Citations

AbstractBackgroundA hallmark of Alzheimer’s disease is amyloid‐β (Aβ) deposition in the brain parenchyma as senile plaques which Aß frequently accompanies in the cerebral vasculature as cerebral amyloid angiopathy (CAA). Previous studies demonstrated that the cerebrovascular system is essential for Aβ clearance and interactions exist between cerebrovascular disease and AD. Neuroimaging data, including measures on small vessel disease, amyloid deposits, and microhemorrhages (MCH), can reflect both cerebrovascular disease and AD pathology, thereby serving as intermediate phenotypes of both conditions. We aim to identify genes and co‐expression networks in blood associated with these neuroimaging endophenotypes and the biological pathways enriched in these perturbed transcriptional networks.MethodWe conducted differential gene expression and Weighted Gene Co‐Expression Network analyses (WGCNA) on blood RNA sequencing data of 407 participants from the longitudinally followed Mayo Clinic Study of Aging cohort. The neuroimaging phenotypes include MCH, infarction, and amyloid deposits measured by Pittsburgh compound B positron emission tomography (PiB PET).ResultWGCNA analysis showed a nominally significant association (p<0.05) between several module eigengenes (MEs) and MCH and PiB PET. One module, M42, remained significantly associated with PiB PET after Bonferroni correction for all 43 modules tested. Pathway enrichment analysis of M42 identified two significant pathways (FDR‐adjusted p<0.05) including phosphodiesterase I activity and IgE binding. These pathways contain genes such as MS4A2 encoding the high‐affinity IgE receptor involved in the immune response. Similarly, ENPP2 in the phosphodiesterase I activity pathway represents an additional candidate gene previously implicated in Alzheimer’s disease.ConclusionWe generated co‐expression networks and identified one network module with a statistically significant association between brain amyloid deposits and blood transcriptome data. Further investigations of this network and its enriched pathways are necessary to understand their roles in amyloidosis, CAA, and neurodegeneration. Future studies will include seeking replication of these results in other datasets. Such studies can help elucidate the biological mechanisms of pathogenesis of cerebrovascular damage and amyloid deposits. In addition, in a clinical setting, blood levels of transcripts discovered in this study may serve as predictive or diagnostic biomarkers with mechanistic implications.

AbstractBackgroundGiven the overlap of symptoms between Alzheimer’s disease (AD) and other dementias, it is essential to develop widely accessible biomarkers that will ensure an accurate diagnosis of AD to enable its proper treatment. African Americans (AA) are twice more likely to develop AD than non‐Hispanic whites (NHW) and yet they remain underrepresented in AD research. The few studies conducted in AA suggest that there are different factors that influence AD in this population; therefore, it is possible that different biomarkers and/or therapies may be needed to effectively treat AD in AA. In this study we are testing the hypothesis that genetic variants that show association with AD‐risk in AA and with plasma levels of transcript and proteins may serve as AD biomarkers which are more accessible and less costly than cerebrospinal fluid or neuroimaging biomarkers.MethodsIn 189 AD cases and 183 cognitively unimpaired (CU) AA participants of the Florida Consortium of African American Alzheimer’s Disease Studies (FCA3DS), we performed targeted DNA sequencing of 10 AD‐associated loci. Genetic variants were tested for association with plasma transcripts and plasma total tau protein levels previously measured in this cohort using a nanoString® custom panel (Reddy J, et al. 2022) and Simoa assays (Deniz K, et al. 2021), respectively. In receiver operating characteristic (ROC) analyses, the most significant expression/protein quantitative trait loci (eQTL/pQTL) were added sequentially to a base model that included only age and sex to identify eQTL/pQTL that improved the accuracy to correctly classify AD cases vs. CU.ResultsOf the 5112 variants identified in the targeted sequencing, 71 were nominally associated (p<0.05) with plasma transcript/protein levels and with AD‐risk. ROC analysis of age, sex, eQTL/pQTL in APOE, ABCA7, AKAP9, EPHA1, SORL1, and TLR4, and plasma levels of APP, ABCA7, AKAP9, CD14, CLU, and APOE‐ɛ4 dosage achieved 77.6% area under the curve (AUC) to discriminate AD vs. CU, a 19.9% improvement over a model that only included age and sex.ConclusionsPlasma transcript levels and eQTL/pQTL are promising diagnostic biomarkers for AD that may improve accessibility and reduce costs for a more accurate diagnosis of AD.

AbstractBackgroundRecent studies increased the number of genes that are associated with the co‐existence of vascular pathologies and Alzheimer’s disease (AD). However, the biological functions and the mechanism of action by which they contribute to the disease pathology are still to be further elucidated. Therefore, animal models that allow streamlined large‐throughput functional screens in a biologically relevant manner are essential. Towards this goal, we generated adult zebrafish models of amyloid toxicity to functionally analyze candidate genes and how they relate to disease perturbations.MethodThrough cerebroventricular injection of human amyloid‐beta42 monomers into the cerebrospinal fluid of the adult zebrafish brain, we inflict acute amyloid toxicity. By performing immunohistological analyses, single cell transcriptomics, gene editing for generating gene variants or full knockouts, transient functional knockdown of candidate genes, pharmacological intervention of selected signalling pathways and comparing the findings to human AD brain transcriptomics datasets, we determined the similarities and differences of the effects of amyloid pathology on the vasculature in zebrafish in relation to the human conditions.ResultsWe found that amyloid pathology in zebrafish brain shows histological and molecular similarities to human brains. Integration of single cell transcriptomics showed remarkable similarities in neurons to amyloid in humans and zebrafish. We identified several molecular mechanisms that underlie this pathology‐induced cellular response and translated to mammalian models including rodent brains. By performing genome‐wide association in AD patients, we identified that the FMNL2 links cerebrovascular disease and AD, by regulating the astroglial and blood vessel interactions and controlling the efficient clearance of toxic proteins from the brain. Our new single cell transcriptomics analyses of the gliovascular niche cells of the zebrafish brain revealed potential cellular interactions and the pathways perturbed in vascular disease and AD.ConclusionWe propose zebrafish as a useful model for functional investigation of vascular pathology and AD‐related genes identified in clinical studies by providing in vivo biological knowledge on disease mechanisms, on which novel drug development strategies can be based.

AbstractBackgroundInter‐cellular communication within the gliovascular unit (GVU) is critical for cerebral blood flow regulation, and maintenance of the blood‐brain‐barrier (BBB) properties. The breakdown of BBB in Alzheimer’s disease (AD) is well‐established, but precise underlying molecular changes remain unclear. Additionally, whether GVU molecular alterations observed in AD brains are also detected in blood from living patients is unknown. Further, these GVU molecular perturbations require further investigation in different model systems to identify both human brain‐specific and cross‐species conserved alterations. In this study, we investigated prioritized GVU molecules altered in AD brains for their conservation in blood and cross‐species model systems.MethodsWe performed single nucleus RNA sequencing (snRNAseq) of temporal cortex tissue in AD and control brains. We analyzed this data to detect cell‐specific GVU molecular perturbations and their interactions. We investigated molecular interactions between vascular and astrocyte clusters, the major cell types of the GVU of the BBB. To determine whether GVU transcriptional alterations detected in the brain are preserved in the blood, existing blood expression, genetic, and neuroimaging data from two longitudinal antemortem cohorts were analyzed. Using model systems, including mouse, drosophila, and zebrafish, we evaluated the cross‐species conservation of the top GVU alterations detected in AD brains.ResultsBrain snRNAseq revealed transcriptional profiles of 6,541 astrocytes and 2,210 vascular cells. The latter formed three distinct vascular clusters characterized as pericytes, endothelia and perivascular fibroblasts. We identified differentially expressed genes and their enriched pathways within these clusters and observed the highest levels of transcriptional changes within pericytes. Vascular targets that interact with astrocytic ligands have biological functions in cell signaling, angiogenesis, amyloid ß metabolism, and cytoskeletal architecture. We discovered that genetic variants influencing blood expression levels of some of the prioritized GVU genes were associated with neuroimaging burden of cerebrovascular disease in living human cohorts. Our ongoing studies in model systems revealed conservation of some of the top prioritized molecular perturbations across species.ConclusionOur findings prioritized by multiscale, cross‐tissue human data revealed GVU perturbations within interacting pericyte and astrocyte molecules, which are conserved across multiple cross‐species models. These results nominate new molecular targets and mechanistic insights for BBB disruptions in AD.

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.

AbstractBackgroundTwo major risk factors of Alzheimer’s disease (AD) are advanced age and the APOE‐ε4 risk allele. However, despite having these risk factors, some individuals escape from developing AD or have slower cognitive decline. In this study we hypothesized that “resilient” individuals, defined as those who remain cognitively normal despite being >80 years‐old and carrying at least one copy of the APOE‐ε4 allele, have concerted changes in their blood transcriptomes that are associated with resilience to AD.MethodWe generated blood RNA‐sequence data from 105 APOE‐ε4 carriers from the Mayo Clinic Study of Aging (MCSA) who remained cognitively normal after age 80 (resilient), or who developed mild cognitive impairment (MCI) or AD (non‐resilient). Weighted gene co‐expression network analysis (WGCNA) was performed to evaluate the association of the blood co‐expression network with primary phenotypes of resilience and memory scores. To validate our findings, we constructed a consensus co‐expression network using both MCSA and a replication cohort of blood microarray data from 91 Alzheimer’s Disease Neuroimaging Initiative (ADNI) study participants who had a similar demographic profile. We annotated the consensus modules for their enrichment of cell types, biological processes, genes within previously published genome‐wide significant (GWS) AD‐associated loci, and genes that reside in consensus modules built from seven brain regions across three different autopsy cohorts. To identify genetic variants regulating blood consensus module co‐expression, we performed module quantitative trait locus (modQTL) analysis.ResultWe found co‐expression modules significantly (q<0.05) correlated with the memory phenotype, logical memory delayed recall, across both MCSA and ADNI cohorts. Some of these modules were enriched with blood cell types and biological processes, including telomere maintenance, mitochondrial functions, and immune response. We found 1 module significantly (q<0.05) enriched with genes near GWS AD‐associated loci and 4 modules with genes in brain consensus modules. Lastly, we found 8 modQTLs significantly (Bonferroni‐p<0.05) associated with the co‐expression of 5 different modules.ConclusionWe identified blood co‐expression modules, biological pathways and specific genetic variants associated with cognitive resilience in the presence of AD risk factors. This study provides novel insights into the potential role of peripheral blood in the development of and resilience to AD.

AbstractBackgroundAlzheimer’s disease (AD) is a complex, multicellular disease with immunological and genetic components. Previous studies demonstrated associations between AD and the missense variants ABI3‐rs616338‐T and PLCG2‐rs72824905‐G. The perturbed expression of these genes has been demonstrated to disrupt microglial function. To date, the transcriptomic profiles of variant carriers in the context of microglial subtype and state have not been explored in human brain. This represents a knowledge gap in our understanding of AD risk and resilience. We have developed and optimized a single nucleus RNAseq (snRNAseq) approach to study the impact of ABI3 and PLCG2 variants on microglial enriched nuclei from frozen human brain.MethodDOCK8 was identified as a nuclear microglial marker based on previous brain snRNAseq data. DOCK8 based microglial enrichment using fluorescent activated nuclei sorting (FANS) was validated using RT‐qPCR and snRNAseq in human cerebellum and temporal cortex (TCX), respectively. We are applying this enrichment and snRNAseq approach to TCX tissue from a cohort of 30 donors harboring ABI3 or PLCG2 missense mutations, or neither. Standard snRNAseq alignment and quality control pipelines will be applied, and nuclei annotated based on putative microglial markers. Differential gene expression will be assessed using pseudo bulk and MAST approaches, for association with ABI3 and PLCG2 genotype, diagnosis groups, risk factors, and pathology scores.ResultDOCK8 has several commercially available antibodies, making it an ideal microglia‐selection marker. Using RT‐qPCR in microglia enriched nuclei, we demonstrated an enrichment of microglia marker genes and depletion of other brain cell type markers. This is further supported by snRNA‐seq data from DOCK8‐sorted TCX tissue that yielded a 5x increase in microglia proportion (56% vs. 11% in unselected nuclei), enabling analyses of microglia sub‐populations. Leveraging this approach, we will investigate affected genes and pathways and uncover the resilience and risk mechanisms within microglia sub‐populations in ABI3 and PLCG2 variant‐carriers.ConclusionOur study presents a novel method for enriching microglial nuclei from frozen human brains and enables transcriptomic profiling of microglial subpopulations in ABI3 and PLCG2 missense variant‐carriers. We expect these insights will reveal novel genes and pathways that may play a key role in microglial function in AD.

AbstractBackgroundContext‐specific networks can capture functionally important genes that may not be differentially expressed. Leveraging seven different gene expression datasets of Alzheimer’s disease (AD) and healthy control brains, we constructed a novel pipeline that includes such network analysis, augmented by in vivo validations and in silico drug repurposing to facilitate prioritization of AD therapeutic targets.MethodWe utilized publicly available gene expression data from 733 AD and 454 control brains across 7 datasets and the NetDecoder (PMID: 26975659) network biology platform to build context‐specific networks. Based on the known protein‐protein interaction and gene expression correlations, information flow from the top differentially expressed genes (DEG) to transcriptional regulator genes was calculated using a process‐guided flow algorithm. Genes with the greatest informational flow changes across AD cohorts were systematically prioritized. We next validated these genes in a medium‐throughput screen of a tau‐transgenic Drosophila model (PMID: 11408621). Further, we identified existing small‐molecule drugs to target the candidate genes utilizing curated databases.ResultsA total of 14 context‐specific networks were constructed across 7 datasets. We found AD‐related genes, such as APP, to have high flow differences between AD and control networks and high connectivity. Notably, many such genes were not DEGs between AD and control, suggesting that the information flow captures novel aspects of AD pathophysiology. We nominated 224 unique genes with the greatest flow differences for functional validation. We screened 111 of the 224 candidate genes with readily available, high‐confidence orthologs in Drosophila. We identified genes that suppress (N = 68) or enhance (N = 27) tau‐related neurodegeneration in this model. We confirmed the effects of previously nominated genes, such as HDAC1, which enhances tau‐neurodegeneration if overexpressed and suppresses it if knocked down. We also identified and validated additional therapeutic targets. Lastly, we applied a network‐proximity‐based algorithm and identified small molecule drugs for the candidate genes.ConclusionWe present a novel pipeline that integrates analytic approaches, experimental validation, and drug repurposing to facilitate therapeutic discovery in AD. We applied this pipeline and nominated previously known and novel genes implicated in AD pathogenesis. Our pipeline demonstrates a cross‐species, systems‐level approach to therapeutic nomination in AD and related neurodegenerative diseases.

AbstractBackgroundAlzheimer’s disease (AD) is neuropathologically characterized by amyloid‐β (Aβ) plaques and tau neurofibrillary tangles often quantified by Thal phase and Braak stage, respectively. Aβ also frequently deposits in the cerebrovasculature with severity categorized by a cerebral amyloid angiopathy (CAA) score. These and related measures often show high variability within AD suggesting distinct underlying mechanisms, the identification of which may reveal important biological insights into AD pathophysiology. We hypothesize that, within the AD brain, neuropathology and levels of core AD‐related proteins are influenced by variations in DNA methylation (DNAm). To test this, we performed epigenome‐wide association studies (EWAS) using DNAm clusters from the temporal cortex (TCX) and cerebellum (CER) with AD‐related neuropathologic measures (Braak, Thal, and CAA) and brain biochemical levels of five proteins (apoE, Aβ40, Aβ42, tau, and p‐tau).MethodsDNAm from neuropathologically‐confirmed AD cases available through the Mayo Clinic brain bank was measured by reduced representation bisulfite sequencing (RRBS) from 471 brain samples, 200 of which had both TCX and CER measures. Neuropathology was scored by an experienced neuropathologist. TCX levels of five AD‐related proteins from three tissue fractions (buffer‐, detergent‐, and in‐soluble) were measured by ELISA (Liu 2020). CpG methylation (CpGm) was binned by a 15‐state chromatin model (Kundaje 2015) and averaged. Averaged CpGm clusters were tested for their association with each endophenotype through multi‐variable linear regression. The influence of CpGm clusters on expression levels of nearby genes was also investigated through multi‐variable regression.ResultsOur innovative binning method demonstrated expected and biologically relevant CpGm patterns. We identified multiple epigenome‐wide significant CpGm clusters unique to each AD‐related endophenotype and brain region. Comparisons between the TCX and CER further revealed brain region‐specific enrichment of distinct CpGm clusters. Significant CpGm clusters were further characterized by their influence on gene expression.ConclusionsAlthough all neuropathologic and biochemical endophenotypes tested are core to AD pathophysiology, our results suggest each has a distinct epigenetic architecture underlying their variability in the AD brain. By discovering brain region‐specific DNAm changes that contribute to each neuropathologic or biochemical outcome, we can identify core components of complex mechanisms that individually and jointly contribute to AD.