Ertekin‐Taner, Nilüfer

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.

AbstractBackgroundCerebral amyloid angiopathy (CAA), characterized by the accumulation of amyloid‐beta in the cerebrovasculature, effects blood vessel integrity leading to brain hemorrhages and an accelerated cognitive decline in Alzheimer’s disease (AD) patients. In a previous genome‐wide association study (GWAS) of 853 neuropathologically confirmed AD cases, we identified a LINC‐PINT splice variant that associated with lower CAA levels amongst individuals that did not have a copy of the APOEe4 AD risk allele (Reddy. J et al 2021). In this study, we perform GWAS to identify further genetic risk factors for CAA and AD neuropathology (Braak stage, Thal phase) in additional AD cases, and participants lacking significant AD pathology (non‐AD).MethodWe expanded the original AD cohort (N = 853) with 400 additional AD cases genotyped using the Infinium Omni2.5+Exome array. We also identified 110 AD and 502 non‐AD donors from the Mayo Clinic Brain Bank, with existing CAA scores and genome‐wide genotypes. We performed QC and imputation of these genotypes to TOPMed. We will conduct GWAS in AD only (N = 1,363), non‐AD only, as well as the combined cohort (N = 1,865). We will test imputed variant dosages for association with square root transformed CAA, Braak and Thal, using linear regression in PLINK, adjusting for relevant covariates. We will perform interaction analysis of CAA‐associated variants with APOEe4 and sex; and pursue stratified analyses. Summary statistics will be utilized to perform SNP‐based gene set enrichment analysis to identify biological pathways implicated in CAA.ResultWe expect that the increased power of this expanded dataset will lead to the identification of additional novel genetic risk factors for CAA and AD neuropathology. Genetic interaction analysis with sex and APOEe4 may further elucidate the role of these risk factors. The addition of 1,102 participants will enable assessment of the LINC‐PINT locus for association with CAA in an independent dataset.ConclusionWe expect this study will provide further insights into the genetic architecture underlying risk for CAA, both in the context of significant AD pathology, and without. Characterization of genetic variants in the context of neuropathology may lead to new avenues of research aimed at identifying biomarkers and therapies to treat CAA, and AD.

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.

AbstractBackgroundThe coarse‐grained plaque is a biochemically and morphologically distinct amyloid‐β (Aβ) deposit that was discovered in clinically‐defined atypical Alzheimer’s disease (AD). Unlike other Aβ deposits, this plaque is not observed in cognitively normal people. This study investigates the hypothesis that the coarse‐grained plaque is distinctly associated with AD neuropathologic change and glial activation within the context of AD subtypes.MethodSixty AD cases from the FLorida Autopsied Multi‐Ethnic (FLAME) cohort were assessed. We included 20 hippocampal sparing (HpSp) AD, 20 typical AD, and 20 limbic predominant AD. Immunohistochemistry was performed on the middle frontal, inferior temporal, and inferior parietal cortex for AD neuropathology (Aβ [6F/3D], phospho‐tau [AT8], tau [GT38]) and glial activation (CD68 [KP1] and GFAP). Digital pathology was used to quantify burden. Thioflavin‐S tangle counts were performed at time of neuropathologic diagnosis. Distinct deposit types, including classic cored plaques, coarse‐grained plaques, cerebral amyloid angiopathy (CAA)‐Type 1, and CAA‐Type 2, were semi‐quantitatively scored on Aβ (Fig. 1). Correlations between demographic, clinical, and neuropathologic characteristics were assessed using Spearman rho analysis and reported if p<0.05. An independent cohort of ∼100 HpSp AD cases will be assessed to validate initial findings.ResultIn the frontal cortex of HpSp AD, higher scores of classic cored plaques correlated with older age at onset and death, non‐APOE ε4 carriership, and less CAA‐Type 1 and Type 2 (Fig. 2). On the contrary, the severity of coarse‐grained plaques correlated with a younger age at onset and death, APOE ε4 carriership, higher scores for CAA‐Type 1, CAA‐Type 2, and increased burden of Aβ, GFAP, and CD68. Classic cored plaques correlated negatively with phospho‐tau and tau, whereas coarse‐grained plaques correlated positively with phospho‐tau, tau, and tangles. The two deposit types were negatively correlated with each other. Correlations were weaker but showed similar trends in other AD subtypes (Fig. 2) and regions.ConclusionWithin HpSp AD, classic cored and coarse‐grained plaques show distinct correlations with glial activation, (phospho‐)tau, and tangles. These results suggest opposite directions of association for these deposits on AD neuropathologic change that may impact clinical symptoms. These findings are relevant to therapies targeting overall Aβ pathology.

AbstractBackgroundBrain glucose hypometabolism is among the earliest pathogenic changes in Alzheimer’s disease (AD). This metabolic dysfunction points to the personal bioenergetic capacity, defined as the ability to maintain energy homeostasis under all circumstances including deregulated glucose uptake, as a potential source of resilience to the disease. Fasting blood acylcarnitine profiles are a central readout for this capacity in the absence of dietary glucose and capture the activity and efficiency of glucose‐independent routes of mitochondrial energy metabolism.MethodWe used fasting serum acylcarnitine profiles of 1,531 participants (465 with normal cognition, 762 with mild cognitive impairment, and 304 with clinical AD) from the AD Neuroimaging Initiative to perform unsupervised subgroup identification using hierarchical clustering. Identified subgroups were investigated for differences in A/T/N biomarker profiles and cognitive status. The contributions of genetic and potentially modifiable factors defining the subgroups were quantified using analysis of explained variance. The influence of the strongest determining factors on longitudinal cognitive trajectories was estimated using linear mixed‐effects models and gene‐by‐environment interaction analysis.ResultWe found several bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. The strongest genetic contribution to this bioenergetic endophenotype seems to be specifically linked to succinylcarnitine metabolism and significantly modulates the rate of future cognitive decline. In contrast, potentially modifiable sustainment of beta‐oxidation efficiency seems to decelerate bioenergetic aging, thus creating a bioenergetic reserve that delays progression of cognitive decline. Using gene‐by‐environment interaction analysis, we demonstrate that this molecular framework identifies a subgroup of individuals that is likely to benefit significantly from personalized therapeutic, dietary or lifestyle interventions tailored to increase resilience against bioenergetic disturbances in AD.ConclusionOur study reports on a set of genetic and metabolic markers that define bioenergetically distinct subgroups with significant differences on the AD biomarker and cognitive level. Longitudinal data suggest that targeting the modifiable fraction of this endophenotype might be a promising strategy to slow down disease progression in individuals with specific allelic configurations.

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.