Longevity Papers

The ubiquitin-proteasome system is essential for neuronal proteostasis, and its activity declines with age. How deubiquitylating enzymes (DUBs) are affected by aging in the vertebrate brain remains unclear. Here, we profiled cysteine protease DUBs using activity-based proteomics in aging mouse and killifish brains. Despite stable protein levels, we identified a subset of DUBs that progressively lose catalytic activity with age. We demonstrated that oxidative stress impairs DUB function through thiol oxidation and that antioxidant treatment restores their activity in vitro and in vivo. Further, inhibition of DUBs in human iPSC-derived neurons significantly recapitulated ubiquitylation changes observed in aged brains, and temporal analysis in mice revealed that DUB inhibition precedes proteasome decline in the brain during aging. Together, these findings indicate a redox-sensitive subset of DUBs that undergo an age-associated decline in activity and suggest that impaired deubiquitylation is an early, yet potentially reversible, driver of proteostasis decline in the aging brain.

Cellular senescence is the major hallmark and therapeutic target of aging and age-related diseases. The role of ALKBH5, one of the main m6A demethylases, in cellular senescence emerges however remains contentious. Herein, we show the reversible ALKBH5 aggregation in cytoplasm promotes cellular senescence. Mechanically, ALKBH5 aggregation causes cytosolic retention, resulting in the m6A dysregulation and m6A hypermethylation of Cdk2, which promotes Cdk2 RNA instability to drive senescence. In addition, m6A imbalance aggravates ALKBH5 cytosolic aggregation in a feedback loop. We further demonstrate that ALKBH5 nuclear translocation required the formation of ALKBH5 droplet phase via binding Nucleoporin p62 (Nup62), while the aggregation of ALKBH5 traps with Nup62 in the cytoplasm. Reduced Nup62 prevents ALKBH5 nuclear entry leading to cellular senescence. Importantly, administration of m6A labeled RNA efficiently reverses ALKBH5 cytosolic aggregates and restores its nuclear entry to alleviate cellular senescence. Forced nuclear entry by NLS-ALKBH5 can prevent senescence in vitro and in vivo. Taken together, these findings unravel a novel paradigm for m6A epigenetic regulation in cellular senescence and offer promising therapeutic targets and strategies for the intervention of aging and age-associated diseases.

Transcriptome analysis has become increasingly utilized in aging research. However, the identification of the key molecular changes underlying aging processes and longevity-promoting regimens from transcriptome data remains challenging. Here, we present Transcriptomic CLassification via Adaptive learning of Signature States (T-CLASS), an online tool that identifies, from transcriptome data, gene sets of several hundred genes that provide an optimal representation of longevity and aging paradigms. We systematically evaluated the effectiveness of T-CLASS with diverse datasets, including longevity-promoting regimens in Caenorhabditis elegans, cellular senescence by different means in both cultured mouse primary cells and cultured human cells, and human sarcopenia. We found that T-CLASS exhibited robust and high classification performance across datasets compared to preexisting machine/deep learning-based gene selection tools. By focusing our further analysis on longevity-promoting regimens in C. elegans, we showed that T-CLASS successfully classified transcriptomic changes caused by ten lifespan-extending small molecules, among which we experimentally validated the effect of rifampicin and atracurium as a proof of principle. Overall, T-CLASS is an effective and practical tool for uncovering and classifying physiological changes caused by genetic and pharmacological interventions that affect aging.

Telomeres, the nucleoprotein structures at the ends of chromosomes, have emerged as critical regulators of cellular aging and key contributors to the pathogenesis of age-related diseases. This comprehensive review examines the evolution of telomere biology from fundamental research to therapeutic applications, analyzing molecular mechanisms of telomere dysfunction across diverse disease categories, including autoimmune disorders, cardiovascular diseases, neurodegeneration, respiratory diseases, metabolic disorders, chronic kidney disease, cancer, and premature aging syndromes. We explore current therapeutic strategies ranging from telomerase modulation to senolytic approaches, highlighting emerging technologies in drug discovery, including CRISPR-based interventions, nanomedicine, mRNA-based therapies, partial cellular reprogramming, and artificial intelligence applications. The convergence of mechanistic understanding with innovative therapeutic approaches positions telomere biology as a promising frontier for addressing multiple age-related conditions simultaneously, potentially shifting medicine from reactive disease treatment toward proactive aging-focused prevention. However, significant challenges remain, including safety considerations, biomarker development, and establishing regulatory frameworks for aging-targeted therapeutics. The success of telomere-targeted interventions could herald a paradigm shift toward geroscience-based medicine, extending lifespan and health span by targeting fundamental biological aging processes.

Aging involves processes spanning orders of magnitude in time, from fast events that occur at the molecular scale to the slow decrease of physiological function. Whether and how fast molecular events lead to the slow progression of aging, and what ultimately sets the timescale of aging, is not understood. Here, by focusing on dynamic changes in DNA methylation, we show how aging phenomena on long timescales emerge from the kinetics of fast molecular processes, providing a bridge between temporal scales. By combining DNA methylation sequencing data across a range of timescales with a statistical modeling-based approach, we show that DNA methylation aging is governed by a three-fold hierarchy of processes that dominate on distinct timescales: individual stochastic events in which enzymes interact with the DNA and with each other (milliseconds); the convergence of molecular concentrations to steady states (days to months); and stochastic transitions between these steady states (years to decades). Our findings provide a unified picture of how DNA methylation aging arises across temporal scales.

The focal adhesion (FA) is the structural basis of the cell-extracellular matrix crosstalk and plays important roles in control of organ formation and function. Here we show that expression of FA protein vinculin is dramatically reduced in osteocytes in patients with aging-related osteoporosis. Vinculin loss severely impaired osteocyte adhesion and dendrite formation. Deleting vinculin using the mouse 10-kb Dmp1-Cre transgenic mice causes dramatic bone loss in the weight-bearing long bones and spine, but not in the skull, in both young and aged mice by impairing osteoblast formation and function without markedly affecting bone resorption. Vinculin loss impairs the anabolic response of skeleton to mechanical loading in mice. Vinculin knockdown increases, while vinculin overexpression decreases, sclerostin expression in osteocytes without impacting expression of Mef2c, a major transcriptional regulator of the Sost gene, which encodes sclerostin. Vinculin interacts with Mef2c and retains the latter in the cytoplasm. Thus, vinculin loss enhances Mef2c nuclear translocation and binding to the Sost enhancer ECR5 to promote sclerostin expression in osteocytes and reduces bone formation. Consistent with this notion, deleting Sost expression in osteocytes reverses the osteopenic phenotypes caused by vinculin loss in mice. Finally, we find that estrogen is a novel regulator of vinculin expression in osteocytes and that vinculin-deficient mice are resistant to ovariectomy-induced bone loss. Thus, we demonstrate a novel mechanism through which vinculin inhibits the Mef2c-driven sclerostin expression in osteocytes to promote bone formation.

Mass spectrometry (MS)-based metabolomics is a key technology for the interrogation of exogenous and endogenous small molecule mediators that influence human health and disease. To date, however, low throughput of MS systems have largely precluded large-scale metabolomics studies of human populations, limiting power to discover physiological roles of metabolites. Here, we introduce a fully automated rapid liquid chromatography-mass spectrometry (rLC-MS) system coupled to an AI-enabled computational pipeline that enables high-throughput, reproducible, non-targeted metabolite measurements across tens of thousands of samples. This system captures thousands of polar, amphipathic and nonpolar (lipid) metabolites in a human plasma sample in 53 seconds of analytical time, enabling analysis of greater than 1,000 samples per day per instrument. To demonstrate the discovery power of the rLC-MS platform, a subset of samples from Sapient\'s DynamiQ biorepository -- comprised of 62,039 total plasma samples collected longitudinally from 11,045 individuals -- were selected for deep analysis by rLC-MS to capture a rich, dynamic landscape of chemical variation that reflects both physiological processes and environmental influences. 26,042 plasma samples with matched real-world data (RWD) were chosen for the study, representing 6,935 individuals with diverse demographic backgrounds and disease profiles. Unbiased exploratory analysis revealed human metabotypes that correlate with heterogenous disease phenotypes, including key sub-populations of cardiometabolic and other human diseases. Moreover, a metabolic aging clock machine learning model trained on healthy individuals in this dataset accurately predicted accelerated aging in various chronic diseases, with dynamic reversal of metabolic aging following definitive therapy. These data demonstrate that the rLC-MS platform enables prediction of clinically relevant physiological states from plasma metabolomics at scale in human populations.

Persistent genomic instability compromises cellular viability while also triggers non-cell-autonomous responses that drive dysfunction across tissues, contributing to aging. Recent evidence suggests that DNA damage activates secretory programs, including the release of inflammatory cytokines, damage-associated molecular patterns, and extracellular vesicles, that reshape immune homeostasis, stem cell function, and metabolic balance. Although these responses may initially support tissue integrity and organismal survival, their chronic activation has been associated with tissue degenerative changes and systemic decline. Here, we discuss how nuclear DNA damage responses trigger the activation of cytoplasmic sensing pathways, promote secretory phenotypes, and affect organismal physiology. Targeting DNA damage-driven mechanisms may help buffer harmful systemic responses while preserving regeneration and immune surveillance, offering new ways to delay aging-related decline.