Tsai, Wei

Abstract Introduction Alzheimer’s disease (AD) is characterized by pathologies including amyloid, tau, neurodegeneration reflected in established blood biomarkers and track with clinical changes. However, the association between peripheral cell-specific signatures and AD-related phenotypes remain poorly characterized. Methods We analyzed bulk blood transcriptome data from the Mayo Clinic Study of Aging (MCSA) and Emory University Vascular study. We used BayesPrism, CIBERSORTx, and an in-house pipeline CNNreg to deconvolute these data and obtain peripheral cell proportions. Cell type specific transcripts were estimated with BayesPrism. We compared cell proportions between cases (AD/MCI) and controls. Association analysis was performed between cell type specific transcripts and AD-related phenotypes including diagnosis and cognition. Meta-analysis of transcripts associations and gene ontology analysis were conducted to assess the enriched pathways for significant genes. Results In MCSA, B and CD4+ T cells proportions are significantly lower while that of myeloid cells higher in cases. The Emory cohort had similar trends. We identified transcripts associated with AD-related phenotypes in CD4+ T and CD8+ T cells. In CD4+ T cells, transcripts downregulated in case are enriched in response to stimulus pathway, whereas those upregulated in negative regulation of immune response. Downregulation of CD4+ T genes enriched in extracellular matrix disassembly and epigenetic regulation and upregulation of those in protein localization associate with better cognition. In CD8+T cells, genes pertinent to vascular development were downregulated while those to metabolic processes were upregulated in cases. Downregulation of CD8+T genes involved in lipid transportation and upregulation of those in mitochondria associate with better cognition. Conclusion We identified peripheral cellular transcriptional changes associated with AD/MCI phenotypes and involved in important biological pathways, revealing potential disease mechanisms in AD. Funding Source RF1 AG051504 Topic Categories Neuroimmunology (NEUR)

Abstract Background Plasma biomarkers offer improved accessibility compared to established diagnostic modalities for Alzheimer's disease (AD). However, current plasma biomarker candidates have not been tested comprehensively in diverse populations, and represent only a fraction of pathways dysregulated in AD. This study seeks to address these knowledge gaps through untargeted plasma biomarker discovery in African Americans (AA), an underrepresented group in AD research despite having a higher risk of developing dementia compared to non‐Hispanic Whites (NHW). Method Untargeted proteomics were generated using the SomaScan 7k platform on plasma samples from AA study participants clinically diagnosed as AD dementia ( n = 181) or cognitively unimpaired (CU; n = 142). Machine learning was implemented to identify a set of plasma proteins to distinguish between AD dementia and CU. Predictive models were constructed by elastic net regression optimized with nested cross‐validation on 6,853 proteins and evaluated by receiver operating characteristic analysis. The generalizability of plasma protein predictors was evaluated in an external Alzheimer's Disease Research Center cohort of 369 NHW and AA study participants diagnosed as AD dementia or CU. Result Thirty‐six proteins were selected as predictors. Proteins selected by machine learning were enriched for processes previously associated with AD including synaptic dysfunction, inflammation, vascular dysfunction, and amyloid processing. The 36 plasma proteins alone achieved an area under the curve (AUC) of 0.94 to classify AD dementia in the discovery cohort, representing a 22% improvement over a model including age and sex. The 36 proteins were validated in an external cohort also achieving AUC=0.94, a 35% improvement over age and sex. Conclusion Biomarker discovery combining untargeted proteomics and machine learning identified a set of plasma proteins that could serve as a novel and highly accurate diagnostic plasma biomarker panel for AD. Work is underway to assess the association of the predictive panel with p ‐tau217, medical comorbidities, and social/structural determinants of health.

Abstract Background More than 85% of Alzheimer's disease (AD) donor brains exhibit some degree of cerebral amyloid angiopathy (CAA), which is characterized predominantly by Aβ40 deposits in the brain vasculature and can cause blood brain barrier (BBB) leakage. CAA is associated with risk of infarcts, cerebral hemorrhages, and cognitive decline. Brain cell type‐specific mechanisms that influence CAA pathology, as well as related measures such as Aβ40 biochemical measures and tight junction proteins, an indicator of BBB integrity, are still elusive. Method We performed single‐nucleus RNA sequencing (snRNAseq) of temporal cortex tissue from 79 AD donors, with varying levels of CAA co‐pathology. Measurements of soluble, insoluble and membrane‐associated fractions of brain Aβ40 and tight junction protein (claudin‐5, occludin) levels were available for these donors. We correlated these measures with cell proportions. We also conducted differential gene expression and analyses for intercellular communications and regulatory networks. Result We identified 25 clusters and annotated them with major brain cell types. There were 3 oligodendrocyte, 7 excitatory and 7 inhibitory neuronal, 2 microglial, 2 astrocytic, and 1 each for endothelial, pericytic, fibroblast, and oligodendrocyte precursor cell clusters. We found that higher CAA and Aβ40 levels correlate with reduced neuronal and increased microglial, astrocytic and vascular cell proportions. Increased tight junction proteins correlate with increased inhibitory neuronal and reduced microglial, astrocytic and vascular cell proportions. Among all clusters, one microglial, both astrocytic and the pericytic clusters have greater number of genes significantly associated with CAA, Aβ40 and tight junction protein levels. Most significant genes have negative associations with CAA and membrane‐bound Aβ40 and positive with tight junction protein measures. Using these genes as input for intercellular communications analysis, we prioritized ligand‐target interactions from microglia or astrocytes to pericytes. Some of the prioritized targets are transcription factors in pericytes. Using regulon analysis, we identified genes downstream of these transcription factors that are also significantly associated with lower CAA and membrane‐bound Aβ40 and higher tight junction protein levels. Conclusion We revealed glia‐to‐pericytes intercellular interactions and pericytic gene regulatory networks that are potentially perturbed by CAA and BBB breakdown. Future directions include analyzing external snRNAseq datasets and performing functional validations.

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 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 INTRODUCTION African American (AA) individuals are underrepresented in biomarker studies for Alzheimer's disease (AD). Biomarkers that reflect the heterogeneity of AD and achieve the greatest accuracy across populations are sorely needed. METHODS Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in AA participants with clinical diagnoses of AD dementia ( n = 181) and cognitively unimpaired (CU, n = 142). Machine learning was used to identify a set of plasma proteins that yielded the best classification accuracy. RESULTS A set of 36 proteins achieved an area under the curve (AUC) of 0.94 to classify AD dementia versus CU, a 16% improvement over age, sex, and apolipoprotein E ( APOE) . This finding was replicated in multiple plasma and brain datasets (AUCs 0.73–0.97). Our findings underscore the importance of matrisome and cerebrovascular dysfunction in AD pathophysiology. DISCUSSION This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning. Highlights Conducted large‐scale plasma proteomics in Alzheimer's disease (AD) versus cognitively unimpaired controls. Machine learning biomarker discovery was replicated in an independent cohort. Novel set of proteins distinguishes AD versus controls with high accuracy (area under the curve [AUC] = 0.94). Achieved reproducibility across multiple replication cohorts (AUC = 0.73–0.97). Network analyses implicates matrisome biology and cerebrovascular dysfunction.

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.

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.

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.