This is a joint project between Dr. Jyoti Jaiswal and Dr. Lorenzo Puri. Dr. Jaiswal is a Professor at the George Washington University School of Medicine and Senior Investigator at the Children’s National Hospital in Washington DC. Dr. Puri is a Professor at Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA.
Dysferlinopathy involves an age-dependent decline in muscle function following the onset of symptoms past adolescence. As the disease progresses, it results in excessive muscle inflammation and the replacement of muscle fibers by fatty deposits. Our studies using the dysferlinopathic mouse model indicate that the accumulation of fat in the dysferlin-null muscle is caused by altered interactions among muscle-resident cell types. Our initial studies reveal the accumulation of a specific population of fibro adipogenic progenitors (FAPs) and macrophages in the dysferlinopathic muscle, as it transitions from pre-symptomatic to symptomatic stages of disease in dysferlin deficient muscles. Of note, the macrophages and FAPs exhibit aberrant gene expression predictive of “pathogenic” features seen in dysferlin null muscles, including activation of the adipogenic FAP fate and chronic macrophage inflammation. While these pathogenic features appear to result from altered cellular interactions, the exact nature and origin of these alterations have not been deciphered.
We aim to fill this gap through our use of single-cell and single-nuclear RNA sequencing approaches to generate transcriptional profiles of dysferlinopathic muscles at sequential stages of disease progression and compare them with healthy mouse muscles. This collaborative study will continue to build on the team’s expertise in muscle biology, dysferlinopathy, RNA sequencing, and muscle cell biological studies. This will generate gene expression datasets at the resolution of individual cell and nuclei to provide a fundamental understanding of the identity of the cell type(s) and myofiber-specific changes during disease progression that contributes to the disease onset. This dataset will help predict altered signaling pathways and their interactions among muscle stromal cells and myofibers that contribute to disease progression and muscle loss in dysferlinopathy. Comparison of these findings with models of other muscular dystrophies is expected to help define common and disease-specific processes in dysferlinopathy and set the groundwork for the translation of these findings into mechanistic studies, as well as potential novel disease biomarkers, and therapeutic targets.