Skeletal muscle has remarkable regenerative capacity, relying on precise coordination between resident muscle stem cells (satellite cells) and the immune system. The age-related decline in skeletal muscle regenerative capacity contributes to the onset of sarcopenia, prolonged hospitalization, and loss of autonomy. Although several age-sensitive pathways have been identified, further investigation is needed to define targets of cellular dysfunction. Autophagy, a process of cellular catabolism, is emerging as a key regulator of muscle regeneration affecting stem cell, immune cell, and myofiber function. Muscle stem cell senescence is associated with a suppression of autophagy during key phases of the regenerative program. Macrophages, a key immune cell involved in muscle repair, also rely on autophagy to aid in tissue repair. This review will focus on the role of autophagy in various aspects of the regenerative program, including adult skeletal muscle stem cells, monocytes/macrophages, and corresponding age-associated dysfunction. Furthermore, we will highlight rejuvenation strategies that alter autophagy to improve muscle regenerative function.

Caloric restriction (CR) can prolong aged skeletal muscle function, yet the molecular mechanisms are not completely understood. We performed phosphoproteomic analysis on muscle from young and old mice fed an ad libitum diet, and old mice fed a CR diet. CR promoted a youthful phosphoproteomic signature, suppressing several known "pro-aging" pathways including Protein kinase A (PKA). This study validates global signaling changes in skeletal muscle during CR.

The incretin peptides glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptors coordinate β cell secretion that is proportional to nutrient intake. This effect permits consistent and restricted glucose excursions across a range of carbohydrate intake. The canonical signaling downstream of ligand-activated incretin receptors involves coupling to Gαs protein and generation of intracellular cAMP. However, recent reports have highlighted the importance of additional signaling nodes engaged by incretin receptors, including other G proteins and β-arrestin proteins. Here, the importance of Gαs signaling was tested in mice with conditional, postdevelopmental β cell deletion of Gnas (encoding Gαs) under physiological and pharmacological conditions. Deletion of Gαs/cAMP signaling induced immediate and profound hyperglycemia that responded minimally to incretin receptor agonists, a sulfonylurea, or bethanechol. While islet area and insulin content were not affected in Gnas ^{βcell–/–} , perifusion of isolated islets demonstrated impaired responses to glucose, incretins, acetylcholine, and IBMX In the absence of Gαs, incretin-stimulated insulin secretion was impaired but not absent, with some contribution from Gαq signaling. Collectively, these findings validate a central role for cAMP in mediating incretin signaling, but also demonstrate broad impairment of insulin secretion in the absence of Gαs that causes both fasting hyperglycemia and glucose intolerance.

Cysteine is a reactive amino acid central to the catalytic activities of many enzymes. It is also a common target of post-translational modifications (PTMs), such as palmitoylation. This long-chain acyl PTM can modify cysteine residues and induce changes in protein subcellular localization. We hypothesized that cysteine could also be modified by short-chain acyl groups, such as cysteine S -acetylation. To test this, we developed sample preparation and non-targeted mass spectrometry protocols to analyze the mouse liver proteome for cysteine acetylation. Our findings revealed hundreds of sites of cysteine acetylation across multiple tissue types, revealing a previously uncharacterized cysteine acetylome. Cysteine acetylation shows a marked cytoplasmic subcellular localization signature, with tissue-specific acetylome patterns and specific changes upon metabolic stress. This study uncovers a novel aspect of cysteine biochemistry, highlighting short-chain modifications alongside known long-chain acyl PTMs. These findings enrich our understanding of the landscape of acyl modifications and suggest new research directions in enzyme activity regulation and cellular signaling in metabolism.

Purpose: Weight loss is often pursued to improve cardiometabolic health and quality of life. However, rapid weight loss can lead to reductions in lean soft tissue mass and strength to compromise body composition and functional ability. Thus, identifying molecular predictors of muscular strength preservation during weight loss is critical to mitigating these effects. Methods: We conducted a secondary analysis of the CALERIE ^TM (Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy) trial, a two-year randomized controlled study of caloric restriction (CR) or ad-libitum intake in healthy adults without obesity. Among 198 participants, changes in whole-body mass and knee extensor strength were assessed over the first 12 months of the study which was primarily characterized by weight loss. Transcriptomic profiling was conducted in a subset of 42 participants who provided skeletal muscle samples. Linear regression was used to model the relationship between strength change and gene expression change, while controlling for changes in whole-body mass. Gene set enrichment analysis (GSEA) was performed using Hallmark pathways. Individual-level pathway analysis was performed via gene set variation analysis (GSVA). Results: We identified 96 out of 198 individuals (48.5%) who maintained or improved strength relative to body mass during weight loss ( i.e. individuals with residuals > 0). Transcriptomics analysis on a subset of 42 individuals revealed 151 genes significantly associated with change in strength after accounting for change in whole-body mass ( p < 0.01). Hub genes were identified as HSP90AA1 (β = 34.45, SE = 7.32, p <0.001), EIF3A (β = 36.14, SE = 10.27, p < 0.001), EIF5B (β = 49.94, SE = 11.28, p < 0.001), and H3C1 (β = −15.87, SE = 4.85, p < 0.001). GSEA revealed significant involvement of pathways related to cellular proliferation, immune regulation, protein secretion, and checkpoint control processes. GSVA identified a similar set of pathways. Conclusions: These findings highlight molecular pathways supporting strength retention during CR-induced weight loss. Heat-shock protein, HSP90AA1 , warrants further investigation as a candidate target for preserving muscle strength during interventions aimed at weight reduction.

Identifying biological and clinical signals that consistently predict physical resilience, defined as one's ability to maintain or regain function following a health stressor, is essential for advancing precision approaches to aging and recovery. High-dimensional datasets hold tremendous promise but pose analytic challenges due to correlation, distributed signals, instability, and sensitivity to analytic choices. The complexity of these data requires strategies that prioritize transparency and stability in variable selection. We present a resampling-based statistical framework, the Exploratory Stability Selection (ESS) framework, designed for hypothesis-generating predictor discovery. ESS is an ensemble-style variable selection technique that integrates multiple resampling strategies and sparsity levels, enabling exploration of robustness and context-dependence across diverse data perturbations. We demonstrate the utilization of the ESS framework with a data example using the PRIME-KNEE study, which examines physical resilience in older adults undergoing elective total knee arthroplasty. ESS analyses were applied to clinical-only, plasma biomarker-only, and combined predictor sets to evaluate the stability and competitiveness of candidate variables associated with the probability of having a highly resilient recovery trajectory for pain interference. The data example highlights how ESS distinguishes highly stable predictors from context-dependent signals whose selection varies with predictor competition and analytic configuration. ESS retains configuration-level results and summarizes stability metrics across configurations to provide insight into the subsequent prioritization and validation of candidate predictors. This framework is well-suited for hypothesis-generating variable selection problems common to exploratory resilience research and other aging-related applications that involve complex, multi-domain predictor sets.