Citations

AbstractBackgroundTriggering receptor expressed on myeloid cell 2 (TREM2) is a surface receptor that, in the central nervous system, is exclusively expressed on microglia. TREM2 variants have been linked to increased risk for neurodegenerative diseases, but the functional effects of microglial TREM2 in TAR‐DNA binding protein 43 kDa (TDP‐43)‐related neurodegeneration remain largely unknown.MethodWe investigated TDP‐43 neurodegeneration via viral‐mediated expression of hTDP‐43 protein in mice or inducible expression of hTDP43 with defective nuclear localization signals in transgenic mice. Mass cytometry analysis, mass spectrometry, surface plasmon resonance, and computational simulation were used to study the interaction between TREM2 and TDP‐43.ResultWe found that TREM2 deficiency impaired microglia phagocytic clearance of pathological TDP‐43, and enhanced neuronal damage and motor function impairments. hTDP‐43 induced a TREM2‐dependent subpopulation of microglia with high CD11c expression and higher phagocytic ability. We further demonstrated an interaction between TDP‐43 and TREM2, in vitro and in vivo, in hTDP‐43‐expressing transgenic mouse brains. We computationally identified the region within hTDP‐43 that interacts with TREM2 and observed the potential interaction in ALS patient tissues.ConclusionOur data reveal the novel interaction between TREM2 and TDP‐43, highlighting that TDP‐43 is a potential ligand for microglial TREM2 and the interaction mediates neuroprotection of microglial TREM2 in TDP‐43‐related neurodegeneration.

AbstractBackgroundAlthough we have greatly increased our ability to identify the presence of neurodegenerative pathology during life, additional techniques and targets are still needed to increase diagnostic specificity. Recently, it has become evident that the neuroinflammatory response might be tailored towards distinct diseases resulting in disease specific “neuroinflammatory signatures”. Therefore, a detailed comparison of neuroinflammatory molecules present among distinct neurodegenerative diseases could provide novel information of disease specific therapeutic targets or differential biomarkers.MethodTo better examine potential neuroinflammatory signatures, a 71 immune‐related protein multiplex ELISA panel was utilized to analyze anterior cingulate grey matter from 127 individuals neuropathologically diagnosed with Alzheimer’s disease (AD), but chronic traumatic encephalopathy (CTE), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and argyrophilic grain disease (AGD). A partial least square regression analysis was used for unbiased clustering and identifying proteins that were distinctly correlated with each disease correcting for age and gender. Receiver operator characteristic and binary logistic regression analyses were then used to examine the ability of each candidate protein to distinguish diseases. Validation in postmortem cerebrospinal fluid (CSF) from 15 AD and 14 CTE cases was performed to determine if candidate proteins could act as possible biomarkers. Finally, histology was performed using candidate proteins to visualize cell type expression for better understanding of potential mechanistic pathways.ResultFive clusters of neuroimmune proteins were identified and compared to determine if clusters were specific to distinct disease. Each cluster was found to correlate with either CTE, AD, PSP, CBD, or AGD. When examining which proteins were the strongest driver of each cluster, it was observed the most distinctive protein for CTE was CCL21, AD was FLT3L, and PSP was IL13. Individual proteins that were specific to CBD and AGD were not observed. CCL21 was observed to be elevated in CTE CSF compared to AD, further validating the use as possible biomarkers.ConclusionOverall, these results highlight that different neuroinflammatory responses might underlie unique mechanisms in related neurodegenerative pathologies. Additionally, the use of distinct neuroinflammatory signatures could help differentiate between neuropathologies and act as novel biomarker candidate to increase specificity for in‐life diagnoses.

AbstractBackgroundGenetic studies in Alzheimer’s disease and related disorders (ADRD) revealed numerous disease risk loci many of which reside in noncoding regions. To translate these discoveries to precision medicine therapies and biomarkers, integrative multi‐omics studies especially combining gene expression and disease phenotypes are necessary.MethodIn this presentation, we will review existing and emerging approaches in the discovery of functional disease risk variants, pathways and key disease molecules in ADRD via integrating multi‐omics and disease phenotypic data using computational systems biology followed by experimental validation.ResultsIntegrative genome‐wide association studies (GWAS) of ADRD risk, ADRD‐related phenotypes, gene expression and epigenetic factors such as methylation nominate disease variants and pinpoint risk mechanisms. Large scale differential gene and co‐expression network analyses discovered novel disease pathways unique to or shared between different ADRD. Experimental validations of these findings in model systems yielded high confidence molecular perturbations in ADRD.ConclusionADRD is characterized by molecular perturbations in myriad pathways including vascular, synaptic, immune, myelination and many others that are revealed through integrative multi‐omics studies in well‐characterized human cohorts and prioritized through experimental model systems. These molecules serve as candidate precision medicine therapeutic targets and biomarkers.

AbstractBackgroundNeurodegenerative disorders affect different brain regions at different time points and to varying degrees, although the underlying reasons for this variability in regional susceptibility is unknown. Brain regions have different proportions of cell types. This cell proportion variability may be further influenced by presence and degree of brain disorders, including neurodegenerative diseases. We postulate that genetic and other factors that influence brain cell type proportions may also provide insights into brain regional susceptibility to neurodegenerative diseases.MethodsUtilizing bulk transcriptomics data from 7 brain regions obtained from 3 cohorts and corresponding whole genome sequencing (WGS) data, genetic variants were evaluated for association with cell type proportions across brain regions. Utilizing cell type specific marker genes for deconvolution provided by McKenzie et.al, we estimated surrogate variables for proportion (SVP) of various cell types using BRETIGEA. We then evaluated relationship between SVP and AD as well as Age, Sex, APOE ε4 and neuropathology measures. Genetic variants from WGS were then tested for association with SVP to identify distinct and conserved patterns across 6 cell types and 7 brain regions.ResultsAnalysis of SVP by diagnosis, sex, age, APOE ε4 and neuropathology measures identified significant differences in cell proportions in various cell types and brain regions. GWAS of SVP identified 134 loci that reached genome‐wide significance (5e‐8). After conservative adjustment for multiple testing (6 cell types and 7 brain regions; 42 tests), variants proximal to SYNPR and VENTXP7 remained significant (p‐value<1.19E‐09). Variants proximal to SYNPR were associated with increased proportion of oligodendrocytes in cerebellum whereas variants near VENTXP7 were associated with reduced proportion of astrocytes in DLPFC. Variants in SYNPR have previously been associated with neurofibrillary tangles and diffuse plaque measurements while variants in VENTXP7 have been associated with measurement of neurofibrillary tangles, cognitive decline, unipolar depression, and bipolar disorder.ConclusionsIt is likely that there are genetic variants associated with cell proportion in brain, may vary by cell type and brain region, while others may act more broadly. Identification of genetic variants associated with cell proportions in different brain regions may provide insights into affected cells and regional vulnerabilities.

AbstractBackgroundGlial activation plays a critical role in Alzheimer’s disease (AD) pathogenesis and is associated with amyloid‐β plaques and neurofibrillary tangles composed of phosphorylated tau (phospho‐tau). With approximately 40%‐60% of AD cases having limbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC), TDP‐43 pathology has become more the rule than the exception in AD brains. However, the effect of LATE‐NC in AD‐related glial activation is unknown.MethodWe analyzed the effect of TDP‐43 pathology on glial activation in three AD subtypes: hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. Twenty cases of each subtype (n = 60 total) were selected from the FLorida Autopsied Multi‐Ethnic (FLAME) cohort. Cases were immunohistochemically evaluated on serial sections of the hippocampus in the CA1 and subiculum subsectors, and superior temporal, inferior parietal, and mid‐frontal cortices with antibodies to CD68 (phagocytosis), GFAP (astrogliosis), GT‐38 (conformation‐specific AD‐type tau), 6F/3D (amyloid‐β), and phospho‐Serine 409/410 of TDP‐43 (pTDP‐43). Digital pathology was employed to quantify immunohistochemical staining. Multivariable regression modeling was utilized to understand the relationship between glial activation and AD pathologies.ResultThe level of glial activation, defined as % of immunoreactive surface area for CD68 and GFAP, was lowest in HpSp AD (0.54%, 13%), whereas typical AD (1.3%, 33%) and limbic predominant AD (1.3%, 30%) had similar levels suggesting a ceiling effect, or plateau, of gliosis levels (both p<0.001). HpSp AD was more often male (p<0.001) and younger at disease onset (p<0.001) compared to both typical AD and limbic predominant AD. Multivariable regression modeling of CD68 (subiculum R2 = 0.60) identified advanced tau to be a significant predictor of CD68 in all brain regions. LATE‐NC stage predicted increased CD68 levels in limbic brain regions. Multivariable regression modeling of GFAP (CA1 R2 = 0.60) showed that Aβ was the strongest predictor of astrogliosis in CA1 and subiculum (p<0.001). Advanced tau in CA1 and parietal cortex, LATE‐NC in subiculum and temporal cortex, and APOE4 genotype in CA1 predicted a significant increase in GFAP.ConclusionThese findings highlight the significant role TDP‐43 pathology plays in affecting the microenvironment and interplay between neuroinflammation and neurodegeneration and have important implications for therapeutic strategies in AD with co‐morbid LATE‐NC.

AbstractBackgroundLewy body dementia (LBD) is one of the most common causes of dementia and is characterized by the deposition of extracellular amyloid plaques and intracellular accumulation of alpha‐synuclein in the form of Lewy bodies and neurites. Many of these individuals also have concomitant neuropathological changes such as vascular disease, tangles, and TDP‐43. Like in Alzheimer’s disease, APOE4 is the most significant genetic risk factor for the development of LBD. We sought to survey the landscape of transcriptional changes in the cingulate cortex from a large cohort of LBD cases (>400) compared to normal controls of both sexes.MethodWe performed bulk tissue RNAseq and aligned reads to a human reference genome that is informed on the sex chromosome complement of the sample. Differentially expressed genes were determined after genome‐wide correction.ResultWe found a core set of transcriptional changes that were shared between both sexes in pathways related to neuronal functions and inflammation. However, we also identified numerous changes in genetic female samples that were not significantly altered in genetic males, or that were significantly altered but in the opposite direction. Similar male‐specific changes were also found. These alterations were present for genes on both sex chromosomes as well as autosomes. Though cohort sizes were smaller, we also compared this dataset to Alzheimer’s disease and found shared but unique transcriptional alterations.ConclusionOverall, genetic males and females exhibit sex‐shared and sex‐specific gene expression patterns in dementia with Lewy bodies. We are currently assessing splice alterations, running causal gene network analysis, and performing histological validation.

AbstractBackgroundEarly Onset Alzheimer’s disease (EOAD) is associated with mutations in APP, PSEN1 and PSEN 2 genes but few studies have assessed its relationship with mutations involved in lipid metabolism and the amyloid cascade. Therefore, this study seeks to determine the association of ABCA7, SORL1 and APOE mutations with EOAD.MethodThe protocol was registered in PROSPERO(CRD42022328366). We included comparative observational studies assessing the association of ABCA7, SORL1 and APOE mutations with the development of EOAD in patients younger than 65 years old. The search strategy was performed in five databases (MEDLINE, Scopus, Web of Science, EMBASE, Opengray) from their inception to May 7th,2022. Abstract and full‐text screening, data extraction and risk of bias (RoB) assessment were performed in duplicates. RoB was assessed with Newcastle‐Ottawa Scale. We performed a random effects meta‐analysis for dichotomous outcomes by using the DerSimoniain Laird method. Effect sizes were reported as odds ratio (OR) with their corresponding 95% confidence intervals (95%CI).ResultA total of 2191 studies were screened and seven studies (n = 8992) were included; one was a retrospective cohort and six were case‐control studies. Three studies had high RoB and four had low RoB. Overall, 60% were female and the mean age ranged from 33.9(SD±17.1) to 57.4(SD±5.6). Three case‐control studies (n = 5098) found a significant association of ABCA7 mutation with EOAD (OR:2.51 95%CI[1.33‐4.73]; tau‐squared = 0.20, I‐squared = 66%). Two case‐control studies (n = 3107) found that SORL1 mutation was not associated with EOAD (OR:3.65 95%CI[0.76‐17.49]; tau‐squared = 1.15, I‐squared = 89%). Two case‐control studies and one cohort study (n = 787) found that APOE4 allelic variant mutation of the APOE gene had significant association with EOAD (OR:13.21; 95%CI[3.81‐45.85]; tau‐squared = 0.90, I‐squared = 75%) and that the APOE2 allelic variant mutation was not associated with EOAD.ConclusionDespite the high heterogeneity in our results and limited number of studies, this study shows that ABCA7 and APOE mutations may be associated with an increased risk of EOAD. Larger high‐quality cohort studies are needed to address this research question.

AbstractBackgroundWhite matter hyper‐intensities (WMH) are observed on FLAIR MRI, increase with age and are more prominent in neurodegenerative diseases such as in Alzheimer’s disease (AD) and Parkinson’s disease (PD). The aim of this study was to assess the association between WMH and neuropathological staging of amyloid‐beta, neurofibrillary tangles (NFT) and α‐synuclein (α‐syn).MethodIn‐situ 3T‐MRI 3DT1‐weighted and 3D‐FLAIR data were collected for 77 clinically defined and pathologically‐confirmed brain donors; 31 AD, 21 PD and PD dementia (PDD), and 25 controls. WMH were segmented on FLAIR using an in‐house developed multiview convolutional neural network, followed by manual checking, and volume was obtained with FSL. Fifteen brain regions were dissected at autopsy, immunostained and evaluated for neuropathological diagnosis, including Thal amyloid phase, CAA type, and Braak stages for NFT and α‐syn. Group differences were assessed with linear models (corrected for age, gender, post‐mortem delay), and associations with Spearman and Pearson partial correlations.ResultCompared to controls, WMH load was higher in AD (p<0.001), specifically amnestic AD (p = 0.003) rather than non‐amnestic AD (p = 0.053), and higher in PD (p = 0.016), specifically non‐demented PD (p = 0.015) rather than PDD (p = 1.0). No difference in gender was observed (p = 0.38). Without the influence of pathology (in controls), WMH was associated with age (r = 0.49,p = 0.016).In the whole cohort, WMH was associated with Fazekas score (r = 0.67; p<0.001), normalized brain volume (NBV; r = ‐0.31,p = 0.007), gray matter volume (NGMV; r = ‐0.36,p = 0.002), Thal phase (r = 0.24,p0.034), Braak NFT stage (r = 0.33,p = 0.004), and Braak α‐syn stage (r = 0.27, p0.018).Across controls and AD, WMH associated with NBV (r = ‐0.39,p = 0.004), NGMV (r = ‐0.42,p = 0.002), left and right hippocampal volume (r = ‐0.36,p = 0.008 and r = ‐0.32,p = 0.022), Braak NFT (r = 0.41,p<0.001), Thal phase (r = 0.41,p = 0.002), and CAA (r = 0.34,p = 0.01). Amnestic and non‐amnestic AD showed similar results, except for CAA, which was associated with WMH in non‐amnestic AD only (r = 0.33,p = 0.04).Across controls and PD(D), WMH associated with Braak NFT stage (r = 0.39,p = 0.008) and Braak α‐syn stage (r = 0.60,p<0.001), but not with MRI outcome measures. In this respect PD and PDD showed different results; in PD WMH associated with NGMV (r = ‐0.37,p = 0.031), in PDD with left and right hippocampal volume (r = ‐0.44,p = 0.014 and r = ‐0.44,p = 0.015).ConclusionWMH associate with pathological stages of AD and PD(D), and contributes to MRI atrophy.

AbstractBackgroundIncreasing evidence suggests that transactive response DNA‐binding protein 43 (TDP‐43) pathology in Alzheimer’s Disease (AD), or AD‐TDP, can be diffuse or limbic‐predominant. Understanding whether diffuse AD‐TDP has genetic, clinical, and pathological features that differ from limbic AD‐TDP could have clinical and research implications. We aimed to better characterize the clinicopathologic features of diffuse AD‐TDP and differentiate it from limbic‐predominant AD‐TDP pathology.MethodThree hundred sixty‐three participants were prospectively recruited and followed in the Mayo Clinic Study of Aging, Alzheimer’s Disease Research Center, and Neurodegenerative Research Group and died between May 1999 and August 2021. All underwent genetic, clinical, neuropsychologic, and neuropathologic evaluations. At postmortem evaluation, they showed Alzheimer’s disease neuropathologic changes and TDP‐43‐immunoreactive inclusions. The distribution of TDP‐43 pathology was assessed using the Josephs 6‐stage scheme. Participants with TDP‐43 stages 1‐3 were classified as Limbic, while those with stages 4‐6 were classified as Diffuse. Neuropathologically defined frontotemporal lobar degeneration cases were excluded. Multivariable logistic regression was used to identify clinicopathological features that predicted diffuse TDP‐43 pathology at postmortem. We hypothesized that the Diffuse group would show impairment in cognitive domains beyond episodic memory and more hippocampal sclerosis.ResultsThe cohort was 61% female and old at onset (median:76 years [IQR:70‐82]) and death (median:88 years [IQR:82‐92]). Fifty‐four percent (n = 197) were classified as Limbic and 46% (n = 166) as Diffuse. A “clinical” multivariate logistic regression fit to 85 participants with all clinical variables of interest showed that later age at onset (OR:1.09, 95%CI:1.01‐1.19;P = 0.03), longer disease duration (OR:1.17, 95%CI:1.02‐1.36;P = 0.03) and higher Neuropsychiatric Inventory scores (OR:1.14, 95%CI:1.01‐1.28;P = 0.03) all independently increased the odds of being Diffuse, while higher Trails Making Test‐B (OR:0.99, 95%CI:0.98‐0.99;P = 0.02) and Block Design Test scores (OR:0.91, 95%CI:0.85‐0.99;P = 0.023) independently decreased the odds of being Diffuse. A “pathology” multivariate logistic regression fit to 233 cases with all neuropathological data revealed that widespread amyloid‐β pathology corresponding to Thal phases 3‐5 all significantly decreased the odds of diffuse pathology by 80‐90%, while hippocampal sclerosis increased it sixfold (OR:6.18, 95% CI:2.93‐13.03;P<0.001).ConclusionsDiffuse AD‐TDP shows clinicopathological features that differ from Limbic AD‐TDP, which could have public health implications relating to treatment and research on AD and dementia.

AbstractBackgroundStructural variants range from simple loss or gain of genetic material to complex events that restructure entire chromosomes. This heterogeneity coupled with variant size greater than sequencing read‐length makes structural variant mapping from short‐read sequencing data difficult and error‐prone. Consequently, the role of structural variants is poorly explored in many phenotypes, including dementia.MethodWe used the GATK‐SV pipeline that combines four external structural variant mapping algorithms with machine‐learning to build high‐quality consensus structural variant calls. We applied GATK‐SV to short‐read whole‐genome sequencing data of 2,601 frontotemporal dementia‐amyotrophic lateral sclerosis spectrum (FTD/ALS) patients, 2,612 Lewy body dementia (LBD) patients, and 4,132 neurologically unaffected participants.ResultAfter stringent quality‐control, we performed genome‐wide association studies on 2,307 ALS/FTD patients versus 3,677 controls with 4,699 common structural variants and 2,355 LBD patients versus 3,700 controls with 4,889 common structural variants. In the FTD/ALS cohort, we identified well‐known risk variants at the C9orf72 (p‐value = 4.99×10‐18, OR = 14.47, 95% CI = 7.90–26.49) and MAPT loci (p‐value = 3.48×10‐6, OR = 0.77, 95% CI = 0.68–0.86) corroborating the ability of our pipeline to identify disease‐associated structural variants. Moreover, we discovered, replicated, and validated TPCN1 as a novel risk locus for LBD (p‐value = 9.18×10‐6, OR = 1.43, 95% CI = 1.22–1.67). Further, we identified rare exonic variants with known pathogenic or likely pathogenic effects, such as an SNCA gene duplication in LBD and a deletion of exons 1 and 2 in CHCHD10 in FTD/ALS. We additionally observed structural variants in regulatory regions, e.g. a deletion of a key PSEN2 enhancer, which resulted in an approximate 0.70 fold‐change in PSEN2 expression in LBD. Finally, we built an interactive web app where structural variants can be visualized and explored (https://ndru-ndrs-lng-nih.shinyapps.io/non_ad_dementias_sv_app/).ConclusionCommon and rare structural variants play a role in LBD and FTD/ALS. Our structural variant resource is publicly available and can be used to further explore the role of structural variants in LBD and FTD/ALS.