Alzheimer’s disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly-funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome. Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify systems level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genomewide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive gene ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank and organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains. The top AD-risk associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains. This provides an objective methodology to localize risk within specific biological endophenotypes, and drill down into the most significantly associated sets of GO-terms and annotated genes for potential therapeutic targets.

Microglia, the innate immune cells of the brain, influence Alzheimer’s disease (AD) progression and are potential therapeutic targets. However, microglia exhibit diverse functions, the regulation of which is not fully understood, complicating therapeutics development. To better define the transcriptomic phenotypes and gene regulatory networks associated with AD, we enriched for microglia nuclei from 12 AD and 10 control human dorsolateral prefrontal cortices (7 males and 15 females, all aged >60 years) before single-nucleus RNA sequencing. Here we describe both established and previously unrecognized microglial molecular phenotypes, the inferred gene networks driving observed transcriptomic change, and apply trajectory analysis to reveal the putative relationships between microglial phenotypes. We identify microglial phenotypes more prevalent in AD cases compared with controls. Further, we describe the heterogeneity in microglia subclusters expressing homeostatic markers. Our study demonstrates that deep profiling of microglia in human AD brain can provide insight into microglial transcriptional changes associated with AD.

Microglia contribute to Alzheimer’s Disease (AD) progression and are candidate therapeutic targets. Human microglia exhibit an array of transcriptional phenotypes implying that accurate manipulation of microglial function will require clarity of their molecular states and context dependent regulation. To increase the number of microglia analyzed per subject we employed fluorescence activated nuclei sorting prior to single-nucleus RNA-seq on human prefrontal cortices. We observed microglia phenotypes previously unrecognized in human brain gene expression studies and mapped their transcriptomic relationships by trajectory inference. Three clusters were enriched for endolysosomal pathways, one of which showed differential expression of AD GWAS genes in addition to genes implicated in nucleic acid detection and interferon signaling. Analysis of the “homeostatic” microglia cluster revealed a uniquely AD subcluster. Our study demonstrates the value of deeply profiling microglia to explore the biological implications of microglia transcriptomic diversity.

Microglia-mediated neuroinflammation contributes to disease progression in Alzheimer’s Disease (AD). Microglia demonstrate heterogeneous states with proposed beneficial, harmful, and disease-specific subtypes. Defining the spectrum of microglia phenotypes is crucially important to the design of neuroinflammation-modulating therapies. We performed single-nucleus RNA-seq on over 120,000 microglia nuclei isolated from dorsolateral prefrontal cortices collected from 12 AD and 10 controls. Nuclei were sorted for PU.1 expression using fluorescence activated nucleus sorting. We detected several microglia clusters that expressed features of “activation” as well as three previously unrecognized microglia transcriptomic subpopulations. These newly described subpopulations are defined by predominant endolysosomal gene expression patterns heterogeneous for metabolic and inflammatory signatures. One endolysosomal subtype is more abundant in AD individuals and uniquely enriched for transcripts involved in nucleic acid detection and interferon signaling. This population is distinct from a classic inflammatory subpopulation also present in our cohort, which upregulates NFKB, TLR and interferon related pathways but does not share the endolysosomal signature. Network analysis revealed subtype specific enrichment of IRF3 and IRF5 regulomes. AD individuals also exhibited increased expression of unfolded protein response and inflammatory genes in homeostatic microglia subpopulations. We demonstrate significant heterogeneity in human brain microglia subtypes including identifying multiple endolysosomal subpopulations. Like neuroinflammation, alterations in the endolysosomal system have been identified as a key component of AD pathology; however, how endolysosomal dysfunction contributes to AD progression remains unresolved. Our study is the first to identify a subpopulation of microglia in human AD brain correlating endolysosomal activity and interferon pathway induction supporting a relationship between these two pathways in AD pathogenesis. These results highlight the heterogenous nature of the microglia response to AD pathology and inform efforts to target specific subtypes of microglia in the development of novel AD therapies.

Background Genetics play an important role in late-onset Alzheimer’s Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. Methods We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants, adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. Results We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1 , ABCA7 , INPP5D , and APOE ), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Conclusions Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

[This corrects the article DOI: 10.3389/fnagi.2021.735524.].

Background: Genetics play an important role in late-onset Alzheimer’s Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease.Methods: We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants, adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed.Results: We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable.Conclusions: Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

The limited success of therapeutics targeting the amyloid hypotheses of Alzheimer’s Disease (AD) suggests the need to examine additional and potentially complementary therapeutic approaches. Recent efforts to amass genome‐scale data from diverse cohorts have revealed thousands of new therapeutic targets and highlighted previously underappreciated molecular and cellular dysfunction associated with disease states. Our understanding of the mechanistic relevance of individual targets and the inter‐relationships of groups of targets remains limited. Here we combine evidence across multiple independent studies to prioritize specific targets for further resource and reagent generation in the Target Enablement to Accelerate Therapy Development for Alzheimer’s Disease (TREAT‐AD) consortium.

Background: Late-onset Alzheimer’s Disease (AD) (LOAD) is the most common neurodegenerative disease. Despite extensive efforts to understand disease progression there are currently no approved disease modifying interventions to delay or reverse neurodegeneration caused by AD. Repeated failures in human trials, despite promising preclinical results in amyloidogenic mouse models, highlight the need for animals that better model human AD. MODEL-AD (Model organism development and evaluation for late-onset AD) are identifying and integrating disease-relevant, humanized gene sequences identified from public AD data repositories to create more translatable mouse models relevant to AD. Methods: Strong risk factors for LOAD, APOEε4 and Trem2*R47H, were expressed alone or in combination on a congenic C57BL/6J (B6) background, in cohorts of mice established on multiple sites and aged to between 4-24 months. A deep phenotyping approach was employed to assess phenotypes relative to human AD. Results: The LOAD1 mouse strain, expressing humanized APOEε4 and Trem2*R47H alleles, was designed to elucidate the disease state of animals expressing the two strongest genetic risk factors of LOAD at endogenous levels. Robust analytical pipelines measured behavioral, transcriptomic, metabolic, and neuropathological phenotypes in cross-sectional cohorts for progression of disease hallmarks at all life stages. In vivo PET/MRI neuroimaging revealed regional alterations in glycolytic metabolism and vascular perfusion. Transcriptional profiling by RNA-Seq of brain hemispheres identified sex and age as the main sources of variation between genotypes including age-specific enrichment of AD-related processes. Similarly, age, but not genotype, was the strongest determinant of behavioral change. In the absence of mouse amyloid plaque formation, many of the hallmarks of AD were not observed in this strain. However, these two alleles together form a sensitized, background strain which will serve as a platform for the characterization of additional genetic and environmental LOAD risk factors. Conclusions: Comprehensive phenotyping provided key insights into genetic and environmental effects aimed at modeling human disease, critical to understand the complex intergenic interactions and subsequent molecular signaling cascades. The data provided by these assays are important for understanding the relative contributions of subsequent risk factors amended to LOAD1.

The ability to investigate therapeutic interventions in animal models of neurodegenerative diseases depends on extensive characterization of the model(s) being used. There are numerous models that have been generated to study Alzheimer’s disease (AD) and the underlying pathogenesis of the disease. While transgenic models have been instrumental in understanding AD mechanisms and risk factors, they are limited in the degree of characteristics displayed in comparison with AD in humans, and the full spectrum of AD effects has yet to be recapitulated in a single mouse model. The Model Organism Development and Evaluation for Late-Onset Alzheimer’s Disease (MODEL-AD) consortium was assembled by the National Institute on Aging (NIA) to develop more robust animal models of AD with increased relevance to human disease, standardize the characterization of AD mouse models, improve preclinical testing in animals, and establish clinically relevant AD biomarkers, among other aims toward enhancing the translational value of AD models in clinical drug design and treatment development. Here we have conducted a detailed characterization of the 5XFAD mouse, including transcriptomics, electroencephalogram, in vivo imaging, biochemical characterization, and behavioral assessments. The data from this study is publicly available through the AD Knowledge Portal.

Deeply phenotyped cohort data can elucidate differences associated with genetic risk for common complex diseases across an age spectrum. Previous work has identified genetic variants associated with Alzheimer’s disease (AD) risk from large-scale genome-wide association study meta-analyses. To explore effects of known AD-risk variants, we performed a phenome-wide association study on ~2000 clinical, proteomic, and metabolic blood-based analytes obtained from 2,831 cognitively normal adult clients of a consumer-based scientific wellness company. Results uncovered statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D , and APOE ). These effects were detectable from early adulthood. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. Sex-stratified GWAS results from an independent AD case-control meta-analysis supported sexspecific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. These analyses support evidence from previous functional genomics studies in the identification of a causal variant within the PILRA gene. Taken together, this study highlights clues to the earliest effects of AD genetic risk variants in individuals where disease symptoms have not (yet) arisen.

Genome-wide association studies (GWAS) have identified many risk loci for Alzheimer's disease (AD)1,2, but how these loci confer AD risk is unclear. Here, we aimed to identify loci that confer AD risk through their effects on brain protein abundance to provide new insights into AD pathogenesis. To that end, we integrated AD GWAS results with human brain proteomes to perform a proteome-wide association study (PWAS) of AD, followed by Mendelian randomization and colocalization analysis. We identified 11 genes that are consistent with being causal in AD, acting via their cis-regulated brain protein abundance. Nine replicated in a confirmation PWAS and eight represent new AD risk genes not identified before by AD GWAS. Furthermore, we demonstrated that our results were independent of APOE e4. Together, our findings provide new insights into AD pathogenesis and promising targets for further mechanistic and therapeutic studies.

Late-onset Alzheimer’s disease (LOAD) is the most common human neurodegenerative disease. Legacy amyloidogenic mouse models have been useful for understanding disease progression, however in the face of failing human trials more focus on disease translation with new mouse strains that better model human Alzheimer’s disease (AD) is required. MODEL-AD (Model Organism Development and Evaluation for Late-onset AD) groups are identifying and integrating disease-relevant, humanized gene sequences from public databases to create more translatable mouse models for therapy development. Mice expressing strong genetic risk factors for LOAD, APOEe4 and Trem2*R47H, were extensively aged and assayed using a multi-disciplined phenotyping approach associated with and relative to human AD pathology. Behavioral, transcriptomic, metabolic, and neuropathological assays identified sex and age as the main sources of variation between genotypes including age-specific enrichment of AD-related processes in the absence of mouse amyloid plaque formation. These data provide an important, baseline understanding of the individual effects and interaction between two strong genetic risk factors for LOAD. These two alleles together form a sensitized, background strain (B6.APOE4.Trem2*R47H, which we have termed ‘LOAD1’) necessary to examine how important underlying risk factors interact with any subsequent genetic or environmental cues to drive pathology.

Microglia-mediated neuroinflammation is hypothesized to contribute to disease progression in neurodegenerative diseases such as Alzheimer's Disease (AD). Microglia demonstrate heterogeneous states in health and disease, with proposed beneficial, harmful, and disease specific subtypes. Defining the spectrum of microglia phenotypes is an important step in rational design of neuroinflammation modulating therapies. To facilitate improved phenotype resolution and group comparisons based on disease state we performed single-nucleus RNA-seq on more than 120,000 microglia nuclei from AD and control dorsolateral prefrontal cortex. We identify clusters of microglia enriched for biological pathways implicating defined myeloid roles. We detected several previously unrecognized microglia populations in human AD brain, including three internalization and trafficking subtypes that were heterogeneous in their metabolic and inflammatory signatures. One of these endolysosomal subtypes is larger in AD individuals and was uniquely enriched for genes involved in nucleic acid detection and activation of interferon signaling. This inflammatory endolysosomal cluster also differentially regulated expression of genes associated with AD risk by genome wide association studies. We also identified a cluster of microglia with upregulated cell cycle and DNA repair genes that is proportionately larger in control individuals. Within cluster comparisons demonstrate that in AD brain, homeostatic microglia subpopulations upregulate inflammatory gene expression. These results highlight the heterogenous nature of the microglia response to AD pathology and will inform efforts to target specific subtypes of microglia in the development of novel AD therapies.

Late-onset Alzheimer’s disease (AD; LOAD) is the most common human neurodegenerative disease, however, the availability and efficacy of disease-modifying interventions is severely lacking. Despite exceptional efforts to understand disease progression via legacy amyloidogenic transgene mouse models, focus on disease translation with innovative mouse strains that better model the complexity of human AD is required to accelerate the development of future treatment modalities. LOAD within the human population is a polygenic and environmentally influenced disease with many risk factors acting in concert to produce disease processes parallel to those often muted by the early and aggressive aggregate formation in popular mouse strains. In addition to extracellular deposits of amyloid plaques and inclusions of the microtubule-associated protein tau, AD is also defined by synaptic/neuronal loss, vascular deficits, and neuroinflammation. These underlying processes need to be better defined, how the disease progresses with age, and compared to human-relevant outcomes. To create more translatable mouse models, MODEL-AD (Model Organism Development and Evaluation for Late-onset AD) groups are identifying and integrating disease-relevant, humanized gene sequences from public databases beginning with APOEε4 and Trem2*R47H, two of the most powerful risk factors present in human LOAD populations. Mice expressing endogenous, humanized APOEε4 and Trem2*R47H gene sequences were extensively aged and assayed using a multi-disciplined phenotyping approach associated with and relative to human AD pathology. Robust analytical pipelines measured behavioral, transcriptomic, metabolic, and neuropathological phenotypes in cross-sectional cohorts for progression of disease hallmarks at all life stages. In vivo PET/MRI neuroimaging revealed regional alterations in glycolytic metabolism and vascular perfusion. Transcriptional profiling by RNA-Seq of brain hemispheres identified sex and age as the main sources of variation between genotypes including age-specific enrichment of AD-related processes. Similarly, age was the strongest determinant of behavioral change. In the absence of mouse amyloid plaque formation, many of the hallmarks of AD were not observed in this strain. However, as a sensitized baseline model with many additional alleles and environmental modifications already appended, the dataset from this initial MODEL-AD strain serves an important role in establishing the individual effects and interaction between two strong genetic risk factors for LOAD in a mouse host.

Background Alzheimer’s disease (AD) is an incurable neurodegenerative disease currently affecting 1.75% of the US population, with projected growth to 3.46% by 2050. Identifying common genetic variants driving differences in transcript expression that confer AD risk is necessary to elucidate AD mechanism and develop therapeutic interventions. We modify the FUSION transcriptome-wide association study (TWAS) pipeline to ingest gene expression values from multiple neocortical regions. Methods A combined dataset of 2003 genotypes clustered to 1000 Genomes individuals from Utah with Northern and Western European ancestry (CEU) was used to construct a training set of 790 genotypes paired to 888 RNASeq profiles from temporal cortex (TCX = 248), prefrontal cortex (FP = 50), inferior frontal gyrus (IFG = 41), superior temporal gyrus (STG = 34), parahippocampal cortex (PHG = 34), and dorsolateral prefrontal cortex (DLPFC = 461). Following within-tissue normalization and covariate adjustment, predictive weights to impute expression components based on a gene’s surrounding cis -variants were trained. The FUSION pipeline was modified to support input of pre-scaled expression values and support cross validation with a repeated measure design arising from the presence of multiple transcriptome samples from the same individual across different tissues. Results Cis -variant architecture alone was informative to train weights and impute expression for 6780 (49.67%) autosomal genes, the majority of which significantly correlated with gene expression; FDR < 5%: N = 6775 (99.92%), Bonferroni: N = 6716 (99.06%). Validation of weights in 515 matched genotype to RNASeq profiles from the CommonMind Consortium (CMC) was (72.14%) in DLPFC profiles. Association of imputed expression components from all 2003 genotype profiles yielded 8 genes significantly associated with AD (FDR < 0.05): APOC1, EED, CD2AP, CEACAM19, CLPTM1, MTCH2, TREM2, and KNOP1. Conclusions We provide evidence of cis-genetic variation conferring AD risk through 8 genes across six distinct genomic loci. Moreover, we provide expression weights for 6780 genes as a valuable resource to the community, which can be abstracted across the neocortex and a wide range of neuronal phenotypes.

Depression is a common condition, but current treatments are only effective in a subset of individuals. To identify new treatment targets, we integrated depression genome-wide association study (GWAS) results (N = 500,199) with human brain proteomes (N = 376) to perform a proteome-wide association study of depression followed by Mendelian randomization. We identified 19 genes that were consistent with being causal in depression, acting via their respective cis-regulated brain protein abundance. We replicated nine of these genes using an independent depression GWAS (N = 307,353) and another human brain proteomic dataset (N = 152). Eleven of the 19 genes also had cis-regulated mRNA levels that were associated with depression, based on integration of the depression GWAS with human brain transcriptomes (N = 888). Meta-analysis of the discovery and replication proteome-wide association study analyses identified 25 brain proteins consistent with being causal in depression, 20 of which were not previously implicated in depression by GWAS. Together, these findings provide promising brain protein targets for further mechanistic and therapeutic studies. Wingo et al. integrate depression GWAS results with human brain proteomes to perform proteome-wide association studies followed by Mendelian randomization. They identify 25 proteins as potential causal mediators of depression, of which 20 are new.

Microglia-mediated neuroinflammation is hypothesized to contribute to disease progression in neurodegenerative diseases such as Alzheimer's Disease (AD). Recent single-cell RNA-seq studies highlight the heterogeneity of microglial subtypes. Since microglia are a relatively small proportion of CNS cells, standard single-cell omics approaches do not fully capture the diversity of microglia in human subjects. Understanding the gene expression and regulatory networks which define the spectrum of microglia phenotypes is critical to identifying specific targets for neuroinflammation modulating therapies.Our study utilized post-mortem brain tissue from 22 total (7 male) participants with an average age of 86.23 years and post-mortem interval of 5.42 hours. Of those individuals, 12 (3 male) had significant AD neuropathic change present in their brains upon postmortem analysis. Nuclei isolated from prefrontal cortex were sorted for PU.1 expression using fluorescence activated nucleus sorting (FANS). The FANS approach yields larger numbers of nuclei annotated as microglia with high quality sequence from each individual. We performed single-nucleus RNA-seq using the 10X Genomics Chromium platform.We isolated more than 4,000 microglia nuclei from each of the 22 participants, facilitating the comparison of profiles at an individual level in addition to group comparisons based on disease state. Unbiased clustering revealed 8 microglia clusters and improved resolution of microglia heterogeneity compared to standard single-cell approaches. Previously identified mouse disease-associated microglia (DAM) genes are not located in a single cluster of human microglia. "DAM" genes are also present in control aged brains. We identify clusters of microglia enriched for biological pathways implicating defined myeloid roles including a cluster of interferon-stimulated microglia as well as a cluster of autophagic/phagocytic microglia. In many, but not all, of our annotated clusters, including that with high expression of homeostatic genes, individuals with AD have greater enrichment of inflammatory pathways using pseudobulk analysis.We demonstrate significant heterogeneity in microglia subtypes in human brain. Regulation of expression in inflammation-related networks is detected in microglia from both control and AD individuals, though enrichment of specific inflammatory pathways differs. This is important as the field looks to target microglia mechanisms for pharmacological intervention while limiting off-target effects.