Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models

Meta-Analysis of Transcriptomic Studies of Blood and Six Brain Regions Identifies a Consensus of 15 Cross-Tissue Mechanisms in Alzheimer's Disease and Suggests an Origin of Cross-Study Heterogeneity

The comprehensive genome-wide nature of transcriptome studies in Alzheimer's disease (AD) should provide a reliable description of disease molecular states. However, the genes and molecular systems nominated by transcriptomic studies do not always overlap. Even when results do align, it is not clear if those observations represent true consensus across many studies. A couple of sources of variation have been proposed to explain this variability, including tissue-of-origin and cohort type, but its basis remains uncertain. To address this variability and extract reliable results, we utilized all publicly available blood or brain transcriptomic datasets of AD, comprised of 24 brain studies with 4007 samples from six different brain regions, and eight blood studies with 1566 samples. We identified a consensus of AD-associated genes across brain regions and AD-associated gene-sets across blood and brain, generalizable machine learning and linear scoring classifiers, and significant contributors to biological diversity in AD datasets. While AD-associated genes did not significantly overlap between blood and brain, our findings highlighted 15 dysregulated processes shared across blood and brain in AD. The top five most significantly dysregulated processes were DNA replication, metabolism of proteins, protein localization, cell cycle, and programmed cell death. Conversely, addressing the discord across studies, we found that large-scale gene co-regulation patterns can account for a significant fraction of variability in AD datasets. Overall, this study ranked and characterized a compilation of genes and molecular systems consistently identified across a large assembly of AD transcriptome studies in blood and brain, providing potential candidate biomarkers and therapeutic targets.

Mitochondrial unfolded protein response (UPRmt) as novel therapeutic targets for neurological disorders

Neurological disorders, including brain cancer, neurodegenerative diseases and ischemic/reperfusion injury, pose a significant threat to global human health. Due to the high metabolic demands of nerve cells, mitochondrial dysfunction is a critical feature of these disorders. The mitochondrial unfolded protein response (UPRmt) is an evolutionarily conserved mitochondrial response, which is critical for maintaining mitochondrial and energetic homeostasis under stress. Previous studies have found that UPRmt participates in diverse physiological processes especially metabolism and immunity. Currently, increasing evidence suggest that targeted regulation of UPRmt can also effectively delay the progression of neurological diseases and improve patients' prognosis. This review provides a comprehensive overview of UPRmt in the context of neurological diseases, with a particular emphasis on its regulatory functions. Additionally, we summarize the mechanistic insights into UPRmt in neurological disorders as investigated in preclinical studies, as well as its potential as a therapeutic target in the clinical management of neurological tumors. By highlighting the importance of UPRmt in the complex processes underlying neurological disorders, this review aims to bridge current knowledge gaps and inspire novel therapeutic strategies for these conditions.

Transcriptomic signatures of oxytosis/ferroptosis are enriched in Alzheimer's disease

BACKGROUND

Oxytosis/ferroptosis is a form of non-apoptotic regulated cell death characterized by specific changes in the redox balance that lead to lethal lipid peroxidation. It has been hypothesized recently that aging predisposes the brain to the activation of oxytosis/ferroptosis in Alzheimer's disease (AD), and consequently that inhibition of oxytosis/ferroptosis offers a path to develop a new class of therapeutics for the disease. The goal of the present study was to investigate the occurrence of oxytosis/ferroptosis in the AD brain by examining transcriptomic signatures of oxytosis/ferroptosis in cellular and animal models of AD as well as in human AD brain samples.

RESULTS

Since oxytosis/ferroptosis has been poorly defined at the RNA level, the publicly available datasets are limited. To address this limitation, we developed TrioSig, a gene signature generated from transcriptomic data of human microglia, astrocytes, and neurons treated with inducers of oxytosis/ferroptosis. It is shown that the different signatures of oxytosis/ferroptosis are enriched to varying extents in the brains of AD mice and human AD patients. The TrioSig signature was the most frequently found enriched, and bioinformatic analysis of its composition identified genes involved in the integrated stress response (ISR). It was confirmed in nerve cell culture that oxytosis/ferroptosis induces the ISR via phosphorylation of eukaryotic translation initiation factor 2 alpha (eIF2α) and activating transcription factor 4 (ATF4) signaling.

CONCLUSIONS

Our data support the involvement of oxytosis/ferroptosis in AD. The implications of the ISR for the progression and prevention of AD are discussed.

Silencer variants are key drivers of gene upregulation in Alzheimer's disease

Alzheimer's disease (AD), particularly late-onset AD, stands as the most prevalent neurodegenerative disorder globally. Owing to its substantial heritability, genetic studies have emerged as indispensable for elucidating genes and biological pathways driving AD onset and progression. However, genetic and molecular mechanisms underlying AD remain poorly defined, largely due to the pronounced heterogeneity of AD and the intricate interactions among AD genetic factors. Notably, approximately 90% of AD-associated genetic variants reside in intronic and intergenic regions, yet their functional significance has remained largely uncharacterized. To address this challenge, we developed a deep learning framework combining bulk and single-cell epigenomic data to evaluate the regulatory potential (i.e., silencing and activating strength) of noncoding AD variants in the dorsolateral prefrontal cortex (DLPFCs) and its major cell types. This model identified 1,457 silencer and 3,084 enhancer AD-associated variants in the DLPFC and binned them into silencer variants only (SL), enhancer variants only (EN), or both variant types (ENSL) classes. Each class exerts distinct cellular and molecular influences on AD pathogenesis. EN loci predominantly regulate housekeeping metabolic processes, whereas SL loci (including the genes MS4A6A , TREM2 , USP6NL , HLA-D ) are selectively linked to immune responses. Notably, 71% of these genes are significantly upregulated in AD and pro-inflammation-stimulated microglia. Furthermore, genes associated with SL loci are, in neuronal cells, often responsive to glutamate receptor antagonists (e.g, NBQX) and anti-inflammatory perturbagens (such as D-64131), the compound classes known for reducing the AD risk. ENSL loci, in contrast, are uniquely implicated in memory maintenance, neurofibrillary tangle assembly, and are also shared by other neurological disorders such as Parkinson's disease and schizophrenia. Key genes in this class of loci, such as MAPT , CR1/2 , and CLU , are frequently upregulated in AD subtypes with hyperphosphorylated tau aggregates. Critically, our model can accurately predict the impact of regulatory variants, with an average Pearson correlation coefficient of 0.54 and a directional concordance rate of 70% between our predictions and experimental outcomes. This model identified rs636317 as a causal AD variant in the MS4A locus, distinguishing it from the 7bp-away allele-neutral variant rs636341. Similarly, rs7922621 was prioritized over its 54-bp-away allele-neutral rs7901634 in the TSPAN14 locus. Additional causal variants include rs6701713 in the CR1 locus, and rs28834970 and rs755951 in the PTK2B locus. Collectively, this work advances our understanding of the regulatory landscape of AD-associated genetic variants, providing a framework to explore their functional roles in the pathogenesis of this complex disease.

Activation of the muscle-to-brain axis ameliorates neurocognitive deficits in an Alzheimer's disease mouse model via enhancing neurotrophic and synaptic signaling

Skeletal muscle regulates central nervous system (CNS) function and health, activating the muscle-to-brain axis through the secretion of skeletal muscle-originating factors ("myokines") with neuroprotective properties. However, the precise mechanisms underlying these benefits in the context of Alzheimer's disease (AD) remain poorly understood. To investigate muscle-to-brain axis signaling in response to amyloid β (Aβ)-induced toxicity, we generated 5xFAD transgenic female mice with enhanced skeletal muscle function (5xFAD;cTFEB;HSACre) at prodromal (4-months old) and late (8-months old) symptomatic stages. Skeletal muscle TFEB overexpression reduced Aβ plaque accumulation in the cortex and hippocampus at both ages and rescued behavioral neurocognitive deficits in 8-month-old 5xFAD mice. These changes were associated with transcriptional and protein remodeling of neurotrophic signaling and synaptic integrity, partially due to the CNS-targeting myokine prosaposin (PSAP). Our findings implicate the muscle-to-brain axis as a novel neuroprotective pathway against amyloid pathogenesis in AD.

A computational framework for extracting biological insights from SRA cancer data

The integration of sequenced samples and clinical data from independent yet related studies from public domain databases, such as The Sequence Read Archive (SRA), has the potential to increase sample sizes and enhance the statistical power needed for more precise bioinformatic analysis. Data mining and sample grouping are the starting points in this process and still present several challenges, including the presence of structured and unstructured data, missing deposited data, and varying experimental conditions and techniques applied across the studies. Designed to address the main challenges of data mining and sample grouping for biomarkers research, the proposed methodology employs a computational approach integrating relational database construction, text and data mining, natural language processing, network analysis, search by Pubmed publications, and combining MeSH, TTD and WordNet database to identify groups of samples with the same characteristics. As a result, it identifies and illustrates relationships among sample collections, aiming to discover potential cancer biomarkers. In colorectal cancer (CRC) and acute lymphoblastic leukemia (ALL) case studies, this methodology effectively navigates SRA metadata, retrieving, extracting, and integrating data. It highlights significant connections between samples and patient clinical data, revealing important biological insights. The study grouped 2,737 (CRC) and 3,655 (ALL) samples into potential comparison groups, demonstrating the method's power in identifying relationships and aiding biomarker discovery.

Multi Layered Omics Approaches Reveal Glia Specific Alterations in Alzheimer's Disease: A Systematic Review and Future Prospects

Alzheimer's disease (AD) is the most common neurodegenerative dementia with multi-layered complexity in its molecular etiology. Multiple omics-based approaches, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and lipidomics are enabling researchers to dissect this molecular complexity, and to uncover a plethora of alterations yielding insights into the pathophysiology of this disease. These approaches reveal multi-omics alterations essentially in all cell types of the brain, including glia. In this systematic review, we screen the literature for human studies implementing any omics approach within the last 10 years, to discover AD-associated molecular perturbations in brain glial cells. The findings from over 200 AD-related studies are reviewed under four different glial cell categories: microglia, oligodendrocytes, astrocytes and brain vascular cells. Under each category, we summarize the shared and unique molecular alterations identified in glial cells through complementary omics approaches. We discuss the implications of these findings for the development, progression and ultimately treatment of this complex disease as well as directions for future omics studies in glia cells.

Microglial Responses to Alzheimer's Disease Pathology: Insights From "Omics" Studies

Human genetics studies lent firm evidence that microglia are key to Alzheimer's disease (AD) pathogenesis over a decade ago following the identification of AD-associated genes that are expressed in a microglia-specific manner. However, while alterations in microglial morphology and gene expression are observed in human postmortem brain tissue, the mechanisms by which microglia drive and contribute to AD pathology remain ill-defined. Numerous mouse models have been developed to facilitate the disambiguation of the biological mechanisms underlying AD, incorporating amyloidosis, phosphorylated tau, or both. Over time, the use of multiple technologies including bulk tissue and single cell transcriptomics, epigenomics, spatial transcriptomics, proteomics, lipidomics, and metabolomics have shed light on the heterogeneity of microglial phenotypes and molecular patterns altered in AD mouse models. Each of these 'omics technologies provide unique information and biological insight. Here, we review the literature on the approaches and findings of these methods and provide a synthesis of the knowledge generated by applying these technologies to mouse models of AD.

Identification of novel proteins associated with intelligence by integrating genome-wide association data and human brain proteomics

While genome-wide association studies (GWAS) have identified genetic variants associated with intelligence, their biological mechanisms remain largely unexplored. This study aimed to bridge this gap by integrating intelligence GWAS data with human brain proteomics and transcriptomics. We conducted proteome-wide (PWAS) and transcriptome-wide (TWAS) association studies, along with enrichment and protein-protein interaction (PPI) network analyses. PWAS identified 44 genes in the human brain proteome that influence intelligence through protein abundance regulation (FDR P <  0.05). Causal analysis revealed 36 genes, including GPX1, involved in the cis-regulation of protein abundance (P <  0.05). In independent PWAS analyses, 17 genes were validated, and 10 showed a positive correlation with intelligence (P <  0.05). TWAS revealed significant SNP-based heritability for mRNA in 28 proteins, and cis-regulation of mRNA levels for 20 genes was nominally associated with intelligence (FDR P <  0.05). This study identifies key genes that bridge genetic variants and protein-level mechanisms of intelligence, providing novel insights into its biological pathways and potential therapeutic targets.

Integrative Epigenomic Landscape of Alzheimer's Disease Brains Reveals Oligodendrocyte Molecular Perturbations Associated with Tau

Alzheimer's disease (AD) brains are characterized by neuropathologic and biochemical changes that are highly variable across individuals. Capturing epigenetic factors that associate with this variability can reveal novel biological insights into AD pathophysiology. We conducted an epigenome-wide association study of DNA methylation (DNAm) in 472 AD brains with neuropathologic measures (Braak stage, Thal phase, and cerebral amyloid angiopathy score) and brain biochemical levels of five proteins (APOE, amyloid-β (Aβ)40, Aβ42, tau, and p-tau) core to AD pathogenesis. Using a novel regional methylation (rCpGm) approach, we identified 5,478 significant associations, 99.7% of which were with brain tau biochemical measures. Of the tau-associated rCpGms, 93 had concordant associations in external datasets comprising 1,337 brain samples. Integrative transcriptome-methylome analyses uncovered 535 significant gene expression associations for these 93 rCpGms. Genes with concurrent transcriptome-methylome perturbations were enriched in oligodendrocyte marker genes, including known AD risk genes such as BIN1 , myelination genes MYRF, MBP and MAG previously implicated in AD, as well as novel genes like LDB3 . We further annotated the top oligodendrocyte genes in an additional 6 brain single cell and 2 bulk transcriptome datasets from AD and two other tauopathies, Pick's disease and progressive supranuclear palsy (PSP). Our findings support consistent rCpGm and gene expression associations with these tauopathies and tau-related phenotypes in both bulk brain tissue and oligodendrocyte clusters. In summary, we uncover the integrative epigenomic landscape of AD and demonstrate tau-related oligodendrocyte gene perturbations as a common potential pathomechanism across different tauopathies.

Mouse-to-human modeling of microglia single-nuclei transcriptomics identifies immune signaling pathways and potential therapeutic candidates associated with Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory loss and behavior change. Studies have found that dysregulation of microglial cells is pivotal to AD pathology. These mechanisms have been studied in mouse models to uncover potential therapeutic biomarkers. Despite these findings, there are limitations to the translatable biological information from mice to humans due to differences in physiology, timeline of disease, and the heterogeneity of humans. To address the inter-species discrepancies, we developed a novel implementation of the Translatable Components Regression (TransComp-R) framework, which integrated microglia single-nuclei mouse and human transcriptomics data to identify biological pathways in mice predictive of human AD. We compared model variations with sparse and traditional principal component analysis. We found that both dimensionality reduction techniques encoded similar AD disease biology on mouse principal components with limited differences in technical performance. Several mouse sparse principal components explained high amounts of variance in humans and significantly differentiated human AD from control microglial cells. Additionally, we identified FDA-approved medications that induced gene expression profiles correlated with projections of healthy human microglia on mouse principal components. Such medications included cabergoline, selumetinib, and palbociclib. This computational framework may support uncovering cross-species disease insights and candidate pharmacological solutions from single-cell datasets.

Ferroptosis and cognitive impairment: Unraveling the link and potential therapeutic targets

Neurodegenerative disorders, such as Alzheimer's and Parkinson's diseases, share key characteristics, notably cognitive impairment and significant cell death in specific brain regions. Cognition, a complex mental process allowing individuals to perceive time and place, is disrupted in these conditions. This consistent disruption suggests the possibility of a shared underlying mechanism across all neurodegenerative diseases. One potential common factor is the activation of pathways leading to cell death. Despite significant progress in understanding cell death pathways, no definitive treatments have emerged. This has shifted focus towards less-explored mechanisms like ferroptosis, which holds potential due to its involvement in oxidative stress and iron metabolism. Unlike apoptosis or necrosis, ferroptosis offers a novel therapeutic avenue due to its distinct biochemical and genetic underpinnings, making it a promising target in neurodegenerative disease treatment. Ferroptosis is distinguished from other cellular death mechanisms, by distinctive characteristics such as an imbalance of iron hemostasis, peroxidation of lipids in the plasma membrane, and dysregulated glutathione metabolism. In this review, we discuss the potential role of ferroptosis in cognitive impairment. We then summarize the evidence linking ferroptosis biomarkers to cognitive impairment brought on by neurodegeneration while highlighting recent advancements in our understanding of the molecular and genetic mechanisms behind the condition. Finally, we discuss the prospective therapeutic implications of targeting ferroptosis for the treatment of cognitive abnormalities associated with neurodegeneration, including natural and synthetic substances that suppress ferroptosis via a variety of mechanisms. Promising therapeutic candidates, including antioxidants and iron chelators, are being explored to inhibit ferroptosis and mitigate cognitive decline.

Integrative pathway analysis across humans and 3D cellular models identifies the p38 MAPK-MK2 axis as a therapeutic target for Alzheimer's disease

Alzheimer's disease (AD) presents a complex pathological landscape, posing challenges to current therapeutic strategies that primarily target amyloid-β (Aβ). Using a novel integrative pathway activity analysis (IPAA), we identified 83 dysregulated pathways common between both post-mortem AD brains and three-dimensional AD cellular models showing robust Aβ42 accumulation. p38 mitogen-activated protein kinase (MAPK) was the most upregulated common pathway. Active p38 MAPK levels increased in the cellular models, human brains, and 5XFAD mice and selectively localized to presynaptic dystrophic neurites. Unbiased phosphoproteomics confirmed increased phosphorylation of p38 MAPK substrates. Downstream activation of MAPK-activated protein kinase 2 (MK2) plays a crucial role in Aβ42-p38 MAPK-mediated tau pathology. Therapeutic targeting of the p38 MAPK-MK2 axis with selective inhibitors significantly reduced Aβ42-driven tau pathology and neuronal loss. IPAA prioritizes the best models to derisk target-drug discovery by integrating human tissue gene expression with functional readouts from cellular models, enabling the identification and validation of high-confidence AD therapeutic targets.

Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer's disease

Background

A significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals.

Methods

We analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on β-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution.

Results

Transcriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience.

Conclusions

We identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

An Overview of the Natural Neuroprotective Agents for the Management of Cognitive Impairment Induced by Scopolamine in Zebrafish (Danio rerio)

Alzheimer's Disease (AD) is a neurodegenerative disorder mainly characterized by dementia and cognitive decline. AD is essentially associated with the presence of aggregates of the amyloid-β peptide and the hyperphosphorylated microtubule-associated protein tau. The available AD therapies can only alleviate the symptoms; therefore, the development of natural treatments that exhibit neuroprotective effects and correct the behavioral impairment is a critical requirement. The present review aims to collect the natural substances that have been evaluated for their neuroprotective profile against AD-like behaviors induced in zebrafish (Danio rerio) by scopolamine. We focused on articles retrieved from the PubMed database via preset searching strings from 2010 to 2023. Our review assembled 21 studies that elucidated the activities of 28 various natural substances, including bioactive compounds, extracts, fractions, commercial compounds, and essential oils. The listed compounds enhanced cognition and showed several mechanisms of action, namely antioxidant potential, acetylcholinesterase's inhibition, and reduction of lipid peroxidation. Additional studies should be achieved to demonstrate their preventive and therapeutic activities in cellular and rodent models. Further clinical trials would be extremely solicited to support more insight into the neuroprotective effects of the most promising drugs in an AD context.

Genome-wide transcriptome analysis of Aβ deposition on PET in a Korean cohort

INTRODUCTION

Despite the recognized importance of including ethnic diversity in Alzheimer's disease (AD) research, substantial knowledge gaps remain, particularly in Asian populations.

METHODS

RNA sequencing was performed on blood samples from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) to perform differential gene expression (DGE), gene co-expression network, gene-set enrichment, and machine learning analyses for amyloid beta (Aβ) deposition on positron emission tomography.

RESULTS

DGE analysis identified 265 dysregulated genes associated with Aβ deposition and replicated three AD-associated genes in an independent Korean cohort. Network analysis identified two modules related to pathways including a natural killer (NK) cell-mediated immunity. Machine learning analysis showed the classification of Aβ positivity improved with the inclusion of gene expression data.

DISCUSSION

Our results in a Korean population suggest Aβ deposition-associated genes are enriched in NK cell-mediated immunity, providing a better understanding of AD molecular mechanisms and yielding potential diagnostic and therapeutic strategies.

HIGHLIGHTS

Dysregulated genes were associated with amyloid beta (Aβ) deposition on positron emission tomography in a Korean cohort. Dysregulated genes in Alzheimer's disease were replicated in an independent Korean cohort. Gene network modules were associated with Aβ deposition. Natural killer (NK) cell proportion in blood was associated with Aβ deposition. Dysregulated genes were related to a NK cell-mediated immunity.

Bridging the gap: Multi-omics profiling of brain tissue in Alzheimer's disease and older controls in multi-ethnic populations

INTRODUCTION

Multi-omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked Black Americans (BA) and Latin Americans (LA), who are disproportionately affected by AD.

METHODS

To bridge this gap, Accelerating Medicines Partnership in Alzheimer's Disease (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors.

RESULTS

We generated multi-omics data and curated and harmonized phenotypic data from BA (n = 306), LA (n = 326), or BA and LA (n = 4) brain donors plus non-Hispanic White (n = 252) and other (n = 20) ethnic groups, to establish a foundational dataset enriched for BA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures.

DISCUSSION

The inclusion of traditionally underrepresented groups in multi-omics studies is essential to discovering the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.

HIGHLIGHTS

Accelerating Medicines Partnership in Alzheimer's Disease Diversity Initiative led brain tissue profiling in multi-ethnic populations. Brain multi-omics data is generated from Black American, Latin American, and non-Hispanic White donors. RNA, whole genome sequencing and tandem mass tag proteomicsis completed and shared. Multiple brain regions including caudate, temporal and dorsolateral prefrontal cortex were profiled.

CYP1B1-RMDN2 Alzheimer's disease endophenotype locus identified for cerebral tau PET

Determining the genetic architecture of Alzheimer's disease pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we perform a genome-wide association study of cortical tau quantified by positron emission tomography in 3046 participants from 12 independent studies. The CYP1B1-RMDN2 locus is associated with tau deposition. The most significant signal is at rs2113389, explaining 4.3% of the variation in cortical tau, while APOE4 rs429358 accounts for 3.6%. rs2113389 is associated with higher tau and faster cognitive decline. Additive effects, but no interactions, are observed between rs2113389 and diagnosis, APOE4, and amyloid beta positivity. CYP1B1 expression is upregulated in AD. rs2113389 is associated with higher CYP1B1 expression and methylation levels. Mouse model studies provide additional functional evidence for a relationship between CYP1B1 and tau deposition but not amyloid beta. These results provide insight into the genetic basis of cerebral tau deposition and support novel pathways for therapeutic development in AD.

BioPathNet: Enhancing Link Prediction in Biomedical Knowledge Graphs through Path Representation Learning

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by the aggregation of β-amyloid (Aβ) and tau in the brain. Breakthroughs in disease-modifying treatments targeting Aβ bring new hope for the management of AD. But to effectively modify and someday even prevent AD, a better understanding is needed of the biological mechanisms that underlie and link Aβ and tau in AD. Developments of high-throughput omics, including genomics, proteomics, and transcriptomics, together with molecular imaging of Aβ and tau with positron emission tomography (PET), allow us to discover and understand the biological pathways that regulate the aggregation and spread of Aβ and tau in living humans. The field of integrated omics and PET studies of Aβ and tau in AD is growing rapidly. We here provide an update of this field, both in terms of biological insights and in terms of future clinical implications of integrated omics-molecular imaging studies.

ABCA7-dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling

Genetic variants in ABCA7, an Alzheimer's disease (AD)-associated gene, elevate AD risk, yet its functional relevance to the etiology is unclear. We generated a CRISPR-Cas9-mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single-cell transcriptomics in heterozygous abca7+/- knockout combined with Aβ42 toxicity revealed that ABCA7 is crucial for neuropeptide Y (NPY), brain-derived neurotrophic factor (BDNF), and nerve growth factor receptor (NGFR) expressions, which are crucial for synaptic integrity, astroglial proliferation, and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density, which are rescuable with ectopic NPY. In induced pluripotent stem cell-derived human neurons exposed to Aβ42, ABCA7-/- suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages, genetic variants in NPY associated with AD, and epigenetic changes in NPY, NGFR, and BDNF promoters linked to ABCA7 variants. Therefore, ABCA7-dependent NPY signaling via BDNF-NGFR maintains synaptic integrity, implicating its impairment in increased AD risk through reduced brain resilience.

Oxygen-induced stress reveals context-specific gene regulatory effects in human brain organoids

The interaction between genetic variants and environmental stressors is key to understanding the mechanisms underlying neurological diseases. In this study, we used human brain organoids to explore how varying oxygen levels expose context-dependent gene regulatory effects. By subjecting a genetically diverse panel of 21 brain organoids to hypoxic and hyperoxic conditions, we identified thousands of gene regulatory changes that are undetectable under baseline conditions, with 1,745 trait-associated genes showing regulatory effects only in response to oxygen stress. To capture more nuanced transcriptional patterns, we employed topic modeling, which revealed context-specific gene regulation linked to dynamic cellular processes and environmental responses, offering a deeper understanding of how gene regulation is modulated in the brain. These findings underscore the importance of genotype-environment interactions in genetic studies of neurological disorders and provide new insights into the hidden regulatory mechanisms influenced by environmental factors in the brain.

Integrative proteomics identifies a conserved Aβ amyloid responsome, novel plaque proteins, and pathology modifiers in Alzheimer's disease

Alzheimer's disease (AD) is a complex neurodegenerative disorder that develops over decades. AD brain proteomics reveals vast alterations in protein levels and numerous altered biologic pathways. Here, we compare AD brain proteome and network changes with the brain proteomes of amyloid β (Aβ)-depositing mice to identify conserved and divergent protein networks with the conserved networks identifying an Aβ amyloid responsome. Proteins in the most conserved network (M42) accumulate in plaques, cerebrovascular amyloid (CAA), and/or dystrophic neuronal processes, and overexpression of two M42 proteins, midkine (Mdk) and pleiotrophin (PTN), increases the accumulation of Aβ in plaques and CAA. M42 proteins bind amyloid fibrils in vitro, and MDK and PTN co-accumulate with cardiac transthyretin amyloid. M42 proteins appear intimately linked to amyloid deposition and can regulate amyloid deposition, suggesting that they are pathology modifiers and thus putative therapeutic targets. We posit that amyloid-scaffolded accumulation of numerous M42+ proteins is a central mechanism mediating downstream pathophysiology in AD.

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI

Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model, mosGraphGPT, for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer's Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link: https://github.com/mosGraph/mosGraphGPT.

Multi-combined QSAR, molecular docking, molecular dynamics simulation, and ADMET of Flavonoid derivatives as potent cholinesterase inhibitors

In searching for a new and efficient therapeutic agent against Alzheimer's disease, a Quantitative structure-activity relationship (QSAR) was derived for 45 Flavonoid derivatives recently synthesized and evaluated as cholinesterase inhibitors. The multiple linear regression method (MLR) was adopted to develop an adequate mathematical model that describes the relationship between a variety of molecular descriptors of the studied compounds and their biological activities (cholinesterase inhibitors). Golbraikh and Tropsha criteria were applied to verify the validity of the built model. The built MLR model was statistically reliable, robust, and predictive (R2 = 0.801, Q2cv = 0.876, R2test = 0.824). Dreiding energy and Molar Refractivity were the major factors that govern the Anti-cholinesterase activity. These results were further exploited to design a new series of Flavonoid derivatives with higher Anti-cholinesterase activities than the existing ones. Thereafter, molecular docking and molecular dynamic studies were performed to predict the binding types of the designed compounds and to investigate their stability at the active site of the Butyrylcholinestérase BuChE protein. The negative and low binding affinity calculated for all designed compounds shows that designed compound 1 has a favorable affinity for the 4TPK. Moreover, molecular dynamics simulation studies confirmed the stability of designed compound 1 in the active pocket of 4TPK over 100 ns. Finally, the ADMET analysis was incorporated to analyze the pharmacokinetics and toxicity parameters. The designed compounds were found to meet the ADMET descriptor criteria at an acceptable level having respectable intestinal permeability and water solubility and can reach the intended destinations.Communicated by Ramaswamy H. Sarma.

Oral administration of liposome-encapsulated thymol could alleviate the inflammatory parameters in serum and hippocampus in a rat model of Alzheimer's disease

BACKGROUND

Neuroinflammation is closely related to Alzheimer's Disease (AD) pathology, hence supplements with anti-inflammatory property could help attenuate the progression of AD. This study was conducted to evaluate the potential anti-inflammatory effects of liposome encapsulated thymol (LET), administered orally, in prevention of Alzheimer in a rat model by anti-inflammatory mechanisms.

METHODS

The rats were grouped into six groups (n = 10 animals per group), including Control healthy (Con), Alzheimer's disease (AD) model, AD model treated with free thymol in 40 and 80 mg/kg body weight (TH40 and TH80), AD model treated with LET in 40 and 80 mg/kg of body weight (LET40 and LET80). The behavioral response of step through latency (Passive Avoidance Test), concentrations of interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) and cyclooxygenase-2 (COX-2) and brain-derived neurotrophic factor (BDNF) were assessed in serum and hippocampus.

RESULTS

The results showed that significant increase in concentrations of IL-1β (P = 0.001), IL-6 (P = 0.001), TNF-α (P = 0.001) and COX-2 (P = 0.001) in AD group compared with healthy control rats. AD induction significantly reduced step through latency and revealed deficits in passive avoidance performance. The results also showed the treatment with free thymol especially in higher concentrations and also LTE could decrease serum concentrations of IL-1β (P < 0.05), IL-6 (P < 0.05), TNF-α (P < 0.05), and COX-2 (P < 0.05) and increase BDNF (P < 0.05) compared with control Alzheimer rats in hippocampus and serum. There were also significant correlations between serum and hippocampus concentrations of IL-1β (r2 = 0.369, P = 0.001), IL-6 (r2 = 0.386, P = 0.001), TNF-α (r2 = 0.412, P = 0.001), and COX-2 (r2 = 0.357, P = 0.001). It means a closed and positive relation between serum and hippocampus concentrations of IL-1β, IL-6, TNF-α, and COX-2.

CONCLUSIONS

LET demonstrates its ability to attenuate neuroinflammatory reaction in AD model through suppression of IL-1β, IL-6, and TNF-α and COX-2 indicators. Hence, it can ameliorate AD pathogenesis by declining inflammatory reaction.

In vivo validation of late-onset Alzheimer's disease genetic risk factors

INTRODUCTION

Genome-wide association studies have identified over 70 genetic loci associated with late-onset Alzheimer's disease (LOAD), but few candidate polymorphisms have been functionally assessed for disease relevance and mechanism of action.

METHODS

Candidate genetic risk variants were informatically prioritized and individually engineered into a LOAD-sensitized mouse model that carries the AD risk variants APOE ε4/ε4 and Trem2*R47H. The potential disease relevance of each model was assessed by comparing brain transcriptomes measured with the Nanostring Mouse AD Panel at 4 and 12 months of age with human study cohorts.

RESULTS

We created new models for 11 coding and loss-of-function risk variants. Transcriptomic effects from multiple genetic variants recapitulated a variety of human gene expression patterns observed in LOAD study cohorts. Specific models matched to emerging molecular LOAD subtypes.

DISCUSSION

These results provide an initial functionalization of 11 candidate risk variants and identify potential preclinical models for testing targeted therapeutics.

HIGHLIGHTS

A novel approach to validate genetic risk factors for late-onset AD (LOAD) is presented. LOAD risk variants were knocked in to conserved mouse loci. Variant effects were assayed by transcriptional analysis. Risk variants in Abca7, Mthfr, Plcg2, and Sorl1 loci modeled molecular signatures of clinical disease. This approach should generate more translationally relevant animal models.

A TrkB and TrkC partial agonist restores deficits in synaptic function and promotes activity-dependent synaptic and microglial transcriptomic changes in a late-stage Alzheimer's mouse model

INTRODUCTION

Tropomyosin related kinase B (TrkB) and C (TrkC) receptor signaling promotes synaptic plasticity and interacts with pathways affected by amyloid beta (Aβ) toxicity. Upregulating TrkB/C signaling could reduce Alzheimer's disease (AD)-related degenerative signaling, memory loss, and synaptic dysfunction.

METHODS

PTX-BD10-2 (BD10-2), a small molecule TrkB/C receptor partial agonist, was orally administered to aged London/Swedish-APP mutant mice (APPL/S) and wild-type controls. Effects on memory and hippocampal long-term potentiation (LTP) were assessed using electrophysiology, behavioral studies, immunoblotting, immunofluorescence staining, and RNA sequencing.

RESULTS

In APPL/S mice, BD10-2 treatment improved memory and LTP deficits. This was accompanied by normalized phosphorylation of protein kinase B (Akt), calcium-calmodulin-dependent kinase II (CaMKII), and AMPA-type glutamate receptors containing the subunit GluA1; enhanced activity-dependent recruitment of synaptic proteins; and increased excitatory synapse number. BD10-2 also had potentially favorable effects on LTP-dependent complement pathway and synaptic gene transcription.

DISCUSSION

BD10-2 prevented APPL/S/Aβ-associated memory and LTP deficits, reduced abnormalities in synapse-related signaling and activity-dependent transcription of synaptic genes, and bolstered transcriptional changes associated with microglial immune response.

HIGHLIGHTS

Small molecule modulation of tropomyosin related kinase B (TrkB) and C (TrkC) restores long-term potentiation (LTP) and behavior in an Alzheimer's disease (AD) model. Modulation of TrkB and TrkC regulates synaptic activity-dependent transcription. TrkB and TrkC receptors are candidate targets for translational therapeutics. Electrophysiology combined with transcriptomics elucidates synaptic restoration. LTP identifies neuron and microglia AD-relevant human-mouse co-expression modules.

Comprehensive Overview of Alzheimer's Disease: Etiological Insights and Degradation Strategies

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and affects millions of individuals globally. AD is associated with cognitive decline and memory loss that worsens with aging. A statistical report using U.S. data on AD estimates that approximately 6.9 million individuals suffer from AD, a number projected to surge to 13.8 million by 2060. Thus, there is a critical imperative to pinpoint and address AD and its hallmark tau protein aggregation early to prevent and manage its debilitating effects. Amyloid-β and tau proteins are primarily associated with the formation of plaques and neurofibril tangles in the brain. Current research efforts focus on degrading amyloid-β and tau or inhibiting their synthesis, particularly targeting APP processing and tau hyperphosphorylation, aiming to develop effective clinical interventions. However, navigating this intricate landscape requires ongoing studies and clinical trials to develop treatments that truly make a difference. Genome-wide association studies (GWASs) across various cohorts identified 40 loci and over 300 genes associated with AD. Despite this wealth of genetic data, much remains to be understood about the functions of these genes and their role in the disease process, prompting continued investigation. By delving deeper into these genetic associations, novel targets such as kinases, proteases, cytokines, and degradation pathways, offer new directions for drug discovery and therapeutic intervention in AD. This review delves into the intricate biological pathways disrupted in AD and identifies how genetic variations within these pathways could serve as potential targets for drug discovery and treatment strategies. Through a comprehensive understanding of the molecular underpinnings of AD, researchers aim to pave the way for more effective therapies that can alleviate the burden of this devastating disease.

Knockout of the longevity gene Klotho perturbs aging and Alzheimer's disease-linked brain microRNAs and tRNA fragments

Overexpression of the longevity gene Klotho prolongs lifespan, while its knockout shortens lifespan and impairs cognition via perturbation of myelination and synapse formation. However, comprehensive analysis of Klotho knockout effects on mammalian brain transcriptomics is lacking. Here, we report that Klotho knockout alters the levels of aging- and cognition related mRNAs, long non-coding RNAs, microRNAs and tRNA fragments. These include altered neuronal and glial regulators in murine models of aging and Alzheimer's disease and in human Alzheimer's disease post-mortem brains. We further demonstrate interaction of the knockout-elevated tRNA fragments with the spliceosome, possibly affecting RNA processing. Last, we present cell type-specific short RNA-seq datasets from FACS-sorted neurons and microglia of live human brain tissue demonstrating in-depth cell-type association of Klotho knockout-perturbed microRNAs. Together, our findings reveal multiple RNA transcripts in both neurons and glia from murine and human brain that are perturbed in Klotho deficiency and are aging- and neurodegeneration-related.

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.

Transcriptomic imputation of genetic risk variants uncovers novel whole-blood biomarkers of Parkinson's disease

Blood-based gene expression signatures could potentially be used as biomarkers for PD. However, it is unclear whether genetically-regulated transcriptomic signatures can provide novel gene candidates for use as PD biomarkers. We leveraged on the Genotype-Tissue Expression (GTEx) database to impute whole-blood transcriptomic expression using summary statistics of three large-scale PD GWAS. A random forest classifier was used with the consensus whole-blood imputed gene signature (IGS) to discriminate between cases and controls. Outcome measures included Area under the Curve (AUC) of Receiver Operating Characteristic (ROC) Curve. We demonstrated that the IGS (n = 37 genes) is conserved across PD GWAS studies and brain tissues. IGS discriminated between cases and controls in an independent whole-blood RNA-sequencing study (1176 PD, 254 prodromal, and 860 healthy controls) with mean AUC and accuracy of 64.8% and 69.4% for PD cohort, and 78.8% and 74% for prodromal cohort. PATL2 was the top-performing imputed gene in both PD and prodromal PD cohorts, whose classifier performance varied with biological sex (higher performance for males and females in the PD and prodromal PD, respectively). Single-cell RNA-sequencing studies (scRNA-seq) of healthy humans and PD patients found PATL2 to be enriched in terminal effector CD8+ and cytotoxic CD4+ cells, whose proportions are both increased in PD patients. We demonstrated the utility of GWAS transcriptomic imputation in identifying novel whole-blood transcriptomic signatures which could be leveraged upon for PD biomarker derivation. We identified PATL2 as a potential biomarker in both clinical and prodromic PD.

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer's Disease and Older Controls in Multi-Ethnic Populations

INTRODUCTION

Multi-omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked African Americans (AA) and Latin Americans (LA), who are disproportionately affected by AD.

METHODS

To bridge this gap, Accelerating Medicines Partnership in AD (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors.

RESULTS

We generated multi-omics data and curated and harmonized phenotypic data from AA (n=306), LA (n=326), or AA and LA (n=4) brain donors plus Non-Hispanic White (n=252) and other (n=20) ethnic groups, to establish a foundational dataset enriched for AA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures.

DISCUSSION

Inclusion of traditionally underrepresented groups in multi-omics studies is essential to discover the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.

Spatial Coding Dysfunction and Network Instability in the Aging Medial Entorhinal Cortex

Across species, spatial memory declines with age, possibly reflecting altered hippocampal and medial entorhinal cortex (MEC) function. However, the integrity of cellular and network-level spatial coding in aged MEC is unknown. Here, we leveraged in vivo electrophysiology to assess MEC function in young, middle-aged, and aged mice navigating virtual environments. In aged grid cells, we observed impaired stabilization of context-specific spatial firing, correlated with spatial memory deficits. Additionally, aged grid networks shifted firing patterns often but with poor alignment to context changes. Aged spatial firing was also unstable in an unchanging environment. In these same mice, we identified 458 genes differentially expressed with age in MEC, 61 of which had expression correlated with spatial firing stability. These genes were enriched among interneurons and related to synaptic transmission. Together, these findings identify coordinated transcriptomic, cellular, and network changes in MEC implicated in impaired spatial memory in aging.

Human brain glycoform coregulation network and glycan modification alterations in Alzheimer's disease

Despite the importance of protein glycosylation to brain health, current knowledge of glycosylated proteoforms or glycoforms in human brain and their alterations in Alzheimer's disease (AD) is limited. Here, we report a proteome-wide glycoform profiling study of human AD and control brains using intact glycopeptide-based quantitative glycoproteomics coupled with systems biology. Our study identified more than 10,000 human brain N-glycoforms from nearly 1200 glycoproteins and uncovered disease signatures of altered glycoforms and glycan modifications, including reduced sialylation and N-glycan branching and elongation as well as elevated mannosylation and N-glycan truncation in AD. Network analyses revealed a higher-order organization of brain glycoproteome into networks of coregulated glycoforms and glycans and discovered glycoform and glycan modules associated with AD clinical phenotype, amyloid-β accumulation, and tau pathology. Our findings provide valuable insights into disease pathogenesis and a rich resource of glycoform and glycan changes in AD and pave the way forward for developing glycosylation-based therapies and biomarkers for AD.

Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification

INTRODUCTION

Blood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells.

METHODS

Bulk RNA-sequencing of blood cells was performed on AD patients of Chinese descent (n = 214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n = 208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co-expression network analysis (WGCNA) and deconvolution analysis identified AD-associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD-associated genes, gene modules, cell types, and established AD classification models.

RESULTS

WGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs] > 0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell-type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis.

DISCUSSION

This study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies.

HIGHLIGHTS

We comprehensively analyze the blood transcriptomes of a well-characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease. Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers. Immune-associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease.

Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

Silencing Apoe with divalent-siRNAs improves amyloid burden and activates immune response pathways in Alzheimer's disease

INTRODUCTION

The most significant genetic risk factor for late-onset Alzheimer's disease (AD) is APOE4, with evidence for gain- and loss-of-function mechanisms. A clinical need remains for therapeutically relevant tools that potently modulate APOE expression.

METHODS

We optimized small interfering RNAs (di-siRNA, GalNAc) to potently silence brain or liver Apoe and evaluated the impact of each pool of Apoe on pathology.

RESULTS

In adult 5xFAD mice, siRNAs targeting CNS Apoe efficiently silenced Apoe expression and reduced amyloid burden without affecting systemic cholesterol, confirming that potent silencing of brain Apoe is sufficient to slow disease progression. Mechanistically, silencing Apoe reduced APOE-rich amyloid cores and activated immune system responses.

DISCUSSION

These results establish siRNA-based modulation of Apoe as a viable therapeutic approach, highlight immune activation as a key pathway affected by Apoe modulation, and provide the technology to further evaluate the impact of APOE silencing on neurodegeneration.

Genetic and multi-omic risk assessment of Alzheimer's disease implicates core associated biological domains

INTRODUCTION

Alzheimer's disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome.

METHODS

Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify system-level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genome-wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive Gene Ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank-and-organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains.

RESULTS

The top AD-risk-associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains.

DISCUSSION

This provides an objective methodology to localize risk within specific biological endophenotypes and drill down into the most significantly associated sets of GO terms and annotated genes for potential therapeutic targets.

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.

TMEM106B coding variant is protective and deletion detrimental in a mouse model of tauopathy

TMEM106B is a risk modifier of multiple neurological conditions, where a single coding variant and multiple non-coding SNPs influence the balance between susceptibility and resilience. Two key questions that emerge from past work are whether the lone T185S coding variant contributes to protection, and if the presence of TMEM106B is helpful or harmful in the context of disease. Here, we address both questions while expanding the scope of TMEM106B study from TDP-43 to models of tauopathy. We generated knockout mice with constitutive deletion of TMEM106B, alongside knock-in mice encoding the T186S knock-in mutation (equivalent to the human T185S variant), and crossed both with a P301S transgenic tau model to study how these manipulations impacted disease phenotypes. We found that TMEM106B deletion accelerated cognitive decline, hind limb paralysis, tau pathology, and neurodegeneration. TMEM106B deletion also increased transcriptional correlation with human AD and the functional pathways enriched in KO:tau mice aligned with those of AD. In contrast, the coding variant protected against tau-associated cognitive decline, synaptic impairment, neurodegeneration, and paralysis without affecting tau pathology. Our findings reveal that TMEM106B is a critical safeguard against tau aggregation, and that loss of this protein has a profound effect on sequelae of tauopathy. Our study further demonstrates that the coding variant is functionally relevant and contributes to neuroprotection downstream of tau pathology to preserve cognitive function.

Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Sex- and region-specific cortical and hippocampal whole genome transcriptome profiles from control and APP/PS1 Alzheimer's disease mice

A variety of Alzheimer's disease (AD) mouse models has been established and characterized within the last decades. To get an integrative view of the sophisticated etiopathogenesis of AD, whole genome transcriptome studies turned out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex and hippocampus of age-matched, eight months old male and female APP/PS1 AD mice and control animals to perform sex- and brain region specific analysis of transcriptome profiles. The results of our studies reveal novel, detailed insight into differentially expressed signature genes and related fold changes in the individual APP/PS1 subgroups. Gene ontology and Venn analysis unmasked that intersectional, upregulated genes were predominantly involved in, e.g., activation of microglial, astrocytic and neutrophilic cells, innate immune response/immune effector response, neuroinflammation, phagosome/proteasome activation, and synaptic transmission. The number of (intersectional) downregulated genes was substantially less in the different subgroups and related GO categories included, e.g., the synaptic vesicle docking/fusion machinery, synaptic transmission, rRNA processing, ubiquitination, proteasome degradation, histone modification and cellular senescence. Importantly, this is the first study to systematically unravel sex- and brain region-specific transcriptome fingerprints/signature genes in APP/PS1 mice. The latter will be of central relevance in future preclinical and clinical AD related studies, biomarker characterization and personalized medicinal approaches.

Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer's disease

Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples. As a result, the co-expression estimates only offer an aggregated view of the underlying gene regulations and can be confounded by heterogeneity in cell type compositions, failing to reveal gene coordination that may be distinct across different cell types. In this paper, we introduce a flexible framework for estimating cell-type-specific gene co-expression networks from bulk sample data, without making specific assumptions on the distributions of gene expression profiles in different cell types. We develop a novel sparse least squares estimator, referred to as CSNet, that is efficient to implement and has good theoretical properties. Using CSNet, we analyzed the bulk gene expression data from a cohort study on Alzheimer's disease and identified previously unknown cell-type-specific co-expressions among Alzheimer's disease risk genes, suggesting cell-type-specific disease mechanisms.

Levetiracetam modulates brain metabolic networks and transcriptomic signatures in the 5XFAD mouse model of Alzheimer's disease

Introduction

Subcritical epileptiform activity is associated with impaired cognitive function and is commonly seen in patients with Alzheimer's disease (AD). The anti-convulsant, levetiracetam (LEV), is currently being evaluated in clinical trials for its ability to reduce epileptiform activity and improve cognitive function in AD. The purpose of the current study was to apply pharmacokinetics (PK), network analysis of medical imaging, gene transcriptomics, and PK/PD modeling to a cohort of amyloidogenic mice to establish how LEV restores or drives alterations in the brain networks of mice in a dose-dependent basis using the rigorous preclinical pipeline of the MODEL-AD Preclinical Testing Core.

Methods

Chronic LEV was administered to 5XFAD mice of both sexes for 3 months based on allometrically scaled clinical dose levels from PK models. Data collection and analysis consisted of a multi-modal approach utilizing 18F-FDG PET/MRI imaging and analysis, transcriptomic analyses, and PK/PD modeling.

Results

Pharmacokinetics of LEV showed a sex and dose dependence in Cmax, CL/F, and AUC0-∞, with simulations used to estimate dose regimens. Chronic dosing at 10, 30, and 56 mg/kg, showed 18F-FDG specific regional differences in brain uptake, and in whole brain covariance measures such as clustering coefficient, degree, network density, and connection strength (i.e., positive and negative). In addition, transcriptomic analysis via nanoString showed dose-dependent changes in gene expression in pathways consistent 18F-FDG uptake and network changes, and PK/PD modeling showed a concentration dependence for key genes, but not for network covariance modeling.

Discussion

This study represents the first report detailing the relationships of metabolic covariance and transcriptomic network changes resulting from LEV administration in 5XFAD mice. Overall, our results highlight non-linear kinetics based on dose and sex, where gene expression analysis demonstrated LEV dose- and concentration-dependent changes, along with cerebral metabolism, and/or cerebral homeostatic mechanisms relevant to human AD, which aligned closely with network covariance analysis of 18F-FDG images. Collectively, this study show cases the value of a multimodal connectomic, transcriptomic, and pharmacokinetic approach to further investigate dose dependent relationships in preclinical studies, with translational value toward informing clinical study design.

Large-scale gene expression changes in APP/PSEN1 and GFAP mutation models exhibit high congruence with Alzheimer's disease

Alzheimer's disease (AD) is a complex neurodegenerative disorder with both genetic and non-genetic causes. Animal research models are available for a multitude of diseases and conditions affecting the central nervous system (CNS), and large-scale CNS gene expression data exist for many of these. Although there are several models specifically for AD, each recapitulates different aspects of the human disease. In this study we evaluate over 500 animal models to identify those with CNS gene expression patterns matching human AD datasets. Approaches included a hypergeometric based scoring system that rewards congruent gene expression patterns but penalizes discordant gene expression patterns. The top two models identified were APP/PS1 transgenic mice expressing mutant APP and PSEN1, and mice carrying a GFAP mutation that is causative of Alexander disease, a primary disorder of astrocytes in the CNS. The APP/PS1 and GFAP models both matched over 500 genes moving in the same direction as in human AD, and both had elevated GFAP expression and were highly congruent with one another. Also scoring highly were the 5XFAD model (with five mutations in APP and PSEN1) and mice carrying CK-p25, APP, and MAPT mutations. Animals with the APOE3 and 4 mutations combined with traumatic brain injury ranked highly. Bulbectomized rats scored high, suggesting anosmia could be causative of AD-like gene expression. Other matching models included the SOD1G93A strain and knockouts for SNORD116 (Prader-Willi mutation), GRID2, INSM1, XBP1, and CSTB. Many top models demonstrated increased expression of GFAP, and results were similar across multiple human AD datasets. Heatmap and Uniform Manifold Approximation Plot results were consistent with hypergeometric ranking. Finally, some gene manipulation models, including for TYROBP and ATG7, were identified with reversed AD patterns, suggesting possible neuroprotective effects. This study provides insight for the pathobiology of AD and the potential utility of available animal models.

Hippocampus Glutathione S Reductase Potentially Confers Genetic Resilience to Cognitive Decline in the AD-BXD Mouse Population

Alzheimer's disease (AD) is a prevalent and costly age-related dementia. Heritable factors account for 58-79% of variation in late-onset AD, but substantial variation remains in age-of- onset, disease severity, and whether those with high-risk genotypes acquire AD. To emulate the diversity of human populations, we utilized the AD-BXD mouse panel. This genetically diverse resource combines AD genotypes with multiple BXD strains to discover new genetic drivers of AD resilience. Comparing AD-BXD carriers to noncarrier littermates, we computed a novel quantitative metric for resilience to cognitive decline in the AD-BXDs. Our quantitative AD resilience trait was heritable and genetic mapping identified a locus on chr8 associated with resilience to AD mutations that resulted in amyloid brain pathology. Using a hippocampus proteomics dataset, we nominated the mitochondrial glutathione S reductase protein (GR or GSHR) as a resilience factor, finding that the DBA/2J genotype was associated with substantially higher GR abundance. By mapping protein QTLs (pQTLs), we identified synaptic organization and mitochondrial proteins coregulated in trans with a cis-pQTL for GR. We found four coexpression modules correlated with the quantitative resilience score in aged 5XFAD mice using paracliques, which were related to cell structure, protein folding, and postsynaptic densities. Finally, we found significant positive associations between human GSR transcript abundance in the brain and better outcomes on AD-related cognitive and pathology traits in the Religious Orders Study/Memory and Aging project (ROSMAP). Taken together, these data support a framework for resilience in which neuronal antioxidant pathway activity provides for stability of synapses within the hippocampus.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease

BACKGROUND

Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

METHODS

We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.

RESULTS

Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.

CONCLUSIONS

Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

A modified Mediterranean-style diet enhances brain function via specific gut-microbiome-brain mechanisms

Alzheimer's disease (AD) is a debilitating brain disorder with rapidly mounting prevalence worldwide, yet no proven AD cure has been discovered. Using a multi-omics approach in a transgenic AD mouse model, the current study demonstrated the efficacy of a modified Mediterranean-ketogenic diet (MkD) on AD-related neurocognitive pathophysiology and underlying mechanisms related to the gut-microbiome-brain axis. The findings revealed that MkD induces profound shifts in the gut microbiome community and microbial metabolites. Most notably, MkD promoted growth of the Lactobacillus population, resulting in increased bacteria-derived lactate production. We discovered elevated levels of microbiome- and diet-derived metabolites in the serum as well, signaling their influence on the brain. Importantly, these changes in serum metabolites upregulated specific receptors that have neuroprotective effects and induced alternations in neuroinflammatory-associated pathway profiles in hippocampus. Additionally, these metabolites displayed strong favorable co-regulation relationship with gut-brain integrity and inflammatory markers, as well as neurobehavioral outcomes. The findings underscore the ameliorative effects of MkD on AD-related neurological function and the underlying gut-brain communication via modulation of the gut microbiome-metabolome arrays.

A Continuous Extension of Gene Set Enrichment Analysis Using the Likelihood Ratio Test Statistics Identifies Vascular Endothelial Growth Factor as a Candidate Pathway for Alzheimer's Disease via ITGA5

BACKGROUND

Alzheimer's disease (AD) involves brain neuropathologies such as amyloid plaque and hyperphosphorylated tau tangles and is accompanied by cognitive decline. Identifying the biological mechanisms underlying disease onset and progression based on quantifiable phenotypes will help understand disease etiology and devise therapies.

OBJECTIVE

Our objective was to identify molecular pathways associated with hallmark AD biomarkers and cognitive status, accounting for variables such as age, sex, education, and APOE genotype.

METHODS

We introduce a pathway-based statistical approach, extending the gene set likelihood ratio test to continuous phenotypes. We first analyzed independently each of the three phenotypes (amyloid-β, tau, cognition) using continuous gene set likelihood ratio tests to account for covariates, including age, sex, education, and APOE genotype. The analysis involved 634 subjects with data available for all three phenotypes, allowing for the identification of common pathways.

RESULTS

We identified 14 pathways significantly associated with amyloid-β; 5 associated with tau; and 174 associated with cognition, which showed a larger number of pathways compared to biomarkers. A single pathway, vascular endothelial growth factor receptor binding (VEGF-RB), exhibited associations with all three phenotypes. Mediation analysis showed that among the VEGF-RB family genes, ITGA5 mediates the relationship between cognitive scores and pathological biomarkers.

CONCLUSIONS

We presented a new statistical approach linking continuous phenotypes, gene expression across pathways, and covariates like sex, age, and education. Our results reinforced VEGF RB2's role in AD cognition and demonstrated ITGA5's significant role in mediating the AD pathology-cognition connection.