Allen, Mariet

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 Cerebral amyloid angiopathy (CAA), characterized by the accumulation of amyloid‐beta in the cerebrovasculature, affects blood vessel integrity leading to brain hemorrhages and an accelerated cognitive decline in Alzheimer’s Disease (AD) patients. Our previous genome‐wide association study (GWAS) identified a LINC‐PINT splice variant associated with lower CAA levels in individuals without the APOE e4 allele and higher levels of LINC‐PINT expression in the brain of AD donors. In this study, we expand our GWAS to include additional donors with AD and those lacking significant AD neuropathology (non‐AD), with available CAA scores. Method In our prior study we assessed 853 AD donors. We expanded this by addition of genetic data from 550 AD and 502 non‐AD donors from the Mayo Clinic Brain Bank, scored for CAA. We performed QC and imputation (TOPMED) of all datasets. We conducted GWAS in AD only ( N = 1,363), non‐AD only, and all donors ( N = 1,865) by testing imputed variant dosages for association with square root transformed CAA using linear regression, adjusting for relevant covariates. To assess associations in the context of major CAA risk factors, we performed interaction analysis with APOEe4 presence and sex; and pursued stratified analyses. Result Variants at the APOE locus were identified as the most significant in our study. In addition, several other variants approached genome‐wide significance after adjusting for AD neuropathology (Braak stage and Thal phase). T he LINC‐PINT splice variant remained associated with lower CAA scores in AD donors without the APOEe4 risk allele. To enhance the robustness of our findings, we are pursuing further expansion of our study cohort to include other available datasets. To explore putative functional consequences of key variants we are collecting peripheral gene expression measures in participants from Mayo Clinic with neuroimaging measures including microhemorrhages. Conclusion We 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 and their functional outcomes may lead to new avenues of research aimed at identifying biomarkers and therapies to treat CAA, a common co‐pathology in those with and without AD.

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.

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.

AbstractBackgroundAlzheimer’s disease (AD) patients have decline in cognitive domains including memory, language, visuospatial, and/or executive function and brain pathology including amyloid‐β and tau deposition, neurodegeneration, and frequent vascular co‐pathologies detectable by neuroimaging and/or cerebrospinal fluid biomarkers. However, molecular disease mechanisms are complex and heterogeneous. It is necessary to develop cost‐effective blood‐based biomarkers reflecting brain molecular perturbations in AD. We identified blood‐based gene and co‐expression network level changes associated with AD/mild cognitive impairment (MCI) diagnosis and AD‐related phenotypes.MethodWe performed differential gene expression and weighted gene co‐expression network analysis, followed by meta‐analysis, using blood transcriptome data of 391 participants from the Mayo Clinic Study of Aging and 654 participants from the Alzheimer's Disease Neuroimaging Initiative. The neuroimaging phenotypes include microhemorrhages, infarcts, amyloid burden, hippocampal volume, and white matter hyperintensities. The cognitive phenotypes include standardized cognitive subtest scores and composite scores for memory, language, visuospatial, and executive function.ResultFive out of 18 modules(M) are significantly associated with diagnosis or cognition (FDR‐adjusted p<0.05). M1 and M15 both positively associates with memory, M1 positively associated with language and M15 with visuospatial function. M1 and M15 are enriched in differentially expressed genes (DEGs) associated with language and executive function, respectively. M2 negatively associates with logical memory delayed recall scores(LMDR), memory, executive, and language functions and is enriched in DEGs for these phenotypes. M8 negatively associates with memory, language and executive functions and is enriched in DEGs for memory and language. M12 positively associates with LMDR. M1 and M15 are down‐regulated while M2 and M8 are up‐regulated in AD/MCI patients. Cell‐type enrichment analysis showed M2 is enriched in monocytes and neutrophils; M8 in monocytes; M15 in B cells (FDR <0.05). Gene ontology terms enriched in these modules indicated broad consistency with their cell types.ConclusionWe identified five modules significantly associated with AD/MCI or cognitive phenotypes using blood transcriptome data. These findings nominate blood transcriptome changes and their enriched biological processes as potential pathomechanisms in cognitive decline and AD/MCI development. We aim to investigate these blood transcripts as potential biomarkers for AD or AD‐related phenotypes and therapeutic targets through additional replication and experimental validation studies.

AbstractBackgroundAfrican Americans (AA) and Latin Americans (LA) are at a higher risk of developing AD compared to non‐Hispanic whites (NHW) but are traditionally underrepresented in AD research. The disproportionate risk is likely multifactorial including differences in co‐morbidities and structural and social determinants of health (SSDoH). AD risk is thought to result from multiple genetic and environmental factors, and their interactions (GxE). However, the molecular mechanisms underlying these interactions are largely unknown. The best‐established genetic risk factor for AD is APOE‐ε4, while APOE‐ε2 is protective. Environmental factors, such as living in neighborhoods with greater socioeconomic disadvantage, have been associated with greater AD neuropathology and worse cognitive function. Studying the potential modifying role of neighborhood disadvantage regarding associations of APOE‐ε4 and APOE‐ε2 with AD risk using a socioeconomic metric like the Area Deprivation Index (ADI) in a multiethnic post‐mortem cohort could offer valuable insights for precision medicine.MethodWe analyzed a post‐mortem cohort of 421 multi‐ethnic donors (NHW: N = 234, AD = 70, non‐AD = 164; LA: N = 145, AD = 102, non‐AD = 43; AA: N = 42, AD = 24, non‐AD = 18), from Mayo Clinic Florida, Banner Sun Health Research Institute, and the University of Florida Brain Banks as part of the AMP AD Diversity initiative. This study used the same neuropathological criteria to categorize AD vs. non‐AD as in the MayoRNAseq study (syn5550404). ADI from the Neighborhood Atlas® was obtained from de‐identified addresses. Associations of APOE‐ε4, APOE‐ε2, and ADI with AD risk were examined using logistic regression models adjusted for age‐at‐death, sex, and ethnic group; interactions of APOE genotype with ADI were also assessed.ResultAPOE‐ε4 and APOE‐ε2 showed a significant association with AD diagnosis (APOE‐ε4combined cohort OR = 3.23, P = 3.14E‐08; APOE‐ε2combined cohort OR = 0.30, P = 0.003). However, contrary to prior findings, our study found no association between ADI and AD diagnosis [OR = 1.023 (per each 10 unit increase in ADI), P = 0.629]. No significant interactions of ADI with APOE‐ε4 or APOE‐ε2 regarding association with AD diagnosis were identified (P = 0.083, P = 0.781).ConclusionThis is the first study evaluating the potential interaction of neighborhood disadvantage with APOE on AD risk in a multi‐ethnic autopsy cohort. However, these findings require further investigation in larger, more well‐powered cohorts for validation.

AbstractBackgroundSystemic inflammation plays a pivotal role in many chronic diseases including Alzheimer’s disease (AD). Assessing the composition of immune pathways in neurodegenerative diseases can contribute to precision medicine. Using publicly available transcriptomic data, we sought to elucidate transcriptional networks pertinent to inflammatory pathways across brain regions and peripheral blood in AD/mild cognitive impairment (MCI) and peripheral blood in Parkinson’s disease (PD).MethodFor the AD/MCI vs. control dataset, we analyzed bulk‐RNAseq collected from 6 brain regions of donors from ROSMAP, Mayo Clinic, and Mount Sinai School of Medicine (MSSM) brain banks available from the AMP‐AD consortium. Ante‐mortem, blood RNAseq expression data was retrieved from the AMP‐AD Emory Vascular cohort and Mayo Clinic Study of Aging (MCSA). We also collected blood‐derived microarray expression data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). For the PD vs Control dataset, blood‐derived bulk‐RNAseq from the PDBP and PPMI cohorts were available through the AMP‐PD consortium. Following quality control, normalization, and residual generation to account for biological and technical variables, co‐expression network modules and their enriched pathways were identified using WGCNA within each dataset. Module/trait correlation tests for aging and diagnosis (cases [AD/MCI or PD] vs control) phenotypes were evaluated. Gene Ontology enrichment analyses were executed to identify enriched pathways and brain or blood cell types within the modules. Modules were tested for preservation across cohorts.ResultWe identified conserved immune signatures across brain regions and cohorts. Modules involved in immune response were preserved across all cohorts. Blood consensus modules involved in immune response were preserved in the brain and vice versa. Some immune modules were associated with AD/MCI, PD, and/or aging. Brain immune modules are significantly associated with aging and/or AD. Significant correlations (q<0.05) with PD diagnosis were present. In the MCSA and Emory vascular cohorts, there were no significant (q<0.05) associations between modules and diagnosis, while in ADNI there were nominal (p<0.05) associations.ConclusionPreserved transcriptional immune networks were identified across blood and brain and across two neurodegenerative diseases. Expanding gene co‐expression network analyses to other diseases and integrating additional omics measures and phenotypes can further strengthen these findings to unravel the immune signatures across complex diseases.