Genome-wide meta-analysis of short-tandem repeats for Parkinson's disease risk using genotype imputation.

Idiopathic Parkinson's disease is determined by a combination of genetic and environmental factors. Recently, the first genome-wide association study on short-tandem repeats in Parkinson's disease reported on eight suggestive short-tandem repeat-based risk loci (α = 5.3 × 10-6), of which four were novel, i.e. they had not been implicated in Parkinson's disease risk by genome-wide association analyses of single-nucleotide polymorphisms before. Here, we tested these eight candidate short-tandem repeats in a large, independent Parkinson's disease case-control dataset (n = 4757). Furthermore, we combined the results from both studies by meta-analysis resulting in the largest Parkinson's disease genome-wide association study of short-tandem repeats to date (n = 43 844). Lastly, we investigated whether leading short-tandem repeat risk variants exert functional effects on gene expression regulation based on methylation quantitative trait locus data in human 'post-mortem' brain (n = 142). None of the eight previously reported short-tandem repeats were significantly associated with Parkinson's disease in our independent dataset after multiple testing correction (α = 6.25 × 10-3). However, we observed modest support for short-tandem repeats near CCAR2 and NCOR1 in the updated meta-analyses of all available data. While the genome-wide meta-analysis did not reveal additional study-wide significant (α = 6.3 × 10-7) short-tandem repeat signals, we identified seven novel suggestive Parkinson's disease short-tandem repeat risk loci (α = 5.3 × 10-6). Of these, especially a short-tandem repeat near MEIOSIN showed consistent evidence for association across datasets. CCAR2, NCOR1 and one novel suggestive locus identified here (LINC01012) emerged from colocalization analyses showing evidence for a shared causal short-tandem repeat variant affecting both Parkinson's disease risk and cis DNA methylation in brain. Larger studies, ideally using short-tandem repeats called from whole-sequencing data, are needed to more fully investigate their role in Parkinson's disease.

Post-GWAS multiomic functional investigation of the TNIP1 locus in Alzheimer's disease highlights a potential role for GPX3.

INTRODUCTION: Recent genome-wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear. METHODS: We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS-implicated variants (rs34294852 and rs871269). RESULTS: CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10-5) and higher CSF phosphorylated tau (p-tau) levels (P = 9.28 × 10-7). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10-6) and CSF p-tau levels (P = 4.38 × 10-9). DISCUSSION: These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels. HIGHLIGHTS: Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau. The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category. GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls. rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia.

Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. METHOD: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. RESULTS: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. CONCLUSIONS: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

Change in body weight of older adults before and during the COVID-19 pandemic: longitudinal results from the Berlin Aging Study II.

OBJECTIVES: Change in body weight during the COVID-19 pandemic as an unintended side effect of lockdown measures has been predominantly reported for younger and middle-aged adults. However, information on older adults for which weight loss is known to result in adverse outcomes, is scarce. In this study we describe the body weight change in older adults before, during, and after the COVID-19 lockdown measures and explore putative associated factors with a focus on the period that includes the first six months of the COVID-19 containment measures. DESIGN: Prospective cohort study with three follow-up examinations over the course of 10 years. SETTING AND PARTICIPANTS: In this study, we analyzed the longitudinal weight change of 472 participants of the Berlin Aging Study II (mean age of 67.5 years at baseline). MEASUREMENTS: Body weight was assessed at four time points. Additionally, differences between subgroups characterized by socio-economic, cognitive, and psychosocial variables as well as morbidity burden, biological age markers (epigenetic clocks, telomere length), and frailty were compared. RESULTS: On average, women and men lost 0.87% (n = 227) and 0.5% (n = 245) of their body weight per year in the study period covering the first six months of the COVID-19 pandemic. Weight loss among men was particularly pronounced among groups characterized by change in physical activity due to COVID-19 lockdown, low positive affect, premature epigenetic age (7-CpG clock), diagnosed metabolic syndrome, and a more masculine gender score (all variables: p < 0.05, n = 245). CONCLUSION: During the COVID-19 pandemic, older participants lost weight with a 2.5-times (women) and 2-times (men) higher rate than what is expected in this age.

No phenotypic or genotypic evidence for a link between sleep duration and brain atrophy.

Short sleep is held to cause poorer brain health, but is short sleep associated with higher rates of brain structural decline? Analysing 8,153 longitudinal MRIs from 3,893 healthy adults, we found no evidence for an association between sleep duration and brain atrophy. In contrast, cross-sectional analyses (51,295 observations) showed inverse U-shaped relationships, where a duration of 6.5 (95% confidence interval, (5.7, 7.3)) hours was associated with the thickest cortex and largest volumes relative to intracranial volume. This fits converging evidence from research on mortality, health and cognition that points to roughly seven hours being associated with good health. Genome-wide association analyses suggested that genes associated with longer sleep for below-average sleepers were linked to shorter sleep for above-average sleepers. Mendelian randomization did not yield evidence for causal impacts of sleep on brain structure. The combined results challenge the notion that habitual short sleep causes brain atrophy, suggesting that normal brains promote adequate sleep duration-which is shorter than current recommendations.

Multivariate GWAS of Alzheimer's disease CSF biomarker profiles implies GRIN2D in synaptic functioning.

BACKGROUND: Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified several risk loci, but many remain unknown. Cerebrospinal fluid (CSF) biomarkers may aid in gene discovery and we previously demonstrated that six CSF biomarkers (ß-amyloid, total/phosphorylated tau, NfL, YKL-40, and neurogranin) cluster into five principal components (PC), each representing statistically independent biological processes. Here, we aimed to (1) identify common genetic variants associated with these CSF profiles, (2) assess the role of associated variants in AD pathophysiology, and (3) explore potential sex differences. METHODS: We performed GWAS for each of the five biomarker PCs in two multi-center studies (EMIF-AD and ADNI). In total, 973 participants (n = 205 controls, n = 546 mild cognitive impairment, n = 222 AD) were analyzed for 7,433,949 common SNPs and 19,511 protein-coding genes. Structural equation models tested whether biomarker PCs mediate genetic risk effects on AD, and stratified and interaction models probed for sex-specific effects. RESULTS: Five loci showed genome-wide significant association with CSF profiles, two were novel (rs145791381 [inflammation] and GRIN2D [synaptic functioning]) and three were previously described (APOE, TMEM106B, and CHI3L1). Follow-up analyses of the two novel signals in independent datasets only supported the GRIN2D locus, which contains several functionally interesting candidate genes. Mediation tests indicated that variants in APOE are associated with AD status via processes related to amyloid and tau pathology, while markers in TMEM106B and CHI3L1 are associated with AD only via neuronal injury/inflammation. Additionally, seven loci showed sex-specific associations with AD biomarkers. CONCLUSIONS: These results suggest that pathway and sex-specific analyses can improve our understanding of AD genetics and may contribute to precision medicine.

Entorhinal cortex epigenome-wide association study highlights four novel loci showing differential methylation in Alzheimer's disease.

BACKGROUND: Studies on DNA methylation (DNAm) in Alzheimer's disease (AD) have recently highlighted several genomic loci showing association with disease onset and progression. METHODS: Here, we conducted an epigenome-wide association study (EWAS) using DNAm profiles in entorhinal cortex (EC) from 149 AD patients and control brains and combined these with two previously published EC datasets by meta-analysis (total n = 337). RESULTS: We identified 12 cytosine-phosphate-guanine (CpG) sites showing epigenome-wide significant association with either case-control status or Braak's tau-staging. Four of these CpGs, located in proximity to CNFN/LIPE, TENT5A, PALD1/PRF1, and DIRAS1, represent novel findings. Integrating DNAm levels with RNA sequencing-based mRNA expression data generated in the same individuals showed significant DNAm-mRNA correlations for 6 of the 12 significant CpGs. Lastly, by calculating rates of epigenetic age acceleration using two recently proposed "epigenetic clock" estimators we found a significant association with accelerated epigenetic aging in the brains of AD patients vs. controls. CONCLUSION: In summary, our study represents the hitherto most comprehensive EWAS in AD using EC and highlights several novel differentially methylated loci with potential effects on gene expression.

Tau pathology epigenetically remodels the neuron-glial cross-talk in Alzheimer's disease.

The neuron-glia cross-talk is critical to brain homeostasis and is particularly affected by neurodegenerative diseases. How neurons manipulate the neuron-astrocyte interaction under pathological conditions, such as hyperphosphorylated tau, a pathological hallmark in Alzheimer's disease (AD), remains elusive. In this study, we identified excessively elevated neuronal expression of adenosine receptor 1 (Adora1 or A1R) in 3×Tg mice, MAPT P301L (rTg4510) mice, patients with AD, and patient-derived neurons. The up-regulation of A1R was found to be tau pathology dependent and posttranscriptionally regulated by Mef2c via miR-133a-3p. Rebuilding the miR-133a-3p/A1R signal effectively rescued synaptic and memory impairments in AD mice. Furthermore, neuronal A1R promoted the release of lipocalin 2 (Lcn2) and resulted in astrocyte activation. Last, silencing neuronal Lcn2 in AD mice ameliorated astrocyte activation and restored synaptic plasticity and learning/memory. Our findings reveal that the tau pathology remodels neuron-glial cross-talk and promotes neurodegenerative progression. Approaches targeting A1R and modulating this signaling pathway might be a potential therapeutic strategy for AD.

Multiomics profiling of human plasma and cerebrospinal fluid reveals ATN-derived networks and highlights causal links in Alzheimer's disease.

INTRODUCTION: This study employed an integrative system and causal inference approach to explore molecular signatures in blood and CSF, the amyloid/tau/neurodegeneration [AT(N)] framework, mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD), and genetic risk for AD. METHODS: Using the European Medical Information Framework (EMIF)-AD cohort, we measured 696 proteins in cerebrospinal fluid (n = 371), 4001 proteins in plasma (n = 972), 611 metabolites in plasma (n = 696), and genotyped whole-blood (7,778,465 autosomal single nucleotide epolymorphisms, n = 936). We investigated associations: molecular modules to AT(N), module hubs with AD Polygenic Risk scores and APOE4 genotypes, molecular hubs to MCI conversion and probed for causality with AD using Mendelian randomization (MR). RESULTS: AT(N) framework associated with protein and lipid hubs. In plasma, Proprotein Convertase Subtilisin/Kexin Type 7 showed evidence for causal associations with AD. AD was causally associated with Reticulocalbin 2 and sphingomyelins, an association driven by the APOE isoform. DISCUSSION: This study reveals multi-omics networks associated with AT(N) and causal AD molecular candidates.

DNA methylation age acceleration is associated with risk of diabetes complications.

BACKGROUND: Patients with Type 2 diabetes mellitus (T2D) are at risk for micro- and macrovascular complications. Implementable risk scores are needed to improve targeted prevention for patients that are particularly susceptible to complications. The epigenetic clock estimates an individual's biological age using DNA methylation profiles. METHODS: In this study, we examined older adults of the Berlin Aging Study II that were reexamined on average 7.4 years after baseline assessment as part of the GendAge study. DNA methylation age (DNAmA) and its deviation from chronological age DNAmA acceleration (DNAmAA) were calculated with the 7-CpG clock (available at both timepoints, n = 1,071), Horvath's clock, Hannum's clock, PhenoAge and GrimAge (available at follow-up only, n = 1,067). T2D associated complications were assessed with the Diabetes Complications Severity Index (DCSI). RESULTS: We report on a statistically significant association between oral glucose tolerance test results and Hannum and PhenoAge DNAmAA. PhenoAge was also associated with fasting glucose. In contrast, we found no cross-sectional association after covariate adjustment between DNAmAA and a diagnosis of T2D. However, longitudinal analyses showed that every additional year of 7-CpG DNAmAA at baseline increased the odds for developing one or more additional complications or worsening of an already existing complication during the follow-up period by 11% in male participants with T2D. This association persisted after covariate adjustment (OR = 1.11, p = 0.045, n = 56). CONCLUSION: Although our results remain to be independently validated, this study shows promising evidence of utility of the 7-CpG clock in identifying patients with diabetes who are at high risk for developing complications.

Deterioration of vision, kidney function and cardiovascular function are just a few examples of diabetes-related complications. However, not all patients develop these complications, and it is desirable to detect patients that have a high risk for the complications early. In this study, we examine markers, which are based on reversible modifications of the DNA, in the context of diabetes and its complications. We found that one of these biomarkers is able to predict the development of diabetes complications over a period of about seven years in our dataset. If these results can be confirmed in other studies, our findings might help physicians to identify patients with diabetes that have an increased risk for developing complications in the future.

Whole-exome rare-variant analysis of Alzheimer's disease and related biomarker traits.

INTRODUCTION: Despite increasing evidence of a role of rare genetic variation in the risk of Alzheimer's disease (AD), limited attention has been paid to its contribution to AD-related biomarker traits indicative of AD-relevant pathophysiological processes. METHODS: We performed whole-exome gene-based rare-variant association studies (RVASs) of 17 AD-related traits on whole-exome sequencing (WES) data generated in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study (n = 450) and whole-genome sequencing (WGS) data from ADNI (n = 808). RESULTS: Mutation screening revealed a novel probably pathogenic mutation (PSEN1 p.Leu232Phe). Gene-based RVAS revealed the exome-wide significant contribution of rare coding variation in RBKS and OR7A10 to cognitive performance and protection against left hippocampal atrophy, respectively. DISCUSSION: The identification of these novel gene-trait associations offers new perspectives into the role of rare coding variation in the distinct pathophysiological processes culminating in AD, which may lead to identification of novel therapeutic and diagnostic targets.

Longitudinal effects of a common UMOD variant on kidney function, blood pressure, cognitive and physical function in older women and men.

Genetic variants in UMOD associate with kidney function and hypertension. These phenotypes are also linked to sex-related differences and impairment in cognitive and physical function in older age. Here we evaluate longitudinal associations between a common UMOD rs4293393-A>G variant and changes in estimated glomerular filtration rate (eGFR), blood pressure (BP), cognitive and physical function parameters in older participants in the BASE-II after long-term follow-up as part of the GendAge study. Overall, 1010 older participants (mean age 75.7 ± 3.7 years, 51.6% women) were analyzed after follow-up (mean 7.4 years) both in cross-sectional analysis and in longitudinal analysis as compared to baseline. In cross-sectional analysis, heterozygous G-allele carriers exhibited significantly higher eGFR values (AA, 71.3 ml/min/1.73 m2, 95% CI, 70.3-72.3 vs. AG, 73.5 ml/min/1.73 m2, 95% CI, 72.1-74.9, P = 0.033). Male heterozygous G-allele carriers had lower odds of eGFR < 60 mL/min/1.73 m2 (OR 0.51, 95% CI, 0.28-0.95, P = 0.032) and in Timed Up and Go-Test ≥ 10 s (OR 0.50, 95% CI, 0.29-0.85, P = 0.011) whereas women were less likely to have hypertension (OR 0.58, CI, 0.37-0.91, P = 0.018). UMOD genotypes were not significantly associated with longitudinal changes in any investigated phenotype. Thus, while the impact of UMOD rs4293393 on kidney function is maintained in aging individuals, this variant has overall no impact on longitudinal changes in BP, kidney, cognitive or functional phenotypes. However, our results suggest a possible sex-specific modifying effect of UMOD on eGFR and physical function in men and hypertension prevalence in women.

A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets.

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.

Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates.

It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.

Genome-wide QTL mapping across three tissues highlights several Alzheimer's and Parkinson's disease loci potentially acting via DNA methylation.

DNA methylation (DNAm) is an epigenetic mark with essential roles in disease development and predisposition. Here, we created genome-wide maps of methylation quantitative trait loci (meQTL) in three peripheral tissues and used Mendelian randomization (MR) analyses to assess the potential causal relationships between DNAm and risk for two common neurodegenerative disorders, i.e. Alzheimer's disease (AD) and Parkinson's disease (PD). Genome-wide single nucleotide polymorphism (SNP; ~5.5M sites) and DNAm (~850K CpG sites) data were generated from whole blood (n=1,058), buccal (n=1,527) and saliva (n=837) specimens. We identified between 11 and 15 million genome-wide significant (p<10-14) SNP-CpG associations in each tissue. Combining these meQTL GWAS results with recent AD/PD GWAS summary statistics by MR strongly suggests that the previously described associations between PSMC3, PICALM, and TSPAN14 and AD may be founded on differential DNAm in or near these genes. In addition, there is strong, albeit less unequivocal, support for causal links between DNAm at PRDM7 in AD as well as at KANSL1/MAPT in AD and PD. Our study adds valuable insights on AD/PD pathogenesis by combining two high-resolution "omics" domains, and the meQTL data shared along with this publication will allow like-minded analyses in other diseases.

Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks.

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid ß (Aß) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aß, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aß, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. Conclusion: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.

A correlation map of genome-wide DNA methylation patterns between paired human brain and buccal samples.

Epigenome-wide association studies (EWAS) assessing the link between DNA methylation (DNAm) and phenotypes related to structural brain measures, cognitive function, and neurodegenerative diseases are becoming increasingly more popular. Due to the inaccessibility of brain tissue in humans, several studies use peripheral tissues such as blood, buccal swabs, and saliva as surrogates. To aid the functional interpretation of EWAS findings in such settings, there is a need to assess the correlation of DNAm variability across tissues in the same individuals. In this study, we performed a correlation analysis between DNAm data of a total of n = 120 matched post-mortem buccal and prefrontal cortex samples. We identified nearly 25,000 (3% of approximately 730,000) cytosine-phosphate-guanine (CpG) sites showing significant (false discovery rate q < 0.05) correlations between buccal and PFC samples. Correlated CpG sites showed a preponderance to being located in promoter regions and showed a significant enrichment of being determined by genetic factors, i.e. methylation quantitative trait loci (mQTL), based on buccal and dorsolateral prefrontal cortex mQTL databases. Our novel buccal-brain DNAm correlation map will provide a valuable resource for future EWAS using buccal samples for studying DNAm effects on phenotypes relating to the brain. All correlation results are made freely available to the public online.

Common signatures of differential microRNA expression in Parkinson's and Alzheimer's disease brains.

Dysregulation of microRNA gene expression has been implicated in many neurodegenerative diseases, including Parkinson's disease. However, the individual dysregulated microRNAs remain largely unknown. Previous meta-analyses have highlighted several microRNAs being differentially expressed in post-mortem Parkinson's disease and Alzheimer's disease brains versus controls, but they were based on small sample sizes. In this study, we quantified the expression of the most compelling Parkinson's and Alzheimer's disease microRNAs from these meta-analyses ('candidate miRNAs') in one of the largest Parkinson's/Alzheimer's disease case-control post-mortem brain collections available (n = 451), thereby quadruplicating previously investigated sample sizes. Parkinson's disease candidate microRNA hsa-miR-132-3p was differentially expressed in our Parkinson's (P = 4.89E-06) and Alzheimer's disease samples (P = 3.20E-24) compared with controls. Alzheimer's disease candidate microRNAs hsa-miR-132-5p (P = 4.52E-06) and hsa-miR-129-5p (P = 0.0379) were differentially expressed in our Parkinson's disease samples. Combining these novel data with previously published data substantially improved the statistical support (α = 3.85E-03) of the corresponding meta-analyses, clearly implicating these microRNAs in both Parkinson's and Alzheimer's disease. Furthermore, hsa-miR-132-3p/-5p (but not hsa-miR-129-5p) showed association with α-synuclein neuropathological Braak staging (P = 3.51E-03/P = 0.0117), suggesting that hsa-miR-132-3p/-5p play a role in α-synuclein aggregation beyond the early disease phase. Our study represents the largest independent assessment of recently highlighted candidate microRNAs in Parkinson's and Alzheimer's disease brains, to date. Our results implicate hsa-miR-132-3p/-5p and hsa-miR-129-5p to be differentially expressed in both Parkinson's and Alzheimer's disease, pinpointing shared pathogenic mechanisms across these neurodegenerative diseases. Intriguingly, based on publicly available high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation data, hsa-miR-132 may interact with SNCA messenger RNA in the human brain, possibly pinpointing novel therapeutic approaches in fighting Parkinson's disease.