Buck Institute for Research on Aging

Using spatially-resolved proteomics via mass spectrometry imaging on fracture callus tissues from young and aged mice, we observed delayed healing in aged animals based on the composition of the extracellular matrices. Higher levels of bone specific collagens were detected in young animals, whereas cartilage specific collagens were detected in aged animals at higher levels. Further, detection of novel, non-canonical callus proteins revealed critical transitional steps that are delayed in aged-callus tissues, and these may also contribute to the delayed healing aged animals.

Plasma biomarkers have emerged as robust indicators of Alzheimer's disease (AD) pathology, offering accessible tools for staging and stratification. However, their expression across globally diverse populations remains poorly characterized. The aim of this study was to evaluate whether the combination of plasma biomarkers could distinguish different stages along the AD continuum and assess their clinical associations in a Latin American cohort. We evaluated plasma amyloid, tau, and neurodegeneration (ATN) biomarkers in 318 older adults from a Chilean community- and clinic-based cohort, including individuals with subjective cognitive complaints (SCC), mild cognitive impairment (MCI), and Alzheimer's disease dementia (ADD), alongside cognitively unimpaired (CU) participants. Plasma ATN biomarkers (Aβ42/Aβ40, p-tau217, NfL, and GFAP) were quantified using Simoa technology. Global cognition was assessed with the Addenbrooke's Cognitive Examination (ACE), memory with the Free and Cued Selective Reminding Test (FCSRT), and functional ability with the Technology-Activities of Daily Living Questionnaire (T-ADLQ). Group differences in plasma biomarkers were examined using ANCOVA models adjusted for age, sex, and education, and associations with cognitive performance were evaluated through linear regression analyses. In addition, supervised machine-learning models were implemented to classify participants across diagnostic categories based on plasma biomarker profiles, using cross-validation to evaluate predictive performance. We observed a progressive decline in the Aβ42/Aβ40 ratio and elevations in p-tau217 and GFAP across the clinical continuum. Additionally, p-tau217 and NfL levels were inversely associated with cognitive, memory, and functional performance. Notably, p-tau217 distinguished ADD from CU with high accuracy (AUC = 0.88), although its performance in earlier stages was limited. These findings support the biological consistency of plasma biomarkers in AD-related neurodegeneration and provide novel evidence from a Latin American population. Further studies are needed to improve early-stage detection and to better understand how genetic, environmental, and health factors shape biomarker expressions in underrepresented regions.

ABSTRACTAging impacts immune function, but the mechanisms driving age‐related changes in immune cell subsets remain unclear. To explore age‐dependent changes in immune cell populations, we analyzed human peripheral blood mononuclear cells (PBMCs) from a cohort of healthy donors aged 20–82 years using a 36‐color spectral flow cytometry panel focused on T cells. We identified a unique population of memory CD8 T cells, which lack CXCR3 and produce a Th2‐like cytokine response, and accumulate with age. We discovered an age‐dependent bias in naïve CD8 T cells toward Th2 cytokine production, accompanied by transcriptional and epigenetic changes supporting this phenotype. Moreover, health outcome association analysis linked the accumulation of these unique CXCR3‐ central memory CD8 T cells to asthma, chronic liver conditions, and type 2 diabetes. Together, our results support the model that an age‐dependent drift in epigenetic regulation toward a Th2‐like phenotype drives a pathogenic Th2‐like immune population.

The regenerative potential of developing cortical axons depends on intrinsic mechanisms, such as axon-autonomous protein synthesis, that are still not fully understood. An emerging factor in this regenerative response is the bidirectional interplay between microtubule dynamics and the axonal ER. We hypothesize that locally synthesized ER proteins regulate microtubule dynamics and the regeneration of cortical axons. RNA data mining identified the ER-shaping protein Reticulon-1 as a relevant candidate across eight axonal transcriptomes. Using microfluidics, we show that axonal treatment with a small RNA against Reticulon-1 mRNA (Reticulon-1 knockdown) increases outgrowth of injured cortical axons while reducing their tubulin levels. We show by live-cell imaging that axonal Reticulon-1 knockdown increases microtubule growth rate in noninjured axons and restores this parameter after injury. Axonal inhibition of the microtubule-severing protein Spastin prevents the effects of Reticulon-1 knockdown over tubulin levels and outgrowth. We provide evidence that the Reticulon-1C isoform is synthesized within axons and attenuates Spastin-mediated microtubule severing. These findings support a model in which axonal protein synthesis regulates microtubule dynamics and axon outgrowth after injury.

Precision medicine demands a shift from static, single-analyte diagnostics toward dynamic, systems-level understanding of health and disease. This review explores how the convergence of systems biology, multiomics, and artificial intelligence (AI) redefines biomarker discovery to drive early disease detection and personalized intervention. We highlight pioneering efforts that use longitudinal, multimodal data to map individual health trajectories and uncover early disease signals. Advances in AI, including machine learning and contextualization using knowledge graphs and digital twins, are accelerating clinical translation by enabling predictive, context-aware analyses. Real-world applications, including omics-informed diagnostics and digital health monitoring, demonstrate the potential of this approach to transform health care from reactive treatment to proactive wellness. These technologies also inform the development of targeted therapeutics that intervene earlier, personalize treatment, and potentially halt or reverse disease progression. We outline challenges, emerging solutions, and future directions that position AI-driven systems biology at the center of next-generation precision health.

Discovery of new compounds that ameliorate the negative health impacts of aging promises to be of tremendous benefit across a number of age-based comorbidities. One method to prioritize a testable subset of the nearly infinite universe of potential compounds is to use computational prediction of their likely anti-aging capacity. Here, we present a survey of longevity effects for 16 compounds suggested by a previously published computational prediction set, capitalizing upon the comprehensive, multi-species approach utilized by the Caenorhabditis Intervention Testing Program. While 11 compounds (aldosterone, arecoline, bortezomib, dasatinib, decitabine, dexamethasone, erlotinib, everolimus, gefitinib, temsirolimus, and thalidomide) either had no effect on median lifespan or were toxic, 5 compounds (all-trans retinoic acid, berberine, fisetin, propranolol, and ritonavir) extended lifespan in Caenorhabditis elegans. These computer predictions yield a remarkable positive hit rate of 30%. Deeper genetic characterization of the longevity effects of one of the most efficacious compounds, the endogenous signaling ligand all-trans retinoic acid (atRA, designated tretinoin in medical products), demonstrated a requirement for the regulatory kinases AKT-1 and AKT-2. While the canonical Akt-target FOXO/DAF-16 was largely dispensable, other conserved Akt-targets (Nrf2/SKN-1 and HSF1/HSF-1), as well as the conserved catalytic subunit of AMPK AAK-2, were all necessary for longevity extension by atRA. Our results highlight the potential of combining computational prediction of longevity interventions with the power of nematode functional genetics and underscore that the manipulation of a conserved metabolic regulatory circuit by co-opting endogenous signaling molecules is a powerful approach for discovering aging interventions.

The worldwide rise of neurological and psychiatric conditions poses major challenges. However, current global research remains fragmented, dominated by limited cohorts and poorly integrated datasets that disconnect whole-body health, exposome, and brain health. Theories rarely unify brain measures with extracerebral factors or capture heterogeneity in individual trajectories. We introduce multimodal diversity, a non-linear, non-simplistic causal and ecological construct integrating data representation, whole-body and exposomic factors, and computational modeling to address this situated, embedded, and embodied complexity. This heuristic metamodel integrates global, multilevel data into personalized predictions fostering population inclusion, multimodal integration, diagnostic precision, and equitable, context-sensitive advances in brain health.