Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer's disease pathogenesis.
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ABSTRACT:
SUBMITTER: Wingo AP
PROVIDER: S-EPMC8130821 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Depression is a common condition, but current treatments are only effective in a subset of individuals. To identify new treatment targets, we integrated depression genome-wide association study (GWAS) results (N = 500,199) with human brain proteomes (N = 376) to perform a proteome-wide association study of depression followed by Mendelian randomization. We identified 19 genes that were consistent with being causal in depression, acting via their respective cis-regulated brain protein abundance. We replicated nine of these genes using an independent depression GWAS (N = 307,353) and another human brain proteomic dataset (N = 152). Eleven of the 19 genes also had cis-regulated mRNA levels that were associated with depression, based on integration of the depression GWAS with human brain transcriptomes (N = 888). Meta-analysis of the discovery and replication proteome-wide association study analyses identified 25 brain proteins consistent with being causal in depression, 20 of which were not previously implicated in depression by GWAS. Together, these findings provide promising brain protein targets for further mechanistic and therapeutic studies.
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Project description:Mitochondrial dysfunction is a key feature in both aging and neurodegenerative diseases including Alzheimer's disease (AD), but the molecular signature that distinguishes pathological changes in the AD from healthy aging in the brain mitochondria remain poorly understood. In order to unveil AD specific mitochondrial dysfunctions, this study adopted a discovery-driven approach with isobaric tag for relative and absolute quantitation (iTRAQ) and label-free quantitative proteomics, and profiled the mitochondrial proteomes in human brain tissues of healthy and AD individuals. LC-MS/MS-based iTRAQ quantitative proteomics approach revealed differentially altered mitochondriomes that distinguished the AD's pathophysiology-induced from aging-associated changes. Our results showed that dysregulated mitochondrial complexes including electron transport chain (ETC) and ATP-synthase are the potential driver for pathology of the AD. The iTRAQ results were cross-validated with independent label-free quantitative proteomics experiments to confirm that the subunit of electron transport chain complex I, particularly NDUFA4 and NDUFA9 were altered in AD patients, suggesting destabilization of the junction between membrane and matrix arms of mitochondrial complex I impacted the mitochondrial functions in the AD. iTRAQ quantitative proteomics of brain mitochondriomes revealed disparity in healthy aging and age-dependent AD.
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Project description:Alzheimer's disease (AD) is a neurodegenerative and progressive disease, which often causes irreversible damages to the cerebrum. The pathogenesis of AD is far from being fully understood, while there are some popular hypotheses. So far, the diagnosis of AD relies only on clinical screening in the form of imaging techniques or cerebrospinal fluid analysis, which may lead to inaccurate evaluation and then cause the delay of suitable treatments. While molecular biomarkers provide promising alternatives of establishing correct relationships between genotypes and phenotypes of clinical symptoms. In this paper, we propose a machine-learning-based method of identifying potential diagnostic biomarkers of AD based on gene coexpression network by integrating gene expression profiles in six brain regions. After building an integrated gene coexpression network of multiple brain regions, we decompose the differential network into some subnetwork modules. The module candidates from these coexpressed gene communities are then identified by screening their discriminative powers in control from disease samples. The potential biomarkers are then validated by multiple cross-validations and functional enrichment analyses. If the biomarkers successfully pass clinical significance tests, they can be used as a reference for clinical diagnosis after wet-experimental validations.
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Project description:In Parkinson's disease (PD) there is a selective degeneration of neuromelanin-containing neurons, especially substantia nigra dopaminergic neurons. In humans, neuromelanin accumulates with age, the latter being the main risk factor for PD. The contribution of neuromelanin to PD pathogenesis remains unknown because, unlike humans, common laboratory animals lack neuromelanin. Synthesis of peripheral melanins is mediated by tyrosinase, an enzyme also present at low levels in the brain. Here we report that overexpression of human tyrosinase in rat substantia nigra results in age-dependent production of human-like neuromelanin within nigral dopaminergic neurons, up to levels reached in elderly humans. In these animals, intracellular neuromelanin accumulation above a specific threshold is associated to an age-dependent PD phenotype, including hypokinesia, Lewy body-like formation and nigrostriatal neurodegeneration. Enhancing lysosomal proteostasis reduces intracellular neuromelanin and prevents neurodegeneration in tyrosinase-overexpressing animals. Our results suggest that intracellular neuromelanin levels may set the threshold for the initiation of PD.
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Project description:BackgroundDiabetes mellitus (DM) is a recognized risk factor for dementia. Because DM is a potentially modifiable condition, greater understanding of the mechanisms linking DM to the clinical expression of Alzheimer's disease dementia may provide insights into much needed dementia therapeutics.ObjectiveIn this feasibility study, we investigated DM as a dementia risk factor by examining genome-wide distributions of the epigenetic DNA modification 5-hydroxymethylcytosine (5hmC).MethodsWe obtained biologic samples from the Rush Memory and Aging Project and used the highly sensitive 5hmC-Seal technique to perform genome-wide profiling of 5hmC in circulating cell-free DNA (cfDNA) from antemortem serum samples and in genomic DNA from postmortem prefrontal cortex brain tissue from 80 individuals across four groups: Alzheimer's disease neuropathologically defined (AD), DM clinically defined, AD with DM, and individuals with neither disease (controls).ResultsDistinct 5hmC signatures and biological pathways were enriched in persons with both AD and DM versus AD alone, DM alone, or controls, including genes inhibited by EGFR signaling in oligodendroglia and those activated by constitutive RHOA. We also demonstrate the potential diagnostic value of 5hmC profiling in circulating cfDNA. Specifically, an 11-gene weighted model distinguished AD from non-AD/non-DM controls (AUC = 91.8%; 95% CI, 82.9-100.0%), while a 4-gene model distinguished DM-associated AD from AD alone (AUC = 87.9%; 95% CI, 77.5-98.3%).ConclusionWe demonstrate in this small sample, the feasibility of detecting and characterizing 5hmC in DM-associated AD and of using 5hmC information contained in circulating cfDNA to detect AD in high-risk individuals, such as those with diabetes.
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Project description:BackgroundThe X chromosome is often omitted in disease association studies despite containing thousands of genes which may provide insight into well-known sex differences in the risk of Alzheimer's Disease.ObjectiveTo model the expression of X chromosome genes and evaluate their impact on Alzheimer's Disease risk in a sex-stratified manner.MethodsUsing elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF>0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome.ResultsAcross different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002).ConclusionsWe optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
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