protein interaction (PPI) networks during infection with an immunostimulatory HSV-1 strain, we integrated nuclear/cytoplasmic fractionation with thermal proximity coaggregation analysis (N/C-TPCA). The resulting expanded depth and spatial resolution of PPIs charted compartment-specific assemblies of protein complexes throughout infection. We find that a broader suite of host chaperones than previously anticipated exhibits nuclear recruitment to form condensates known as virus-induced chaperone-enriched (VICE) domains. Monitoring protein and RNA constituents and ribosome activity, we establish that VICE domains sequester ribosome biogenesis factors from ribosomal RNA, accompanying a cell-wide defect in ribosome supply. These findings highlight infection-driven VICE domains as nodes of translational remodeling and demonstrate the utility of N/C-TPCA to study dynamic biological contexts.

A lack of tools for detecting receptor activity in vivo has limited our ability to fully explore receptor-level control of developmental patterning. Here, we extend phospho-tyrosine tag (pYtag) biosensors to visualize endogenous receptor tyrosine kinase (RTK) activity in Drosophila. We build biosensors for three RTKs that function across developmental stages and tissues. By characterizing Torso::pYtag during embryonic terminal patterning, we find that Torso activity differs from downstream extracellular signal-regulated kinase (ERK) activity in two surprising ways: Torso activity is narrowly restricted to the poles but produces a broader gradient of ERK and decreases over developmental time, while ERK activity is sustained, an effect mediated by ERK pathway-dependent negative feedback. Our results suggest that a narrow domain of Torso activity, tuned in amplitude by negative feedback, locally activates signaling effectors, which diffuse through the syncytial embryo to form the ERK gradient. Altogether, the results of this work highlight the usefulness of pYtags for investigating receptor-level regulation of developmental patterning.

Microtubules are essential cytoskeletal filaments involved in cell motility, division, and intracellular transport, exhibiting complex structural dynamics governed by diverse biophysical factors. Atomistic simulations of microtubule assemblies remain challenging due to their extensive spatiotemporal scales. To address this, we present a multiscale approach combining the primarily top-down Martini 3 coarse-grained (CG) model with an appropriately parameterized heterogeneous elastic network to capture microtubule mechanics and molecular detail efficiently. By iteratively tuning the elastic network, we matched the structural fluctuations of CG heterodimeric building blocks to atomistic reference data, reproducing experimentally consistent mechanical properties. This framework helped us identify stabilizing long-lived interactions between charged C-terminal tails and the folded domain of neighboring tubulin subunits, offering insight into sequence-specific contributions to lattice stability. Our efforts culminated in the construction of a 200 nm microtubule composed of million interaction centers, enabling exploration of large-scale microtubule-associated processes with amino acid-level resolution. This work bridges the gap between molecular specificity and computational scalability, offering a platform for simulating biophysical processes across cellular length and time scales.

Understanding protein conformation is key to understanding their function. Importantly, most proteins adopt multiple conformations with nontrivial ensemble distributions that change depending on their environment to perform functions like catalysis, signaling, and transport. Recently, machine learning techniques, especially deep generative models, have been employed to develop protein conformation generators. These models, known as unified protein ensemble samplers, are trained on the Protein Data Bank (PDB) dataset and can generate diverse protein conformation ensembles given a protein sequence. However, their reliance solely on structural data from the PDB, which primarily captures folded protein states, restricts the diversity of the generated ensembles and can result in physically unrealistic conformations. In this paper, we overcome these challenges by introducing ExEnDiff, an experiment-guided diffusion model for protein conformation generation. ExEnDiff integrates experimental measurements as a physical prior, enabling the generation of protein conformations with desired properties. Our experiments on a variety of fast-folding and intrinsically disordered proteins demonstrate that ExEnDiff significantly advances the capabilities of current unified protein ensemble samplers. With little computational cost, ExEnDiff can capture important proteins' configuration properties and the underlying Boltzmann distribution, paving the way for a next-generation molecular dynamics engine. We further demonstrate the effectiveness of ExEnDiff to capture conformational changes in the presence of mutations and as an efficient tool for determining a reasonable collective variable space for protein ensembles. With these results, ExEnDiff is well poised to push the study of protein ensembles into a data-rich regime currently available to few problems in biology.

Active nematics exhibit spontaneous flows through a well-known linear instability of the uniformly aligned quiescent state. Here, we show that even a linearly stable uniform state can experience a nonlinear instability, resulting in a discontinuous transition to spontaneous flows. In this case, quiescent and flowing states may coexist. Through a weakly nonlinear analysis and a numerical study, we trace the bifurcation diagram of striped patterns and show that the underlying pitchfork bifurcation switches from supercritical (continuous) to subcritical (discontinuous) by varying the flow-alignment parameter. We predict that the discontinuous spontaneous flow transition occurs for a wide range of parameters, including systems of contractile flow-aligning rods. Our predictions are relevant to active nematic turbulence and can potentially be tested in experiments on either cell layers or active cytoskeletal suspensions.

Studies of fate patterning during development typically emphasize cell-cell communication via diffusible chemical signals. Recent experiments on stem cell colonies, however, suggest that in some cases mechanical stresses, rather than secreted chemicals, enable long-ranged cell-cell interactions that specify positional information and pattern cell fates. These findings inspire a model of mechanical patterning: fate affects cell contractility, and pressure in the cell layer biases fate. Cells at the colony edge, more contractile than cells at the center, seed a pattern that propagates via force transmission. Strikingly, our model implies that the width of the outer fate domain varies nonmonotonically with substrate stiffness, a prediction that we confirm experimentally; we argue that a similar dependence on substrate stiffness can be achieved by a chemical morphogen only if strong constraints on the signaling pathway's mechanobiology are met. Our findings thus support the idea that mechanical stress can mediate patterning in the complete absence of chemical morphogens, even in nonmotile cell layers, thus expanding the repertoire of possible roles for mechanical signals in development and morphogenesis. Future tests of additional model predictions, like the effect of anisotropic substrate rigidity, will further broaden the range of achievable fate patterns.

Characterizing the conformational ensemble of biomolecular systems is key to understand their functions. Cryoelectron microscopy (cryo-EM) captures two-dimensional snapshots of biomolecular ensembles, giving in principle access to thermodynamics. However, these images are very noisy and show projections of the molecule in unknown orientations, making it very difficult to identify the biomolecule’s conformation in each individual image. Here, we introduce cryo-EM simulation-based inference (cryoSBI) to infer the conformations of biomolecules and the uncertainties associated with the inference from individual cryo-EM images. CryoSBI builds on simulation-based inference, a merger of physics-based simulations and probabilistic deep learning, allowing us to use Bayesian inference even when likelihoods are too expensive to calculate. We begin with an ensemble of conformations, templates from experiments, and molecular modeling, serving as structural hypotheses. We train a neural network approximating the Bayesian posterior using simulated images from these templates and then use it to accurately infer the conformation of the biomolecule from each experimental image. Training is only done once on simulations, and after that, it takes just a few milliseconds to make inference on an image, making cryoSBI suitable for arbitrarily large datasets and direct analysis on micrographs. CryoSBI eliminates the need to estimate particle pose and imaging parameters, significantly enhancing the computational speed compared to explicit likelihood methods. Importantly, we obtain interpretable machine learning models by integrating physics-based approaches with deep neural networks, ensuring that our results are transparent and reliable. We illustrate and benchmark cryoSBI on synthetic data and showcase its promise on experimental single-particle cryo-EM data.

The remarkable cohesion and coordination observed in moving animal groups and their collective responsiveness to threats are thought to be mediated by scale-free correlations, where changes in the behavior of one animal influence others in the group, regardless of the distance between them. But are these features independent of group size? Here, we investigate group cohesiveness and collective responsiveness in computational models of massive schools of fish of up to 50,000 individuals. We show that as the number of swimmers increases, flow interactions destabilize the school, creating clusters that constantly fragment, disperse, and regroup, similar to their biological counterparts. We calculate the spatial correlation and speed of information propagation in these dynamic clusters. Spatial correlations in cohesive and polarized clusters are indeed scale free, much like in natural animal groups, but fragmentation events are preceded by a decrease in correlation length, thus diminishing the group's collective responsiveness, leaving it more vulnerable to predation events. Importantly, in groups undergoing collective turns, the information about the change in direction propagates linearly in time among group members, thanks to the non-reciprocal nature of the visual interactions between individuals. Merging speeds up the transfer of information within each cluster by several fold, while fragmentation slows it down. Our findings suggest that flow interactions may have played an important role in group size regulation, behavioral adaptations, and dispersion in living animal groups.