A suppressive type of immune cell called a regulatory T cell has a key role in helping muscles to adapt to exercise — guarding muscle mitochondrial organelles against damage mediated by proinflammatory factors generated during physical activity.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the patchy distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology.

This study aimed to evaluate the correlation between various outcome measures used to assess disease severity and progression in inclusion body myositis (IBM) clinical trials. A cross-sectional study, involving 51 IBM patients meeting the European Neuromuscular Center (ENMC) 2011 criteria for clinically defined or probable IBM, was completed at the University of California, Irvine. Clinical details, demographic data, and functional data including timed get up (TGU), manual muscle testing, hand grip, pinch dynamometry, as well as IBM functional rating scale (IBMFRS), modified Rankin score, forced vital capacity (FVC), and modified ocular bulbar facial respiratory scale (mOBFRS) were collected on all patients. Descriptive statistics and Pearsons r correlation were performed to analyze the data. Age of the patient did not correlate with any of the outcome measures. Greater severity of IBMFRS scores correlated with longer disease duration as well as greater severity for FVC, strength outcomes, TGU, modified Rankin, and mOBFRS. Additionally, TGU strongly correlated with muscle strength measurements, modified Rankin, and mOBFRS. mOBFRS moderately correlated with IBMFRS, muscle strength, FVC, TGU and modified Rankin score. We demonstrate moderate to strong correlations among the disease severity outcome measures in this study.

Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.

Duchenne muscular dystrophy (DMD) is an X-linked muscle wasting disease that is caused by the loss of functional dystrophin protein in cardiac and skeletal muscles. DMD patient muscles become weakened, leading to eventual myofiber breakdown and replacement with fibrotic and adipose tissues. Inflammation drives the pathogenic processes through releasing inflammatory cytokines and other factors that promote skeletal muscle degeneration and contributing to the loss of motor function. Selective inhibitors of nuclear export (SINEs) are a class of compounds that function by inhibiting the nuclear export protein exportin 1 (XPO1). The XPO1 protein is an important regulator of key inflammatory and neurological factors that drive inflammation and neurotoxicity in various neurological and neuromuscular diseases. Here, we demonstrate that SINE compound KPT-350 can ameliorate dystrophic-associated pathologies in the muscles of DMD models of zebrafish and mice. Thus, SINE compounds are a promising novel strategy for blocking dystrophic symptoms and could be used in combinatorial treatments for DMD.

Endogenous retroviruses (ERVs) are remnants of ancient parasitic infections and comprise sizable portions of most genomes. Although epigenetic mechanisms silence most ERVs by generating a repressive environment that prevents their expression (heterochromatin), little is known about mechanisms silencing ERVs residing in open regions of the genome (euchromatin). This is particularly important during embryonic development, where induction and repression of distinct classes of ERVs occur in short temporal windows. Here, we demonstrate that transcription-associated RNA degradation by the nuclear RNA exosome and Integrator is a regulatory mechanism that controls the productive transcription of most genes and many ERVs involved in preimplantation development. Disrupting nuclear RNA catabolism promotes dedifferentiation to a totipotent-like state characterized by defects in RNAPII elongation and decreased expression of long genes (gene-length asymmetry). Our results indicate that RNA catabolism is a core regulatory module of gene networks that safeguards RNAPII activity, ERV expression, cell identity, and developmental potency.