Organ aging signatures in the plasma proteome track health and disease.

Animal studies show aging varies between individuals as well as between organs within an individual1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

Sex and aging signatures of proteomics in human cerebrospinal fluid identify distinct clusters linked to neurodegeneration.

Sex and age are major risk factors for chronic diseases. Recent studies examining age-related molecular changes in plasma provided insights into age-related disease biology. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and neurodegeneration. By comprehensively examining 7,006 aptamers targeting 6,139 proteins in CSF obtained from 660 healthy individuals aged from 43 to 91 years old, we subsequently identified significant sex and aging effects on 5,097 aptamers in CSF. Many of these effects on CSF proteins had different magnitude or even opposite direction as those on plasma proteins, indicating distinctive CSF-specific signatures. Network analysis of these CSF proteins revealed not only modules associated with healthy aging but also modules showing sex differences. Through subsequent analyses, several modules were highlighted for their proteins implicated in specific diseases. Module 2 and 6 were enriched for many aging diseases including those in the circulatory systems, immune mechanisms, and neurodegeneration. Together, our findings fill a gap of current aging research and provide mechanistic understanding of proteomic changes in CSF during a healthy lifespan and insights for brain aging and diseases.

Genetic and multi-omic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center.

The Knight-Alzheimer Disease Research Center (Knight-ADRC) at Washington University in St. Louis has pioneered and led worldwide seminal studies that have expanded our clinical, social, pathological, and molecular understanding of Alzheimer Disease. Over more than 40 years, research volunteers have been recruited to participate in cognitive, neuropsychologic, imaging, fluid biomarkers, genomic and multi-omic studies. Tissue and longitudinal data collected to foster, facilitate, and support research on dementia and aging. The Genetics and high throughput -omics core (GHTO) have collected of more than 26,000 biological samples from 6,625 Knight-ADRC participants. Samples available include longitudinal DNA, RNA, non-fasted plasma, cerebrospinal fluid pellets, and peripheral blood mononuclear cells. The GHTO has performed deep molecular profiling (genomic, transcriptomic, epigenomic, proteomic, and metabolomic) from large number of brain (n = 2,117), CSF (n = 2,012) and blood/plasma (n = 8,265) samples with the goal of identifying novel risk and protective variants, identify novel molecular biomarkers and causal and druggable targets. Overall, the resources available at GHTO support the increase of our understanding of Alzheimer Disease.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer's and coronary disease pathways.

Identification of proteins dysregulated by COVID-19 infection is critically important for better understanding of its pathophysiology, building prognostic models, and identifying new targets. Plasma proteomic profiling of 4,301 proteins was performed in two independent datasets and tested for the association for three COVID-19 outcomes (infection, ventilation, and death). We identified 1,449 proteins consistently associated in both datasets with any of these three outcomes. We subsequently created highly accurate models that distinctively predict infection, ventilation, and death. These proteins were enriched in specific biological processes including cytokine signaling, Alzheimer's disease, and coronary artery disease. Mendelian randomization and gene network analyses identified eight causal proteins and 141 highly connected hub proteins including 35 with known drug targets. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes, reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways.

Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal ( https://covid.proteomics.wustl.edu/ ). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR ≤ 3.72×10 -14 ), Alzheimer's disease (FDR ≤ 5.46×10 -10 ) and coronary artery disease (FDR ≤ 4.64×10 -2 ). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.