Proteomic signatures improve risk prediction for common and rare diseases.

For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81-6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02-0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases.

Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures for asymptomatic and symptomatic Alzheimer's disease.

Changes in Amyloid-ß (A), hyperphosphorylated Tau (T) in brain and cerebrospinal fluid (CSF) precedes AD symptoms, making CSF proteome a potential avenue to understand the pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 proteins dysregulated in AD, that were further validated in a third totally independent cohort. Machine learning was implemented to create and validate highly accurate and replicable (AUC>0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD and those AD cases with faster progression. The associated proteins cluster in four different protein pseudo-trajectories groups spanning the AD continuum and were enrichment in specific pathways including neuronal death, apoptosis and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfuncton(mid-stages), brain plasticity and longevity (mid-stages) and late microglia-neuron crosstalk (late stages).

Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Brain DNA Methylation Patterns in CLDN5 Associated With Cognitive Decline.

BACKGROUND: Cognitive trajectory varies widely and can distinguish people who develop dementia from people who remain cognitively normal. Variation in cognitive trajectory is only partially explained by traditional neuropathologies. We sought to identify novel genes associated with cognitive trajectory using DNA methylation profiles from human postmortem brain. METHODS: We performed a brain epigenome-wide association study of cognitive trajectory in 636 participants from the ROS (Religious Orders Study) and MAP (Rush Memory and Aging Project) using DNA methylation profiles of the dorsolateral prefrontal cortex. To maximize our power to detect epigenetic associations, we used the recently developed Gene Association with Multiple Traits test to analyze the 5 measured cognitive domains simultaneously. RESULTS: We found an epigenome-wide association for differential methylation of sites in the CLDN5 locus and cognitive trajectory (p = 9.96 × 10-7) that was robust to adjustment for cell type proportions (p = 8.52 × 10-7). This association was primarily driven by association with declines in episodic (p = 4.65 × 10-6) and working (p = 2.54 × 10-7) memory. This association between methylation in CLDN5 and cognitive decline was significant even in participants with no or little signs of amyloid-ß and neurofibrillary tangle pathology. CONCLUSIONS: Differential methylation of CLDN5, a gene that encodes an important protein of the blood-brain barrier, is associated with cognitive trajectory beyond traditional Alzheimer's disease pathologies. The association between CLDN5 methylation and cognitive trajectory in people with low pathology suggests an early role for CLDN5 and blood-brain barrier dysfunction in cognitive decline and Alzheimer's disease.

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer's disease.

Alzheimer's disease (AD) is influenced by both genetic and environmental factors; thus, brain epigenomic alterations may provide insights into AD pathogenesis. Multiple array-based Epigenome-Wide Association Studies (EWASs) have identified robust brain methylation changes in AD; however, array-based assays only test about 2% of all CpG sites in the genome. Here, we develop EWASplus, a computational method that uses a supervised machine learning strategy to extend EWAS coverage to the entire genome. Application to six AD-related traits predicts hundreds of new significant brain CpGs associated with AD, some of which are further validated experimentally. EWASplus also performs well on data collected from independent cohorts and different brain regions. Genes found near top EWASplus loci are enriched for kinases and for genes with evidence for physical interactions with known AD genes. In this work, we show that EWASplus implicates additional epigenetic loci for AD that are not found using array-based AD EWASs.

Genetic control of the human brain proteome.

We generated an online brain pQTL resource for 7,376 proteins through the analysis of genetic and proteomic data derived from post-mortem samples of the dorsolateral prefrontal cortex of 330 older adults. The identified pQTLs tend to be non-synonymous variation, are over-represented among variants associated with brain diseases, and replicate well (77%) in an independent brain dataset. Comparison to a large study of brain eQTLs revealed that about 75% of pQTLs are also eQTLs. In contrast, about 40% of eQTLs were identified as pQTLs. These results are consistent with lower pQTL mapping power and greater evolutionary constraint on protein abundance. The latter is additionally supported by observations of pQTLs with large effects' tending to be rare, deleterious, and associated with proteins that have evidence for fewer protein-protein interactions. Mediation analyses using matched transcriptomic and proteomic data provided additional evidence that pQTL effects are often, but not always, mediated by mRNA. Specifically, we identified roughly 1.6 times more mRNA-mediated pQTLs than mRNA-independent pQTLs (550 versus 341). Our pQTL resource provides insight into the functional consequences of genetic variation in the human brain and a basis for novel investigations of genetics and disease.

Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer's disease pathogenesis.

Genome-wide association studies (GWAS) have identified many risk loci for Alzheimer's disease (AD)1,2, but how these loci confer AD risk is unclear. Here, we aimed to identify loci that confer AD risk through their effects on brain protein abundance to provide new insights into AD pathogenesis. To that end, we integrated AD GWAS results with human brain proteomes to perform a proteome-wide association study (PWAS) of AD, followed by Mendelian randomization and colocalization analysis. We identified 11 genes that are consistent with being causal in AD, acting via their cis-regulated brain protein abundance. Nine replicated in a confirmation PWAS and eight represent new AD risk genes not identified before by AD GWAS. Furthermore, we demonstrated that our results were independent of APOE e4. Together, our findings provide new insights into AD pathogenesis and promising targets for further mechanistic and therapeutic studies.

Association between DNA methylation levels in brain tissue and late-life depression in community-based participants.

OBJECTIVE: Major depressive disorder (MDD) arises from a combination of genetic and environmental risk factors and DNA methylation is one of the molecular mechanisms through which these factors can manifest. However, little is known about the epigenetic signature of MDD in brain tissue. This study aimed to investigate associations between brain tissue-based DNA methylation and late-life MDD. METHODS: We performed a brain epigenome-wide association study (EWAS) of late-life MDD in 608 participants from the Religious Order Study and the Rush Memory and Aging Project (ROS/MAP) using DNA methylation profiles of the dorsal lateral prefrontal cortex generated using the Illumina HumanMethylation450 Beadchip array. We also conducted an EWAS of MDD in each sex separately. RESULTS: We found epigenome-wide significant associations between brain tissue-based DNA methylation and late-life MDD. The most significant and robust association was found with altered methylation levels in the YOD1 locus (cg25594636, p value = 2.55 × 10-11; cg03899372, p value = 3.12 × 10-09; cg12796440, p value = 1.51 × 10-08, cg23982678, p value = 7.94 × 10-08). Analysis of differentially methylated regions (p value = 5.06 × 10-10) further confirmed this locus. Other significant loci include UGT8 (cg18921206, p value = 1.75 × 10-08), FNDC3B (cg20367479, p value = 4.97 × 10-08) and SLIT2 (cg10946669, p value = 8.01 × 10-08). Notably, brain tissue-based methylation levels were strongly associated with late-life MDD in men more than in women. CONCLUSIONS: We identified altered methylation in the YOD1, UGT8, FNDC3B, and SLIT2 loci as new epigenetic factors associated with late-life MDD. Furthermore, our study highlights the sex-specific molecular heterogeneity of MDD.

An integrated -omics analysis of the epigenetic landscape of gene expression in human blood cells.

BACKGROUND: Gene expression can be influenced by DNA methylation 1) distally, at regulatory elements such as enhancers, as well as 2) proximally, at promoters. Our current understanding of the influence of distal DNA methylation changes on gene expression patterns is incomplete. Here, we characterize genome-wide methylation and expression patterns for ~ 13 k genes to explore how DNA methylation interacts with gene expression, throughout the genome. RESULTS: We used a linear mixed model framework to assess the correlation of DNA methylation at ~ 400 k CpGs with gene expression changes at ~ 13 k transcripts in two independent datasets from human blood cells. Among CpGs at which methylation significantly associates with transcription (eCpGs), > 50% are distal (> 50 kb) or trans (different chromosome) to the correlated gene. Many eCpG-transcript pairs are consistent between studies and ~ 90% of neighboring eCpGs associate with the same gene, within studies. We find that enhancers (P < 5e-18) and microRNA genes (P = 9e-3) are overrepresented among trans eCpGs, and insulators and long intergenic non-coding RNAs are enriched among cis and distal eCpGs. Intragenic-eCpG-transcript correlations are negative in 60-70% of occurrences and are enriched for annotated gene promoters and enhancers (P < 0.002), highlighting the importance of intragenic regulation. Gene Ontology analysis indicates that trans eCpGs are enriched for transcription factor genes and chromatin modifiers, suggesting that some trans eCpGs represent the influence of gene networks and higher-order transcriptional control. CONCLUSIONS: This work sheds new light on the interplay between epigenetic changes and gene expression, and provides useful data for mining biologically-relevant results from epigenome-wide association studies.

Testing Two Evolutionary Theories of Human Aging with DNA Methylation Data.

The evolutionary theories of mutation accumulation (MA) and disposable soma (DS) provide possible explanations for the existence of human aging. To better understand the relative importance of these theories, we devised a test to identify MA- and DS-consistent sites across the genome using familial DNA methylation data. Two key characteristics of DNA methylation allowed us to do so. First, DNA methylation exhibits distinct and widespread changes with age, with numerous age-differentially-methylated sites observed across the genome. Second, many sites show heritable DNA methylation patterns within families. We extended heritability predictions of MA and DS to DNA methylation, predicting that MA-consistent age-differentially-methylated sites will show increasing heritability with age, while DS-consistent sites will show the opposite. Variance components models were used to test for changing heritability of methylation with age at 48,601 age-differentially-methylated sites across the genome in 610 individuals from 176 families. Of these, 102 sites showed significant MA-consistent increases in heritability with age, while 2266 showed significant DS-consistent decreases in heritability. These results suggest that both MA and DS play a role in explaining aging and aging-related changes, and that while the majority of DNA methylation changes observed in aging are consistent with epigenetic drift, targeted changes exist and may mediate effects of aging-related genes.

A Genome-wide RNAi Screen for Microtubule Bundle Formation and Lysosome Motility Regulation in Drosophila S2 Cells.

Long-distance intracellular transport of organelles, mRNA, and proteins ("cargo") occurs along the microtubule cytoskeleton by the action of kinesin and dynein motor proteins, but the vast network of factors involved in regulating intracellular cargo transport are still unknown. We capitalize on the Drosophila melanogaster S2 model cell system to monitor lysosome transport along microtubule bundles, which require enzymatically active kinesin-1 motor protein for their formation. We use an automated tracking program and a naive Bayesian classifier for the multivariate motility data to analyze 15,683 gene phenotypes and find 98 proteins involved in regulating lysosome motility along microtubules and 48 involved in the formation of microtubule filled processes in S2 cells. We identify innate immunity genes, ion channels, and signaling proteins having a role in lysosome motility regulation and find an unexpected relationship between the dynein motor, Rab7a, and lysosome motility regulation.

Testing evolutionary models of senescence: traditional approaches and future directions.

From an evolutionary perspective, the existence of senescence is a paradox. Why has senescence not been more effectively selected against given its associated decreases in Darwinian fitness? Why does senescence exist and how has it evolved? Three major theories offer explanations: (1) the theory of mutation accumulation suggested by PB Medawar; (2) the theory of antagonistic pleiotropy suggested by GC Williams; and (3) the disposable soma theory suggested by TBL Kirkwood. These three theories differ in the underlying causes of aging that they propose but are not mutually exclusive. This paper compares the specific biological predictions of each theory and discusses the methods and results of previous empirical tests. Lifespan is found to be the most frequently used estimate of senescence in evolutionary investigations. This measurement acts as a proxy for an individual's rate of senescence, but provides no information on an individual's senescent state or "biological age" throughout life. In the future, use of alternative longitudinal measures of senescence may facilitate investigation of previously neglected aspects of evolutionary models, such as intra- and inter-individual heterogeneity in the process of aging. DNA methylation data are newly proposed to measure biological aging and are suggested to be particularly useful for such investigations.