Citations

Abstract Background Individual environmental exposures, the exposome, can play an influential role in the development and progression of neurodegenerative diseases such as AD. The protein components of the main AD neuropathologies, amyloid (Aβ) plaques and tau neurofibrillary tangles (NFTs), can be measured using immunohistochemical and biochemical approaches. The main genetic risk factor for AD is the APOE e4 allele. APOE levels can also be measured in the brain. We hypothesize that social determinants of health, specifically alcohol and tobacco consumption, and the area deprivation index (ADI), could play a considerable role in severity of neuropathology and brain biochemical phenotypes of AD. Method We evaluated 478 neuropathologically characterized AD donors, from the Mayo Clinic Brain Bank. 469 samples had available biochemical measures (APOE, Aβ40, Aβ42, tau, and phospho‐tau (Thr231)), collected across three isolated fractions (soluble (TBS), membrane‐bound (TX), and insoluble (FA)) of the superior temporal gyrus. Exposome variables were extracted from the available records. ADI from the Neighborhood Atlas® version from the year 2015 was obtained from de‐identified addresses. ADI, smoking and alcohol were available for 263 of these donors and were examined for association with biochemical measures using linear regression adjusting for age at death and sex. Result Higher ADI levels representing neighborhoods with increased deprivation, were nominally associated (β=0.006, p =0.01) with soluble levels of APOE. Alcohol consumption was associated with lower levels of membrane bound APOE (β=‐0.23, p =0.04). Analysis with neuropathological variables including CAA ( N =454), Braak ( N =454), Thal ( N =454), Hippocampal sclerosis ( N =453), Lewy bodies ( N =455) will also be performed. Conclusion Different components of the social exposome may impact levels of core AD proteins. Additional analyses in expanded cohorts to further explore the associations with neuropathology are ongoing. We expect that understanding how the social exposome impacts specific aspects of neurodegenerative diseases will provide key insights into underlying, potentially modifiable, mechanisms of disease.

Abstract Background Alzheimer's disease (AD) affects all brain cells and has complex genomic and immunological alterations. Previous research discovered missense AD risk or protective variants in microglial genes ABI3 and PLCG2 , respectively. Expression levels of these genes are altered in AD and can influence microglial function. This study aims to uncover protective, and risk microglial molecular signatures associated with these variants to determine their role in microglial subtypes and states in AD by single cell expression and functional studies. Method We generated microglia‐enriched snRNAseq data from donors harboring either AD protective PLCG2 or AD risk ABI3 missense mutations, or neither mutation. After standard snRNAseq QC, we performed differential expressed gene (DEG) analysis of all microglial cells between variant carriers and non‐carriers using MAST. We defined protective signature as genes that are both down in ABI3 and up in PLCG2 mutation‐carriers. In contrast, risk signature genes are up in ABI3 and down in PLCG2 mutation‐carriers. We investigated the conservation of protective and risk signatures across multiple datasets, including scRNAseq data from iPSC‐derived microglia cells carrying PLCG2 protective variant, snRNAseq data from AD‐resilient donors, and data from AD and other diagnostic groups across multiple brain regions sourced from external datasets. Result We obtained snRNAseq profiles of 35,000 microglia from AD variant carriers. Our DEG analysis among all microglia cells provided 227 microglial protective and 293 risk signature genes defined by these variants. Using integrated analysis of multiple internal and external datasets, we further narrowed down these signatures. We determined that these high‐confidence protective signature genes are downregulated in early AD, upregulated in late AD brains and positively‐correlated with protective variant load in in vitro models. In contrast, risk signature genes are upregulated in early AD, downregulated in late AD and resilient donors. Risk signature expression is decreased with protective PLCG2 variant load, and altered with Aβ treatment in in vitro models. Conclusion Our study uncovers microglia specific protective and risk signatures associated with AD using sn/scRNAseq datasets from multiple sources and models. These findings nominate novel immune targets and pathways with implications for microglial function in health and disease, and ultimately therapeutic potential.

Abstract Background Most existing biomarkers for Alzheimer’s disease (AD) inform on core AD pathologies in the central nervous system, namely amyloid, tau and neurodegeneration (ATN). However, molecular disease mechanisms underlying AD are complex and heterogeneous, highlighting the need for cost‐effective blood‐based biomarkers that reflect brain molecular changes in AD beyond ATN. Method Using peripheral blood transcriptome data from the Mayo Clinic Study of Aging(MCSA) and the Alzheimer's Disease Neuroimaging Initiative, we identified genes associated with AD or mild cognitive impairment (MCI) diagnosis, cognitive, and harmonized neuroimaging measures in two cohorts, followed by meta‐analysis. We identified consensus gene co‐expression modules in MCSA and ADNI using WGCNA and assessed the association between module eigengenes and aforementioned measures. Using seven bulk brain RNAseq cohorts, we confirmed the preservation of coexpression modules across blood and brain. Enrichment analyses of Gene Ontology (GO) terms and cell‐types were performed using significant and/or hub genes. The MCSA transcriptome was further deconvoluted to obtain cell‐type proportions and cell‐type specific expression. Result Blood transcriptome modules M5 and M8 are upregulated in AD/MCI and associate with worse cognition, whereas M1, M10, M13, M16, and M21 are downregulated in AD/MCI and associate with better cognition. M17 associates with more microhemorrhages. Cell‐type enrichment analysis revealed that M5 is enriched in basophils, monocytes, and neutrophils; M8 in monocytes and neutrophils; M13 in natural kills cells; M17 in megakaryocytes; M21 in B cells. GO analysis shows broad consistency with the cell‐type enrichment analysis. M1, 5, and 8 are preserved in the brain network modules. From these modules, we prioritized transcripts significantly associated with at least one AD‐related phenotype in both blood and brain. Cell‐type deconvolution analysis shows that prioritized transcripts in M1 associate with better cognition and higher metabolic activity in B cells. Conclusion We identified blood transcripts and modules associated with AD/MCI, cognition, or neuroimaging, with three preserved in brain networks. These three modules represent centrally linked peripheral molecular signatures that can reveal perturbed brain pathways and cell level transcriptional changes in AD/MCI. We aim to further investigate the prioritized transcripts in additional datasets to establish them as potential novel biomarkers and therapeutic targets.

Abstract Background Alzheimer's disease (AD) is pathologically characterized by the presence of extracellular amyloid beta plaques and intracellular tau tangles. Related disorders share aspects of AD pathology including progressive supranuclear palsy (PSP), a tauopathy, and pathologic aging (PA) defined by presence of amyloid but not tau. We hypothesize that there are shared, and distinct, brain molecular changes associated with amyloid and tau protein levels in AD and related disorders and that identifying these will provide novel insights into disease pathogenesis. Methods We collected brain multiomics measures (genetic, transcriptomic, epigenomic, proteomic and metabolomic) from donors diagnosed with AD, PSP, PA and controls. Following quality control and normalization, omics measures were tested for association with disease diagnosis and amyloid and tau neuropathology using regression models adjusting for relevant covariates. Association with biochemical measures of core AD proteins were also assessed in a subset of AD cases. Gene sets were evaluated for enrichment of cellular pathways and cell type signatures. Results We found remarkably conserved transcriptomic changes associated with AD and PSP, and downregulation of myelination pathway genes in both diseases. Among PSP cases, we found divergent patterns of transcriptional associations with distinct tau lesions. In a cohort of AD cases, we found broad transcriptional and epigenetic dysregulation associated with levels of tau protein, but not amyloid beta. Conversely a genetic analysis implicated more variants associated with amyloid levels than tau. Conclusion Multi‐omics studies across various proteinopathies can identify convergent and divergent molecular signatures that are candidates for shared therapeutic targets and disease differentiating biomarkers.

Abstract This study benchmarks the robustness and resilience of computational deconvolution methods for estimating cell-type proportions in bulk tissues, with a focus on comparing reference-based and reference-free methods. Robustness is evaluated by generating in silico pseudo-bulk tissue RNA sequencing data from cell-level gene expression profiles derived from four different tissue types, with simulated cellular composition at varying levels of heterogeneity. To assess resilience, we intentionally alter single-cell RNA profiles to create pseudo-bulk tissue RNA-seq data. Deconvolution estimates are compared with ground truth using Pearson’s correlation coefficient, root mean squared deviation, and mean absolute deviation. The results show that reference-based methods are more robust when reliable reference data are available, whereas reference-free methods excel in scenarios lacking suitable reference data. Furthermore, variations in cell-level transcriptomic profiles and cell composition have emerged as critical factors influencing the performance of deconvolution methods. This study provides significant insights into the factors affecting bulk tissue deconvolution performance, which are essential for guiding users and advancing the development of more powerful and reliable algorithms in the future.

ABSTRACT Recent development of single-cell technology across multiple omics platforms has provided new ways to obtain holistic views of cells to study disease pathobiology. Alzheimer’s disease (AD) is the most common form of dementia worldwide, yet the detailed understanding of its cellular and molecular mechanisms remains limited. In this study, we analyzed paired single-cell transcriptomic (scRNA-seq) and chromatin accessibility (scATAC-seq) data from the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) Consortium to investigate the molecular mechanisms of AD at a cell-subpopulation-specific resolution focusing on glial cells. We benchmarked various multi-omics integration methods using diverse metrics and built an analytic workflow that enabled effective batch correction and cross-modality alignment, creating a unified cell state space. Through integrative analysis of 26 human brain samples, we uncovered AD-associated gene expression and pathway changes in glial subpopulations and highlighted important transcriptomic and epigenomic signatures via functional inference and interpretable machine learning paradigms, discovering the profound involvement of the Solute Carrier proteins (SLC) family genes in multiple glial cell types. We also identified glial cell-specific regulatory programs mediated by key transcription factors such as JUN and FOSL2 in astrocytes, the Zinc Finger (ZNF) family genes in microglia, and the SOX family of transcription factors in oligodendrocytes. Our study provides a comprehensive workflow and a high-resolution view of how glial regulatory programs are disrupted in AD. Our findings offer novel insights into disease-related changes in gene regulation and suggest potential targets for further research and therapy.

Abstract Blood-brain barrier (BBB) dysfunction is a key feature of Alzheimer’s disease (AD), particularly in individuals carrying the APOE-ε4 allele. This dysfunction worsens neuroinflammation and hinders the removal of toxic proteins, such as amyloid-beta (Aβ42), from the brain. In post-mortem brain tissues and in animal models, we previously reported that fibronectin accumulates at the BBB predominantly in APOE-ε4 carriers. Furthermore, we found a loss-of-function variant in the fibronectin 1 ( FN1 ) gene significantly reduces aggregated fibronectin levels and decreases AD risk among APOE-ε4 carriers. Yet, the molecular mechanisms downstream of fibronectin at the BBB remain unclear. The extracellular matrix (ECM) plays a crucial role in maintaining BBB homeostasis and orchestrating the interactions between BBB cell types, including endothelia and astrocytes. Understanding the mechanisms affecting the ECM and BBB cell types will be critical for developing effective therapies against AD, especially among APOE-ε4 carriers. Here, we demonstrate that APOE-ε4 , Aβ42, and inflammation drive the induction of FN1 expression in several models including zebrafish, mice, iPSC-derived human 3D astrocyte and 3D cerebrovascular cell cultures, and in human brains. Fibronectin accumulation disrupts astroglial-endothelial interactions and the signalling cascade between vascular endothelial growth factor (VEGF), heparin-binding epidermal growth factor (HBEGF) and Insulin-like growth factor 1 (IGF1). This accumulation of fibronectin in APOE-ε4- associated AD potentiates BBB dysfunction, which strongly implicates reducing fibronectin deposition as a potential therapeutic target for AD. Graphical abstract Accessibility text This image illustrates the effects of different APOE isoforms (ApoE-ε3 and ApoE-ε4) on blood-brain barrier (BBB) integrity, focusing on the molecular interactions between astrocytes and endothelial cells. This figure emphasizes the detrimental effects of ApoE-ε4 on BBB integrity via fibronectin accumulation and altered signaling pathways. The top section provides a schematic overview of the blood-brain barrier, highlighting astrocytes, endothelial cells, and their interface. The left panel represents the ApoE-ε3 condition: Normal fibronectin (FN1) levels support healthy interactions between astrocytes and endothelial cells. Growth factors, including VEGFA, HBEGF, and IGF1, maintain BBB integrity through their respective receptors (VEGFR and EGFR). Green arrows indicate activation of these signaling pathways. The right panel depicts the ApoE-ε4 condition: Elevated fibronectin (FN1) disrupts astrocyte-endothelium interactions. FN1 binds integrins and activates focal adhesion kinase (FAK), inhibiting VEGFA, which is required for endothelial HBEGF that in turn activates IGF1 signaling. Red symbols indicate inhibition of HBEGF, VEGFA, and IGF1 pathways, leading to BBB dysfunction. Highlights APOE-ε4 drives fibronectin deposition in Alzheimer’s, disrupting astrocyte-endothelia interactions. APOE-ε4 and fibronectin co-localize, forming aggregates at blood-brain barrier (BBB). Fibronectin alters the signaling between VEGF, IGF1, and HBEGF impairing BBB function. Reducing fibronectin restores BBB integrity and offsets APOE-ε4 pathology.

AbstractBackgroundAlzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within the non‐Hispanic White (NHW) population. To address this, Accelerating Medicines Partnership in AD (AMP‐AD) aimed to promote inclusivity in multi‐omics AD research, to unravel unique molecular signatures and pathways. The study aimed to provide comprehensive insights into the proteomic landscape of AD across diverse racial groups.MethodDorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) brain tissues were donated from multiple centers (Mayo Clinic, Emory University, Rush University, Mt. Sinai School of Medicine) and were harmonized through neuropathological evaluation, specifically adhering to the Braak staging and CERAD criteria. Among 1105 DLPFC tissue samples (980 unique individuals), 322 were from African American donors, 214 from Latino Americans, 551 from NHW donors, and the rest were from a mixed or unknown racial background. Among 280 STG samples, 86 were African American, 76 Latino American, 116 NHW and the rest were mixed or unknown ethnicity. All tissues were uniformly homogenized and analyzed by tandem mass tag‐based mass spectrometry (TMT‐MS). Following quality control (QC), proteins with more than 50% missing values were removed and iterative principal component analysis was conducted to remove outliers within brain regions. Differential protein expression was conducted within and across races after adjusting for batch using regression‐analysis.ResultIn total, 9,180 proteins from the DLPFC, and 9,734 proteins from STG brain tissues were quantified by TMT‐MS. Further analysis identified approximately 3,000 differentially abundant proteins in AD brains compared to controls within race. The AD‐associated differentially abundant proteins were strongly correlated between DLPFC and STG regions and with CERAD and Braak. Protein levels of MAPT and APP demonstrated equivalent disease‐related elevations in both African American and NHW individuals with AD. Notably, after adjusting for AD, significant differences in protein levels were identified across race.ConclusionThe proteomics approach employed by AMP‐AD sheds light on the brain proteome in AD across diverse racial groups, emphasizing the importance of inclusive research in establishing robust and generalizable discoveries. The identified proteins here could underscore racial disparities beyond disease association, contributing to a more nuanced understanding of AD pathophysiology.

AbstractBackgroundTwo main risk factors of Alzheimer’s disease (AD) are aging and APOE‐ε4. However, some individuals remain cognitively normal despite having these risk factors. They are considered “cognitively resilient”. This study aimed to identify molecular factors that confer cognitive resilience in APOE‐ε4 carriers ≥ 80 years of age and may serve as biomarkers.MethodWe applied weighted gene co‐expression network analysis (WGCNA) to generate consensus co‐expression networks from blood of participants in two antemortem cohorts, the Mayo Clinic Study of Aging (MCSA, n=105), and the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n=91), using RNA‐sequencing and microarray data, respectively. We associated these networks with resilience (resilient vs non‐resilient), cognitive endophenotypes and hippocampal volume. Preservation between consensus networks from blood and those derived from postmortem brain tissues of AD and control donors from AMP‐AD (n=1174) was evaluated. We validated the human findings in four AD mouse models. Finally, machine learning models were utilized to discriminate cases (AD+mild cognitive impairment (MCI)) from controls in MCSA, ADNI and ANMerge antemortem cohorts.ResultFour consensus networks were significantly correlated with a memory phenotype (logical memory delayed recall=LMDR) and hippocampal volume in both MCSA and ADNI. Among these, blood expression module M3 was most preserved with the brain transcriptome. M3 was enriched with NDUF hub genes that are involved in the mitochondrial respiratory chain. Expression levels of M3 and many blood NDUFs had significant associations with better LMDR and hippocampal volume. In brain, NDUFs were upregulated in controls compared to AD, and their expression levels were associated with better global cognition and decreased AD neuropathology. Many NDUFs were significantly downregulated in the hippocampus or cortex of AD mice compared to wild‐types. Lastly, models that included blood NDUFs improved diagnostic accuracy of AD+MCI compared to models that only included demographic and risk variables (age, sex, APOE‐ε4) in MCSA, ADNI and ANMerge. In MCSA and ADNI, adding NDUFs’ expression to models that included established blood biomarkers (Aβ42/40, ptau181, NFL) further improved diagnostic accuracy.ConclusionOur results suggest that mitochondrial NDUFs are centrally‐linked peripheral molecular signatures that may be resilience factors against AD and serve as both therapeutic targets and novel diagnostic biomarkers.

AbstractBackgroundOur Alzheimer Disease Metabolomics Consortium (ADMC), part of the Accelerating Medicines Partnership for AD (AMP‐AD) and in partnership with AD Neuroimaging Initiative (ADNI), applied state‐of‐the‐art metabolomics and lipidomics technologies combined with genomic and imaging data to map metabolic failures across the trajectory of the disease. Our studies confirmed that peripheral metabolic changes influenced by the exposome inform about cognitive changes, brain imaging changes, and ATN markers for disease confirming that peripheral and central changes are connected, in part through the metabolome.MethodsTo map the biochemical changes in AD, we used various targeted and untargeted metabolic platforms to profile ∼800 postmortem brain tissue, and ∼ 5000 blood samples.ResultsRecently, we built a comprehensive reference map of extensive AD‐related metabolic changes in brain, spanning multiple AD‐related traits, including neuropathological b‐amyloid and tau tangle burden, as well as late‐life cognitive performance. Using this resource, we extracted novel metabolic including bioenergetic pathways, cholesterol metabolism, neuroinflammation, broad impairment of osmoregulation, an imbalance between excitatory/inhibitory neurotransmitter ratios and identification of tau load as a potential driver of metabolic dysfunction in the AD brain, with minimal contributions from b‐amyloid load. As AD and progressive supranuclear palsy (PSP) share the pathological feature of tauopathy and metabolic alterations, we compared their metabolomic profiles to identify shared biological pathways that could be targeted for therapeutic interventions. Our findings indicate that both diseases display oxidative stress, mitochondrial dysfunction, and tau‐induced polyamine stress response.ConclusionOverall, through our studies, (1) We identified biochemical processes altered in AD, with findings supported across both metabolomic and proteomic data, indicating multimodal deregulation. (2) Our research pinpointed widespread AD‐related biochemical changes across various brain regions with differing levels of neuropathology. While there are many overlapping changes across the brain regions, each region also has its distinct metabolic alterations. (3) We identified biochemical processes disrupted by AD, with parallel findings in other neurodegenerative diseases, hinting at broader implications in neurodegenerative research. Currently, we are working on mapping widespread connections of the brain metabolome with various determinants of AD namely genome, gut microbiome, exposome, and linking with peripheral metabolic alterations in AD.