Duchenne muscular dystrophy (DMD) is a lethal genetic disorder caused by a mutation of the dystrophin gene, causing muscle fibers to have reduced ability to bind to the extracellular matrix (ECM). The result of this defect is weakened myofiber-ECM connection, making muscle more prone to injury and eventually leading to chronic inflammation, remodeling, and fibrosis. In the DMD mouse model (mdx), we have previously shown increased mechanical stiffness of fibrotic ECM. How this mechanical environment affects mechanosignaling and function of resident inflammatory cells is unknown. Much of DMD pathogenesis is modulated by macrophages, with pro-inflammatory (M1) subsets exacerbating muscle damage and anti- inflammatory (M2) subsets losing normal regenerative potential in favor of pro-fibrotic activity. Pro-inflammatory macrophages are pivotal for removal of necrotic debris by phagocytosis, and research implicates phagocytosis as essential for shifting from M1 to M2 polarization during muscle repair. However, it is unknown if phagocytosis is altered in the stiffened dystrophic ECM. Increased mechanical stiffness can activate YAP, a mechanosensitive transcriptional co- activator, in macrophages and enhance inflammatory activity. Therefore, this project examines how macrophage phagocytic activity changes in different ECM microenvironments to better characterize the linkage between YAP-mediated macrophage mechanosensing and disease pathogenesis. Phagocytic dysregulation may provide a mechanism by which fibrosis-mediated stiffness directly influences macrophage inflammatory behavior and drives pathogenesis. Additionally, efforts to improve myofiber-ECM connection with transgenic models that ameliorate muscle pathology cause an increase of fibrosis, which has an uncertain effect on macrophage phagocytosis and will be explored.

The Mammalian Reovirus (MRV), a non-enveloped dsRNA segmented virus with two capsid layers, is part of the Reovirdae family of viruses. MRV is often found in the respiratory and gastrointestinal tracts of all mammals but has low pathogenicity towards humans. Despite its use as a model virus for more pathogenic viruses in the Reoviridae family and for its oncolytic potential, an atomic model of the virion has yet to be reported. Using a high-resolution cryo-electron micrograph (cryoEM) map with 2.9 Å resolution, our goal is to construct an atomic model that will reveal the architecture of the MRV virion. An initial atomic model of the virion was reconstructed by docking structures of the individual components obtained previously with cryoEM and X-ray crystallography into the EM map. In order to yield a high-quality model, the structure was further refined and regularized through the use of Coot, ISOLDE, and Phenix. The finished atomic model consists of the spike, outer capsid, and inner capsid proteins, including 𝜎3 which has not been included in previous atomic models of the intermediate MRV particles. The finished atomic model presents the detailed architecture of the intact virus and contains sufficient details to interpret assembly interactions.

Tuberculosis (TB) continues to pose a significant global health challenge, necessitating innovative diagnostic strategies. In this study, we present a novel approach combining environment-sensitive probes with a machine learning-based fluorescence microscope, Octopi, for rapid and accurate detection of live Mycobacterium tuberculosis (Mtb), which is the causative agent of TB. Environment-sensitive probes, DMN-Tre and 3HC-Tre, are employed to label Mtb cells, enabling rapid visualization within 30 minutes. These probes exploit hydrophobicity changes upon bacterial metabolic activation, offering a cost-effective and efficient alternative to traditional methods. We harnessed the potential of Octopi, an automated slide scanning portable fluorescence microscope, to acquire fluorescence and brightfield images of labeled Mycobacterium smegmatis (Msmeg) in different conditions. These images served as inputs for a machine learning pipeline that seamlessly integrates automated data acquisition and analysis. By leveraging machine learning, we bridge the gap between manual intervention and accurate detection, enhancing diagnostic precision. Our results showcase the versatility of the labeling approach across different microscopy platforms. Looking ahead, we outline crucial future directions for our research. These include finalizing the machine learning pipeline, transitioning to acquiring Mtb images instead of using Msmeg as the model organism, determining the limit of detection, and optimizing Octopi for Mtb analysis in clinical samples. By validating the pipeline's performance with clinical samples, we aim to establish its clinical utility. This study lays the groundwork for a transformative diagnostic tool that combines environment-sensitive probes, automated microscopy, and machine learning, offering promise for enhanced TB diagnosis and patient care.

Induced pluripotent stem cells are a valuable resource for diagnosis, research, and treatment of human diseases. A major barrier to the use of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) for drug screening, disease modeling, and cell-replacement therapy is their immaturity compared to adult human cardiomyocytes. Previous research indicated that a combination of biochemical factors, thyroid hormone, dexamethasone, and insulin-like growth factor-1 (TDI), could enhance maturation of iPSC-CMs. In this study, we show that a combination of TDI with protein X, a modulator of Wnt signaling pathway, could accelerate and further enhance the maturation process of iPSC-CMs. Based on a comparison of gene expression profile, sarcomere assembly, and contractile properties of iPSC-CMs treated with TDI and TDI with protein X, we find that protein X, on basis of TDI treatment, yields more mature iPSC-CMs with phenotypes similar to adult human cardiomyocytes, including contractility, sarcomere assembly, and gene expression profiles consistent with maturation of iPSC-CMs. Thus, the combination of TDI with protein X is a de novo method to accelerate the maturation of iPSC-CMs towards adult-like CMs for successful drug discovery and disease modeling.