Biology subjects

Hur, M.

Publications and source records attributed to Hur, M..

7 recordsLinked to original sources

bioRxiv2026

Implementation of ddaE neuron growth mechanism in graph grammar replicates biological features

Dendrites develop branching patterns that are critical for their function, yet the mechanisms guiding arbor morphology remain incompletely understood, and quantitative models predicting how signals guide morphology remain limited. The ddaE neuron in Drosophila larvae is a proprioceptive sensory neuron with a characteristic asymmetric dendrite arbor that exhibits posterior-biased branching. We developed a computational model using Dynamical Graph Grammar (DGG) to simulate ddaE dendrite development as a graph-based dynamical system, using a single morphogen gradient to establish arbor architecture. Our simulations of ddaE dendrites, guided by the spatial gradient of the Teneurin-m (Tenm) morphogen combined with resource constraints and self-avoidance rules, accurately recapitulate the morphological features of biological ddaE neurons, including primary branch orientation, posterior bias, branch tree distributions, and branch length statistics. We find that the response to a single morphogen gradient is sufficient to guide the computerized dendritic arbor. Null model analyses demonstrate that simulated arbors exhibit non-random spatial and topological organization consistent with biological constraints. Our results demonstrate that rules based on a single morphogen gradient are sufficient to generate complex asymmetric dendritic patterns and provide a validated computational framework for testing perturbations in silico. SIGNIFICANCEWe develop and simulate a minimal computational model of dendritic arbor morphogenesis based on a single morphogen gradient. Asymmetric dendrite arbors, such as the ddaE proprioceptive neuron in Drosophila larvae, have not previously been computationally modeled. Using the Dynamical Graph Grammar framework, we create a mathematical model from 17 dynamical rules governing changes in arbor structure, spatial position, morphogen-directed growth, and the local dynamic state of each tip. Each tip switches among three states: growth/pause/shrinkage, while tips that encounter another branch additionally enter a retraction state. Using a large simulation sample size, we characterize our model systems generative outputs and verify that they match imaged biological dendrites across the majority of morphological statistics, including posterior branch bias, branch degree distributions, branch number, and dendrite length. We demonstrate that efficient spacing is guided by the orientation of branch junctions. We make our simulation publicly available to function with high-throughput investigation of gene-to-phenotype relationships in dendrite development.

neuroscience↗

bioRxiv2025

Global Landscape of Human Kinase Motifs in Viral Proteomes

Viruses are classically viewed as targets of host sensing, yet whether they also sense and respond to host cues remains largely unexplored. We propose that host-driven post-translational modification of viral proteins allows viruses to dynamically sense host cellular states. We annotated human kinase motifs in 1,505 viral proteomes and discovered an enrichment for stress, inflammation, and cell-cycle kinases. Mapping kinase motifs onto 21,606 viral protein structures and integrating with phosphoproteomics of infected cells revealed surface-accessible residues were preferentially phosphorylated, showed greater kinase specificity, and were under positive selection for stress and immune kinase motifs. Temporal phosphoproteomics of alphavirus-infected cells confirmed stress kinase activation and viral protein phosphorylation, and MAP kinase inhibition reduced alphavirus replication and phosphorylation of ERK and JNK motifs on viral proteins. Our findings suggest that viruses evolved as biosensors of the host signaling state, unveiling new antiviral opportunities aimed at disrupting virus decision-making.

microbiology↗

bioRxiv2025

Synaptic Spine Head Morphodynamics from Graph Grammar Rules for Actin Dynamics

There is a morphodynamic component to synaptic learning by which changes in dendritic (postsynaptic) spine head size are associated with the strengthening or weakening of the synaptic connection between two neurons. The membrane shape and size dynamics is sculpted by the growth dynamics of the enclosed actin cytoskeleton. We use Dynamical Graph Grammars (DGGs) governing dynamic labelled graphs embedded in two dimensions to model networks of actin filaments and the enclosing membrane in spine head morphology. We demonstrate the flexibility and extensibility of the framework by encoding detailed biophysical as well as biochemical models, obeying constraints of invariance and conservation, in DGG rule sets. From graph-local energy functions for cytoskeleton actin interacting and membrane, we specialize dissipative stochastic dynamics to an exhaustive collection of graph-local neighborhood types for the rule left hand sides. Extensively simulating the resulting model delineates effects of four actin-binding proteins, and their epistatic relationships, on morphology.

biophysics↗

bioRxiv2022

The last missing piece of the Triangle of U: the evolution of the tetraploid Brassica carinata genome

Ethiopian mustard (Brassica carinata) is an ancient crop with significant potential for expanded cultivation as a biodiesel feedstock. The remarkable stress resilience of B. carinata and desirable seed fatty acid profile addresses the ongoing food vs. fuel debate as the crop is productive on marginal lands otherwise not suitable for even closely related species. B. carinata is one of six key Brassica spp. that share three major genomes: three diploid species (AA, BB, CC) that spontaneously hybridized in a pairwise manner, forming three allotetraploid species (AABB, AACC, and BBCC). Each of these genomes has been researched extensively, except for that of B. carinata. In the present study, we report a high-quality, 1.31 Gbp genome with 156.9-fold sequencing coverage for B. carinata var. Gomenzer, completing and confirming the classic Triangle of U, a theory of the evolutionary relationships among these six species that arose almost a century ago. Our assembly provides insights into the genomic features that give rise to B. carinatas superior agronomic traits for developing more climate-resilient Brassica crops with excellent oil production. Notably, we identified an expansion of transcription factor networks and agronomically-important gene families. Completing the Triangle of U comparative genomics platform allowed us to examine the dynamics of polyploid evolution and the role of subgenome dominance in domestication and agronomical improvement.

genetics↗

bioRxiv2019

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

The diverse and growing omics data in public domains provide researchers with a tremendous opportunity to extract hidden knowledge. However, the challenge of providing domain experts with easy access to these big data has resulted in the vast majority of archived data remaining unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory data analysis of massive datasets by scientific researchers. Using MOG, a researcher can interactively visualize and statistically analyze the data, in the context of its metadata. Researchers can interactively hone-in on groups of experiments or genes based on attributes such as expression values, statistical results, metadata terms, and ontology annotations. MOGs statistical tools include coexpression, differential expression, and differential correlation analysis, with permutation test-based options for significance assessments. Multithreading and indexing enable efficient data analysis on a personal computer, with no need for writing code. Data can be visualized as line charts, box plots, scatter plots, and volcano plots. A researcher can create new MOG projects from any data or analyze an existing one. An R-wrapper lets a researcher select and send smaller data subsets to R for additional analyses. A researcher can save MOG projects with a history of the exploratory progress and later reopen or share them. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, in which we assembled a list of novel putative biomarker genes in different tumors, and microarray and metabolomics from A. thaliana.

bioinformatics↗

bioRxiv2019

synder: inferring genomic orthologs from synteny maps

Ortholog inference is a key step in understanding the evolution and function of a gene or other genomic feature. Yet often no similar sequence can be identified, or the true ortholog is hidden among false positives. A solution is to consider the sequences genomic context. We present the generic program, synder, for tracing features of interest between genomes based on a synteny map. This approach narrows genomic search-space independently of the sequence of the feature of interest. We illustrate the utility of synder by finding orthologs for the Arabidopsis thaliana 13-member gene family of Nuclear Factor YC transcription factor across the Brassicaceae clade.

bioinformatics↗

bioRxiv2017

microCT-Based Skeletal Phenomics in Zebrafish Reveals Virtues of Deep Phenotyping at the Whole-Organism Scale

Phenomics, which ideally involves in-depth phenotyping at the whole-organism scale, may enhance our functional understanding of genetic variation. Here, we demonstrate methods to profile hundreds of measures comprised of morphological and densitometric traits from a large number sites in the axial skeleton of adult zebrafish. We show the potential for vertebral patterns to confer heightened sensitivity, with similar specificity, in discriminating mutant populations compared to analyzing individual vertebrae in isolation. We identify phenotypes associated with human brittle bone disease and thyroid stimulating hormone receptor hyperactivity. Finally, we develop allometric models and show their potential to aid in the discrimination of mutant phenotypes masked by alterations in growth. Our studies demonstrate virtues of deep phenotyping in a spatially distributed organ. Analyzing phenotypic patterns may increase productivity in genetic screens, and could facilitate the study of genetic variants associated with smaller effect sizes, such as those that underlie complex diseases.

genetics↗