To investigate whether antidiabetic drugs have a biological basis to be repurposed in PD prevention, we applied a drug target Mendelian randomization framework to assess associations between genetic variation in antidiabetic drug targets and PD risk or age at onset (AAO). Instrumental variables (IVs) were derived from GWAS summary statistics on fasting glucose (FG), glycated hemoglobin (HbA1c), and gene expression data from GTEx. Apart from SGLT2 inhibitors, all other antidiabetic drugs of interest could be instrumented through our methods. Positive and negative control analyses were carried out to validate 20 IVs in the FG arm and 23 IVs in the HbA1c arm. DPP-4 inhibitors failed the positive control. GWAS summary statistics for PD risk and AAO data were sourced from the IPDGC and COURAGE-PD consortia, resulting in 42 083 cases/457 090 controls for risk and 37 103 PD cases for AAO. MR analyses showed no significant associations across consortia or in meta-analysis. These findings do not support a causal role of genetic variation in antidiabetic drug targets in PD risk or AAO.

Cerebrovascular pathology is increasingly implicated in neurodegenerative diseases, yet its pathomechanistic contribution remains poorly defined. Building on prior evidence of dysregulated iron and oxygen homeostasis in early-affected brain regions of progressive supranuclear palsy (PSP), we hypothesized that brain microvascular alterations may play an etiological role in select neurodegenerative proteinopathies. First, we conducted a systematic neuropathological evaluation of 178 brains from the University Health Network Neurodegenerative Brain Collection, including Alzheimer's disease-related neuropathologic change (ADNC; n=30), Lewy body disease with high or intermediate ADNC (n=38) and low ADNC (n=16), multiple system atrophy (MSA; n=14), PSP (n=39), frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP; n=10), and controls (n=31). Arteriolosclerosis, microinfarction, and calcification were assessed in the basal ganglia and frontal cortex. Iron burden was correlated by quantification of Perl's staining in MSA and PSP, where vessel pathology was most severe. Single-nucleus RNA-sequencing (snRNA-seq) of frontal cortex tissue from control (n=5) and PSP (n=8) cases with varying arteriolosclerosis severity was performed to characterize the vascular transcriptome, with validation against an independent snRNA-seq evaluation of PSP (n= 11), Pick's disease (n=9), AD (n=10), and control (n=10) brains. Histological analysis revealed disease-specific involvement of microvascular pathology in neurodegenerative diseases, identifying PSP to demonstrate most prominent and widespread vessel wall thickening across regions examined. Regression analysis using demographic, APOE and MAPT genetic risk status, and neuropathological features of cases corroborated the distinct association with PSP pathology. Elevated iron load in early affected regions of MSA and PSP brains correlated with greater vessel wall thickening, suggesting a possible pathomechanistic relationship between the two disease physiologies. snRNA-seq analysis of vascular transcriptome identified robust upregulation of heat shock proteins and hypoxia-related genes in PSP endothelial cells and pericytes across both datasets. Importantly, we found the proteotoxic signature to be strongly associated with higher vessel scores in PSP cases, linking microvascular morphology to endothelial dysfunction. Our comprehensive neuropathological evaluation coupled with correlative snRNA-seq analysis establish PSP-specific arteriolar thickening associated with endothelial proteotoxic state as a candidate pathogenic mechanism. The cerebral arteriolar unit represents a compelling therapeutic target for disease modification in PSP.

Oxylipin species are generated from omega-3 and omega-6 fatty acids during inflammation, including fatty acid epoxides by cytochrome P450s (CYP450) and their diol metabolites by soluble epoxide hydrolase (sEH). The CYP450-sEH pathway has been implicated in Alzheimer’s disease (AD) but it remains unclear how plasma oxylipins relate to AD biomarkers and neurodegeneration. Fasting plasma CYP450-sEH total (free + esterified) oxylipins were assayed by ultra-high pressure liquid chromatography-tandem mass spectrometry in participants (NCT04104373) clinically diagnosed with AD or mild cognitive impairment (MCI). Plasma AD biomarkers were assayed by SiMoA. At baseline, 1 and 2 years, regional grey matter and white matter hyperintensity (WMH) volumes were quantified using 3.0 T MRI, and cognitive assessments were performed. At baseline (n = 125), participants with AD vs. MCI had higher CYP450-sEH pathway metabolites, apolipoprotein E (APOE) ε4 carriers vs. non-carriers had higher diol levels, and females vs. males had higher omega-6 vs. omega-3 oxylipins. An oxylipin profile of higher diols and lower epoxides was associated with higher pTau181, temporal lobe and hippocampal atrophy, and declines in cognitive performance in multiple domains over 2 years. Higher CYP450-sEH pathway metabolites were associated with the progression of WMH. Larger oxylipin relationships with atrophy and cognitive decline were seen at the MCI stage, in APOE ε4 carriers, and in females. Elevated sEH metabolites were related to biomarkers of AD pathogenesis, and they predicted atrophy, white matter changes, and cognitive decline over 2 years.

There is substantial heterogeneity in clinical presentation of genetic Frontotemporal Dementia (FTD), even within the same family. This suggests that additional heritability may exist and contribute to this variable presentation. We examined whether gene-based aggregate burden of genome-wide rare variants (minor allele frequency [MAF]: ≤1%) contribute to variation in regional cortical and subcortical grey matter volumes, after controlling for effects of causative mutations in GRN , MAPT , and C9orf72 . This study was embedded within the GENetic Frontotemporal dementia Initiative (GENFI), which recruits genetic FTD cases and their asymptomatic at-risk family members, both carriers and non-carriers of FTD mutations. We included 518 participants with genotype (Neurochip; imputed against TOPMed), and T1w-MRI brain volumetric data. Gene-based burden tests that aggregate the number of rare variants by gene were used to examine the association of rare variants (MAF: ≤1%) with regional cortical and subcortical grey matter volumes (70 regions of interest [ROIs]), controlling for age, sex, total intracranial volume, mutation status, scanner site, population stratification, and family membership (kinship matrix) using RVTests. Annotations for loss of function mutations (LOF): start gain, stop loss, start loss, essential splice site, stop gain, normal splice site, and non-synonymous. Multiple testing correction accounted for the number of genes and number of independent grey matter volumes as calculated by matSpD ( p -value threshold: 0.05/(17,053x42) =  6.98 x10 -8 ) . Aggregate burden of LOF mutations ( DNAJB8-AS1, WDR26, RDM1P5, BSND, CNOT2, DDA1, ASAH2B, PPM1A, HOXD13, ALDH1A1, CENATAC, ANKRD45) was associated with significantly lower volumes within the left temporal lobe (ROIs: left temporal and lateral temporal left), and greater volume in the putamen bilaterally ( TSACC) . All genes are protein coding, except the DNAJB8-AS1 (antisense RNA) and RDM1P5 (pseudogene), and are variably expressed in the brain. Molecular functions of significant genes involve regulation of gene expression, transcription, and cell cycle, ion channel function, and chromosomal segregation. WDR26 and CNOT2 genes have been implicated in neurodevelopment and neurological disorders respectively; BSND gene is involved in neurotransmission. Identification of deleterious or protective rare variants contributing to FTD imaging phenotypes may help identify genetic modifiers of familial FTD. Replication in larger cohorts is needed.

We investigated the role of copy number variations (CNVs) in Parkinson's disease (PD) using genotyping data from 10,815 patients (2731 early-onset PD, EOPD) and 8901 controls from the COURAGE-PD consortium. CNVs were analyzed using a sliding window genome-wide association and burden approach. No genome-wide significant CNVs were detected in the overall cohort, but a robust deletion spanning exons 2-6 of PRKN was identified in EOPD cases, validated by MLPA, and replicated in the GP2 dataset (23,089 cases, 18,824 controls). CNV burden was significantly enriched in PD-related genes, primarily driven by PRKN, with the strongest effect observed in EOPD. PRKN CNV carriers showed earlier age at onset, confirmed by survival analysis. No association was observed for genome-wide or large CNV burden. Our findings reinforce the pivotal role of PRKN deletions in early-onset PD and highlight the need for high-resolution CNV analysis in large cohorts to uncover additional rare contributors to PD risk.

Amyotrophic lateral sclerosis (ALS) is a heritable disorder where rare variants with low-to-moderate penetrance are thought to dominate genetic risk. To identify such rare variants, we harmonized and analyzed exome data from 22 cohorts, totaling 17,919 individuals with ALS and 200,703 controls across discovery and replication phases. Rare variant analyses identified several new risk genes, with replication confirming association of YKT6 and supporting HTR3C, GBGT1 and KNTC1. We also provide strong, independent validation for genes with limited previous evidence: ARPP21, DNAJC7 and CFAP410. Notably, in ARPP21, we identified a new high-effect variant (p.P747L) and confirmed that p.P563L is an ALS-associated variant leading to an aggressive disease course. Beyond new discoveries, our analyses largely recapitulated the known genetic architecture of ALS, identifying risk variants in over 20% of cases and supporting a cumulative oligogenic risk model. These findings highlight new translational targets and show that rare variant analyses capture substantially more genetic risk than common variant genome-wide association studies.

Background Repetitive head impacts in former contact sport athletes are associated with cognitive impairment, accelerated cerebral atrophy and risk of neurodegenerative disease. Epigenetic clocks derived from age-associated DNA methylation (DNAm) profiles may capture accelerated biological ageing in neurodegenerative conditions; however, their application in former athletes remains unexplored. Here we aim to explore the application of epigenetic clocks as a measure of accelerated biological ageing in a cohort of former athletes. Methods In 126 former athletes (96% male; mean age: 54.5±14.4 years; mean concussions: 6.8±6.7), we examined associations of brain volumes, plasma neurofilament light levels and cognitive/behavioural scores with DNAmAge-acceleration, AgeAccelResidual and DNAmFitAge-acceleration. Results We only found an association between the number of concussions and DNAmFitAge-acceleration (p=0.003, B=0.46, R²=0.063), indicating that every two additional concussions were associated with a 5-year increase in DNAmFitAge-acceleration. There was also a trend towards an association between years of play and DNAmAge-acceleration in older athletes. Conclusions These preliminary findings suggest that specific epigenetic clock measures may serve as early markers of biological ageing related to repetitive head impacts.

Atypical frontotemporal lobar degeneration with ubiquitin-positive inclusions (aFTLD-U) is neuropathologically characterized by aggregation of the FET family of proteins and clinically manifests as sporadic young-onset frontotemporal dementia. Here we describe a major risk locus on chr15q14 identified through a genome-wide association study in 59 pathologically confirmed aFTLD-U cases and 3,153 controls (lead single nucleotide polymorphism rs549846383, P = 5.85 × 10-21, odds ratio 26.7). When combined with data from 28 additional aFTLD-U cases, 3,712 controls and 3,215 individuals with other neurodegenerative diseases and by leveraging in-house and public long-read genome sequencing data from 1,715 individuals, we identified a tandem repeat expansion on the associated haplotypes in an intron of GOLGA8A. We found variation in repeat length, motif length, and motif sequence, with long CT-dimer expansions strongly associated with aFTLD-U. Although the functional consequence of this repeat remains unknown, its presence in nearly 60% of aFTLD-U cases points to a fundamental role in disease pathogenesis.

Brain white matter hyperintensities (WMH) are vascular lesions commonly observed in Alzheimer's disease (AD). WMH were previously shown to be associated with greater brain amyloid, but the molecular pathways underlying their complex relation remain unclear. Here, we aim to identify single nucleotide polymorphisms (SNP) that modify the relationship between WMH and AD amyloid biomarkers. We conducted a genome-wide interaction study in participants with AD, mild cognitive impairment, and normal cognition from the Alzheimer's Disease Neuroimaging Initiative (ADNI). WMH were measured from FLAIR MRI using an automated atlas-based segmentation. Amyloid-β 42 (Aβ42) in cerebrospinal fluid (CSF) were measured using immunoassays. Interactions between SNPs and WMH volumes on CSF-Aβ42 were assessed via a linear regression model adjusting for age, sex, diagnosis, MMSE, APOE -ε4 status, head-size, and 4 genetic principal components in PLINK2. The most influential SNP was identified via Sum of Single Effects (SuSiE) regression. Significant SNP-WMH interactions were validated in participants from the UK Biobank (UKB) with available plasma-Aβ42 data quantified by liquid chromatography-mass spectrometry. SNP-WMH interactions in relation to amyloid pathology (diffuse and neuritic plaque burden) was investigated in the Religious Orders Study/Rush Memory and Aging Project (ROSMAP). A 28-variant intergenic locus on chromosome 18 (top SNP: rs72899960 T>A, p  = 5.66x10 -9 , MAF=11.1%, Imputation R 2 >0.99%, n  = 863) interacted with WMH to predict Aβ42 (nearest gene: U7 small nuclear RNA (snRNA) XR_007066478.1 [-111KB]). The effect of this SNP was also significant in the dominant and recessive genetic models (relative to the minor A-allele). This SNP-WMH interaction was replicated in UKB ( n  = 645) in both additive (B=0.91, p  = 0.017) and dominant models (B=0.96, p  = 0.024). In ROSMAP ( n  = 195), significant SNP-WMH interaction was observed on diffuse plaque burden in the additive (B=-0.46, p  = 0.049) and dominant (B=-0.51, p  = 0.046) models. The minor A-allele exhibited a protective effect by being associated with higher circulating Aβ42 (in ADNI and UKB) and lower diffuse plaque burden (in ROSMAP) in individuals with greater WMH. Genomic variants moderated the relationship between WMH and amyloid biomarkers, suggesting a novel regulatory role for snRNA. This genome-wide interaction study highlights the potential contributions of snRNA and related pathways to the relationship between vascular disease and amyloid biomarkers in AD.

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease, with most sporadic cases lacking clear genetic causes. Abnormal pre-mRNA splicing is a fundamental mechanism in neurodegenerative diseases. For example, TAR DNA-binding protein 43 (TDP-43) loss of function causes widespread RNA mis-splicing events in ALS. Additionally, splicing mutations are major contributors to neurological disorders. However, the role of intronic variants driving RNA mis-splicing in ALS remains poorly understood.To address this, we developed Spliformer to predict RNA splicing. Spliformer is a transformer-based deep learning model trained and tested on splicing events from the GENCODE database, in addition to RNA-sequencing data from blood and CNS tissues. We benchmarked Spliformer against SpliceAI and Pangolin using testing datasets and paired whole-genome sequencing with RNA-sequencing data. We also developed the Spliformer-motif model to identify splicing regulatory motifs. We analysed the Clinvar dataset to identify the link of splicing variants with disease pathogenicity. Additionally, we analysed whole-genome sequencing data of ALS patients and controls to identify common intronic splicing variants linked to ALS risk or disease phenotypes. We also profiled rare intronic splicing variants in ALS patients to identify known or novel ALS-associated genes. Minigene assays were used to validate candidate splicing variants. Finally, we measured spine density in neurons with a specific gene knockdown or those expressing a TDP-43 disease-causing mutant.Spliformer accurately predicts the possibilities of a nucleotide within a pre-mRNA sequence being a splice donor, acceptor or neither. Spliformer outperformed SpliceAI and Pangolin in both speed and accuracy in tested splicing events and/or paired whole-genome sequencing/RNA-sequencing data. Spliformer-motif successfully identified canonical and novel splicing regulatory motifs. In the Clinvar dataset, splicing variants are highly related to disease pathogenicity. Genome-wide analyses of common intronic splicing variants nominated one variant linked to ALS progression. Deep learning analyses of whole-genome sequencing data from 1370 ALS patients revealed rare splicing variants in reported ALS genes (such as PTPRN2 and CFAP410, validated through minigene assays and RNA sequencing) and TDP-43 loss-of-function-related RNA mis-splicing genes (such as PTPRD). Further genetic analysis and minigene assays nominated PCP4 and TMEM63A as ALS-associated genes. Functional assays demonstrated that PCP4 is crucial for maintaining spine density and can rescue spine loss in neurons expressing a disease-causing TDP-43 mutant. In summary, we developed Spliformer and Spliformer-motif, which accurately predict and interpret pre-mRNA splicing. Our findings highlight an intronic genetic mechanism driving RNA mis-splicing in ALS and nominate PCP4 as an ALS-associated gene. Tang et al. present a new deep learning tool, Spliformer, that accurately predicts RNA splicing. Genome-wide analyses using this tool identified intronic variants driving RNA mis-splicing in amyotrophic lateral sclerosis, while additional genetic and functional analyses pinpointed PCP4 as an ALS-associated gene.

The ordered assembly of α-synuclein protein encoded by SNCA into filaments characterizes neurodegenerative synucleinopathies. Lewy body disease (LBD) shows predominantly neuronal and multiple system atrophy (MSA), predominantly oligodendrocytic α-synuclein pathology affecting subcortical brain structures. Based on cryo-electron microscopy, it was reported that the structures of α-synuclein filaments from LBD differ from MSA and juvenile-onset synucleinopathy (JOS). The rare atypical MSA subtype shows abundant neuronal argyrophilic α-synuclein inclusions in the limbic system. Current concepts indicate that disease entities are characterized by unique protofilament folds. Here we demonstrate that α-synuclein can form a Lewy-MSA hybrid fold, leading to the atypical histopathological form of MSA. Distinct biochemical characteristics of α-synuclein, as demonstrated by protease-sensitivity digestion assay, seed amplification assays (SAAs), and conformational stability assays (CSA), are also linked to cytopathological differences. We expand the current structure-based classification of α-synucleinopathies and propose that cell-specific protein pathologies can be associated with distinct filament folds.

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are fatal neurodegenerative diseases sharing clinical and pathological features. Both involve complex neuron-glia interactions, but cell-type-specific alterations remain poorly defined. We performed single-nucleus RNA sequencing of the frontal cortex from C9orf72-related ALS (with and without FTLD) and sporadic ALS (sALS). Neurons showed prominent changes in mitochondrial function, protein homeostasis, and chromatin remodeling. Comparison with independent datasets from other cortical regions revealed consistent pathway alterations, including upregulation of STMN2 and NEFL across brain regions and subtypes. We further examined dysregulation of alternative polyadenylation (APA), an understudied post-transcriptional mechanism, uncovering cell-type-specific APA patterns. To investigate its regulation, we developed the alternative polyadenylation network (APA-Net), a multi-modal deep learning model integrating transcript sequences and RNA-binding protein (RBP) expression profiles to predict APA. This atlas advances our understanding of ALS/FTLD molecular pathology and provides a valuable resource for future mechanistic studies.

Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder with both genetic and environmental factors contributing to its pathogenesis. While early-onset AD has well-established genetic determinants, the genetic basis for late-onset AD remains less clear. This study investigates a large Italian family with late-onset autosomal dominant AD, identifying a novel rare missense variant in GRIN2C gene associated with the disease, and evaluates the functional impact of this variant. Methods Affected and unaffected members from a Northern Italian family were included. Genomic DNA from family members was extracted and initially screened for pathogenic mutations in APP, PSEN1, and PSEN2, and screened for 77 genes associated with neurodegenerative conditions using NeuroX array assay. Exome sequencing was performed on three affected individuals and two healthy relatives. Bioinformatics analyses were conducted. Functional analysis was performed using primary neuronal cultures, and the impact of the variant was assessed through immunocytochemistry and electrophysiology. Results Pathogenic variants were not identified in APP, PSEN1, or PSEN2, nor in the 77 genes in NeuroX array assay. Exome Sequencing revealed the c.3215C > T p.(A1072V) variant in GRIN2C gene (NM 000835.6), encoding for the glutamate ionotropic receptor N-methyl-D-aspartate receptor (NMDA) type subunit 2C (GluN2C). This variant segregated in 6 available AD patients in the family and was absent in 9 healthy relatives. Primary rat hippocampal neurons overexpressing GluN2C(A1072V) showed an increase in NMDAR-induced currents, suggesting altered glutamatergic transmission. Surface expression assays demonstrated an elevated surface/total ratio of the mutant GluN2C, correlating with the increased NMDAR current. Additionally, immunocytochemistry revealed in neurons expressing the mutant variant a reduced colocalization between the GluN2C subunit and 14-3-3 proteins, which are known to facilitate membrane trafficking of NMDARs. Discussion We identified a rare missense variant in GRIN2C associated with late-onset autosomal dominant Alzheimer's disease. These findings highlight the role of GluN2C-containing NMDARs in glutamatergic signaling and their potential contribution to AD pathogenesis.

OBJECTIVES:Progressive supranuclear palsy (PSP) is a neurodegenerative disease showing pathological tau accumulation in subcortical neurons and glial cells. The human leukocyte antigen (HLA) locus on chromosome 6 is a polymorphic region with complex linkage patterns that has been implicated in several autoimmune and neurological disorders. The HLA locus has not been systematically examined in PSP. It is unclear whether tau and HLA can interact to induce an autoimmune disease mechanism. METHODS:We evaluated an autopsy confirmed PSP cohort (n = 44) and compared allele/haplotype frequencies to those of the reference group of a local deceased Canadian donor pool. We performed HLA-Tau peptide binding prediction and modelling of HLA Class II - Tau Peptide interactions. FINDINGS:Odds ratio was 2.94 (95 % CI 1.01 to 8.55; p = 0.047) for DQB1*06:01 allele, and 2.59 (95 % CI 1.39 to 4.83; p = 0.0025) for the narcolepsy-associated haplotype (DRB1*15:01-DQB1*06:02). One patient with 4-repeat tau PSP-type pathology was a carrier of the IgLON5-associated haplotype (DRB1*10:01-DQB1*05:01). HLA-Tau peptide binding prediction and modelling of HLA Class II - Tau Peptide interactions revealed strong-binding tau peptides but not the PSP-protofilament fold for alleles DQA1*01:02-DQB1*06:02 and DQA1*01:03-DQB1*06:01. CONCLUSION:Our study suggests that epitopes within the tau peptide may bind to HLA alleles that are found in a subset of PSP patients supporting the notion of an autoimmune pathophysiological component. These findings have implications for subtyping and stratifying patients for therapies, including those targeting immune modulation.

Objective:To investigate the impact of copy number variations (CNVs) on Parkinson's disease (PD) pathogenesis using genome-wide data and explore their role in sporadic PD. Methods:We analyzed CNV data from 11,035 PD patients (including 2,731 early-onset PD (EOPD)) and 8,901 controls from the COURAGE-PD consortium using a sliding window CNV-GWAS and genome-wide burden analysis. The independent dataset from the Global Parkinson Genetics Program (GP2) consisted of 23,089 cases and 18,824 controls were used to validate our initial findings. Results:The exploratory dataset identifies multiple CNV regions associated with PD risk. The nominated CNV loci were not confirmed in an independent dataset, except that only a deletion in the PRKN gene, a well-established EOPD locus, remained genome-wide significant and robustly supported. CNV burden analysis showed a higher prevalence of CNVs in PD-related genes in patients compared to controls (OR=1.56 [1.18-2.09], p=0.0013), with PRKN showing the highest burden (OR=1.47 [1.10-1.98], p=0.026). Patients with CNVs in PRKN had an earlier disease onset. Burden analysis with controls and EOPD patients showed similar results. Interpretation:The largest CNV-based GWAS on PD highlights both the promise and pitfalls of array-based CNV detection in PD and underscores the relevance of whole-genome sequencing approaches in resolving the role of CNV in PD. The array-based findings are prone towards false positive findings that might arise either from platform limitations and/or cohort biases. Future studies require improved genotyping resolution and rigorous cross-cohort validation to reliably assess CNV contributions to PD risk.

The long-term consequences of repetitive head impacts in contact sports include cognitive deficits, accelerated brain atrophy, and neurodegenerative diseases. Currently, no studies of former athletes have addressed the connection between brain aging and biological aging, which can be assessed using age-related DNA methylation (DNAm) profiles. The most studied epigenetic clock is DNAm-age, which is providing consistent results across tissues (e.g., blood and brain). It includes several measures like DNAm-age acceleration (DNAmAA), the difference between DNAm-age and chronological age, and DNAmAA-residual (DNAmAAr), independent of chronological age. In a cohort of retired athletes, we explored the link between measures of brain age and epigenetic age, including the recently developed DNAmFit-age reported to be younger in physically fit individuals. We investigated 126 former contact sports athletes (mean age: 54.5±14.4; 96% male; mean concussion number: 6.8±6.7). Longitudinal assessments were available for 21 athletes (2–3 time points over 1–10 years). Bisulfite-converted blood DNA was analyzed using the Infinium MethylationEPIC chip, and the DNAm data were submitted to the Horvath calculator ( https://dnamage.clockfoundation.org/ ) to obtain DNAmFit-age, DNAm-age, and DNAmAAr. T1-weighted MRIs were processed using the CAT12-Toolbox. Regional gray matter volume was examined in relation to DNAmAA, DNAmAAr and DNAmFit-age acceleration (DNAmFitAA). Consistent with previous findings, chronological age was significantly associated with brain volumes and cortical thickness. DNAmAA, DNAmAAr, and DNAmFitAA remained stable over a period of up to 10 years, and none of these measures were associated with brain volumes or cortical thickness. Notably, multivariate linear regression analysis revealed a significant positive association between DNAmFitAA and the number of concussions ( p  = 0.0027, B=0.46, R 2 =0.063), indicating that every additional two concussions correspond to a 5-year increase in DNAmFitAA. In former athletes, we confirmed that chronological age is associated with cerebral atrophy and identified an association between increased DNAmFitAA and a higher number of concussions. This finding requires validation in independent studies, along with an assessment of the relationship between DNAmFitAA and neurodegeneration. Our analysis did not reveal a link of the examined epigenetic clocks with brain volumes and cortical thickness. Exploration of other epigenetic clocks may provide new insights into the mechanisms underlying brain aging.

A common variant within TMEM106B is associated with risk for Frontotemporal Lobar Degeneration-Tar DNA binding Protein-43 (FTLD-TDP). A recent study has shown that the minor allele G of TMEM106B-rs1990622 confers protection against FTLD-TDP in symptomatic mutation carriers through reductions in NfL serum levels, brain atrophy, and cognitive decline. It is unknown whether this protective effect is present in phenoconverters of the disease. We included 518 participants from the GENetic Frontotemporal dementia Initiative (GENFI), which recruits genetic FTD cases and their family members, both carriers and non-carriers of FTD mutations. Of these, 21 were phenoconverters, 209 were non-carrier controls, 70 were presymptomatic and 45 symptomatic C9orf72 carriers, 92 presymptomatic and 29 symptomatic GRN carriers, and 39 presymptomatic and 13 symptomatic MAPT carriers. Effects of interaction between TMEM106B-rs1990622 and phenoconverter status were examined using mixed effects models, with a random effects structure featuring subjects nested within families and fixed effects for age at baseline and sex. Serum neurofilament light chain (NfL) was measured using the Simoa platform. Cognitive assessment included the Mini-Mental State Examination (MMSE), tests of attention, processing speed, executive function, and language, as well as the Cambridge Behavioural Inventory (CBI), with mixed effects also including years of education as a covariate. Brain volumetry was assessed using T1-weighted MRI and these mixed effect models also included additional covariates of total intracranial volume and scanner site. In phenoconverters, each copy of the protective allele G was associated with a significant reduction in the rate of serum NfL accumulation (-5.33 pg/mL/year; p  =  7.79 × 10 −9 ). Structural imaging analyses revealed decreased rates of atrophy in fronto-orbital regions and the insular cortex among protective allele carriers. Cognitive trajectories showed significantly slower decline across multiple domains including general cognition (MMSE; p  =  0.003), attention and processing speed ( p  = 2.2 × 10 −4 ), executive function ( p  = 2.6 × 10 −7 ), language ( p  = 2.9 × 10 −3 ), and behavioural symptoms as measured by CBI ( p  = 9.5 × 10 −3 ). The TMEM106B-rs1990622 protective variant significantly modulates disease progression in genetic FTD phenoconverters across multiple markers, suggesting its potential as a therapeutic target.

BACKGROUND:The marked heterogeneity of Amyotrophic Lateral Sclerosis (ALS) combined with a lack of biomarkers are key contributing factors to the lack of disease-modifying treatments. The Comprehensive Analysis Platform to Understand Remedy and Eliminate ALS (CAPTURE ALS) is a Canadian platform designed to create the most comprehensive picture of people living with ALS with the objective of facilitating ALS research initiatives worldwide. OBJECTIVES:The main aims of CAPTURE ALS include: (1) to characterize ALS and healthy controls with biosamples and data in order to provide the most comprehensive picture of individuals living with ALS to date; (2) to create a de-identified database and biosample repository linked to detailed clinical information; and (3) to develop and implement an inclusive and transparent participant engagement strategy to be active throughout all stages of CAPTURE ALS. METHODS/RESULTS:CAPTURE ALS is a prospective, multicenter, observational, longitudinal study. People living with ALS, or a related disease and healthy controls undergo a harmonized protocol including the collection of detailed clinical information, neurological and cognitive examination, speech recording, advanced magnetic resonance imaging, and biosampling. Data and samples are stored in a biobank operating under an open science governance framework. An inclusive and transparent participant engagement strategy was designed and implemented throughout all stages of CAPTURE ALS. Four sites are operating in the consortium with a fifth being onboarded. The target enrollment is 120 affected participants and 50 controls, with the first participant visit having occurred in March 2022. Recruitment is ongoing. DISCUSSION:CAPTURE ALS is a scalable clinical research platform that connects scientists and patients to facilitate efficient translational research. The unique and deeply phenotyped data and biosamples are a global resource towards the development of biomarkers and understanding ALS biology. This study is registered at clinicaltrials.gov (NCT: NCT05204017).

Background Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease, with highly diverse survival time. However, genetic and epigenetic factors influencing ALS survival across diverse populations remain unclear. Methods We performed whole-genome sequencing (WGS) and DNA methylome array in blood DNA of patients with ALS. For survival analysis, we used Cox proportional hazards model for genetic variants, DNA methylation (DNAm) of CpG sites or CpG-SNPs in Chinese and Canadian cohorts, followed by meta-analysis. We performed pathway enrichment analysis for candidate genes inferred from DNAm events associated with survival. In paired genome and methylome data, we analysed the effect of the candidate CpG-SNP genotypes on DNAm status. Findings Genome-wide cross-population meta-analysis of common variants in 511 patients with ALS showed a suggestive association of CAV1/CAV2 rs117002347 genotypes with survival. Epigenome-wide cross-population meta-analysis in 459 patients revealed that ALS survival was significantly linked to DNAm of 88 CpGs on 40 genes, and highlighted the AMPK and cytoskeleton pathways. Epigenome-wide cross-population meta-analysis of CpG-SNPs in 459 patients identified 8 loci on 4 genes, including BAG6 (cg27014438/rs28732154), which was further validated in another 204 patients with ALS. Moreover, analysis of paired genome/epigenome data (n = 454) indicated that BAG6 rs28732154 genotypes may modulate cg27014438 methylation, which is also a cis-eQTM of BAG6 expression in blood. Interpretation Our study identified BAG6 cg27014438 methylation as a potential epigenetic modifier of ALS survival. BAG6 cg27014438 methylation is modulated by rs28732154 genotypes, and linked to BAG6 expression. Our findings extended our understanding of epigenetic modifiers in ALS survival. Funding This work was supported by the National Natural Science Foundation of China (82071430, 82371878) (MZ), Shanghai Municipal Natural Science Foundation General Program (22ZR1466400) (MZ), the Fundamental Research Funds for the Central Universities (MZ), the G. Harry Sheppard Memorial Research Fund, and Canadian Consortium on Neurodegeneration in Aging (ER).

INTRODUCTION:Recent research has suggested that neuroinflammation may be important in the pathogenesis of neurodegenerative diseases. Free-water diffusion (FWD) has been proposed as a non-invasive neuroimaging-based biomarker for neuroinflammation. METHODS:Free-water maps were generated using diffusion MRI data in 367 patients from the Ontario Neurodegenerative Disease Research Initiative (108 Alzheimer's Disease/Mild Cognitive Impairment, 42 Frontotemporal Dementia, 37 Amyotrophic Lateral Sclerosis, 123 Parkinson's Disease, and 58 vascular disease-related Cognitive Impairment). The ability of FWD to predict neuroinflammation and neurodegeneration from biofluids was estimated using plasma glial fibrillary-associated protein (GFAP) and neurofilament light chain (NfL), respectively. RESULTS:Recursive Feature Elimination (RFE) performed the strongest out of all feature selection algorithms used and revealed regional specificity for areas that are the most important features for predicting GFAP over NfL concentration. Deep learning models using selected features and demographic information revealed better prediction of GFAP over NfL. DISCUSSION:Based on feature selection and deep learning methods, FWD was found to be more strongly related to GFAP concentration (measure of astrogliosis) over NfL (measure of neuro-axonal damage), across neurodegenerative disease groups, in terms of predictive performance. Non-invasive markers of neurodegeneration such as MRI structural imaging that can reveal neurodegeneration already exist, while non-invasive markers of neuroinflammation are not available. Our results support the use of FWD as a non-invasive neuroimaging-based biomarker for neuroinflammation.