Ertekin‐Taner, Nilüfer

AbstractBackgroundUsing an objective mathematical algorithm to assess corticolimbic involvement of neurofibrillary tangle pathology, we identified three AD subtypes: hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. Typical AD brains were representative of the expected patterns of hippocampal and cortical involvement as outlined by Braak tangle stage. In contrast, we discovered an extreme phenotype that exists outside of the Braakian‐concept of neurofibrillary tangle patterns. Hippocampal sparing AD cases demonstrate unexpected sparing of the hippocampus relative to severely involved association cortices and limbic predominant AD cases demonstrate inundation of the hippocampus relative to mildly involved association cortices. Using these AD subtypes, our objective was to leverage our understanding of disease spectrum to uncover transcriptomic changes that underlie selective vulnerability of the hippocampus in AD.MethodWe performed RNA‐sequencing in 40 neuropathologically diagnosed AD cases (10 HpSp, 20 Typical and 10 Limbic) and 15 controls to uncover gene expression changes associated with phenotype differences allowing us to prioritize 44 genes for further exploration. Validation with NanoString and quantification with digital pathology were performed in an expanded cohort of 158 AD cases (36 HpSp, 79 Typical and 35 Limbic) and 32 controls. We quantified digital pathology measures of early tangle pathology (CP13), mature tangle pathology (Ab39), amyloid‐β (33.1.1). In addition, we measured cellular markers for microglia (CD68), endothelia (CD34), and astroglia (GFAP). Deep learning based prediction models were employed, which nominated the SLC38A2 gene as a predictor of AD.ResultThe SLC38A2 gene was originally prioritized in our RNA‐sequencing data based upon differential expression between controls and typical AD. We observed upregulation of SLC38A2 in all AD subtypes. RNA‐seq gene expression measures validated well with NanoString for SLC38A2 (R=0.97, p<0.001). Regression analysis identified early tangle (p=0.011) and late tangle measures (p=0.026) as significant predictors of gene expression, but not amyloid measures (p=0.74). Microgliosis approached significance (p=0.052), but with no contribution from endothelial cell burden (p=0.17) or astrogliosis (p=0.58). Age approached significance (p=0.06), but not sex (p=0.88) or APOE‐ε4 status (p=0.10).ConclusionOur data supports consideration of intra‐disease divergence with regard to case stratification and may reveal genes previously masked by heterogeneity of cohorts.

AbstractBackgroundOur understanding of the biological changes in the brain associated with Alzheimer’s disease (AD) pathology and cognitive impairment remains incomplete. Unbiased discovery proteomic analysis of AD brain can help to increase our understanding of these changes.MethodWe analyzed dorsolateral prefrontal cortex, precuneus, and temporal cortex of control, asymptomatic AD, and AD brains from multiple different centers by label‐free quantitative mass spectrometry and weighted protein co‐expression analysis to obtain a consensus protein co‐expression network of AD brain. We also analyzed cerebrospinal fluid to investigate potential biomarkers related to this network.ResultThe network consisted of 13 protein co‐expression modules. Six of these modules correlated with amyloid‐β plaque burden, tau neurofibrillary tangle burden, cognitive function, and clinical functional status, and were altered in asymptomatic AD, AD, or in both disease states. These six modules reflected synaptic, mitochondrial, sugar metabolism, extracellular matrix, cytoskeletal, and RNA binding/splicing biological functions. The identified protein network modules were preserved in a community‐based cohort analyzed by a different quantitative mass spectrometry approach. They were also preserved in temporal lobe and precuneus brain regions. Some of the modules were influenced by aging, and showed changes in other neurodegenerative diseases such as frontotemporal dementia and corticobasal degeneration. The module most strongly associated with AD pathology and cognitive impairment was the sugar metabolism module. This module was enriched in AD genetic risk factors, and was also highly enriched in microglia and astrocyte protein markers associated with an anti‐inflammatory state, suggesting that the biological functions it represents may serve a protective role in AD. Proteins from the sugar metabolism module were increased in cerebrospinal fluid from asymptomatic AD and AD cases, highlighting their potential as biomarkers of the altered brain network.ConclusionIn this study of >2000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brain that may serve as therapeutic targets and fluid biomarkers for the disease.

Background/Objective: The aim of this study was to determine if plasma concentrations of 5 surrogate markers of Alzheimer’s disease (AD) pathology and neuroinflammation are associated with disease status in African Americans. Methods: We evaluated 321 African Americans (159 AD, 162 controls) from the Florida Consortium for African-American Alzheimer’s Disease Studies (FCA3DS). Five plasma proteins reflecting AD neuropathology or inflammation (Aβ42, tau, IL6, IL10, TNFα) were tested for associations with AD, age, sex, APOE and MAPT genotypes, and for pairwise correlations. Results: Plasma tau levels were higher in AD when adjusted for biological and technical covariates. APOE ɛ4 was associated with lower plasma Aβ42 and tau levels. Older age was associated with higher plasma Aβ42, tau, and TNFα. Females had lower IL10 levels. Inflammatory proteins had strong pairwise correlations amongst themselves and with Aβ42. Conclusion: We identified effects of demographic and genetic variants on five potential plasma biomarkers in African Americans. Plasma inflammatory biomarkers and Aβ42 may reflect correlated pathologies and elevated plasma tau may be a biomarker of AD in this population.

Background: Plasma NfL (pNfL) levels are elevated in many neurological disorders. However, the utility of pNfL in a clinical setting has not been established. Objective: In a cohort of diverse older participants, we examined: 1) the association of pNfL to age, sex, Hispanic ethnicity, diagnosis, and structural and amyloid imaging biomarkers; and 2) its association to baseline and longitudinal cognitive and functional performance. Methods: 309 subjects were classified at baseline as cognitively normal (CN) or with cognitive impairment. Most subjects had structural MRI and amyloid PET scans. The most frequent etiological diagnosis was Alzheimer’s disease (AD), but other neurological and neuropsychiatric disorders were also represented. We assessed the relationship of pNfL to cognitive and functional status, primary etiology, imaging biomarkers, and to cognitive and functional decline. Results: pNfL increased with age, degree of hippocampal atrophy, and amyloid load, and was higher in females among CN subjects, but was not associated with Hispanic ethnicity. Compared to CN subjects, pNfL was elevated among those with AD or FTLD, but not those with neuropsychiatric or other disorders. Hippocampal atrophy, amyloid positivity and higher pNfL levels each added unique variance in predicting greater functional impairment on the CDR-SB at baseline. Higher baseline pNfL levels also predicted greater cognitive and functional decline after accounting for hippocampal atrophy and memory scores at baseline. Conclusion: pNfL may have a complementary and supportive role to brain imaging and cognitive testing in a memory disorder evaluation, although its diagnostic sensitivity and specificity as a stand-alone measure is modest. In the absence of expensive neuroimaging tests, pNfL could be used for differentiating neurodegenerative disease from neuropsychiatric disorders.

Abstract Introduction The cytoprotective PTEN‐induced kinase 1 (PINK1)‐parkin RBR E3 ubiquitin protein ligase (PRKN) pathway selectively labels damaged mitochondria with phosphorylated ubiquitin (pS65‐Ub) for their autophagic removal (mitophagy). Because dysfunctions of mitochondria and degradation pathways are early features of Alzheimer's disease (AD), mitophagy impairments may contribute to the pathogenesis. Methods Morphology, levels, and distribution of the mitophagy tag pS65‐Ub were evaluated by biochemical analyses combined with tissue and single cell imaging in AD autopsy brain and in transgenic mouse models. Results Analyses revealed significant increases of pS65‐Ub levels in AD brain, which strongly correlated with granulovacuolar degeneration (GVD) and early phospho‐tau deposits, but were independent of amyloid beta pathology. Single cell analyses revealed predominant co‐localization of pS65‐Ub with mitochondria, GVD bodies, and/or lysosomes depending on the brain region analyzed. Discussion Our study highlights mitophagy alterations in AD that are associated with early tau pathology, and suggests that distinct mitochondrial, autophagic, and/or lysosomal failure may contribute to the selective vulnerability in disease.

AbstractIntroductionCerebrovascular pathologies including cerebral amyloid angiopathy (CAA) and blood‐brain barrier (BBB) dysregulation are prominent features in the majority of Alzheimer's disease (AD) cases.MethodsWe performed neuropathologic and biochemical studies on a large, neuropathologically confirmed human AD cohort (N = 469). Amounts of endothelial tight junction proteins claudin‐5 (CLDN5) and occludin (OCLN), and major AD‐related molecules (amyloid beta [Aβ40], Aβ42, tau, p‐tau, and apolipoprotein E) in the temporal cortex were assessed by ELISA.ResultsHigher levels of soluble tau, insoluble p‐tau, and apolipoprotein E (apoE) were independently correlated with lower levels of endothelial tight junction proteins CLDN5 and OCLN in AD brains. Although high Aβ40 levels, APOE ε4, and male sex were predominantly associated with exacerbated CAA severity, those factors did not influence tight junction protein levels.DiscussionRefining the molecular mechanisms connecting tau, Aβ, and apoE with cerebrovascular pathologies is critical for greater understanding of AD pathogenesis and establishing effective therapeutic interventions for the disease.

Abstract Background: Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power but often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. Results: In the Alzheimer’s Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that the association signals of a subset of top disease risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Conclusions: Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordance came largely from quality differences and if the variants from one batch had better quality scores.

AbstractIntroductionAbnormal gene expression patterns may contribute to the onset and progression of late‐onset Alzheimer's disease (LOAD).MethodsWe performed transcriptome‐wide meta‐analysis (N = 1440) of blood‐based microarray gene expression profiles as well as neuroimaging and cerebrospinal fluid (CSF) endophenotype analysis.ResultsWe identified and replicated five genes (CREB5, CD46, TMBIM6, IRAK3, and RPAIN) as significantly dysregulated in LOAD. The most significantly altered gene, CREB5, was also associated with brain atrophy and increased amyloid beta (Aβ) accumulation, especially in the entorhinal cortex region. cis‐expression quantitative trait loci mapping analysis of CREB5 detected five significant associations (P < 5 × 10−8), where rs56388170 (most significant) was also significantly associated with global cortical Aβ deposition measured by [18F]Florbetapir positron emission tomography and CSF Aβ1‐42.DiscussionRNA from peripheral blood indicated a differential gene expression pattern in LOAD. Genes identified have been implicated in biological processes relevant to Alzheimer's disease. CREB, in particular, plays a key role in nervous system development, cell survival, plasticity, and learning and memory.

AbstractIntroductionMAPT H1 haplotype is implicated as a risk factor for neurodegenerative diseases including Alzheimer's disease (AD).MethodsUsing Alzheimer's Disease Genetics Consortium (ADGC) genome‐wide association study (GWAS) data (n = 18,841), we conducted a MAPT H1/H2 haplotype–stratified association to discover MAPT haplotype–specific AD risk loci.ResultsWe identified 11 loci—5 in H2‐non‐carriers and 6 in H2‐carriers—although none of the MAPT haplotype–specific associations achieved genome‐wide significance. The most significant H2 non‐carrier–specific association was with a NECTIN2 intronic (P = 1.33E‐07) variant, and that for H2 carriers was near NKX6‐1 (P = 1.99E‐06). The GABRG2 locus had the strongest epistasis with MAPT H1/H2 variant rs8070723 (P = 3.91E‐06). Eight of the 12 genes at these loci had transcriptome‐wide significant differential expression in AD versus control temporal cortex (q < 0.05). Six genes were members of the brain transcriptional co‐expression network implicated in “synaptic transmission” (P = 9.85E‐59), which is also enriched for neuronal genes (P = 1.0E‐164), including MAPT.DiscussionThis stratified GWAS identified loci that may confer AD risk in a MAPT haplotype–specific manner. This approach may preferentially enrich for neuronal genes implicated in synaptic transmission.