Microglia activation is the brain's major immune response to amyloid plaques in Alzheimer's disease (AD). Both cerebrospinal fluid (CSF) levels of soluble TREM2 (sTREM2), a biomarker of microglia ...activation, and microglia PET are increased in AD; however, whether an increase in these biomarkers is associated with reduced amyloid‐beta (Aβ) accumulation remains unclear. To address this question, we pursued a two‐pronged translational approach. Firstly, in non‐demented and demented individuals, we tested CSF sTREM2 at baseline to predict (i) amyloid PET changes over ∼2 years and (ii) tau PET cross‐sectionally assessed in a subset of patients. We found higher CSF sTREM2 associated with attenuated amyloid PET increase and lower tau PET. Secondly, in the AppNL‐G-F mouse model of amyloidosis, we studied baseline 18F‐GE180 microglia PET and longitudinal amyloid PET to test the microglia vs. Aβ association, without any confounding co‐pathologies often present in AD patients. Higher microglia PET at age 5 months was associated with a slower amyloid PET increase between ages 5‐to‐10 months. In conclusion, higher microglia activation as determined by CSF sTREM2 or microglia PET shows protective effects on subsequent amyloid accumulation.
Synopsis
TREM2 is a protein almost exclusively expressed by microglia in the brain. This study investigates the association between soluble TREM2 (sTREM2) levels in cerebrospinal fluid and the longitudinal Aβ accumulation in human and mouse.
In patients with Aβ pathology, higher cerebrospinal fluid (CSF) levels of sTREM2 are associated with lower rates of Aβ accumulation.
Higher CSF sTREM2 levels are associated with lower neurofibrillary tangles.
In the Aβ mouse model, higher microglia activation at baseline is associated with lower rates of Aβ accumulation between 5 and 10 months of age, when Aβ deposition primarily takes place.
TREM2 is a protein almost exclusively expressed by microglia in the brain. This study investigates the association between soluble TREM2 (sTREM2) levels in cerebrospinal fluid and the longitudinal Aβ accumulation in human and mouse.
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 (β= 0.146, p = 0.03). In the case of rare variants, TREM2 (β= 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.
Endophenotypes, as measurable intermediate features of human diseases, reflect underlying molecular mechanisms. The use of quantitative endophenotypes in genetic studies has improved our ...understanding of pathophysiological changes associated with diseases. The main advantage of the quantitative endophenotypes approach to study human diseases over a classic case-control study design is the inferred biological context that can enable the development of effective disease-modifying treatments. Here, we summarize recent progress on biomarkers for neurodegenerative diseases, including cerebrospinal fluid and blood-based, neuroimaging, neuropathological, and clinical studies. This review focuses on how endophenotypic studies have successfully linked genetic modifiers to disease risk, disease onset, or progression rate and provided biological context to genes identified in genome-wide association studies. Finally, we review critical methodological considerations for implementing this approach and future directions.
Deposition of amyloid plaques in the brain is one of the two main pathological hallmarks of Alzheimer's disease (AD). Amyloid positron emission tomography (PET) is a neuroimaging tool that ...selectively detects in vivo amyloid deposition in the brain and is a reliable endophenotype for AD that complements cerebrospinal fluid biomarkers with regional information. We measured in vivo amyloid deposition in the brains of ~1000 subjects from three collaborative AD centers and ADNI using
C-labeled Pittsburgh Compound-B (PiB)-PET imaging followed by meta-analysis of genome-wide association studies, first to our knowledge for PiB-PET, to identify novel genetic loci for this endophenotype. The APOE region showed the most significant association where several SNPs surpassed the genome-wide significant threshold, with APOE*4 being most significant (P-meta = 9.09E-30; β = 0.18). Interestingly, after conditioning on APOE*4, 14 SNPs remained significant at P < 0.05 in the APOE region that were not in linkage disequilibrium with APOE*4. Outside the APOE region, the meta-analysis revealed 15 non-APOE loci with P < 1E-05 on nine chromosomes, with two most significant SNPs on chromosomes 8 (P-meta = 4.87E-07) and 3 (P-meta = 9.69E-07). Functional analyses of these SNPs indicate their potential relevance with AD pathogenesis. Top 15 non-APOE SNPs along with APOE*4 explained 25-35% of the amyloid variance in different datasets, of which 14-17% was explained by APOE*4 alone. In conclusion, we have identified novel signals in APOE and non-APOE regions that affect amyloid deposition in the brain. Our data also highlights the presence of yet to be discovered variants that may be responsible for the unexplained genetic variance of amyloid deposition.
Alzheimer's disease (AD) is the most common form of dementia. This neurodegenerative disorder is associated with neuronal death and gliosis heavily impacting the cerebral cortex. AD has a substantial ...but heterogeneous genetic component, presenting both Mendelian and complex genetic architectures. Using bulk RNA-seq from the parietal lobes and deconvolution methods, we previously reported that brains exhibiting different AD genetic architecture exhibit different cellular proportions. Here, we sought to directly investigate AD brain changes in cell proportion and gene expression using single-cell resolution.
We generated unsorted single-nuclei RNA sequencing data from brain tissue. We leveraged the tissue donated from a carrier of a Mendelian genetic mutation, PSEN1 p.A79V, and two family members who suffer from sporadic AD, but do not carry any autosomal mutations. We evaluated alternative alignment approaches to maximize the titer of reads, genes, and cells with high quality. In addition, we employed distinct clustering strategies to determine the best approach to identify cell clusters that reveal neuronal and glial cell types and avoid artifacts such as sample and batch effects. We propose an approach to cluster cells that reduces biases and enable further analyses.
We identified distinct types of neurons, both excitatory and inhibitory, and glial cells, including astrocytes, oligodendrocytes, and microglia, among others. In particular, we identified a reduced proportion of excitatory neurons in the Mendelian mutation carrier, but a similar distribution of inhibitory neurons. Furthermore, we investigated whether single-nuclei RNA-seq from the human brains recapitulate the expression profile of disease-associated microglia (DAM) discovered in mouse models. We also determined that when analyzing human single-nuclei data, it is critical to control for biases introduced by donor-specific expression profiles.
We propose a collection of best practices to generate a highly detailed molecular cell atlas of highly informative frozen tissue stored in brain banks. Importantly, we have developed a new web application to make this unique single-nuclei molecular atlas publicly available.
Genetic factors predictive of severe adolescent idiopathic scoliosis (AIS) are largely unknown. To identify genetic variation associated with severe AIS, we performed an exome-wide association study ...of 457 severe AIS cases and 987 controls. We find a missense SNP in SLC39A8 (p.Ala391Thr, rs13107325) associated with severe AIS (P = 1.60 × 10
, OR = 2.01, CI = 1.54-2.62). This pleiotropic SNP was previously associated with BMI, blood pressure, cholesterol, and blood manganese level. We replicate the association in a second cohort (841 cases and 1095 controls) resulting in a combined P = 7.02 × 10
, OR = 1.94, CI = 1.63-2.34. Clinically, the minor allele of rs13107325 is associated with greater spinal curvature, decreased height, increased BMI and lower plasma manganese in our AIS cohort. Functional studies demonstrate reduced manganese influx mediated by the SLC39A8 p.Ala391Thr variant and vertebral abnormalities, impaired growth, and decreased motor activity in slc39a8 mutant zebrafish. Our results suggest the possibility that scoliosis may be amenable to dietary intervention.
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex ...relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.
Human proteins are widely used as drug targets. Integration of large-scale protein-level genome-wide association studies (GWAS) and disease-related GWAS has thus connected genetic variation to ...disease mechanisms via protein. Previous proteome-by-phenome-wide Mendelian randomization (MR) studies have been mainly focused on plasma proteomes. Previous MR studies using the brain proteome only reported protein effects on a set of pre-selected tissue-specific diseases. No studies, however, have used high-throughput proteomics from multiple tissues to perform MR on hundreds of phenotypes.
Here, we performed MR and colocalization analysis using multi-tissue (cerebrospinal fluid (CSF), plasma, and brain from pre- and post-meta-analysis of several disease-focus cohorts including Alzheimer disease (AD)) protein quantitative trait loci (pQTLs) as instrumental variables to infer protein effects on 211 phenotypes, covering seven broad categories: biological traits, blood traits, cancer types, neurological diseases, other diseases, personality traits, and other risk factors. We first implemented these analyses with cis pQTLs, as cis pQTLs are known for being less prone to horizontal pleiotropy. Next, we included both cis and trans conditionally independent pQTLs that passed the genome-wide significance threshold keeping only variants associated with fewer than five proteins to minimize pleiotropic effects. We compared the tissue-specific protein effects on phenotypes across different categories. Finally, we integrated the MR-prioritized proteins with the druggable genome to identify new potential targets.
In the MR and colocalization analysis including study-wide significant cis pQTLs as instrumental variables, we identified 33 CSF, 13 plasma, and five brain proteins to be putative causal for 37, 18, and eight phenotypes, respectively. After expanding the instrumental variables by including genome-wide significant cis and trans pQTLs, we identified a total of 58 CSF, 32 plasma, and nine brain proteins associated with 58, 44, and 16 phenotypes, respectively. For those protein-phenotype associations that were found in more than one tissue, the directions of the associations for 13 (87%) pairs were consistent across tissues. As we were unable to use methods correcting for horizontal pleiotropy given most of the proteins were only associated with one valid instrumental variable after clumping, we found that the observations of protein-phenotype associations were consistent with a causal role or horizontal pleiotropy. Between 66.7 and 86.3% of the disease-causing proteins overlapped with the druggable genome. Finally, between one and three proteins, depending on the tissue, were connected with at least one drug compound for one phenotype from both DrugBank and ChEMBL databases.
Integrating multi-tissue pQTLs with MR and the druggable genome may open doors to pinpoint novel interventions for complex traits with no effective treatments, such as ovarian and lung cancers.
Background
Genetic studies have been highly successful in identifying genetic regions associated with Alzheimer’s disease, and we are now at the exciting juncture of applying this knowledge to ...understanding disease mechanisms. The ability to generate and mine clinical and omicdata is advancing our understanding of neurodegeneration. The Alzheimer’s Disease Sequencing Project (ADSP) Functional Genomics Consortium (FunGen‐AD; https://adsp‐fgc.niagads.org/) aims to apply cutting‐edge genomics technologies, high‐throughput genetic screening and cutting edge disease modeling to understand the functional consequences of genetic susceptibility and resilience to Alzheimer’s Disease, and to identify genetics‐guided targets for the prevention, diagnosis, and treatment of Alzheimer’s disease and related dementias (AD/ADRD).
Method
The investigators of the FunGen‐AD consortium are using multiple approaches, including brain single cell data acquisition, human stem cell modeling and high through put CRISPR‐based screens, to understand how AD variants lead to changes across molecular networks, and understand how specific risk variants affect disease in diverse populations. Multi‐omic data generated in well characterized cohorts are being used to identify systems‐level alterations in and provide insight into the mechanisms underlying genetic variants.
Result
The FunGen‐AD consortium is organizing and facilitating large collaborative projects. Currently we are working on a xQTL project putting together data from multi‐tissue (brain, myeloid cells, CSF and plasma), and multi‐omic layers (transcriptomic in bulk and at single cell level, epigenetic, proteomic, and metabolomic). In order to fully understand the biology of AD we recognize that multiple tissues, and ‐omic layers need to be studied. All the omic data are being processed, harmonized and analyzed using standard pipelines, and mapping on a reference genome and annotation developed by the group. This QTL atlas will be used to perform colocalization and identify the functional genes within AD risk loci, and Mendelian Randomization to identify novel causal genes, proteins and druggable targets. This xQTL project will provide a rich resource of multi‐omics datasets, analysis pipelines, and an xQTL atlas for the research community studying AD/ADRD and other complex neurodegenerative traits.
Conclusions
In summary, the FunGen‐AD consortium is using multidisciplinary and collaborative research approaches to identify genetics‐guided targets for the prevention, diagnosis, and treatment of AD/ADRD.
The temporal evolutions and relative orderings of Alzheimer disease biomarkers, including CSF amyloid-β42 (Aβ42), Aβ40, total tau (Tau) and phosphorylated tau181 (pTau181), standardized uptake value ...ratio (SUVR) from the molecular imaging of cerebral fibrillar amyloid-β with PET using the 11C-Pittsburgh Compound-B (PiB), MRI-based hippocampal volume and cortical thickness and cognition have been hypothesized but not yet fully tested with longitudinal data for all major biomarker modalities among cognitively normal individuals across the adult lifespan starting from 18 years. By leveraging a large harmonized database from 8 biomarker studies with longitudinal data from 2609 participants in cognition, 873 in MRI biomarkers, 519 in PET PiB imaging and 475 in CSF biomarkers for a median follow-up of 5-6 years, we estimated the longitudinal trajectories of all major Alzheimer disease biomarkers as functions of baseline age that spanned from 18 to 103 years, located the baseline age window at which the longitudinal rates of change accelerated and further examined possible modifying effects of apolipoprotein E (APOE) genotype. We observed that participants 18-45 years at baseline exhibited learning effects on cognition and unexpected directions of change on CSF and PiB biomarkers. The earliest acceleration of longitudinal change occurred for CSF Aβ42 and Aβ42/Aβ40 ratio (with an increase) and for Tau, and pTau181 (with a decrease) at the next baseline age interval of 45-50 years, followed by an accelerated increase for PiB SUVR at the baseline age of 50-55 years and an accelerated decrease for hippocampal volume at the baseline age of 55-60 years and finally by an accelerated decline for cortical thickness and cognition at the baseline age of 65-70 years. Another acceleration in the rate of change occurred at the baseline age of 65-70 years for Aβ42/Aβ40 ratio, Tau, pTau181, PiB SUVR and hippocampal volume. Accelerated declines in hippocampal volume and cognition continued after 70 years. For participants 18-45 years at baseline, significant increases in Aβ42 and Aβ42/Aβ40 ratio and decreases in PiB SUVR occurred in APOE ɛ4 non-carriers but not carriers. After age 45 years, APOE ɛ4 carriers had greater magnitudes than non-carriers in the rates of change for all CSF biomarkers, PiB SUVR and cognition. Our results characterize the temporal evolutions and relative orderings of Alzheimer disease biomarkers across the adult lifespan and the modification effect of APOE ɛ4. These findings may better inform the design of prevention trials on Alzheimer disease.
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