Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease.

Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.

Cell-free RNA signatures predict Alzheimer's disease.

There is a need for affordable, scalable, and specific blood-based biomarkers for Alzheimer's disease that can be applied to a population level. We have developed and validated disease-specific cell-free transcriptomic blood-based biomarkers composed by a scalable number of transcripts that capture AD pathobiology even in the presymptomatic stages of the disease. Accuracies are in the range of the current CSF and plasma biomarkers, and specificities are high against other neurodegenerative diseases.

Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer's disease.

Proteomic studies for Alzheimer's disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson's disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer's and coronary disease pathways.

Identification of proteins dysregulated by COVID-19 infection is critically important for better understanding of its pathophysiology, building prognostic models, and identifying new targets. Plasma proteomic profiling of 4,301 proteins was performed in two independent datasets and tested for the association for three COVID-19 outcomes (infection, ventilation, and death). We identified 1,449 proteins consistently associated in both datasets with any of these three outcomes. We subsequently created highly accurate models that distinctively predict infection, ventilation, and death. These proteins were enriched in specific biological processes including cytokine signaling, Alzheimer's disease, and coronary artery disease. Mendelian randomization and gene network analyses identified eight causal proteins and 141 highly connected hub proteins including 35 with known drug targets. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes, reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

MS4A4A modifies the risk of Alzheimer disease by regulating lipid metabolism and immune response in a unique microglia state.

Genome-wide association studies (GWAS) have identified many modifiers of Alzheimer disease (AD) risk enriched in microglia. Two of these modifiers are common variants in the MS4A locus (rs1582763: protective and rs6591561: risk) and serve as major regulators of CSF sTREM2 levels. To understand their functional impact on AD, we used single nucleus transcriptomics to profile brains from carriers of these variants. We discovered a "chemokine" microglial subpopulation that is altered in MS4A variant carriers and for which MS4A4A is the major regulator. The protective variant increases MS4A4A expression and shifts the chemokine microglia subpopulation to an interferon state, while the risk variant suppresses MS4A4A expression and reduces this subpopulation of microglia. Our findings provide a mechanistic explanation for the AD variants in the MS4A locus. Further, they pave the way for future mechanistic studies of AD variants and potential therapeutic strategies for enhancing microglia resilience in AD pathogenesis.

Metabolomic and lipidomic signatures in autosomal dominant and late-onset Alzheimer's disease brains.

INTRODUCTION: The identification of multiple genetic risk factors for Alzheimer's disease (AD) suggests that many pathways contribute to AD onset and progression. However, the metabolomic and lipidomic profiles in carriers of distinct genetic risk factors are not fully understood. The metabolome can provide a direct image of dysregulated pathways in the brain. METHODS: We interrogated metabolomic signatures in the AD brain, including carriers of pathogenic variants in APP, PSEN1, and PSEN2 (autosomal dominant AD; ADAD), APOE ɛ4, and TREM2 risk variant carriers, and sporadic AD (sAD). RESULTS: We identified 133 unique and shared metabolites associated with ADAD, TREM2, and sAD. We identified a signature of 16 metabolites significantly altered between groups and associated with AD duration. DISCUSSION: AD genetic variants show distinct metabolic perturbations. Investigation of these metabolites may provide greater insight into the etiology of AD and its impact on clinical presentation. HIGHLIGHTS: APP/PSEN1/PSEN2 and TREM2 variant carriers show distinct metabolic changes. A total of 133 metabolites were differentially abundant in AD genetic groups. ß-citrylglutamate is differentially abundant in autosomal dominant, TREM2, and sporadic AD. A 16-metabolite profile shows differences between Alzheimer's disease (AD) genetic groups. The identified metabolic profile is associated with duration of disease.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways.

Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal ( https://covid.proteomics.wustl.edu/ ). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR ≤ 3.72×10 -14 ), Alzheimer's disease (FDR ≤ 5.46×10 -10 ) and coronary artery disease (FDR ≤ 4.64×10 -2 ). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

Circular RNA detection identifies circPSEN1 alterations in brain specific to autosomal dominant Alzheimer's disease.

BACKGROUND: Autosomal-dominant Alzheimer's disease (ADAD) is caused by pathogenic mutations in APP, PSEN1, and PSEN2, which usually lead to an early age at onset (< 65). Circular RNAs are a family of non-coding RNAs highly expressed in the nervous system and especially in synapses. We aimed to investigate differences in brain gene expression of linear and circular transcripts from the three ADAD genes in controls, sporadic AD, and ADAD. METHODS: We obtained and sequenced RNA from brain cortex using standard protocols. Linear counts were obtained using the TOPMed pipeline; circular counts, using python package DCC. After stringent quality control (QC), we obtained the counts for PSEN1, PSEN2 and APP genes. Only circPSEN1 passed QC. We used DESeq2 to compare the counts across groups, correcting for biological and technical variables. Finally, we performed in-silico functional analyses using the Circular RNA interactome website and DIANA mirPath software. RESULTS: Our results show significant differences in gene counts of circPSEN1 in ADAD individuals, when compared to sporadic AD and controls (ADAD = 21, AD = 253, Controls = 23-ADADvsCO: log2FC = 0.794, p = 1.63 × 10-04, ADADvsAD: log2FC = 0.602, p = 8.22 × 10-04). The high gene counts are contributed by two circPSEN1 species (hsa_circ_0008521 and hsa_circ_0003848). No significant differences were observed in linear PSEN1 gene expression between cases and controls, indicating that this finding is specific to the circular forms. In addition, the high circPSEN1 levels do not seem to be specific to PSEN1 mutation carriers; the counts are also elevated in APP and PSEN2 mutation carriers. In-silico functional analyses suggest that circPSEN1 is involved in several pathways such as axon guidance (p = 3.39 × 10-07), hippo signaling pathway (p = 7.38 × 10-07), lysine degradation (p = 2.48 × 10-05) or Wnt signaling pathway (p = 5.58 × 10-04) among other KEGG pathways. Additionally, circPSEN1 counts were able to discriminate ADAD from sporadic AD and controls with an AUC above 0.70. CONCLUSIONS: Our findings show the differential expression of circPSEN1 is increased in ADAD. Given the biological function previously ascribed to circular RNAs and the results of our in-silico analyses, we hypothesize that this finding might be related to neuroinflammatory events that lead or that are caused by the accumulation of amyloid-beta.

Examination of the Effect of Rare Variants in TREM2, ABI3, and PLCG2 in LOAD Through Multiple Phenotypes.

BACKGROUND: Rare variants in PLCG2 (p.P522R), ABI3 (p.S209F), and TREM2 (p.R47H, p.R62H) have been associated with late onset Alzheimer's disease (LOAD) risk in Caucasians. After the initial report, several studies have found positive results in cohorts of different ethnic background and with different phenotype. OBJECTIVE: In this study, we aim to evaluate the association of rare coding variants in PLCG2, ABI3, and TREM2 with LOAD risk and their effect at different time points of the disease. METHODS: We used a European American cohort to assess the association of the variants prior onset (using CSF Aß42, tau, and pTau levels, and amyloid imaging as endophenotypes) and after onset (measured as rate of memory decline). RESULTS: We confirm the association with LOAD risk of TREM2 p.R47H, p.R62H and ABI3 p.S209F variants, and the protective effect of PLCG2 p.P522R. In addition, ABI3 and TREM2 gene-sets showed significant association with LOAD risk. TREM2 p.R47H and PLCG2 p.P522R variants were also statistically associated with increase of amyloid imaging and AD progression, respectively. We did not observe any association of ABI3 p.S209F with any of the other AD endophenotypes. CONCLUSION: The results of this study highlight the importance of including biomarkers and alternative phenotypes to better understand the role of novel candidate genes with the disease.

An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations.

Parietal cortex RNA-sequencing (RNA-seq) data were generated from individuals with and without Alzheimer disease (AD; ncontrol = 13; nAD = 83) from the Knight Alzheimer Disease Research Center (Knight ADRC). Using this and an independent (Mount Sinai Brain Bank (MSBB)) AD RNA-seq dataset, cortical circular RNA (circRNA) expression was quantified in the context of AD. Significant associations were identified between circRNA expression and AD diagnosis, clinical dementia severity and neuropathological severity. It was demonstrated that most circRNA-AD associations are independent of changes in cognate linear messenger RNA expression or estimated brain cell-type proportions. Evidence was provided for circRNA expression changes occurring early in presymptomatic AD and in autosomal dominant AD. It was also observed that AD-associated circRNAs co-expressed with known AD genes. Finally, potential microRNA-binding sites were identified in AD-associated circRNAs for miRNAs predicted to target AD genes. Together, these results highlight the importance of analyzing non-linear RNAs and support future studies exploring the potential roles of circRNAs in AD pathogenesis.

A Comprehensive Resource for Induced Pluripotent Stem Cells from Patients with Primary Tauopathies.

Primary tauopathies are characterized neuropathologically by inclusions containing abnormal forms of the microtubule-associated protein tau (MAPT) and clinically by diverse neuropsychiatric, cognitive, and motor impairments. Autosomal dominant mutations in the MAPT gene cause heterogeneous forms of frontotemporal lobar degeneration with tauopathy (FTLD-Tau). Common and rare variants in the MAPT gene increase the risk for sporadic FTLD-Tau, including progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We generated a collection of fibroblasts from 140 MAPT mutation/risk variant carriers, PSP, CBD, and cognitively normal controls; 31 induced pluripotent stem cell (iPSC) lines from MAPT mutation carriers, non-carrier family members, and autopsy-confirmed PSP patients; 33 genome engineered iPSCs that were corrected or mutagenized; and forebrain neural progenitor cells (NPCs). Here, we present a resource of fibroblasts, iPSCs, and NPCs with comprehensive clinical histories that can be accessed by the scientific community for disease modeling and development of novel therapeutics for tauopathies.

Human fibroblast and stem cell resource from the Dominantly Inherited Alzheimer Network.

BACKGROUND: Mutations in amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2) cause autosomal dominant forms of Alzheimer disease (ADAD). More than 280 pathogenic mutations have been reported in APP, PSEN1, and PSEN2. However, understanding of the basic biological mechanisms that drive the disease are limited. The Dominantly Inherited Alzheimer Network (DIAN) is an international observational study of APP, PSEN1, and PSEN2 mutation carriers with the goal of determining the sequence of changes in presymptomatic mutation carriers who are destined to develop Alzheimer disease. RESULTS: We generated a library of 98 dermal fibroblast lines from 42 ADAD families enrolled in DIAN. We have reprogrammed a subset of the DIAN fibroblast lines into patient-specific induced pluripotent stem cell (iPSC) lines. These cells were thoroughly characterized for pluripotency markers. CONCLUSIONS: This library represents a comprehensive resource that can be used for disease modeling and the development of novel therapeutics.

Discovery and validation of autosomal dominant Alzheimer's disease mutations.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease that is clinically characterized by progressive cognitive decline. Mutations in amyloid-ß precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2) are the pathogenic cause of autosomal dominant AD (ADAD). However, polymorphisms also exist within these genes. METHODS: In order to distinguish polymorphisms from pathogenic mutations, the DIAN Expanded Registry has implemented an algorithm for determining ADAD pathogenicity using available information from multiple domains, including genetic, bioinformatic, clinical, imaging, and biofluid measures and in vitro analyses. RESULTS: We propose that PSEN1 M84V, PSEN1 A396T, PSEN2 R284G, and APP T719N are likely pathogenic mutations, whereas PSEN1 c.379_382delXXXXinsG and PSEN2 L238F have uncertain pathogenicity. CONCLUSIONS: In defining a subset of these variants as pathogenic, individuals from these families can now be enrolled in observational and clinical trials. This study outlines a critical approach for translating genetic data into meaningful clinical outcomes.

Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease.

Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families (N = 1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, and MAS1L) as candidate genes for familial LOAD.

Assessment of the Genetic Architecture of Alzheimer's Disease Risk in Rate of Memory Decline.

Many genetic studies for Alzheimer's disease (AD) have been focused on the identification of common genetic variants associated with AD risk and not on other aspects of the disease, such as age at onset or rate of dementia progression. There are multiple approaches to untangling the genetic architecture of these phenotypes. We hypothesized that the genetic architecture of rate of progression is different than the risk for developing AD dementia. To test this hypothesis, we used longitudinal clinical data from ADNI and the Knight-ADRC at Washington University, and we calculated PRS (polygenic risk score) based on the IGAP study to compare the genetic architecture of AD risk and dementia progression. Dementia progression was measured by the change of Clinical Dementia Rating Sum of Boxes (CDR)-SB per year. Out of the 21 loci for AD risk, no association with the rate of dementia progression was found. The PRS rate was significantly associated with the rate of dementia progression (ß= 0.146, p = 0.03). In the case of rare variants, TREM2 (ß= 0.309, p = 0.02) was also associated with the rate of dementia progression. TREM2 variant carriers showed a 23% faster rate of dementia compared with non-variant carriers. In conclusion, our results indicate that the recently identified common and rare variants for AD susceptibility have a limited impact on the rate of dementia progression in AD patients.

Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease.

Alzheimer disease (AD), Frontotemporal lobar degeneration (FTD), Amyotrophic lateral sclerosis (ALS) and Parkinson disease (PD) have a certain degree of clinical, pathological and molecular overlap. Previous studies indicate that causative mutations in AD and FTD/ALS genes can be found in clinical familial AD. We examined the presence of causative and low frequency coding variants in the AD, FTD, ALS and PD Mendelian genes, in over 450 families with clinical history of AD and over 11,710 sporadic cases and cognitive normal participants from North America. Known pathogenic mutations were found in 1.05% of the sporadic cases, in 0.69% of the cognitively normal participants and in 4.22% of the families. A trend towards enrichment, albeit non-significant, was observed for most AD, FTD and PD genes. Only PSEN1 and PINK1 showed consistent association with AD cases when we used ExAC as the control population. These results suggest that current study designs may contain heterogeneity and contamination of the control population, and that current statistical methods for the discovery of novel genes with real pathogenic variants in complex late onset diseases may be inadequate or underpowered to identify genes carrying pathogenic mutations.

SORL1 variants across Alzheimer's disease European American cohorts.

The accumulation of the toxic Aß peptide in Alzheimer's disease (AD) largely relies upon an efficient recycling of amyloid precursor protein (APP). Recent genetic association studies have described rare variants in SORL1 with putative pathogenic consequences in the recycling of APP. In this work, we examine the presence of rare coding variants in SORL1 in three different European American cohorts: early-onset, late-onset AD (LOAD) and familial LOAD.

Phenotypic Similarities Between Late-Onset Autosomal Dominant and Sporadic Alzheimer Disease: A Single-Family Case-Control Study.

IMPORTANCE: The amyloid hypothesis posits that disrupted ß-amyloid homeostasis initiates the pathological process resulting in Alzheimer disease (AD). Autosomal dominant AD (ADAD) has an early symptomatic onset and is caused by single-gene mutations that result in overproduction of ß-amyloid 42. To the extent that sporadic late-onset AD (LOAD) also results from dysregulated ß-amyloid 42, the clinical phenotypes of ADAD and LOAD should be similar when controlling for the effects of age. OBJECTIVE: To use a family with late-onset ADAD caused by a presenilin 1 (PSEN1) gene mutation to mitigate the potential confound of age when comparing ADAD and LOAD. DESIGN, SETTING, AND PARTICIPANTS: This case-control study was conducted at the Knight Alzheimer Disease Research Center at Washington University, St Louis, Missouri, and other National Institutes of Aging-funded AD centers in the United States. Ten PSEN1 A79V mutation carriers from multiple generations of a family with late-onset ADAD and 12 noncarrier family members were followed up at the Knight Alzheimer Disease Research Center (1985-2015) and 1115 individuals with neuropathologically confirmed LOAD were included from the National Alzheimer Coordinating Center database (September 2005-December 2014). Data analysis was completed in January 2016, including Knight Alzheimer Disease Research Center patient data collected up until the end of 2015. MAIN OUTCOMES AND MEASURES: Planned comparison of clinical characteristics between cohorts, including age at symptom onset, associated symptoms and signs, rates of progression, and disease duration. RESULTS: Of the PSEN1 A79V carriers in the family with late-onset ADAD, 4 were female (57%); among those with LOAD, 529 were female (47%). Seven mutation carriers (70%) developed AD dementia, while 3 were yet asymptomatic in their seventh and eighth decades of life. No differences were observed between mutation carriers and individuals with LOAD concerning age at symptom onset (mutation carriers: mean, 75 years [range, 63-77 years] vs those with LOAD: mean, 74 years [range, 60-101 years]; P = .29), presenting symptoms (memory loss in 7 of 7 mutation carriers [100%] vs 958 of 1063 individuals with LOAD [90.1%]; P ≥ .99) and duration (mutation carriers: mean, 9.9 years [range, 2.3-12.8 years] vs those with LOAD: 9 years [range, 1-27 years]; P = .73), and rate of progression of dementia (median annualized change in Clinical Dementia Rating-Sum of Boxes score, mutation carriers: 1.2 [range, 0.1-3.3] vs those with LOAD: 1.9 [range, -3.5 to 11.9]; P = .73). Early emergence of comorbid hallucinations and delusions were observed in 57% of individuals with ADAD (4 of 7) vs 19% of individuals with LOAD (137 of 706) (P = .03). Three of 12 noncarriers (25%) from the PSEN1 A79V family are potential phenocopies as they also developed AD dementia (median age at onset, 76.0 years). CONCLUSIONS AND RELEVANCE: In this family, the amyloidogenic PSEN1 A79V mutation recapitulates the clinical attributes of LOAD. Previously reported clinical phenotypic differences between individuals with ADAD and LOAD may reflect age- or mutation-dependent effects.