Data-Modeling Identifies Conflicting Signaling Axes Governing Myoblast Proliferation and Differentiation Responses to Diverse Ligand Stimuli

Inhibitor of Myom3 inhibits proliferation and promotes differentiation of sheep myoblasts

Skeletal muscle quality and yield are important production traits in livestock, and improving skeletal muscle quality while increasing its yield is an important goal of economic breeding. The proliferation and differentiation process of sheep myoblasts directly affects the growth and development of their muscles, thereby affecting the yield of mutton. Myomesin 3 (Myom3), as a functional gene related to muscle growth, currently lacks research on its function in myoblasts. This study aims to investigate the effect of the Myom3 gene on the proliferation and differentiation of sheep myoblasts and its potential molecular mechanisms. The results showed that inhibitor of Myom3 in the proliferation phase of myoblasts resulted in significant downregulation of the proliferation marker gene paired box 7 (Pax7) and myogenic regulatory factors (MRFs; Myf5, Myod1, Myog, P < 0.01), a significant decrease in the EdU-positive cell rate (P < 0.05), and a significant increase in the cell apoptosis rate (P < 0.01), which inhibited the proliferation of myoblasts and promoted their apoptosis. During the differentiation phase of myoblasts, the inhibitor of Myom3 resulted in significant downregulation of the Pax7 gene, upregulation of MRFs (Myod1, Myog, P < 0.05), and a significant increase in fusion index (P < 0.05), promoting the differentiation of myoblasts. Further transcriptome sequencing revealed that differentially expressed genes in the Myom3 interference group were mainly enriched in the MAPK signaling pathway, TNF signaling pathway, and IL-17 signaling pathway. In summary, the inhibitor of Myom3 inhibits myoblast proliferation and promotes myoblast differentiation. Therefore, Myom3 has a potential regulatory effect on the growth and development of sheep muscles, and in-depth functional research can be used for molecular breeding practices in sheep.

Tumor Necrosis Factor-Alpha Modulates Expression of Genes Involved in Cytokines and Chemokine Pathways in Proliferative Myoblast Cells

Skeletal muscle regeneration after injury is a complex process involving inflammatory signaling and myoblast activation. Pro-inflammatory cytokines like tumor necrosis factor-alpha (TNF-α) are key mediators, but their effects on gene expression in proliferating myoblasts are unclear. We performed the RNA sequencing of TNF-α treated C2C12 myoblasts to elucidate the signaling pathways and gene networks regulated by TNF-α during myoblast proliferation. The TNF-α (10 ng/mL) treatment of C2C12 cells led to 958 differentially expressed genes compared to the controls. Pathway analysis revealed significant regulation of TNF-α signaling, along with the chemokine and IL-17 pathways. Key upregulated genes included cytokines (e.g., IL-6), chemokines (e.g., CCL7), and matrix metalloproteinases (MMPs). TNF-α increased myogenic factor 5 (Myf5) but decreased MyoD protein levels and stimulated the release of MMP-9, MMP-10, and MMP-13. TNF-α also upregulates versican and myostatin mRNA. Overall, our study demonstrates the TNF-α modulation of distinct gene expression patterns and signaling pathways that likely contribute to enhanced myoblast proliferation while suppressing premature differentiation after muscle injury. Elucidating the mechanisms involved in skeletal muscle regeneration can aid in the development of regeneration-enhancing therapeutics.

Endothelial cell signature in muscle stem cells validated by VEGFA-FLT1-AKT1 axis promoting survival of muscle stem cell

Endothelial and skeletal muscle lineages arise from common embryonic progenitors. Despite their shared developmental origin, adult endothelial cells (ECs) and muscle stem cells (MuSCs; satellite cells) have been thought to possess distinct gene signatures and signaling pathways. Here, we shift this paradigm by uncovering how adult MuSC behavior is affected by the expression of a subset of EC transcripts. We used several computational analyses including single-cell RNA-seq (scRNA-seq) to show that MuSCs express low levels of canonical EC markers in mice. We demonstrate that MuSC survival is regulated by one such prototypic endothelial signaling pathway (VEGFA-FLT1). Using pharmacological and genetic gain- and loss-of-function studies, we identify the FLT1-AKT1 axis as the key effector underlying VEGFA-mediated regulation of MuSC survival. All together, our data support that the VEGFA-FLT1-AKT1 pathway promotes MuSC survival during muscle regeneration, and highlights how the minor expression of select transcripts is sufficient for affecting cell behavior.

Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity

Due to the variability of protein expression, cells of the same population can exhibit different responses to stimuli. It is important to understand this heterogeneity at the individual level, as population averages mask these underlying differences. Using computational modeling, we can interrogate a system much more precisely than by using experiments alone, in order to learn how the expression of each protein affects a biological system. Here, we examine a mechanistic model of CAR T cell signaling, which connects receptor-antigen binding to MAPK activation, to determine intracellular modulations that can increase cellular response. CAR T cell cancer therapy involves removing a patient's T cells, modifying them to express engineered receptors that can bind to tumor-associated antigens to promote tumor cell killing, and then injecting the cells back into the patient. This population of cells, like all cell populations, would have heterogeneous protein expression, which could affect the efficacy of treatment. Thus, it is important to examine the effects of cell-to-cell heterogeneity. We first generated a dataset of simulated cell responses via Monte Carlo simulations of the mechanistic model, where the initial protein concentrations were randomly sampled. We analyzed the dataset using partial least-squares modeling to determine the relationships between protein expression and ERK phosphorylation, the output of the mechanistic model. Using this data-driven analysis, we found that only the expressions of proteins relating directly to the receptor and the MAPK cascade, the beginning and end of the network, respectively, are relevant to the cells' response. We also found, surprisingly, that increasing the amount of receptor present can actually inhibit the cell's ability to respond due to increasing the strength of negative feedback from phosphatases. Overall, we have combined data-driven and mechanistic modeling to generate detailed insight into CAR T cell signaling.