Publications

IgA nephropathy (IgAN), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and minimal change disease (MCD) account for the majority of idiopathic glomerulopathies (GN). However, there are no powered transcriptomic datasets coupled to genetic data to investigate the genetic mechanisms underlying gene regulation in the context of GN.

The highly conserved extracellular signal–regulated kinase (ERK) regulates diverse cellular processes by phosphorylating a wide range of intracellular substrates. Its catalytic activity relies on phosphorylation by a single upstream kinase, mitogen-activated protein kinase kinase (MEK), which interacts with only a few binding partners. Here, we test whether the asymmetry in protein–protein interaction network architecture influences the coevolution of the MEK–ERK complex. Phylogenetic sequence analysis across metazoan species revealed accelerated divergence in MEK’s intrinsically disordered N-terminal docking motif (docking site [D-site]), whereas ERK remained highly conserved. Structure prediction with AlphaFold2 and extensive molecular dynamics simulations showed that five conserved D-site residues form stable hydrophobic and electrostatic contacts with ERK’s D-recruitment site. Functional assays in Drosophila melanogaster confirmed that these D-site interactions are essential for proper downstream signaling and support an allosteric role for this motif. Our results demonstrate that MEK uses a structurally simple yet evolutionarily adaptable motif to regulate MEK–ERK complex stability and binding dynamics. The D-site is strongly conserved within phylogenetic groups such as insects or terrestrial vertebrates, yet diverges across them, reflecting evolutionary pressures that balance functional conservation with signaling adaptability. The presented approach illustrates how the combined approach using sequencing data, molecular simulations, and targeted perturbations can be used to address fundamental questions about the evolution of protein–protein interaction networks.

Spontaneous pattern formation in homogeneous systems is ubiquitous in nature. Although Turing demonstrated that spatial patterns can emerge in reaction-diffusion (RD) systems when the homogeneous state becomes linearly unstable, it remains unclear whether Turing mechanism is the only route for pattern formation. Here, we develop an efficient algorithm to systematically construct the solution landscape to find all steady-state solutions connected to a homogeneous state. By applying our method to generic RD models, we find that stable spatial patterns can emerge via saddle-node bifurcations before the onset of Turing instability, and reveal a general nonlinear mechanism that increases the parameter space over which pattern formation occurs in the RD systems. Furthermore, by using a generalized action in the functional space based on large deviation theory, our method is extended to evaluate stability of spatial patterns against noise. Applying this general approach in a three-species RD model, we show that though formation of Turing patterns only requires two chemical species, the third species is critical for stabilizing patterns against strong intrinsic noise in small biochemical systems.

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our non-minimal formulation ensures numerical stability, making it effective for real-world applications.

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our non-minimal formulation ensures numerical stability, making it effective for real-world applications.

Simulation-based inference provides a powerful framework for cryoelectron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable information about the physical system and the inference process. Harnessing this potential hinges on understanding the underlying geometric structure of these representations. We investigate this structure by applying manifold learning techniques to CryoSBI representations of a simulated benchmark dataset and both simulated and experimental images of hemagglutinin. We reveal that these high-dimensional data inherently populate low-dimensional, smooth manifolds, with simulated data effectively covering the experimental counterpart. By characterizing the manifold's geometry using Diffusion Maps and identifying its principal axes of variation via coordinate interpretation methods, we establish a direct link between the latent structure and key physical parameters. Discovering this intrinsic low-dimensionality and interpretable geometric organization not only validates the CryoSBI approach but also enables us to learn more from the data structure and provides opportunities for improving future inference strategies by exploiting this revealed manifold geometry.

Wnt-signaling, via β-catenin or the planar cell polarity (PCP) branch, is crucial for development, tissue homeostasis, and linked to many diseases. LRP5/6, arrow (arr) in Drosophila, is the obligate co-receptor in Wnt/β-catenin signaling, with ligand binding to a Frizzled (Fz) family member and LRP5/6 mediating formation of the signalosome complex with Dishevelled (Dsh/Dvl in mammals) and Axin. Current models for Wnt/PCP signaling omit Arr/LRP5/6 and the notion is that it functions without these co-receptors. Here we show that arr/LRP5/6 is positively required in Wnt/PCP signaling. In Drosophila, loss of arr results in PCP mediated cellular orientation defects, aberrant wing hair formation, and loss of polarity, as described for core PCP factors fz, fmi/Celsr, and dsh. In the eye, arr mutant tissue displays cell fate changes in photoreceptors R3/R4 and chirality defects, classical PCP phenotypes. During Wnt/PCP establishment, defects are manifest as reduced levels of Fmi/Celsr and Dsh along with loss of their asymmetric localization. Functional interactions indicate that Fz can recruit Arr, and this potentiates Fz and Dsh function in PCP signaling in all tissues tested. Taken together, our data support an essential Arr/LRP5/6 function in promoting Wnt/Fz-Dsh PCP-complex activity.

Simulation-based inference provides a powerful framework for cryoelectron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable information about the physical system and the inference process. Harnessing this potential hinges on understanding the underlying geometric structure of these representations. We investigate this structure by applying manifold learning techniques to CryoSBI representations of a simulated benchmark dataset and both simulated and experimental images of hemagglutinin. We reveal that these high-dimensional data inherently populate low-dimensional, smooth manifolds, with simulated data effectively covering the experimental counterpart. By characterizing the manifold's geometry using Diffusion Maps and identifying its principal axes of variation via coordinate interpretation methods, we establish a direct link between the latent structure and key physical parameters. Discovering this intrinsic low-dimensionality and interpretable geometric organization not only validates the CryoSBI approach but also enables us to learn more from the data structure and provides opportunities for improving future inference strategies by exploiting this revealed manifold geometry.

The collective migration of epithelial groups of cells plays a central role in processes such as embryo development, wound healing, and cancer invasion. While finite cell clusters are known to collectively migrate in response to external gradients, the competing effect of possible endogenous cues is largely this http URL, we demonstrate that the polarization of peripheral cells that pull the cluster's edge outward is sufficient to induce and sustain the collective migration of confluent clusters. We use a general continuum model to show that the underlying shape-sensing mechanism is purely mechanical, relying on long-range hydrodynamic interactions and cell-cell alignment forces. As a proof-of-concept, we validate our findings with experiments on monolayer clusters from various cell lines, where we control initial shapes and sizes. The mechanism operates independently of external signals and will generally interfere with them. Specifically, we predict and observe experimentally that it can override collective durotaxis, reversing the direction of migration. Together, our results offer a physical framework for understanding how cell interactions govern the interplay between global shape and collective motion and afford engineering principles for optimal control and manipulation of cell cluster shape and motion.

nferring dynamic cellular processes from static single-cell measurements remains a central challenge in genomics. Here we introduce ArchVelo, a new method for modeling gene regulation and inferring cell trajectories using single-cell simultaneous chromatin accessibility (scATAC-seq) and transcriptomic (scRNA-seq) profiling. ArchVelo represents chromatin accessibility as a set of archetypes—shared regulatory programs—and models their dynamic influence on transcription. Compared to previous methods, ArchVelo improves inference accuracy and gene-level latent time alignment, and enables identification of the underlying transcription factor activity. We benchmark ArchVelo on developing mouse brain and human hematopoiesis datasets and apply it to CD8 T cells responding to viral infection, revealing distinct trajectories of differentiation and proliferation. Focusing on the progenitor CD8 T cell population with key roles in sustaining immune responses and translationally linked to immunotherapy outcomes, we identify a previously uncharacterized differentiation trajectory from Ccr6− to Ccr6+ progenitors, shared between acute and chronic infection. In sum, ArchVelo provides a principled framework for modeling dynamic gene regulation in multi-omic single-cell data across biological systems.