Biology subjects

Maddox, A.

Publications and source records attributed to Maddox, A..

3 recordsLinked to original sources

bioRxiv2026

Integrated Analysis of Skeletal Muscle Transcriptional Networks Characterizes Dysregulation in Pathways and Trait-Associated Regulatory Regions in Type 2 Diabetes

Skeletal muscle, a primary site of insulin-mediated glucose uptake, plays a central role in the pathogenesis of type 2 diabetes. It is therefore critical to understand the disease-associated alterations in skeletal muscle and identify the underlying drivers of this dysregulation. Here, we characterize type 2 diabetes associated transcriptional dysregulation using 301 skeletal muscle biopsies from living donors with and without diabetes. Using weighted gene co-expression network analysis, we identify 56 distinct gene modules, which we further characterize using single-nucleus RNA-seq-derived cell type signatures and pathway enrichment analysis. We identify numerous cell type-associated dysregulated pathways in skeletal muscle tissue from individuals with diabetes, including muscle fiber-associated mitochondrial function and mRNA splicing and processing; endothelial vascularization and phospholipase D signaling; and macrophage- and T-cell-associated inflammation. Through analysis of module hub genes and transcription factor regulatory network analysis, we further identify candidate driver genes of this dysregulation including ATP5L, ATF2, SIRT1, and THRAP3 in muscle fibers; JAM2 and CLEC14A in endothelial cells; and F13A1 and IRF8 in immune cells. Finally, we integrate our co-expression networks with single-nucleus ATAC-seq data to identify proximal and distal genomic regulatory elements and identify context-specific enrichment for type 2 diabetes and related trait GWAS signals in muscle fiber and endothelial modules. Together, our results reveal dysregulation in pathways in muscle tissue from individuals with diabetes, identify candidate drivers, and connect the genomic drivers of this dysregulation across type 2 diabetes and related metabolic traits.

bioinformatics↗

bioRxiv2025

Spatial transcriptomics of glioblastoma defines biologically and clinically significant reprogramming patterns across unique spatial microenvironments

Intratumoral heterogeneity is thought to hinder targeted therapy in glioblastoma (GBM), but the extent of variation in known targets and tumor cell-intrinsic and extrinsic regulatory mechanisms within tissue remains unclear. In this work, we use Visium spatial transcriptomics to profile 44 tumor slides and 128,176 niche-annotated ST spots, including 14 new slides from 7 GBM specimens, along with spatial annotation from the Ivy Glioblastoma Atlas Project. First, we characterize the differential expression of AVB3, EGFR, VEGF, and PDGFRA pathway activities across space, elucidating the limited success of individual targeted therapies. Second, we identify transcription factor modules driving intratumoral heterogeneity and characterize a novel transcription factor module at the interface between core tumor and the invasive edge. Third, we identify cell type-specific receptor-ligand signaling enriched in specific spatial niches and relate these to specific molecular program alterations. Finally, we identify and characterize transcriptional pathways and their activity across spatial niches through transcription network analysis and demonstrate how these pathways can inform a combined therapeutic approach. Together, through novel insights into the spatial patterning of transcription factor regulation, cellular interactions, and biological pathway activity, our work informs rational combination therapies targeting spatial niche specific vulnerabilities.

bioinformatics↗

bioRxiv2019

Bond type and discretization of non-muscle myosin II are critical for simulated contractile dynamics

Molecular motors drive cytoskeletal rearrangements to change cell shape. Myosins are the motors that move, crosslink, and modify the actin cytoskeleton. The primary force generator in contractile actomyosin networks is non-muscle myosin II (NMMII), a molecular motor that assembles into ensembles that bind, slide, and crosslink actin filaments (F-actin). The multivalence of NMMII ensembles and their multiple roles have confounded the resolution of crucial questions including how the number of NMMII subunits affects dynamics, and what affects the relative contribution of ensembles crosslinking versus motoring activities. Since biophysical measurements of ensembles are sparse, modeling of actomyosin networks has aided in discovering the complex behaviors of NMMII ensembles. Myosin ensembles have been modeled via several strategies with variable discretization/coarse-graining and unbinding dynamics, and while general assumptions that simplify motor ensembles result in global contractile behaviors, it remains unclear which strategies most accurately depict cellular activity. Here, we used an agent-based platform, Cytosim, to implement several models of NMMII ensembles. Comparing the effects of bond type, we found that ensembles of catch-slip and catch motors were the best force generators and binders of filaments. Slip motor ensembles were capable of generating force but unbound frequently, resulting in slower contractile rates of contractile networks. Coarse-graining of these ensemble types from two sets of 16 motors on opposite ends of a stiff rod to two binders, each representing 16 motors, reduced force generation, contractility, and the total connectivity of filament networks for all ensemble types. A parallel cluster model (PCM) previously used to describe ensemble dynamics via statistical mechanics, allowed better contractility with coarse-graining, though connectivity was still markedly reduced for this ensemble type with coarse-graining. Together our results reveal substantial trade-offs associated with the process of coarse-graining NMMII ensembles and highlight the robustness of discretized catch-slip ensembles in modeling actomyosin networks. STATEMENT OF SIGNIFICANCEAgent-based simulations of contractile networks allow us to explore the mechanics of actomyosin contractility, which drives many cell shape changes including cytokinesis, the final step of cell division. Such simulations should be able to predict the mechanics and dynamics of non-muscle contractility, however recent work has highlighted a lack of consensus on how to best model the non-muscle myosin II. These ensembles of approximately 32 motors are the key components responsible for driving contractility. Here, we explored different methods for modeling non-muscle myosin II ensembles within the context of contractile actomyosin networks. We show that the level of coarse-graining and the choice of unbinding model used to model motor unbinding under load indeed has profound effects on contractile network dynamics.

cell biology↗