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.

Single-nucleus RNA-sequencing of autosomal dominant Alzheimer disease and risk variant carriers.

Genetic studies of Alzheimer disease (AD) have prioritized variants in genes related to the amyloid cascade, lipid metabolism, and neuroimmune modulation. However, the cell-specific effect of variants in these genes is not fully understood. Here, we perform single-nucleus RNA-sequencing (snRNA-seq) on nearly 300,000 nuclei from the parietal cortex of AD autosomal dominant (APP and PSEN1) and risk-modifying variant (APOE, TREM2 and MS4A) carriers. Within individual cell types, we capture genes commonly dysregulated across variant groups. However, specific transcriptional states are more prevalent within variant carriers. TREM2 oligodendrocytes show a dysregulated autophagy-lysosomal pathway, MS4A microglia have dysregulated complement cascade genes, and APOEε4 inhibitory neurons display signs of ferroptosis. All cell types have enriched states in autosomal dominant carriers. We leverage differential expression and single-nucleus ATAC-seq to map GWAS signals to effector cell types including the NCK2 signal to neurons in addition to the initially proposed microglia. Overall, our results provide insights into the transcriptional diversity resulting from AD genetic architecture and cellular heterogeneity. The data can be explored on the online browser ( http://web.hararilab.org/SNARE/ ).

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.

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.

Multi-ancestry GWAS reveals excitotoxicity associated with outcome after ischaemic stroke.

During the first hours after stroke onset, neurological deficits can be highly unstable: some patients rapidly improve, while others deteriorate. This early neurological instability has a major impact on long-term outcome. Here, we aimed to determine the genetic architecture of early neurological instability measured by the difference between the National Institutes of Health Stroke Scale (NIHSS) within 6 h of stroke onset and NIHSS at 24 h. A total of 5876 individuals from seven countries (Spain, Finland, Poland, USA, Costa Rica, Mexico and Korea) were studied using a multi-ancestry meta-analyses. We found that 8.7% of NIHSS at 24 h of variance was explained by common genetic variations, and also that early neurological instability has a different genetic architecture from that of stroke risk. Eight loci (1p21.1, 1q42.2, 2p25.1, 2q31.2, 2q33.3, 5q33.2, 7p21.2 and 13q31.1) were genome-wide significant and explained 1.8% of the variability suggesting that additional variants influence early change in neurological deficits. We used functional genomics and bioinformatic annotation to identify the genes driving the association from each locus. Expression quantitative trait loci mapping and summary data-based Mendelian randomization indicate that ADAM23 (log Bayes factor = 5.41) was driving the association for 2q33.3. Gene-based analyses suggested that GRIA1 (log Bayes factor = 5.19), which is predominantly expressed in the brain, is the gene driving the association for the 5q33.2 locus. These analyses also nominated GNPAT (log Bayes factor = 7.64) ABCB5 (log Bayes factor = 5.97) for the 1p21.1 and 7p21.1 loci. Human brain single-nuclei RNA-sequencing indicates that the gene expression of ADAM23 and GRIA1 is enriched in neurons. ADAM23, a presynaptic protein and GRIA1, a protein subunit of the AMPA receptor, are part of a synaptic protein complex that modulates neuronal excitability. These data provide the first genetic evidence in humans that excitotoxicity may contribute to early neurological instability after acute ischaemic stroke.

Functional genomic analyses uncover APOE-mediated regulation of brain and cerebrospinal fluid beta-amyloid levels in Parkinson disease.

Alpha-synuclein is the main protein component of Lewy bodies, the pathological hallmark of Parkinson's disease. However, genetic modifiers of cerebrospinal fluid (CSF) alpha-synuclein levels remain unknown. The use of CSF levels of amyloid beta1-42, total tau, and phosphorylated tau181 as quantitative traits in genetic studies have provided novel insights into Alzheimer's disease pathophysiology. A systematic study of the genomic architecture of CSF biomarkers in Parkinson's disease has not yet been conducted. Here, genome-wide association studies of CSF biomarker levels in a cohort of individuals with Parkinson's disease and controls (N = 1960) were performed. PD cases exhibited significantly lower CSF biomarker levels compared to controls. A SNP, proxy for APOE ε4, was associated with CSF amyloid beta1-42 levels (effect = - 0.5, p = 9.2 × 10-19). No genome-wide loci associated with CSF alpha-synuclein, total tau, or phosphorylated tau181 levels were identified in PD cohorts. Polygenic risk score constructed using the latest Parkinson's disease risk meta-analysis were associated with Parkinson's disease status (p = 0.035) and the genomic architecture of CSF amyloid beta1-42 (R2 = 2.29%; p = 2.5 × 10-11). Individuals with higher polygenic risk scores for PD risk presented with lower CSF amyloid beta1-42 levels (p = 7.3 × 10-04). Two-sample Mendelian Randomization revealed that CSF amyloid beta1-42 plays a role in Parkinson's disease (p = 1.4 × 10-05) and age at onset (p = 7.6 × 10-06), an effect mainly mediated by variants in the APOE locus. In a subset of PD samples, the APOE ε4 allele was associated with significantly lower levels of CSF amyloid beta1-42 (p = 3.8 × 10-06), higher mean cortical binding potentials (p = 5.8 × 10-08), and higher Braak amyloid beta score (p = 4.4 × 10-04). Together these results from high-throughput and hypothesis-free approaches converge on a genetic link between Parkinson's disease, CSF amyloid beta1-42, and APOE.

Multi-ancestry genetic study in 5,876 patients identifies an association between excitotoxic genes and early outcomes after acute ischemic stroke.

During the first hours after stroke onset neurological deficits can be highly unstable: some patients rapidly improve, while others deteriorate. This early neurological instability has a major impact on long-term outcome. Here, we aimed to determine the genetic architecture of early neurological instability measured by the difference between NIH stroke scale (NIHSS) within six hours of stroke onset and NIHSS at 24h (ΔNIHSS). A total of 5,876 individuals from seven countries (Spain, Finland, Poland, United States, Costa Rica, Mexico and Korea) were studied using a multi-ancestry meta-analyses. We found that 8.7% of ΔNIHSS variance was explained by common genetic variations, and also that early neurological instability has a different genetic architecture than that of stroke risk. Seven loci (2p25.1, 2q31.2, 2q33.3, 4q34.3, 5q33.2, 6q26 and 7p21.1) were genome-wide significant and explained 2.1% of the variability suggesting that additional variants influence early change in neurological deficits. We used functional genomics and bioinformatic annotation to identify the genes driving the association from each loci. eQTL mapping and SMR indicate that ADAM23 (log Bayes Factor (LBF)=6.34) was driving the association for 2q33.3. Gene based analyses suggested that GRIA1 (LBF=5.26), which is predominantly expressed in brain, is the gene driving the association for the 5q33.2 locus. These analyses also nominated PARK2 (LBF=5.30) and ABCB5 (LBF=5.70) for the 6q26 and 7p21.1 loci. Human brain single nuclei RNA-seq indicates that the gene expression of ADAM23 and GRIA1 is enriched in neurons. ADAM23 , a pre-synaptic protein, and GRIA1 , a protein subunit of the AMPA receptor, are part of a synaptic protein complex that modulates neuronal excitability. These data provides the first evidence in humans that excitotoxicity may contribute to early neurological instability after acute ischemic stroke. RESEARCH INTO CONTEXT: Evidence before this study: No previous genome-wide association studies have investigated the genetic architecture of early outcomes after ischemic stroke.Added Value of this study: This is the first study that investigated genetic influences on early outcomes after ischemic stroke using a genome-wide approach, revealing seven genome-wide significant loci. A unique aspect of this genetic study is the inclusion of all of the major ethnicities by recruiting from participants throughout the world. Most genetic studies to date have been limited to populations of European ancestry.Implications of all available evidence: The findings provide the first evidence that genes implicating excitotoxicity contribute to human acute ischemic stroke, and demonstrates proof of principle that GWAS of acute ischemic stroke patients can reveal mechanisms involved in ischemic brain injury.

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.

The TMEM106B FTLD-protective variant, rs1990621, is also associated with increased neuronal proportion.

Apart from amyloid ß deposition and tau neurofibrillary tangles, Alzheimer's disease (AD) is a neurodegenerative disorder characterized by neuronal loss and astrocytosis in the cerebral cortex. The goal of this study is to investigate genetic factors associated with the neuronal proportion in health and disease. To identify cell-autonomous genetic variants associated with neuronal proportion in cortical tissues, we inferred cellular population structure from bulk RNA-Seq derived from 1536 individuals. We identified the variant rs1990621 located in the TMEM106B gene region as significantly associated with neuronal proportion (p value = 6.40 × 10-07) and replicated this finding in an independent dataset (p value = 7.41 × 10-04) surpassing the genome-wide threshold in the meta-analysis (p value = 9.42 × 10-09). This variant is in high LD with the TMEM106B non-synonymous variant p.T185S (rs3173615; r2 = 0.98) which was previously identified as a protective variant for frontotemporal lobar degeneration (FTLD). We stratified the samples by disease status, and discovered that this variant modulates neuronal proportion not only in AD cases, but also several neurodegenerative diseases and in elderly cognitively healthy controls. Furthermore, we did not find a significant association in younger controls or schizophrenia patients, suggesting that this variant might increase neuronal survival or confer resilience to the neurodegenerative process. The single variant and gene-based analyses also identified an overall genetic association between neuronal proportion, AD and FTLD risk. These results suggest that common pathways are implicated in these neurodegenerative diseases, that implicate neuronal survival. In summary, we identified a protective variant in the TMEM106B gene that may have a neuronal protection effect against general aging, independent of disease status, which could help elucidate the relationship between aging and neuronal survival in the presence or absence of neurodegenerative disorders. Our findings suggest that TMEM106B could be a potential target for neuronal protection therapies to ameliorate cognitive and functional deficits.

A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Overlap in the Genetic Architecture of Stroke Risk, Early Neurological Changes, and Cardiovascular Risk Factors.

Background and Purpose- The genetic relationships between stroke risk, stroke severity, and early neurological changes are complex and not completely understood. Genetic studies have identified 32 all stroke risk loci. Polygenic risk scores can be used to compare the genetic architecture of related traits. In this study, we compare the genetic architecture of stroke risk, stroke severity, and early neurological changes with that of 2 stroke risk factors: type 2 diabetes mellitus (T2DM) and hypertension. Methods- We assessed the degree of overlap in the genetic architecture of stroke risk, T2DM, hypertension, and 2 acute stroke phenotypes based on the National Institutes of Health Stroke Scale (NIHSS), which ranges from 0 for no stroke symptoms to 21 to 42 for a severe stroke: baseline (within 6 hours after onset) and change in NIHSS (ΔNIHSS=NIHSS at baseline-NIHSS at 24 hours). This was done by (1) single-nucleotide polymorphism by single-nucleotide polymorphism comparison, (2) weighted polygenic risk scores with sentinel variants, and (3) whole-genome polygenic risk scores using multiple P thresholds. Results- We found evidence of genetic architecture overlap between stroke risk and T2DM ( P=2.53×10-169), hypertension ( P=3.93×10-04), and baseline NIHSS ( P=0.03). However, there was no evidence of overlap between ΔNIHSS and stroke risk, T2DM, or hypertension. Conclusions- The genetic architecture of stroke risk is correlated with that of T2DM, hypertension, and initial stroke severity (NIHSS within 6 hours of stroke onset). However, the genetic architecture of early neurological change after stroke (ΔNIHSS) is not correlated with that of ischemic stroke risk, T2DM, or hypertension. Thus, stroke risk and early neurological change after stroke have distinct genetic architectures.