👤 Tony Wyss-Coray

Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality remains unknown. We developed machine learning models to estimate the biological age of more than 40 distinct cell types-spanning neuronal, immune, glial, endocrine, epithelial, and musculoskeletal origins-using over 7,000 plasma proteins measured in 60,000 individuals across three cohorts, comprising the largest human plasma proteomics aging study to date. Individuals showed heterogeneous aging profiles, with 20-25% exhibiting accelerated aging in a single cell type and 1-3% across ten or more cell types. APOE genotype showed antagonistic aging effects in different cell types: APOE4 carriers exhibited older astrocytes but younger macrophages, while APOE2 carriers showed the inverse. Cellular aging signatures were uniquely associated with disease status and predicted incident disease and mortality over 15 years of follow-up. Amyotrophic lateral sclerosis (ALS) showed the strongest association with skeletal myocyte aging (hazard ratio = 12.7 for extreme accelerated versus youthful aging). In Alzheimer's disease (AD), prevalent cases showed accelerated aging across multiple neural and peripheral cell types, with extreme astrocyte aging conferring AD risk comparable to APOE4 carrier status. Moreover, extreme astrocyte aging increased AD risk in APOE4/4 carriers threefold, while youthful astrocytes strikingly reduced risk. Beyond neurodegeneration, respiratory cell aging identified smokers at 58% higher lung cancer risk, and myeloid aging identified normoglycemic individuals at higher diabetes risk. Both specific cellular vulnerabilities and cumulative aging burden influenced survival, wherein youthful immune or neuronal profiles were protective. A polycellular aging risk score provided robust mortality risk stratification across platforms and cohorts. These findings establish a framework for quantifying biological aging at the cellular resolution using plasma proteomics, revealing heterogeneity in aging trajectories and their impact on disease susceptibility and resilience. Show less

Apolipoprotein E (APOE) ε4 is the strongest genetic risk factor for Alzheimer's disease (AD). However, it is known that other pathways independent of APOE also play a role in AD. Disentangling APOE-dependent and independent effects is instrumental for understanding the biology of AD. We conducted an APOE-stratified multi-omic analysis in multiple large datasets to identify AD-associated plasma proteins and metabolites. More than 64% of the identified proteins were not found in non-APOE stratified studies, and 17% of the proteins showed APOE-specific trends. Mitochondrial dysfunction was associated in AD independently of APOE and was accompanied by disruptions in glucose and lipid metabolism and cell death and increased in inflammatory signaling activation. Lipid upregulation was found in AD cases when compared with controls with the same APOE genotype, indicating that additional factors beyond APOE affect lipid regulation and AD risk. These findings may be informative in guiding the development of effective medications for AD. Show less

Autophagy is an intracellular degradation pathway that functions in protein and organelle turnover in response to starvation and cellular stress. Autophagy is initiated by the formation of a complex containing Beclin 1 (BECN1) and its binding partner Phosphoinositide-3-kinase, class 3 (PIK3C3). Recently, BECN1 deficiency was shown to enhance the pathology of a mouse model of Alzheimer Disease (AD). However, the mechanism by which BECN1 or autophagy mediate these effects are unknown. Here, we report that the levels of Amyloid precursor protein (APP) and its metabolites can be reduced through autophagy activation, indicating that they are a substrate for autophagy. Furthermore, we find that knockdown of Becn1 in cell culture increases the levels of APP and its metabolites. Accumulation of APP and APP C-terminal fragments (APP-CTF) are accompanied by impaired autophagosomal clearance. Pharmacological inhibition of autophagosomal-lysosomal degradation causes a comparable accumulation of APP and APP-metabolites in autophagosomes. Becn1 reduction in cell culture leads to lower levels of its binding partner Pik3c3 and increased presence of Microtubule-associated protein 1, light chain 3 (LC3). Overexpression of Becn1, on the other hand, reduces cellular APP levels. In line with these observations, we detected less BECN1 and PIK3C3 but more LC3 protein in brains of AD patients. We conclude that BECN1 regulates APP processing and turnover. BECN1 is involved in autophagy initiation and autophagosome clearance. Accordingly, BECN1 deficiency disrupts cellular autophagy and autophagosomal-lysosomal degradation and alters APP metabolism. Together, our findings suggest that autophagy and the BECN1-PIK3C3 complex regulate APP processing and play an important role in AD pathology. Show less