AD Risk Genes Research Articles

Characterizing dysregulations via cell-cell communications in Alzheimer’s brains using single-cell transcriptomes

BackgroundAlzheimer’s disease (AD) is a devastating neurodegenerative disorder affecting 44 million people worldwide, leading to cognitive decline, memory loss, and significant impairment in daily functioning. The recent single-cell sequencing technology has revolutionized genetic and genomic resolution by enabling scientists to explore the diversity of gene expression patterns at the finest resolution. Most existing studies have solely focused on molecular perturbations within each cell, but cells live in microenvironments rather than in isolated entities. Here, we leveraged the large-scale and publicly available single-nucleus RNA sequencing in the human prefrontal cortex to investigate cell-to-cell communication in healthy brains and their perturbations in AD. We uniformly processed the snRNA-seq with strict QCs and labeled canonical cell types consistent with the definitions from the BRAIN Initiative Cell Census Network. From ligand and receptor gene expression, we built a high-confidence cell-to-cell communication network to investigate signaling differences between AD and healthy brains.ResultsSpecifically, we first performed broad communication pattern analyses to highlight that biologically related cell types in normal brains rely on largely overlapping signaling networks and that the AD brain exhibits the irregular inter-mixing of cell types and signaling pathways. Secondly, we performed a more focused cell-type-centric analysis and found that excitatory neurons in AD have significantly increased their communications to inhibitory neurons, while inhibitory neurons and other non-neuronal cells globally decreased theirs to all cells. Then, we delved deeper with a signaling-centric view, showing that canonical signaling pathways CSF, TGFβ, and CX3C are significantly dysregulated in their signaling to the cell type microglia/PVM and from endothelial to neuronal cells for the WNT pathway. Finally, after extracting 23 known AD risk genes, our intracellular communication analysis revealed a strong connection of extracellular ligand genes APP, APOE, and PSEN1 to intracellular AD risk genes TREM2, ABCA1, and APP in the communication from astrocytes and microglia to neurons.ConclusionsIn summary, with the novel advances in single-cell sequencing technologies, we show that cellular signaling is regulated in a cell-type-specific manner and that improper regulation of extracellular signaling genes is linked to intracellular risk genes, giving the mechanistic intra- and inter-cellular picture of AD.

Genome-wide association studies identify novel loci in rapidly progressive Alzheimer's disease.

Recent data suggest that distinct prion-like amyloid beta and tau strains are associated with rapidly progressive Alzheimer's disease (rpAD). The role of genetic factors in rpAD is largely unknown. Previously known AD risk loci were examined in rpAD cases.Genome-wide association studies (GWAS) were performed to identify variants that influence rpAD. We identified 115 pathology-confirmed rpAD cases and 193 clinical rpAD cases, 80% and 69% were of non-Hispanic European ancestry. Compared to the clinical cohort, pathology-confirmed rpAD had higher frequencies of apolipoprotein E (APOE) ε4 and rare missense variants in AD risk genes. A novel genome-wide significant locus (P<5×10-8 ) was observed for clinical rpAD on chromosome 21 (rs2832546); 102 loci showed suggestive associations with pathology-confirmed rpAD (P<1×10-5 ). DISCUSSION rpAD constitutes an extreme subtype of AD with distinct features. GWAS found previously known and novel loci associated with rpAD. Highlights Rapidly progressive Alzheimer's disease (rpAD) was defined with different criteria. Whole genome sequencing identified rare missense variants in rpAD. Novel variants were identified for clinical rpAD on chromosome 21.

Alzheimer’s disease risk genes are differentially expressed upon Lipopolysaccharide stimulation in a myelogenic cell model

AbstractBackgroundLipopolysaccharides (LPS) is a molecule present in the outer membrane of Gram‐negative bacteria. Previous studies have shown that LPS treatment can lead to upregulated pro‐inflammatory and chemotactic cytokines, or elevate the expression of anti‐inflammatory factors in mouse or cell models. Inflammation is seen around amyloid plaques and around small cerebral vessels with Ab1‐40 deposits in AD brains. Increased levels of LPS have been found in the hippocampus of patients with Alzheimer’s disease, compared to age‐matched controls. Other studies demonstrated that LPS is also present and associated with white matter injury in AD and aging brains. Continued inflammation activates microglia and macrophages, which are ialso localized in perivascular tissue. However, to what extent the increased inflammation influences the expression of AD risk genes in myelogenic cells is not well known. Here we utilized RNA sequencing on THP‐1 derived macrophages using Aβ and LPS as stressors to further elucidate this.MethodWe performed RNA sequencing on THP1‐derived macrophages, with or without Aβ and LPS stimulation. 50 known AD risk genes were cross‐checked against the differentially expressed genes discovered. Furthermore, KEGG pathway analysis was performed on the Aβ versus the LPS and Aβ group.ResultOut of 50 AD risk genes, we identified a total of 24 genes in the various comparison groups (Figure 1). 14 AD risk genes were discovered in all the groups: TREM2, LILRB2, BIN1, TNIP1, CD2AP, CD33, CASS4, MS4A4A, NCK2, ADAMTS1, SPI1, RIN3, ECHDC3, AGRN (Figure 1 and2). KEGG pathway analysis pointed out TNF signaling pathway, lysosome, endocytosis and ubiquitin mediated proteolysis as enriched for cells stimulated by LPS with Aβ present (Figure 3).ConclusionOur study demonstrates that LPS activates inflammatory responses in myelogenic cells that further affects AD risk gene expression. Enriched pathways not only relate to Aβ uptake and clearance, but also reflect AD pathology. LPS and/or LPS induced inflammation may be a significant initiator or progressive contributor to inflammatory degeneration in AD.

Elucidating the role of the AD risk gene SORL1 in hiPSC‐derived microglial cells

AbstractBackgroundhiPSC models, from which all cell types of the CNS can be generated, have provided key insights into cell‐type specific processes implicated by AD genetics. Endo‐lysosomal network (ELN) trafficking is a biological pathway implicated in AD and the endosomal receptor SORL1 is strongly associated with AD risk, however studies of SORL1 function in glial cells are lacking. Because ELN trafficking is highly related to neuroimmune function, we modeled SORL1 deficiency in hiPSC‐derived microglia and tested whether pharmacologic or genetic rescue with components of retromer improve cellular phenotypes.MethodWe used an established isogenic series of hiPSC lines that harbor deficiencies in SORL1 (SORL1 KO or SORL1 +/‐) or missense variants in SORL1 (SORL1Var) associated with AD. We differentiated microglial‐like cells (hiPSC‐MGLs) and used cellular assays that examined the function of the ELN. In hiPSC‐MGLs we measured phagocytosis, lysosome function, and pro‐inflammatory cytokine release. For rescue experiments, we treated cells with TPT‐260, a pharmacologic chaperone that stabilizes retromer, and lentiviral vectors expressing retromer subunits.ResultIn SORL1 deficient and SORL1 variant hiPSC‐MGLs, we document ELN phenotypes that converge on the lysosome, including lysosomal swelling and impaired degradation. Interestingly, we also document decreased lysosomal exocytosis, a calcium‐regulated process by which immune cells release lysosomal enzymes and undegraded substrates to the extracellular space to aid in cellular homeostasis, immune‐related signaling and tissue remodeling. In SORL1 deficient cells, lysosomal impairments were observed with abnormal neuroimmune function including differential expression and secretion of cytokines, in response to pro‐inflammatory stimuli Our preliminary data suggests that enhancement of retromer function, either genetically or pharmacologically, can improve these phenotypes.ConclusionUsing an hiPSC‐based model, we show that we can uncover dynamic phenotypes that are linked to AD risk genes. Mutations in SORL1 are now frequently discovered in early‐onset AD patients and the SORL1 locus has a strong genome‐wide signal for late‐onset AD. Together, this suggests that therapeutic strategies targeting the SORL1‐retromer pathway are greatly needed. In this study, we elucidate a role for the SORL1‐retromer pathway in neuroimmune function and provide validation that enhancing this pathway can improve cell‐type specific AD phenotypes in a human model.

Interpretable Deep Learning and MultiOme data repurposing in Alzheimer’s target and drug discovery: NIA Artificial Intelligence and Machine Learning Consortium

AbstractBackgroundHigh‐throughput DNA/RNA sequencing technologies have rapidly led to a robust body of genetic and genomic data in the Alzheimer’s Disease Sequencing Project (ADSP); however, approaching AD with a simplistic single‐target approach has been demonstrated effective for developing symptomatic therapies but ineffective when attempted for disease modification. Therapeutic approaches by specifically modulating genetic risk genes are essential for development of disease‐modifying treatments in AD. However, existing data, including genetics, genomics, transcriptomics, proteomics, and interactomics, have not yet been fully utilized and integrated to explore the roles of targeted therapeutic development for AD.MethodWe will introduce how our team combines tools from artificial intelligence (AI), network medicine, endophenotype models, multi‐omics (genomics, transcriptomics, and proteomics), and electronic health records (EHRs), to identify potential drug targets and repurposable drugs for AD. The fundamental premise of our method is that AD risk genes exhibit distinct functional characteristics compared to non‐risk genes and, therefore, can be distinguished by their aggregated human brain‐specific functional genomic features from various quantitative trait loci (X‐QTL), including expression QTL (eQLT), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL).ResultVia applying our deep learning model to the latest AD GWAS data, we identified 156 putative AD‐risk genes that were differentially expressed in AD‐associated microglia and astrocytes from large single‐nuclei RNA‐sequencing data of human postmortem brains with AD. Combining network‐based prediction and retrospective case‐control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is significantly associated with reduced likelihood of AD incidence, after adjusting for various confounding factors. Importantly, gemfibrozil (an approved lipid regulator) is significantly associated with reduced risk of AD compared to simvastatin (another approved anti‐lipid medicine under Phase II AD trials), using an active user design (odds ratio 0.57, 95% CI 0.51‐0.63, P &lt; 0.0001).ConclusionFrom a translational perspective, the deep learning and network‐based approaches presented here, if broadly applied would significantly catalyze effective treatment development in AD and other neurodegenerative diseases from the human genome sequencing findings.

Genomic atlas of human cerebrospinal fluid and brain metabolomics provides in‐depth understanding of cellular mechanism for neurodegeneration

AbstractBackgroundBrain metabolism is critical for the pathological mechanism of neurodegeneration. However, most metabolite quantitative trait loci (MQTL) studies have used tissues other than brain or cerebrospinal fluid (CSF) and are not ideal for neurodegeneration. We therefore pursued a large‐scale brain and CSF study to identify metabolites contributing to 27 brain‐related diseases.MethodWe obtained 440 metabolites in 2311 CSF samples and 962 metabolites in 1016 brain samples (WashU, ROSMAP, MAYO). We identified MQTL and performed metabolome‐wide association study (MWAS) to identify metabolites affecting diseases. Mendelian randomization (MR) and colocalization was performed for causality and shared genetic regulations.ResultWe identified 192 associations for 144 CSFmetabolites at 102 loci. Of those, 122 associations and 24 loci were novel, indicating tissue specificity. The brain study found 35 associations at 27 loci. Through MWAS, we identified 57 metabolites associated with 17 traits, in which 21 colocalized. Of the eight metabolites associated with AD, succinylcarnitine and adenine were causal. Succinylcarnitine was associated with AD risk gene LACTB, which product regulates lipid metabolism in mitochondrial, where succinylcarnitine generates energy. Both succinylcarnitine and LACTB have been implicated in obesity, a risk factor for AD. For Parkinson disease (PD), low galactosylglycerol level was causal, with signal colocalized at GALC with PD. The knockout of GALC gene prevented alpha‐synuclein accumulation in PD mice model, indicating that galactosylceramidase contributed to the development of PD. For cognitive performance, high levels of three metabolites (6‐oxopiperidine‐2‐carboxylate, 3‐hydroxyisobutyrate and argininosuccinate) were causal for low cognition, with additional support from colocalization and literature evidences. Pyridoxine‐dependent epilepsy patients, often with impaired cognition, had increased level of 6‐oxopiperidine‐2‐carboxylate. AD patients who underwent medium chain triglycerides treatment and improved cognition had changed level of beta‐hydroxybutyrate, related to 3‐hydroxyisobutyrate. Moreover, argininosuccinate lyase deficiency patients often develop severe cognitive impairment and seizures.ConclusionOur large‐scale CSF and brain MQTL study identified tissue‐specific MQTLs and multiple metabolites contributing to neurodegenerative disorders. Our results expand the knowledge on neurodegeneration, providing insights to neurodegeneration etiology, including AD, PD, and cognitive function.

Morphological connectivity differences in Alzheimer's disease correlate with gene transcription and cell-type.

Alzheimer's disease (AD) is one of the most prevalent forms of dementia in older individuals. Convergent evidence suggests structural connectome abnormalities in specific brain regions are linked to AD progression. The biological basis underpinnings of these connectome changes, however, have remained elusive. We utilized an individual regional mean connectivity strength (RMCS) derived from a regional radiomics similarity network to capture altered morphological connectivity in 1654 participants (605 normal controls, 766 mild cognitive impairment [MCI], and 283 AD). Then, we also explored the biological basis behind these morphological changes through gene enrichment analysis and cell-specific analysis. We found that RMCS probes of the hippocampus and medial temporal lobe were significantly altered in AD and MCI, with these differences being spatially related to the expression of AD-risk genes. In addition, gene enrichment analysis revealed that the modulation of chemical synaptic transmission is the most relevant biological process associated with the altered RMCS in AD. Notably, neuronal cells were found to be the most pertinent cells in the altered RMCS. Our findings shed light on understanding the biological basis of structural connectome changes in AD, which may ultimately lead to more effective diagnostic and therapeutic strategies for this devastating disease.

Human microglial state dynamics in Alzheimer’s disease progression

Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer's disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution.

Microglial senescence contributes to female-biased neuroinflammation in the aging mouse hippocampus: implications for Alzheimer’s disease

BackgroundMicroglia, the brain’s principal immune cells, have been implicated in the pathogenesis of Alzheimer’s disease (AD), a condition shown to affect more females than males. Although sex differences in microglial function and transcriptomic programming have been described across development and in disease models of AD, no studies have comprehensively identified the sex divergences that emerge in the aging mouse hippocampus. Further, existing models of AD generally develop pathology (amyloid plaques and tau tangles) early in life and fail to recapitulate the aged brain environment associated with late-onset AD. Here, we examined and compared transcriptomic and translatomic sex effects in young and old murine hippocampal microglia.MethodsHippocampal tissue from C57BL6/N and microglial NuTRAP mice of both sexes were collected at young (5–6 month-old [mo]) and old (22–25 mo) ages. Cell sorting and affinity purification techniques were used to isolate the microglial transcriptome and translatome for RNA-sequencing and differential expression analyses. Flow cytometry, qPCR, and imaging approaches were used to confirm the transcriptomic and translatomic findings.ResultsThere were marginal sex differences identified in the young hippocampal microglia, with most differentially expressed genes (DEGs) restricted to the sex chromosomes. Both sex chromosomally and autosomally encoded sex differences emerged with aging. These sex DEGs identified at old age were primarily female-biased and enriched in senescent and disease-associated microglial signatures. Normalized gene expression values can be accessed through a searchable web interface (https://neuroepigenomics.omrf.org/). Pathway analyses identified upstream regulators induced to a greater extent in females than in males, including inflammatory mediators IFNG, TNF, and IL1B, as well as AD-risk genes TREM2 and APP.ConclusionsThese data suggest that female microglia adopt disease-associated and senescent phenotypes in the aging mouse hippocampus, even in the absence of disease pathology, to a greater extent than males. This sexually divergent microglial phenotype may explain the difference in susceptibility and disease progression in the case of AD pathology. Future studies will need to explore sex differences in microglial heterogeneity in response to AD pathology and determine how sex-specific regulators (i.e., sex chromosomal or hormonal) elicit these sex effects.

Identification of novel genes for age-at-onset of Alzheimer's disease by combining quantitative and survival trait analyses.

Our understanding of the genetic predisposition for age-at-onset (AAO) of Alzheimer's disease (AD) is limited. Here, we sought to identify genes modifying AAO and examined whether any have sex-specific effects. Genome-wide association analysis were performed on imputed genetic data of 9219 AD cases and 10,345 controls from 20 cohorts of the Alzheimer's Disease Genetics Consortium. AAO was modeled from cases directly and as a survival outcome. We identified 11 genome-wide significant loci (P<5×10-8 ), including six known AD-risk genes and five novel loci, UMAD1, LUZP2, ARFGEF2, DSCAM, and 4q25, affecting AAO of AD. Additionally, 39 suggestive loci showed strong association. Twelve loci showed sex-specific effects on AAO including CD300LG and MLX/TUBG2 for females and MIR4445 for males. Genes that influence AAO of AD are excellent therapeutic targets for delaying onset of AD. Several loci identified include genes with promising functional implications for AD.

Spatial Distribution of Rare Missense Variants Within Protein Structures is Associated with AD Risk

AbstractBackgroundDespite large genome‐wide association studies, only ∼30% of the heritability of Alzheimer’s disease is explained. The Alzheimer’s Disease Sequencing Project Whole Exome Sequencing (ADSP WES) has identified millions of genetic variants, over 97% of which are rare (MAF&lt;1%), with 23% appearing in only one person. These rare variants could provide valuable information about new and previously identified risk loci for AD. However, current analysis strategies do not have power to detect associations for such rare variants.MethodWe have developed a protein structure‐based approach that evaluates rare missense variants based on their spatial distribution in a known protein structure rather than on their allele frequency. We hypothesize that AD cases exhibit clustering of rare variants within a protein structure relative to cognitive normal controls. We applied our approach to the ADSP WES Discovery Dataset with 5,522 AD cases and 4,919 controls on 5,969 genes with known structures from the Protein Data Bank (PDB) and 17,450 genes with Alpha Fold2 predicted structures. Only rare variants(MAF&lt;0.05) were included in the analysis. We validated the identified genes within an independent dataset with multi‐ancestry individuals (ADSP WGS Replication) and a European‐ancestry dataset with 15,078 individuals (ADSP validation dataset).ResultWe identified three significant genes (TREM2, SORL1, and EXOC3L4) and one suggestive gene (CSF1R) with AD‐associated spatial clusterings from the ADSP WES data. For TREM2(PDB:6XDS; p‐value = 3.592E‐07) and SORL1(PDB:3WSY;p‐value = 6.701E‐05), two known AD genes, the spatial clusters are significant after excluding known AD risk variants, indicating the presence of additional low‐frequency risk variants within these genes. EXOC3L4(AlphaFold2:Q17RC7;p‐value = 2.504E‐05) is a novel AD risk gene that has a cluster of variants primarily shared by AD cases around the C terminal end of the Sec6 domain. This cluster replicated with significant associations in the ADSP WGS Replication and ADSP validation dataset.ConclusionOur result suggests multiple rare missense variants in TREM2, SORL1, EXOC3L4, and likely CSF1R are associated with AD risk by spatial clustering within the protein structure. These spatial clusterings have been replicated in two additional datasets. These spatial patterns may indicate potential functional regions in the protein structure associated with AD risk and are prime targets for further experimental validation.

Brain Region-Dependent Alternative Splicing of Alzheimer Disease (AD)-Risk Genes Is Associated With Neuropathological Features in AD.

Alzheimer disease (AD) is one of the most complex diseases and is characterized by AD-related neuropathological features, including accumulation of amyloid-β plaques and tau neurofibrillary tangles. Dysregulation of alternative splicing (AS) contributes to these features, and there is heterogeneity in features across brain regions between AD patients, leading to different severity and progression rates; however, brain region-specific AS mechanisms still remain unclear. Therefore, we aimed to systemically investigate AS in multiple brain regions of AD patients and how they affect clinical features. We analyzed RNA sequencing (RNA-Seq) data obtained from brain regions (frontal and temporal) of AD patients. Reads were mapped to the hg19 reference genome using the STAR aligner, and exon skipping (ES) rates were estimated as percent spliced in (PSI) by rMATs. We focused on AD-risk genes discovered by genome-wide association studies, and accordingly evaluated associations between PSI of skipped exons in AD-risk genes and Braak stage and plaque density mean (PM) for each brain region. We also integrated whole-genome sequencing data of the ascertained samples with RNA-Seq data to identify genetic regulators of feature-associated ES. We identified 26 and 41 ES associated with Braak stage in frontal and temporal regions, respectively, and 10 and 50 ES associated with PM. Among those, 10 were frontal-specific (CLU and NTRK2), 65 temporal-specific (HIF1A and TRPC4AP), and 26 shared ES (APP) that accompanied functional Gene Ontology terms, including axonogenesis in shared-ES genes. We further identified genetic regulators that account for 44 ES (44% of the total). Finally, we present as a case study the systematic regulation of an ES in APP, which is important in AD pathogenesis. This study provides new insights into brain region-dependent AS regulation of the architecture of AD-risk genes that contributes to AD pathologies, ultimately allowing identification of a treatment target and region-specific biomarkers for AD.

Synthesize heterogeneous biological knowledge via representation learning for Alzheimer’s disease drug repurposing

Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

Integrative cross-species analysis of GABAergic neuron cell types and their functions in Alzheimer’s disease

Understanding the phenotypic and functional diversity of cerebral cortical GABAergic neurons requires a comprehensive analysis of key transcriptional signatures and neuronal subtype identity. However, the diversity and conservation of GABAergic neurons across multiple mammals remain unclear. Here, we collected the single-nucleus RNA sequencing (snRNA-seq) datasets of cerebral cortex from human, macaque, mouse, and pig to identify the conserved neuronal cell types across species. After systematic analysis of the heterogeneity of GABAergic neurons, we defined four major conserved GABAergic neuron subclasses (Inc SST, Inc LAMP5, Inc PVALB, and Inc VIP) across species. We characterized the species-enriched subclasses of GABAergic neurons from four mammals, such as Inc Meis2 in mouse. Then, we depicted the genetic regulatory network (GRNs) of GABAergic neuron subclasses, which showed the conserved and species-specific GRNs for GABAergic neuron cell types. Finally, we investigated the GABAergic neuron subclass-specific expression modules of Alzheimer’s disease (AD)-related genes in GABAergic neuron cell types. Overall, our study reveals the conserved and divergent GABAergic neuron subclasses and GRNs across multiple species and unravels the gene expression modules of AD-risk genes in GABAergic neuron subclasses, facilitating the GABAergic neurons research and clinical treatment.

Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease.

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p<0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.

Cellular transcriptional alterations of peripheral blood in Alzheimer’s disease

BackgroundAlzheimer’s disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD.MethodsTo explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion.ResultsWe detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B. Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains.ConclusionsWe revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology.

Anesthesia promotes acute expression of genes related to Alzheimer’s disease and latent tau aggregation in transgenic mouse models of tauopathy

BackgroundExposure to anesthesia in the elderly might increase the risk of dementia. Although the mechanism underlying the association is uncertain, anesthesia has been shown to induce acute tau hyperphosphorylation in preclinical models. We sought to investigate the impact of anesthesia on gene expression and on acute and long-term changes in tau biochemistry in transgenic models of tauopathy in order to better understand how anesthesia influences the pathophysiology of dementia.MethodsWe exposed mice with over-expressed human mutant tau (P301L and hyperdopaminergic COMTKO/P301L) to two hours of isoflurane and compared anesthetized mice to controls at several time points. We evaluated tau hyperphosphorylation with quantitative high-sensitivity enzyme-linked immunosorbent assay and performed differential expression and functional transcriptome analyses following bulk mRNA-sequencing.ResultsAnesthesia induced acute hyperphosphorylation of tau at epitopes related to Alzheimer’s disease (AD) in both P301L-based models. Anesthesia was associated with differential expression of genes in the neurodegenerative pathways (e.g., AD-risk genes ApoE and Trem2) and thermogenesis pathway, which is related to both mammalian hibernation and tau phosphorylation. One and three months after anesthesia, hyperphosphorylated tau aggregates were increased in the anesthetized mice.ConclusionsAnesthesia may influence the expression of AD-risk genes and induce biochemical changes in tau that promote aggregation even after single exposure. Further preclinical and human studies are necessary to establish the relevance of our transcriptomic and biochemical findings in these preclinical models to the pathogenesis of dementia following anesthesia.Trial registration: Not applicable.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease.

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Identifying Alzheimer’s genes via brain transcriptome mapping

BackgroundAlzheimer’s disease (AD) is one of the most common neurodegenerative disorders characterized by progressive decline in cognitive function. Targeted genetic analyses, genome-wide association studies, and imaging genetic analyses have been performed to detect AD risk and protective genes and have successfully identified dozens of AD susceptibility loci. Recently, brain imaging transcriptomics analyses have also been conducted to investigate the relationship between neuroimaging traits and gene expression measures to identify interesting gene-traits associations. These imaging transcriptomic studies typically do not involve the disease outcome in the analysis, and thus the identified brain or transcriptomic markers may not be related or specific to the disease outcome.ResultsWe propose an innovative two-stage approach to identify genes whose expression profiles are related to diagnosis phenotype via brain transcriptome mapping. Specifically, we first map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model. Then, the gene-diagnosis association is assessed by spatially correlating the brain transcriptome map with the diagnostic effect map on the brain-wide imaging traits. To demonstrate the promise of our approach, we apply it to the integrative analysis of the brain transcriptome data from the Allen Human Brain Atlas (AHBA) and the amyloid imaging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our method identifies 12 genes whose brain-wide transcriptome patterns are highly correlated with six different diagnostic effect maps on the amyloid imaging traits. These 12 genes include four confirmatory findings (i.e., AD genes reported in DisGeNET) and eight novel genes that have not be associated with AD in DisGeNET.ConclusionWe have proposed a novel disease-related brain transcriptomic mapping method to identify genes whose expression profiles spatially correlated with regional diagnostic effects on a studied brain trait. Our empirical study on the AHBA and ADNI data shows the promise of the approach, and the resulting AD gene discoveries provide valuable information for better understanding biological pathways from transcriptomic signatures to intermediate brain traits and to phenotypic disease outcomes.

Gene co-expression changes underlying the functional connectomic alterations in Alzheimer\u2019s disease

BackgroundThere is growing evidence indicating that a number of functional connectivity networks are disrupted at each stage of the full clinical Alzheimer’s disease spectrum. Such differences are also detectable in cognitive normal (CN) carrying mutations of AD risk genes, suggesting a substantial relationship between genetics and AD-altered functional brain networks. However, direct genetic effect on functional connectivity networks has not been measured.MethodsLeveraging existing AD functional connectivity studies collected in NeuroSynth, we performed a meta-analysis to identify two sets of brain regions: ones with altered functional connectivity in resting state network and ones without. Then with the brain-wide gene expression data in the Allen Human Brain Atlas, we applied a new biclustering method to identify a set of genes with differential co-expression patterns between these two set of brain regions.ResultsDifferential co-expression analysis using biclustering method led to a subset of 38 genes which showed distinctive co-expression patterns between AD-related and non AD-related brain regions in default mode network. More specifically, we observed 4 sub-clusters with noticeable co-expression difference, where the difference in correlations is above 0.5 on average.ConclusionsThis work applies a new biclustering method to search for a subset of genes with altered co-expression patterns in AD-related default mode network regions. Compared with traditional differential expression analysis, differential co-expression analysis yielded many more significant hits with extra insights into the wiring mechanism between genes. Particularly, the differential co-expression pattern was observed between two sets of genes, suggesting potential upstream genetic regulators in AD development.