Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer's disease

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, suggesting that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, suggesting that DEGs exert more impact on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we observe an overall downregulation of astrocyte and microglia modules across all brain regions in AD, indicating a prevailing trend of functional repression in glial cells across these regions. Notable genes from the CALM and HSP90 families emerged as hub genes across neuronal modules in all brain regions, suggesting conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis to comprehensively understand the cell-type-specific roles of genes in AD-related biological processes.

Urolithin A improves Alzheimer's disease cognition and restores mitophagy and lysosomal functions

BACKGROUND

Compromised autophagy, including impaired mitophagy and lysosomal function, plays pivotal roles in Alzheimer's disease (AD). Urolithin A (UA) is a gut microbial metabolite of ellagic acid that stimulates mitophagy. The effects of UA's long-term treatment of AD and mechanisms of action are unknown.

METHODS

We addressed these questions in three mouse models of AD with behavioral, electrophysiological, biochemical, and bioinformatic approaches.

RESULTS

Long-term UA treatment significantly improved learning, memory, and olfactory function in different AD transgenic mice. UA also reduced amyloid beta (Aβ) and tau pathologies and enhanced long-term potentiation. UA induced mitophagy via increasing lysosomal functions. UA improved cellular lysosomal function and normalized lysosomal cathepsins, primarily cathepsin Z, to restore lysosomal function in AD, indicating the critical role of cathepsins in UA-induced therapeutic effects on AD.

CONCLUSIONS

Our study highlights the importance of lysosomal dysfunction in AD etiology and points to the high translational potential of UA.

HIGHLIGHTS

Long-term urolithin A (UA) treatment improved learning, memory, and olfactory function in Alzheimer's disease (AD) mice. UA restored lysosomal functions in part by regulating cathepsin Z (Ctsz) protein. UA modulates immune responses and AD-specific pathophysiological pathways.

Post-GWAS multiomic functional investigation of the TNIP1 locus in Alzheimer's disease highlights a potential role for GPX3

INTRODUCTION

Recent genome-wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear.

METHODS

We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS-implicated variants (rs34294852 and rs871269).

RESULTS

CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10-5) and higher CSF phosphorylated tau (p-tau) levels (P = 9.28 × 10-7). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10-6) and CSF p-tau levels (P = 4.38 × 10-9).

DISCUSSION

These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels.

HIGHLIGHTS

Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau. The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category. GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls. rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia.

Alzheimer's disease rewires gene coexpression networks coupling different brain regions

Connectome studies have shown how Alzheimer's disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.

Gene networks and systems biology in Alzheimer's disease: Insights from multi-omics approaches

Despite numerous studies in the field of dementia and Alzheimer's disease (AD), a comprehensive understanding of this devastating disease remains elusive. Bulk transcriptomics have provided insights into the underlying genetic factors at a high level. Subsequent technological advancements have focused on single-cell omics, encompassing techniques such as single-cell RNA sequencing and epigenomics, enabling the capture of RNA transcripts and chromatin states at a single cell or nucleus resolution. Furthermore, the emergence of spatial omics has allowed the study of gene responses in the vicinity of amyloid beta plaques or across various brain regions. With the vast amount of data generated, utilizing gene regulatory networks to comprehensively study this disease has become essential. This review delves into some techniques employed in the field of AD, explores the discoveries made using these techniques, and provides insights into the future of the field.

Genetic diversity promotes resilience in a mouse model of Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is a neurodegenerative disorder with multifactorial etiology, including genetic factors that play a significant role in disease risk and resilience. However, the role of genetic diversity in preclinical AD studies has received limited attention.

METHODS

We crossed five Collaborative Cross strains with 5xFAD C57BL/6J female mice to generate F1 mice with and without the 5xFAD transgene. Amyloid plaque pathology, microglial and astrocytic responses, neurofilament light chain levels, and gene expression were assessed at various ages.

RESULTS

Genetic diversity significantly impacts AD-related pathology. Hybrid strains showed resistance to amyloid plaque formation and neuronal damage. Transcriptome diversity was maintained across ages and sexes, with observable strain-specific variations in AD-related phenotypes. Comparative gene expression analysis indicated correlations between mouse strains and human AD.

DISCUSSION

Increasing genetic diversity promotes resilience to AD-related pathogenesis, relative to an inbred C57BL/6J background, reinforcing the importance of genetic diversity in uncovering resilience in the development of AD.

HIGHLIGHTS

Genetic diversity's impact on AD in mice was explored. Diverse F1 mouse strains were used for AD study, via the Collaborative Cross. Strain-specific variations in AD pathology, glia, and transcription were found. Strains resilient to plaque formation and plasma neurofilament light chain (NfL) increases were identified. Correlations with human AD transcriptomics were observed.

The Abca7V1613M variant reduces Aβ generation, plaque load, and neuronal damage

BACKGROUND

Variants in ABCA7, a member of the ABC transporter superfamily, have been associated with increased risk for developing late onset Alzheimer's disease (LOAD).

METHODS

CRISPR-Cas9 was used to generate an Abca7V1613M variant in mice, modeling the homologous human ABCA7V1599M variant, and extensive characterization was performed.

RESULTS

Abca7V1613M microglia show differential gene expression profiles upon lipopolysaccharide challenge and increased phagocytic capacity. Homozygous Abca7V1613M mice display elevated circulating cholesterol and altered brain lipid composition. When crossed with 5xFAD mice, homozygous Abca7V1613M mice display fewer Thioflavin S-positive plaques, decreased amyloid beta (Aβ) peptides, and altered amyloid precursor protein processing and trafficking. They also exhibit reduced Aβ-associated inflammation, gliosis, and neuronal damage.

DISCUSSION

Overall, homozygosity for the Abca7V1613M variant influences phagocytosis, response to inflammation, lipid metabolism, Aβ pathology, and neuronal damage in mice. This variant may confer a gain of function and offer a protective effect against Alzheimer's disease-related pathology.

HIGHLIGHTS

ABCA7 recognized as a top 10 risk gene for developing Alzheimer's disease. Loss of function mutations result in increased risk for LOAD. V1613M variant reduces amyloid beta plaque burden in 5xFAD mice. V1613M variant modulates APP processing and trafficking in 5xFAD mice. V1613M variant reduces amyloid beta-associated damage in 5xFAD mice.

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, indicating that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, highlighting the differential impact of DEGs on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we note an overall downregulation of both astrocyte and microglia modules in AD across all brain regions, suggesting a prevailing trend of functional repression in glial cells across these regions. Notable genes, including those of the CALM and HSP90 family genes emerged as hub genes across neuronal modules in all brain regions, indicating conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis for a comprehensive understanding of the cell-type-specific roles of genes in AD-related biological processes.

Epigenomic analysis of Alzheimer's disease brains reveals diminished CTCF binding on genes involved in synaptic organization

Epigenetic aberrations are suggested to play an important role in transcriptional alterations in Alzheimer's disease (AD). One of the key mechanisms of epigenetic regulation of gene expression is through the dynamic organization of chromatin structure via the master genome architecture protein, CCCTC-binding factor (CTCF). By forming chromatin loops, CTCF can influence gene transcription in a complex manner. To find out whether genome-wide DNA binding sites for CTCF are altered in AD, we compared CTCF chromatin immunoprecipitation sequencing (ChIP-Seq) data from frontal cortex of human AD patients and normal controls (n = 9 pairs, all females). We have revealed that CTCF-binding affinity on many genes is significantly reduced in AD patients, and these genes are enriched in synaptic organization, cell adhesion, and actin cytoskeleton, including synaptic scaffolding molecules and receptors, such as SHANK2, HOMER1, NRXN1, CNTNAP2 and GRIN2A, and protocadherin (PCDH) and cadherin (CDH) family members. By comparing transcriptomic data from AD patients, we have discovered that many of the synaptic and adhesion genes with reduced CTCF binding in AD are significantly reduced in their mRNA expression. Moreover, a significant overlap of genes with the diminished CTCF binding and the reduced H3K27ac is identified in AD, with the common genes enriched in synaptic organization. These data suggest that the CTCF-controlled 3D chromatin organization is perturbed in AD, which may be linked to the diminished expression of target genes, probably through changes in histone modification.

Longitudinal APOE4- and amyloid-dependent changes in the blood transcriptome in cognitively intact older adults

BACKGROUND

Gene expression is dysregulated in Alzheimer's disease (AD) patients, both in peripheral blood and post mortem brain. We investigated peripheral whole-blood gene (co)expression to determine molecular changes prior to symptom onset.

METHODS

RNA was extracted and sequenced for 65 cognitively healthy F-PACK participants (65 (56-80) years, 34 APOE4 non-carriers, 31 APOE4 carriers), at baseline and follow-up (interval: 5.0 (3.4-8.6) years). Participants received amyloid PET at both time points and amyloid rate of change derived. Accumulators were defined with rate of change ≥ 2.19 Centiloids. We performed differential gene expression and weighted gene co-expression network analysis to identify differentially expressed genes and networks of co-expressed genes, respectively, with respect to traits of interest (APOE4 status, amyloid accumulation (binary/continuous)), and amyloid positivity status, followed by Gene Ontology annotation.

RESULTS

There were 166 significant differentially expressed genes at follow-up compared to baseline in APOE4 carriers only, whereas 12 significant differentially expressed genes were found only in APOE4 non-carriers, over time. Among the significant genes in APOE4 carriers, several had strong evidence for a pathogenic role in AD based on direct association scores generated from the DISQOVER platform: NGRN, IGF2, GMPR, CLDN5, SMIM24. Top enrichment terms showed upregulated mitochondrial and metabolic pathways, and an exacerbated upregulation of ribosomal pathways in APOE4 carriers compared to non-carriers. Similarly, there were 33 unique significant differentially expressed genes at follow-up compared to baseline in individuals classified as amyloid negative at baseline and positive at follow-up or amyloid positive at both time points and 32 unique significant differentially expressed genes over time in individuals amyloid negative at both time points. Among the significant genes in the first group, the top five with the highest direct association scores were as follows: RPL17-C18orf32, HSP90AA1, MBP, SIRPB1, and GRINA. Top enrichment terms included upregulated metabolism and focal adhesion pathways. Baseline and follow-up gene co-expression networks were separately built. Seventeen baseline co-expression modules were derived, with one significantly negatively associated with amyloid accumulator status (r² =  - 0.25, p = 0.046). This was enriched for proteasomal protein catabolic process and myeloid cell development. Thirty-two follow-up modules were derived, with two significantly associated with APOE4 status: one downregulated (r² =  - 0.27, p = 0.035) and one upregulated (r² = 0.26, p = 0.039) module. Top enrichment processes for the downregulated module included proteasomal protein catabolic process and myeloid cell homeostasis. Top enrichment processes for the upregulated module included cytoplasmic translation and rRNA processing.

CONCLUSIONS

We show that there are longitudinal gene expression changes that implicate a disrupted immune system, protein removal, and metabolism in cognitively intact individuals who carry APOE4 or who accumulate in cortical amyloid. This provides insight into the pathophysiology of AD, whilst providing novel targets for drug and therapeutic development.

hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data

Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.

Phenome-wide analyses identify an association between the parent-of-origin effects dependent methylome and the rate of aging in humans

BACKGROUND

The variation in the rate at which humans age may be rooted in early events acting through the genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions enriched for genetically controlled imprinting effects (the typical type of POE) and regions influenced by environmental effects associated with parents (the atypical POE). This part of the methylome is heavily influenced by early events, making it a potential route connecting early exposures, the epigenome, and aging. We aim to test the association of POE-CpGs with early and later exposures and subsequently with health-related phenotypes and adult aging.

RESULTS

We perform a phenome-wide association analysis for the POE-influenced methylome using GS:SFHS (Ndiscovery = 5087, Nreplication = 4450). We identify and replicate 92 POE-CpG-phenotype associations. Most of the associations are contributed by the POE-CpGs belonging to the atypical class where the most strongly enriched associations are with aging (DNAmTL acceleration), intelligence, and parental (maternal) smoking exposure phenotypes. A proportion of the atypical POE-CpGs form co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased within-module methylation connectivity with age. The atypical POE-CpGs also display high levels of methylation heterogeneity, fast information loss with age, and a strong correlation with CpGs contained within epigenetic clocks.

CONCLUSIONS

These results identify the association between the atypical POE-influenced methylome and aging and provide new evidence for the "early development of origin" hypothesis for aging in humans.

The combined signatures of the tumour microenvironment and nucleotide metabolism-related genes provide a prognostic and therapeutic biomarker for gastric cancer

The tumour microenvironment (TME) is vital to tumour development and influences the immunotherapy response. Abnormal nucleotide metabolism (NM) not only promotes tumour cell proliferation but also inhibits immune responses in the TME. Therefore, this study aimed to determine whether the combined signatures of NM and the TME could better predict the prognosis and treatment response in gastric cancer (GC). 97 NM-related genes and 22 TME cells were evaluated in TCGA-STAD samples, and predictive NM and TME characteristics were determined. Subsequent correlation analysis and single-cell data analysis illustrated a link between NM scores and TME cells. Thereafter, NM and TME characteristics were combined to construct an NM-TME classifier. Patients in the NMlow/TMEhigh group exhibited better clinical outcomes and treatment responses, which could be attributed to the differences in immune cell infiltration, immune checkpoint genes, tumour somatic mutations, immunophenoscore, immunotherapy response rate and proteomap. Additionally, the NMhigh/TMElow group benefited more from Imatinib, Midostaurin and Linsitinib, while patients in the NMlow/TMEhigh group benefited more from Paclitaxel, Methotrexate and Camptothecin. Finally, a highly reliable nomogram was developed. In conclusion, the NM-TME classifier demonstrated a pretreatment predictive value for prognosis and therapeutic responses, which may offer novel strategies for strategizing patients with optimal therapies.

Revealing cell vulnerability in Alzheimer's disease by single-cell transcriptomics

Alzheimer's disease (AD) is a neurodegenerative disorder that by affecting specific brain cell types and regions cause severe pathological and functional changes in memory neural circuits. A comprehensive knowledge of the pathogenic mechanisms underlying AD requires a deeper understanding of the cell-specific pathological responses through integrative molecular analyses. Recent application of high-throughput single-cell transcriptomics to postmortem tissue has proved powerful to unravel cell susceptibility and biological networks responding to amyloid and tau pathologies. Here, we review single-cell transcriptomic studies successfully applied to decipher cell-specific gene expression programs and pathways in the brain of AD patients. Transcriptional information reveals both specific and common gene signatures affecting the major cerebral cell types, including astrocytes, endothelial cells, microglia, neurons, and oligodendrocytes. Cell type-specific transcriptomes associated with AD pathology and clinical symptoms are related to common biological networks affecting, among others pathways, synaptic function, inflammation, proteostasis, cell death, oxidative stress, and myelination. The general picture that emerges from systems-level single-cell transcriptomics is a spatiotemporal pattern of cell diversity and biological pathways, and novel cell subpopulations affected in AD brain. We argue that broader implementation of cell transcriptomics in larger AD human cohorts using standardized protocols is fundamental for reliable assessment of temporal and regional cell-type gene profiling. The possibility of applying this methodology for personalized medicine in clinics is still challenging but opens new roads for future diagnosis and treatment in dementia.

Identification of gene networks mediating regional resistance to tauopathy in late-onset Alzheimer’s disease

Neurofibrillary lesions composed of tau protein aggregates are defining hallmarks of Alzheimer’s Disease. Despite tau filaments appearing to spread between networked brain regions in a prion-like manner, certain areas including cerebellum resist trans-synaptic spread of tauopathy and degeneration of their constituent neuronal cell bodies. To identify molecular correlates of resistance, we derived and implemented a ratio of ratios approach for disaggregating gene expression data on the basis of regional vulnerability to tauopathic neurodegeneration. When applied to vulnerable pre-frontal cortex as an internal reference for resistant cerebellum, the approach segregated adaptive changes in expression into two components. The first was enriched for neuron-derived transcripts associated with proteostasis including specific members of the molecular chaperone family and was unique to resistant cerebellum. When produced as purified proteins, each of the identified chaperones depressed aggregation of 2N4R tau in vitro at sub-stoichiometric concentrations, consistent with the expression polarity deduced from ratio of ratios testing. In contrast, the second component enriched for glia- and microglia-derived transcripts associated with neuroinflammation, segregating these pathways from susceptibility to tauopathy. These data support the utility of ratio of ratios testing for establishing the polarity of gene expression changes with respect to selective vulnerability. The approach has the potential to identify new targets for drug discovery predicated on their ability to promote resistance to disease in vulnerable neuron populations.

Mechanism of action deconvolution of the small-molecule pathological tau aggregation inhibitor Anle138b

BACKGROUND

A key histopathological hallmark of Alzheimer's disease (AD) is the presence of neurofibrillary tangles of aggregated microtubule-associated protein tau in neurons. Anle138b is a small molecule which has previously shown efficacy in mice in reducing tau aggregates and rescuing AD disease phenotypes.

METHODS

In this work, we employed bioinformatics analysis-including pathway enrichment and causal reasoning-of an in vitro tauopathy model. The model consisted of cultured rat cortical neurons either unseeded or seeded with tau aggregates derived from human AD patients, both of which were treated with Anle138b to generate hypotheses for its mode of action. In parallel, we used a collection of human target prediction models to predict direct targets of Anle138b based on its chemical structure.

RESULTS

Combining the different approaches, we found evidence supporting the hypothesis that the action of Anle138b involves several processes which are key to AD progression, including cholesterol homeostasis and neuroinflammation. On the pathway level, we found significantly enriched pathways related to these two processes including those entitled "Superpathway of cholesterol biosynthesis" and "Granulocyte adhesion and diapedesis". With causal reasoning, we inferred differential activity of SREBF1/2 (involved in cholesterol regulation) and mediators of the inflammatory response such as NFKB1 and RELA. Notably, our findings were also observed in Anle138b-treated unseeded neurons, meaning that the inferred processes are independent of tau pathology and thus represent the direct action of the compound in the cellular system. Through structure-based ligand-target prediction, we predicted the intracellular cholesterol carrier NPC1 as well as NF-κB subunits as potential targets of Anle138b, with structurally similar compounds in the model training set known to target the same proteins.

CONCLUSIONS

This study has generated feasible hypotheses for the potential mechanism of action of Anle138b, which will enable the development of future molecular interventions aiming to reduce tau pathology in AD patients.

Golgi apparatus, endoplasmic reticulum and mitochondrial function implicated in Alzheimer's disease through polygenic risk and RNA sequencing

Polygenic risk scores (PRS) have been widely adopted as a tool for measuring common variant liability and they have been shown to predict lifetime risk of Alzheimer's disease (AD) development. However, the relationship between PRS and AD pathogenesis is largely unknown. To this end, we performed a differential gene-expression and associated disrupted biological pathway analyses of AD PRS vs. case/controls in human brain-derived cohort sample (cerebellum/temporal cortex; MayoRNAseq). The results highlighted already implicated mechanisms: immune and stress response, lipids, fatty acids and cholesterol metabolisms, endosome and cellular/neuronal death, being disrupted biological pathways in both case/controls and PRS, as well as previously less well characterised processes such as cellular structures, mitochondrial respiration and secretion. Despite heterogeneity in terms of differentially expressed genes in case/controls vs. PRS, there was a consensus of commonly disrupted biological mechanisms. Glia and microglia-related terms were also significantly disrupted, albeit not being the top disrupted Gene Ontology terms. GWAS implicated genes were significantly and in their majority, up-regulated in response to different PRS among the temporal cortex samples, suggesting potential common regulatory mechanisms. Tissue specificity in terms of disrupted biological pathways in temporal cortex vs. cerebellum was observed in relation to PRS, but limited tissue specificity when the datasets were analysed as case/controls. The largely common biological mechanisms between a case/control classification and in association with PRS suggests that PRS stratification can be used for studies where suitable case/control samples are not available or the selection of individuals with high and low PRS in clinical trials.

Neurodegenerative disorders: From clinicopathology convergence to systems biology divergence

Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders.

Endothelial Cells are Heterogeneous in Different Brain Regions and are Dramatically Altered in Alzheimer's Disease

Vascular endothelial cells play an important role in maintaining brain health, but their contribution to Alzheimer's disease (AD) is obscured by limited understanding of the cellular heterogeneity in normal aged brain and in disease. To address this, we performed single nucleus RNAseq on tissue from 32 AD and non-AD donors each with five cortical regions: entorhinal cortex, inferior temporal gyrus, prefrontal cortex, visual association cortex and primary visual cortex. Analysis of 51,586 endothelial cells revealed unique gene expression patterns across the five regions in non-AD donors. Alzheimer's brain endothelial cells were characterized by upregulated protein folding genes and distinct transcriptomic differences in response to amyloid beta plaques and cerebral amyloid angiopathy (CAA). This dataset demonstrates previously unrecognized regional heterogeneity in the endothelial cell transcriptome in both aged non-AD and AD brain.

Significance Statement

In this work, we show that vascular endothelial cells collected from five different brain regions display surprising variability in gene expression. In the presence of Alzheimer's disease pathology, endothelial cell gene expression is dramatically altered with clear differences in regional and temporal changes. These findings help explain why certain brain regions appear to differ in susceptibility to disease-related vascular remodeling events that may impact blood flow.

Phenome-wide analysis identifies parent-of-origin effects on the human methylome associated with changes in the rate of aging

Variation in the rate at which humans age may be rooted in early life events acting through genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions either enriched for genetically controlled imprinting effects (the typical type of POE) or atypical POE introduced by environmental effects associated with parents. This part of the methylome is heavily influenced by early life events, making it a potential route connecting early environmental exposures, the epigenome and the rate of aging. Here, we aim to test the association of POE-influenced methylation of CpG dinucleotides (POE-CpG sites) with early and later environmental exposures and subsequently with health-related phenotypes and adult aging phenotypes. We do this by performing phenome-wide association analyses of the POE-influenced methylome using a large family-based population cohort (GS:SFHS, Ndiscovery=5,087, Nreplication=4,450). At the single CpG level, 92 associations of POE-CpGs with phenotypic variation were identified and replicated. Most of the associations were contributed by POE-CpGs belonging to the atypical class and the most strongly enriched associations were with aging (DNAmTL acceleration), intelligence and parental (maternal) smoking exposure phenotypes. We further found that a proportion of the atypical-POE-CpGs formed co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased internal module connectivity (strength of methylation correlation across constituent CpGs) with age. Atypical POE-CpGs also displayed high levels of methylation heterogeneity and epigenetic drift (i.e. information loss with age) and a strong correlation with CpGs contained within epigenetic clocks. These results identified associations between the atypical-POE-influenced methylome and aging and provided new evidence for the "early development of origin" hypothesis for aging in humans.

Molecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer's disease

Tau aggregation in neurofibrillary tangles (NFTs) is closely associated with neurodegeneration and cognitive decline in Alzheimer's disease (AD). However, the molecular signatures that distinguish between aggregation-prone and aggregation-resistant cell states are unknown. We developed methods for the high-throughput isolation and transcriptome profiling of single somas with NFTs from the human AD brain, quantified the susceptibility of 20 neocortical subtypes for NFT formation and death, and identified both shared and cell-type-specific signatures. NFT-bearing neurons shared a marked upregulation of synaptic transmission-related genes, including a core set of 63 genes enriched for synaptic vesicle cycling. Oxidative phosphorylation and mitochondrial dysfunction were highly cell-type dependent. Apoptosis was only modestly enriched, and the susceptibilities of NFT-bearing and NFT-free neurons for death were highly similar. Our analysis suggests that NFTs represent cell-type-specific responses to stress and synaptic dysfunction. We provide a resource for biomarker discovery and the investigation of tau-dependent and tau-independent mechanisms of neurodegeneration.

Loss of CaV1.3 RNA editing enhances mouse hippocampal plasticity, learning, and memory

L-type Ca_V1.3 calcium channels are expressed on the dendrites and soma of neurons, and there is a paucity of information about its role in hippocampal plasticity. Here, by genetic targeting to ablate Ca_V1.3 RNA editing, we demonstrate that unedited Ca_V1.3^ΔECS mice exhibited improved learning and enhanced long-term memory, supporting a functional role of RNA editing in behavior. Significantly, the editing paradox that functional recoding of Ca_V1.3 RNA editing sites slows Ca²⁺-dependent inactivation to increase Ca²⁺ influx but reduces channel open probability to decrease Ca²⁺ influx was resolved. Mechanistically, using hippocampal slice recordings, we provide evidence that unedited Ca_V1.3 channels permitted larger Ca²⁺ influx into the hippocampal pyramidal neurons to bolster neuronal excitability, synaptic transmission, late long-term potentiation, and increased dendritic arborization. Of note, RNA editing of the Ca_V1.3 IQ-domain was found to be evolutionarily conserved in mammals, which lends support to the importance of the functional recoding of the Ca_V1.3 channel in brain function.

scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene–gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.

Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning

Atopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options.

Integrative genomic and transcriptomic analyses on publicly available data-sets together with in silico drug repositioning identifies alternative therapeutic options to treat atopic dermatitis.

Absence of microglia promotes diverse pathologies and early lethality in Alzheimer's disease mice

Microglia are strongly implicated in the development and progression of Alzheimer's disease (AD), yet their impact on pathology and lifespan remains unclear. Here we utilize a CSF1R hypomorphic mouse to generate a model of AD that genetically lacks microglia. The resulting microglial-deficient mice exhibit a profound shift from parenchymal amyloid plaques to cerebral amyloid angiopathy (CAA), which is accompanied by numerous transcriptional changes, greatly increased brain calcification and hemorrhages, and premature lethality. Remarkably, a single injection of wild-type microglia into adult mice repopulates the microglial niche and prevents each of these pathological changes. Taken together, these results indicate the protective functions of microglia in reducing CAA, blood-brain barrier dysfunction, and brain calcification. To further understand the clinical implications of these findings, human AD tissue and iPSC-microglia were examined, providing evidence that microglia phagocytose calcium crystals, and this process is impaired by loss of the AD risk gene, TREM2.

The Key Factors Predicting Dementia in Individuals With Alzheimer’s Disease-Type Pathology

Dementia affects millions of individuals worldwide, yet there are no effective treatments. Alzheimer’s disease, the most common form of dementia, is characterized by amyloid and tau pathology with amyloid accumulation thought to precipitate tau pathology, neurodegeneration, and dementia. The Religious Orders Study and Memory and Aging Project (ROSMAP) cohort is a unique resource with quantitative pathology from multiple brain regions, RNA sequencing, and longitudinal cognitive data. Our previous work applying machine learning to the RNA sequencing data identified lactoferrin (LTF) as the gene most predictive of amyloid accumulation with a potential amyloidogenic mechanism identified in vitro and with cell-culture models. In the present study, we examined which pathologies and genes were related to cognitive status (dementia, mild impairment, and no cognitive impairment) and rate of cognitive decline. Tau load in the anterior cingulate and ADAMTS2, encoding a metallopeptidase, were the respective regional pathology and gene most associated with cognitive decline, while PRTN3, encoding a serine protease, was the key protective feature. ADAMTS2, but not PRTN3, was related to amyloid and tau load in the previous study while LTF was not related to cognitive decline here. These findings confirm a general relationship between tau pathology and dementia, show the specific importance of tau pathology in the anterior cingulate cortex and identify ADAMTS2 as a potential target for slowing cognitive decline.

Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain

A key aspect of nearly all single-cell sequencing experiments is dissociation of intact tissues into single-cell suspensions. While many protocols have been optimized for optimal cell yield, they have often overlooked the effects that dissociation can have on ex vivo gene expression. Here, we demonstrate that use of enzymatic dissociation on brain tissue induces an aberrant ex vivo gene expression signature, most prominently in microglia, which is prevalent in published literature and can substantially confound downstream analyses. To address this issue, we present a rigorously validated protocol that preserves both in vivo transcriptional profiles and cell-type diversity and yield across tissue types and species. We also identify a similar signature in postmortem human brain single-nucleus RNA-sequencing datasets, and show that this signature is induced in freshly isolated human tissue by exposure to elevated temperatures ex vivo. Together, our results provide a methodological solution for preventing artifactual gene expression changes during fresh tissue digestion and a reference for future deeper analysis of the potential confounding states present in postmortem human samples.

Association of SPI1 Haplotypes with Altered SPI1 Gene Expression and Alzheimer’s Disease Risk

Background:

Genetic studies reveal that single-nucleotide polymorphisms (SNPs) of SPI1 are associated with Alzheimer’s disease (AD), while their effects in the Chinese population remain unclear.

Objective:

We aimed to examine the AD-association of SPI1 SNPs in the Chinese population and investigate the underlying mechanisms of these SNPs in modulating AD risk.

Methods:

We conducted a genetic analysis of three SPI1 SNPs (i.e., rs1057233, rs3740688, and rs78245530) in a Chinese cohort (n = 333 patients with AD, n = 721 normal controls). We also probed public European-descent AD cohorts and gene expression datasets to investigate the putative functions of those SNPs.

Results:

We showed that SPI1 SNP rs3740688 is significantly associated with AD in the Chinese population (odds ratio [OR] = 0.72 [0.58–0.89]) and identified AD-protective SPI1 haplotypes β (tagged by rs1057233 and rs3740688) and γ (tagged by rs3740688 and rs78245530). Specifically, haplotypes β and γ are associated with decreased SPI1 gene expression level in the blood and brain tissues, respectively. The regulatory roles of these haplotypes are potentially mediated by changes in miRNA binding and the epigenetic landscape. Our results suggest that the AD-protective SPI1 haplotypes regulate pathways involved in immune and neuronal functions.

Conclusion:

This study is the first to report a significant association of SPI1 with AD in the Chinese population. It also identifies SPI1 haplotypes that are associated with SPI1 gene expression and decreased AD risk.

Systems biology approaches to unravel the molecular and genetic architecture of Alzheimer's disease and related tauopathies

Over the years, genetic studies have identified multiple genetic risk variants associated with neurodegenerative disorders and helped reveal new biological pathways and genes of interest. However, genetic risk variants commonly reside in non-coding regions and may regulate distant genes rather than the nearest gene, as well as a gene's interaction partners in biological networks. Systems biology and functional genomics approaches provide the framework to unravel the functional significance of genetic risk variants in disease. In this review, we summarize the genetic and transcriptomic studies of Alzheimer's disease and related tauopathies and focus on the advantages of performing systems-level analyses to interrogate the biological pathways underlying neurodegeneration. Finally, we highlight new avenues of multi-omics analysis with single-cell approaches, which provide unparalleled opportunities to systematically explore cellular heterogeneity, and present an example of how to integrate publicly available single-cell datasets. Systems-level analysis has illuminated the function of many disease risk genes, but much work remains to study tauopathies and to understand spatiotemporal gene expression changes of specific cell types.

Colony stimulating factors in the nervous system

Although traditionally seen as regulators of hematopoiesis, colony-stimulating factors (CSFs) have emerged as important players in the nervous system, both in health and disease. This review summarizes the cellular sources, patterns of expression and physiological roles of the macrophage (CSF-1, IL-34), granulocyte-macrophage (GM-CSF) and granulocyte (G-CSF) colony stimulating factors within the nervous system, with a particular focus on their actions on microglia. CSF-1 and IL-34, via the CSF-1R, are required for the development, proliferation and maintenance of essentially all CNS microglia in a temporal and regional specific manner. In contrast, in steady state, GM-CSF and G-CSF are mainly involved in regulation of microglial function. The alterations in expression of these growth factors and their receptors, that have been reported in several neurological diseases, are described and the outcomes of their therapeutic targeting in mouse models and humans are discussed.

Transcriptome analyses reveal tau isoform-driven changes in transposable element and gene expression

Alternative splicing of the gene MAPT produces several isoforms of tau protein. Overexpression of these isoforms is characteristic of tauopathies, which are currently untreatable neurodegenerative diseases. Though non-canonical functions of tau have drawn interest, the role of tau isoforms in these diseases has not been fully examined and may reveal new details of tau-driven pathology. In particular, tau has been shown to promote activation of transposable elements-highly regulated nucleotide sequences that replicate throughout the genome and can promote immunologic responses and cellular stress. This study examined tau isoforms' roles in promoting cell damage and dysregulation of genes and transposable elements at a family-specific and locus-specific level. We performed immunofluorescence, Western blot and cytotoxicity assays, along with paired-end RNA sequencing on differentiated SH-SY5Y cells infected with lentiviral constructs of tau isoforms and treated with amyloid-beta oligomers. Our transcriptomic findings were validated using publicly available RNA-sequencing data from Alzheimer's disease, progressive supranuclear palsy and control human samples from the Accelerating Medicine's Partnership for AD (AMP-AD). Significance for biochemical assays was determined using Wilcoxon ranked-sum tests and false discovery rate. Transcriptome analysis was conducted through DESeq2 and the TEToolkit suite available from the Hammell lab at Cold Spring Harbor Laboratory. Our analyses show overexpression of different tau isoforms and their interactions with amyloid-beta in SH-SY5Y cells result in isoform-specific changes in the transcriptome, with locus-specific transposable element dysregulation patterns paralleling those seen in patients with Alzheimer's disease and progressive supranuclear palsy. Locus-level transposable element expression showed increased dysregulation of L1 and Alu sites, which have been shown to drive pathology in other neurological diseases. We also demonstrated differences in rates of cell death in SH-SY5Y cells depending on tau isoform overexpression. These results demonstrate the importance of examining tau isoforms' role in neurodegeneration and of further examining transposable element dysregulation in tauopathies and its role in activating the innate immune system.

Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer's disease phenotypes

BACKGROUND

Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.

METHOD

First, given the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes.

RESULTS

Our analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.

Integrative Network-Based Analysis Reveals Gene Networks and Novel Drug Repositioning Candidates for Alzheimer Disease

Background and Objectives

To integrate genome-wide association study data with tissue-specific gene expression information to identify coexpression networks, biological pathways, and drug repositioning candidates for Alzheimer disease.

Methods

We integrated genome-wide association summary statistics for Alzheimer disease with tissue-specific gene coexpression networks from brain tissue samples in the Genotype-Tissue Expression study. We identified gene coexpression networks enriched with genetic signals for Alzheimer disease and characterized the associated networks using biological pathway analysis. The disease-implicated modules were subsequently used as a molecular substrate for a computational drug repositioning analysis, in which we (1) imputed genetically regulated gene expression within Alzheimer disease implicated modules; (2) integrated the imputed gene expression levels with drug-gene signatures from the connectivity map to identify compounds that normalize dysregulated gene expression underlying Alzheimer disease; and (3) prioritized drug compounds and mechanisms of action based on the extent to which they normalize dysregulated expression signatures.

Results

Genetic factors for Alzheimer disease are enriched in brain gene coexpression networks involved in the immune response. Computational drug repositioning analyses of expression changes within the disease-associated networks retrieved known Alzheimer disease drugs (e.g., memantine) as well as biologically meaningful drug categories (e.g., glutamate receptor antagonists).

Discussion

Our results improve the biological interpretation of genetic data for Alzheimer disease and provide a list of potential antidementia drug repositioning candidates for which the efficacy should be investigated in functional validation studies.

Sex dependent glial-specific changes in the chromatin accessibility landscape in late-onset Alzheimer's disease brains

BACKGROUND

In the post-GWAS era, there is an unmet need to decode the underpinning genetic etiologies of late-onset Alzheimer's disease (LOAD) and translate the associations to causation.

METHODS

We conducted ATAC-seq profiling using NeuN sorted-nuclei from 40 frozen brain tissues to determine LOAD-specific changes in chromatin accessibility landscape in a cell-type specific manner.

RESULTS

We identified 211 LOAD-specific differential chromatin accessibility sites in neuronal-nuclei, four of which overlapped with LOAD-GWAS regions (±100 kb of SNP). While the non-neuronal nuclei did not show LOAD-specific differences, stratification by sex identified 842 LOAD-specific chromatin accessibility sites in females. Seven of these sex-dependent sites in the non-neuronal samples overlapped LOAD-GWAS regions including APOE. LOAD loci were functionally validated using single-nuclei RNA-seq datasets.

CONCLUSIONS

Using brain sorted-nuclei enabled the identification of sex-dependent cell type-specific LOAD alterations in chromatin structure. These findings enhance the interpretation of LOAD-GWAS discoveries, provide potential pathomechanisms, and suggest novel LOAD-loci.

RNA Dynamics in Alzheimer's Disease

Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder that heavily burdens healthcare systems worldwide. There is a significant requirement to understand the still unknown molecular mechanisms underlying AD. Current evidence shows that two of the major features of AD are transcriptome dysregulation and altered function of RNA binding proteins (RBPs), both of which lead to changes in the expression of different RNA species, including microRNAs (miRNAs), circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs). In this review, we will conduct a comprehensive overview of how RNA dynamics are altered in AD and how this leads to the differential expression of both short and long RNA species. We will describe how RBP expression and function are altered in AD and how this impacts the expression of different RNA species. Furthermore, we will also show how changes in the abundance of specific RNA species are linked to the pathology of AD.

Inflammatory gene expression profiling in peripheral blood from patients with Alzheimer's disease reveals key pathways and hub genes with potential diagnostic utility: a preliminary study

BACKGROUND

Alzheimer's disease (AD) is an age-related neurodegenerative disease caused by central nervous system disorders. Late-onset Alzheimer disease (LOAD) is the most common neurodegenerative disorder worldwide. Differences at the expression level of certain genes, resulting from either genetic variations or environmental interactions, might be one of the mechanisms underlying differential risks for developing AD. Peripheral blood genome transcriptional profiling may provide a powerful and minimally invasive tool for the identification of novel targets beyond Aβ and tau for AD research.

METHODS

This preliminary study explores molecular pathogenesis of LOAD-related inflammation through next generation sequencing, to assess RNA expression profiles in peripheral blood from five patients with LOAD and 10 healthy controls.

RESULTS

The analysis of RNA expression profiles revealed 94 genes up-regulated and 147 down-regulated. Gene function analysis, including Gene Ontology (GO) and KOBAS-Kyoto Encyclopedia of DEGs and Genomes (KEGG) pathways indicated upregulation of interferon family (INF) signaling, while the down-regulated genes were mainly associated with the cell cycle process. KEGG metabolic pathways mapping showed gene expression alterations in the signaling pathways of JAK/STAT, chemokines, MAP kinases and Alzheimer disease. The results of this preliminary study provided not only a comprehensive picture of gene expression, but also the key processes associated with pathology for the regulation of neuroinflammation, to improve the current mechanisms to treat LOAD.

Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer's disease

The gene-regulatory landscape of the brain is highly dynamic in health and disease, coordinating a menagerie of biological processes across distinct cell types. Here, we present a multi-omic single-nucleus study of 191,890 nuclei in late-stage Alzheimer's disease (AD), accessible through our web portal, profiling chromatin accessibility and gene expression in the same biological samples and uncovering vast cellular heterogeneity. We identified cell-type-specific, disease-associated candidate cis-regulatory elements and their candidate target genes, including an oligodendrocyte-associated regulatory module containing links to APOE and CLU. We describe cis-regulatory relationships in specific cell types at a subset of AD risk loci defined by genome-wide association studies, demonstrating the utility of this multi-omic single-nucleus approach. Trajectory analysis of glial populations identified disease-relevant transcription factors, such as SREBF1, and their regulatory targets. Finally, we introduce single-nucleus consensus weighted gene coexpression analysis, a coexpression network analysis strategy robust to sparse single-cell data, and perform a systems-level analysis of the AD transcriptome.

Gene co-expression network analysis in human spinal cord highlights mechanisms underlying amyotrophic lateral sclerosis susceptibility

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease defined by motor neuron (MN) loss. Multiple genetic risk factors have been identified, implicating RNA and protein metabolism and intracellular transport, among other biological mechanisms. To achieve a systems-level understanding of the mechanisms governing ALS pathophysiology, we built gene co-expression networks using RNA-sequencing data from control human spinal cord samples, identifying 13 gene co-expression modules, each of which represents a distinct biological process or cell type. Analysis of four RNA-seq datasets from a range of ALS disease-associated contexts reveal dysregulation in numerous modules related to ribosomal function, wound response, and leukocyte activation, implicating astrocytes, oligodendrocytes, endothelia, and microglia in ALS pathophysiology. To identify potentially causal processes, we partitioned heritability across the genome, finding that ALS common genetic risk is enriched within two specific modules, SC.M4, representing genes related to RNA processing and gene regulation, and SC.M2, representing genes related to intracellular transport and autophagy and enriched in oligodendrocyte markers. Top hub genes of this latter module include ALS-implicated risk genes such as KPNA3, TMED2, and NCOA4, the latter of which regulates ferritin autophagy, implicating this process in ALS pathophysiology. These unbiased, genome-wide analyses confirm the utility of a systems approach to understanding the causes and drivers of ALS.

The acute phase protein lactoferrin is a key feature of Alzheimer's disease and predictor of Aβ burden through induction of APP amyloidogenic processing

Amyloidogenic processing of the amyloid precursor protein (APP) forms the amyloid-β peptide (Aβ) component of pathognomonic extracellular plaques of AD. Additional early cortical changes in AD include neuroinflammation and elevated iron levels. Activation of the innate immune system in the brain is a neuroprotective response to infection; however, persistent neuroinflammation is linked to AD neuropathology by uncertain mechanisms. Non-parametric machine learning analysis on transcriptomic data from a large neuropathologically characterised patient cohort revealed the acute phase protein lactoferrin (Lf) as the key predictor of amyloid pathology. In vitro studies showed that an interaction between APP and the iron-bound form of Lf secreted from activated microglia diverted neuronal APP endocytosis from the canonical clathrin-dependent pathway to one requiring ADP ribosylation factor 6 trafficking. By rerouting APP recycling to the Rab11-positive compartment for amyloidogenic processing, Lf dramatically increased neuronal Aβ production. Lf emerges as a novel pharmacological target for AD that not only modulates APP processing but provides a link between Aβ production, neuroinflammation and iron dysregulation.