Longevity Papers

Immunosenescence, the gradual deterioration of the immune system with age, leads to an increased susceptibility to a range of diseases associated with immune dysfunction. Notably, sex is an important variable underlying how immune aging unfolds, as, for instance, autoimmunity develops with aging differently between males and females. Even though some clinical and molecular differences have been identified between male and female immunosenescence, it is not known to what extent sex affects the dynamic composition of immune cells over time. Here, we analyze a large single-cell RNA-sequencing dataset of peripheral blood mononuclear cells from a sex-balanced cohort of 982 human donors providing novel transcriptional and cellular insights into immune aging at an unprecedented resolution. We uncover that aging induces cell type-dependent and sex-specific transcriptional shifts that translate into a differential abundance of distinct immune cell subpopulations. These shifts predominantly involve translation-related genes, indicating a strong link between transcriptional and translational throughput with cell function and consequent immune cell composition. This sexual dimorphism overlaps known autoimmune disease-related genetic variants and results in the differential enrichment of functionally distinct immune populations. Specifically, we uncover that a cytotoxic CD8+ T effector memory subpopulation with an NK-like phenotype accumulates with age only in females and identify a distinct B cell subpopulation that expands with age exclusively in males. These cell subpopulations represent novel sex-specific hallmarks of immune aging. Our findings underscore the hidden complexity of immune aging and demonstrate the value of high-resolution, single-cell analyses in large population cohorts. This research paves the way for future sex-specific interventions targeting immunosenescence to ultimately promote a personalized approach to foster healthy aging.

The dorsolateral prefrontal cortex is central to higher cognitive functions and is particularly vulnerable to age-related decline. To advance our understanding of the molecular mechanisms underlying brain development, maturation, and aging, we constructed a detailed single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex, encompassing over 1.3 million nuclei from 284 postmortem samples spanning the full human lifespan (0-97 years). This atlas reveals distinct phases of transcriptomic activity: a dynamic developmental period, stabilization during midlife, and subtle yet coordinated changes in late adulthood. Modeling non-linear age trends across the lifespan shows ten distinct trajectories of the entire transcriptome from all cell types, with notable findings in neurons and microglia, linked to neurodevelopmental disorders and Alzheimers disease risk, respectively. Moreover, excitatory neurons exhibit a convergence of gene expression patterns across the lifespan, suggesting the emergence of a common molecular signature of aging. Pseudotime analysis tracing the progression of cellular lineages throughout life reveals key gene clusters with dynamic expression changes that reflect development, maturation, and aging, as well as their connection to brain-related diseases. We uncover significant circadian rhythm reprogramming in late adulthood, characterized by disruption of core clock gene rhythmicity and the emergence of new rhythmic patterns, particularly within microglia and oligodendrocytes. This comprehensive single-cell atlas provides a baseline for understanding the molecular transitions from development through successful aging in the human dorsolateral prefrontal cortex.

Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.

Recent advancements in cancer immunotherapy have improved patient outcomes, yet responses to immunotherapy remain moderate. We conducted a Phase II clinical trial (NCT04718415) involving 51 cancer patients undergoing neoadjuvant chemoimmunotherapy and applied single-cell RNA and T/BCR sequencing on tumor and blood samples to elucidate the immune cell perturbations. Our findings associate poor response with reduced levels of CCR7+CD4 Naive T cells and CD27+ Memory B cells, as well as higher expression of immunosenescence-related genes in T and B cell subsets. Using naturally aged and Ercc1+/- transgenic aging mouse models, we found that senolytics enhance the therapeutic efficacy of immunotherapy in multiple solid tumors by mitigating tumor immunosenescence. Notably, we launched a Phase II clinical trial, COIS-01 (NCT05724329), which pioneers the combination of senolytics with anti-PD-1 therapy. The clinical results demonstrate that this therapeutic strategy is associated with a favorable safety profile and therapeutic efficacy, significantly mitigating adverse effects and alleviating immunosenescence. These findings underscore the pivotal role of immunosenescence characteristics in influencing the effectiveness of immunotherapy and suggest a promising therapeutic efficacy along with a beneficial safety assessment for the combination of senolytics with anti-PD-1 therapy.

How organisms age is a question with broad implications for human health. In mammals, DNA methylation is a biomarker for biological age, which may predict age more accurately than date of birth. However, limitations in mammalian models make it difficult to identify mechanisms underpinning age-related DNA methylation changes. Here, we show that the short-lived model plant Arabidopsis thaliana exhibits a loss of epigenetic integrity during aging, causing heterochromatin DNA methylation decay and the expression of transposable elements. We show that the rate of epigenetic aging can be manipulated by extending or curtailing lifespan, and that shoot apical meristems are protected from this aging process. We demonstrate that a program of transcriptional repression suppresses DNA methylation maintenance pathways during aging, and that mutants of this mechanism display a complete absence of epigenetic decay. This presents a new paradigm in which a gene regulatory program sets the rate of epigenomic information loss during aging.

Aging biomarkers can potentially allow researchers to rapidly monitor the impact of an aging intervention, without the need for decade-spanning trials, by acting as surrogate endpoints. Prior to testing whether aging biomarkers may be useful as surrogate endpoints, it is first necessary to determine whether they are responsive to interventions that target aging. Epigenetic clocks are aging biomarkers based on DNA methylation with prognostic value for many aging outcomes. Many individual studies are beginning to explore whether epigenetic clocks are responsive to interventions. However, the diversity of both interventions and epigenetic clocks in different studies make them difficult to compare systematically. Here, we curate TranslAGE-Response, a harmonized database of 51 public and private longitudinal interventional studies and calculate a consistent set of 16 prominent epigenetic clocks for each study, along with 95 other DNAm biomarkers that help explain changes in each clock. With this database, we discover patterns of responsiveness across a variety of interventions and DNAm biomarkers. For example, clocks trained to predict mortality or pace of aging have the strongest response across all interventions and show consistent agreement with each other, pharmacological and lifestyle interventions drive the strongest response from DNAm biomarkers, and study population and study duration are key factors in driving responsiveness of DNAm biomarkers in an intervention. Some classes of interventions such as TNF-alpha inhibitors have strong, consistent effects across multiple studies, while others such as senolytic drugs have inconsistent effects. Clocks with multiple sub-scores (i.e. "explainable clocks") provide specificity and greater mechanistic insight into responsiveness of interventions than single-score clocks. Our work can help the geroscience field design future clinical trials, by guiding the choice of interventions, specific subsets of epigenetic clocks to minimize multiple testing, study duration, study population, and sample size, with the eventual aim of determining whether epigenetic clocks can be used as surrogate endpoints.