Novel methods for integration and visualization of genomics and genetics data in Alzheimer's disease

TYK2 as a novel therapeutic target in Alzheimer's Disease with TDP-43 inclusions

Neuroinflammation is a pathological feature of many neurodegenerative diseases, including Alzheimer's disease (AD) 1,2 and amyotrophic lateral sclerosis (ALS) 3 , raising the possibility of common therapeutic targets. We previously established that cytoplasmic double-stranded RNA (cdsRNA) is spatially coincident with cytoplasmic pTDP-43 inclusions in neurons of patients with C9ORF72-mediated ALS 4 . CdsRNA triggers a type-I interferon (IFN-I)-based innate immune response in human neural cells, resulting in their death 4 . Here, we report that cdsRNA is also spatially coincident with pTDP-43 cytoplasmic inclusions in brain cells of patients with AD pathology and that type-I interferon response genes are significantly upregulated in brain regions affected by AD. We updated our machine-learning pipeline DRIAD-SP (Drug Repurposing In Alzheimer's Disease with Systems Pharmacology) to incorporate cryptic exon (CE) detection as a proxy of pTDP-43 inclusions and demonstrated that the FDA-approved JAK inhibitors baricitinib and ruxolitinib that block interferon signaling show a protective signal only in cortical brain regions expressing multiple CEs. Furthermore, the JAK family member TYK2 was a top hit in a CRISPR screen of cdsRNA-mediated death in differentiated human neural cells. The selective TYK2 inhibitor deucravacitinib, an FDA-approved drug for psoriasis, rescued toxicity elicited by cdsRNA. Finally, we identified CCL2, CXCL10, and IL-6 as candidate predictive biomarkers for cdsRNA-related neurodegenerative diseases. Together, we find parallel neuroinflammatory mechanisms between TDP-43 associated-AD and ALS and nominate TYK2 as a possible disease-modifying target of these incurable neurodegenerative diseases.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Alzheimer DataLENS: An Open Data Analytics Portal for Alzheimer's Disease Research

Background

Recent Alzheimer's disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner.

Objective

Create an online portal of public omics datasets for AD research.

Methods

We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline.

Results

Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas.

Conclusions

Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at https://alzdatalens.partners.org.

Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain.

Epigenomics and Lipidomics Integration in Alzheimer Disease: Pathways Involved in Early Stages

Background: Alzheimer Disease (AD) is the most prevalent dementia. However, the physiopathological mechanisms involved in its development are unclear. In this sense, a multi-omics approach could provide some progress. Methods: Epigenomic and lipidomic analysis were carried out in plasma samples from patients with mild cognitive impairment (MCI) due to AD (n = 22), and healthy controls (n = 5). Then, omics integration between microRNAs (miRNAs) and lipids was performed by Sparse Partial Least Squares (s-PLS) regression and target genes for the selected miRNAs were identified. Results: 25 miRNAs and 25 lipids with higher loadings in the sPLS regression were selected. Lipids from phosphatidylethanolamines (PE), lysophosphatidylcholines (LPC), ceramides, phosphatidylcholines (PC), triglycerides (TG) and several long chain fatty acids families were identified as differentially expressed in AD. Among them, several fatty acids showed strong positive correlations with miRNAs studied. In fact, these miRNAs regulated genes implied in fatty acids metabolism, as elongation of very long-chain fatty acids (ELOVL), and fatty acid desaturases (FADs). Conclusions: The lipidomic–epigenomic integration showed that several lipids and miRNAs were differentially expressed in AD, being the fatty acids mechanisms potentially involved in the disease development. However, further work about targeted analysis should be carried out in a larger cohort, in order to validate these preliminary results and study the proposed pathways in detail.

Computational Interspecies Translation Between Alzheimer's Disease Mouse Models and Human Subjects Identifies Innate Immune Complement, TYROBP, and TAM Receptor Agonist Signatures, Distinct From Influences of Aging

Mouse models are vital for preclinical research on Alzheimer’s disease (AD) pathobiology. Many traditional models are driven by autosomal dominant mutations identified from early onset AD genetics whereas late onset and sporadic forms of the disease are predominant among human patients. Alongside ongoing experimental efforts to improve fidelity of mouse model representation of late onset AD, a computational framework termed Translatable Components Regression (TransComp-R) offers a complementary approach to leverage human and mouse datasets concurrently to enhance translation capabilities. We employ TransComp-R to integratively analyze transcriptomic data from human postmortem and traditional amyloid mouse model hippocampi to identify pathway-level signatures present in human patient samples yet predictive of mouse model disease status. This method allows concomitant evaluation of datasets across different species beyond observational seeking of direct commonalities between the species. Additional linear modeling focuses on decoupling disease signatures from effects of aging. Our results elucidated mouse-to-human translatable signatures associated with disease: excitatory synapses, inflammatory cytokine signaling, and complement cascade- and TYROBP-based innate immune activity; these signatures all find validation in previous literature. Additionally, we identified agonists of the Tyro3 / Axl / MerTK (TAM) receptor family as significant contributors to the cross-species innate immune signature; the mechanistic roles of the TAM receptor family in AD merit further dedicated study. We have demonstrated that TransComp-R can enhance translational understanding of relationships between AD mouse model data and human data, thus aiding generation of biological hypotheses concerning AD progression and holding promise for improved preclinical evaluation of therapies.

MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.

Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer's disease etiopathogenesis

Alzheimer's disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Aiming for a better understanding of APOE-specific effects, the ADAPTED consortium analysed and integrated publicly available data of multiple OMICS technologies from both plasma and brain stratified by APOE haplotype (APOE2, APOE3 and APOE4). Combining genome-wide association studies (GWAS) with differential mRNA and protein expression analyses and single-nuclei transcriptomics, we identified genes and pathways contributing to AD in both APOE dependent and independent fashion. Interestingly, we characterised a set of biomarkers showing plasma and brain consistent protein profiles and opposite trends in APOE2 and APOE4 AD cases that could constitute screening tools for a disease that lacks specific blood biomarkers. Beside the identification of APOE-specific signatures, our findings advocate that this novel approach, based on the concordance across OMIC layers and tissues, is an effective strategy for overcoming the limitations of often underpowered single-OMICS studies.

IGF2R circular RNA hsa_circ_0131235 expression in the middle temporal cortex is associated with AD pathology

OBJECTIVE

To identify circular RNAs as candidates for differential expression in the middle temporal (MT) cortex in a well-characterized cohort with contrasting Alzheimer disease (AD) pathology and cognition. Top screen candidates were assessed for proof of circularity and then quantified by qPCR in a larger number of samples.

METHODS

An initial RNA sequencing screen was performed on n = 20 frozen human tissue samples. Filters were applied to select candidate circular RNAs for further investigation. Frozen human tissue samples were selected for global AD pathology burden and global cognition scores (n = 100). Linear and divergent primers were used to assess circularity using RNaseR digestion. RT-qPCR was performed to quantify relative hsa_circ_0131235 abundance.

RESULTS

Eleven circular RNAs were selected for further investigation. Four candidates produced circular RNA primers with appropriate efficiencies for qPCR. RNaseR treatment and analysis by both basic PCR and qPCR confirmed hsa_circ_0131235 circularity. There was a significant main effect of AD pathology on hsa_circ_0131235 expression.

CONCLUSIONS

Elevated hsa_circ_0131235 expression in the MT cortex was significantly associated with AD pathology.

Systematic analysis to identify transcriptome-wide dysregulation of Alzheimer's disease in genes and isoforms

Alzheimer's disease (AD) is one of the most common neurodegeneration diseases caused by multiple factors. The mechanistic insight of AD remains limited. To disclose molecular mechanisms of AD, many studies have been proposed from transcriptome analyses. However, no analysis across multiple levels of transcription has been conducted to discover co-expression networks of AD. We performed gene-level and isoform-level analyses of RNA sequencing (RNA-seq) data from 544 brain tissues of AD patients, mild cognitive impaired (MCI) patients, and healthy controls. Gene and isoform levels of co-expression modules were constructed by RNA-seq data. The associations of modules with AD were evaluated by integrating cognitive scores of patients, Genome-wide association studies (GWAS), alternative splicing analysis, and dementia-related genes expressed in brain tissues. Totally, 29 co-expression modules were found with expressions significantly correlated with the cognitive scores. Among them, two isoform modules were enriched with AD-associated SNPs and genes whose mRNA splicing displayed significant alteration in relation to AD disease. These two modules were further found enriched with dementia-related genes expressed in four brain regions of 125 AD patients. Analyzing expressions of these two modules revealed expressions of 39 isoforms (corresponding to 35 genes) significantly correlated with cognitive scores of AD patients, in which 38 isoforms were significantly up-regulated in AD patients comparing to controls, and 33 isoforms (corresponding to 29 genes) were not reported as AD-related previously. Employing the co-expression modules and the drug-induced gene expression data from Connectivity Map (CMAP), 12 drugs were predicted as significant in restoring the gene expression of AD patients towards health, which include nine drugs reported for relieving AD. In comparison, four of the top 12 significant drugs were known for relieving AD if the drug prediction was performed by the genes expressed significantly different in AD and healthy controls. Analysis of multiple levels of the transcriptomic organization is useful in suggesting AD-related co-expression networks and discovering drugs.

Meta-analysis of mouse transcriptomic studies supports a context-dependent astrocyte reaction in acute CNS injury versus neurodegeneration

Background

Neuronal damage in acute CNS injuries and chronic neurodegenerative diseases is invariably accompanied by an astrocyte reaction in both mice and humans. However, whether and how the nature of the CNS insult—acute versus chronic—influences the astrocyte response, and whether astrocyte transcriptomic changes in these mouse models faithfully recapitulate the astrocyte reaction in human diseases remains to be elucidated. We hypothesized that astrocytes set off different transcriptomic programs in response to acute versus chronic insults, besides a shared “pan-injury” signature common to both types of conditions, and investigated the presence of these mouse astrocyte signatures in transcriptomic studies from human neurodegenerative diseases.

Methods

We performed a meta-analysis of 15 published astrocyte transcriptomic datasets from mouse models of acute injury (n = 6) and chronic neurodegeneration (n = 9) and identified pan-injury, acute, and chronic signatures, with both upregulated (UP) and downregulated (DOWN) genes. Next, we investigated these signatures in 7 transcriptomic datasets from various human neurodegenerative diseases.

Results

In mouse models, the number of UP/DOWN genes per signature was 64/21 for pan-injury and 109/79 for acute injury, whereas only 13/27 for chronic neurodegeneration. The pan-injury-UP signature was represented by the classic cytoskeletal hallmarks of astrocyte reaction (Gfap and Vim), plus extracellular matrix (i.e., Cd44, Lgals1, Lgals3, Timp1), and immune response (i.e., C3, Serping1, Fas, Stat1, Stat2, Stat3). The acute injury-UP signature was enriched in protein synthesis and degradation (both ubiquitin-proteasome and autophagy systems), intracellular trafficking, and anti-oxidant defense genes, whereas the acute injury-DOWN signature included genes that regulate chromatin structure and transcriptional activity, many of which are transcriptional repressors. The chronic neurodegeneration-UP signature was further enriched in astrocyte-secreted extracellular matrix proteins (Lama4, Cyr61, Thbs4), while the DOWN signature included relevant genes such as Agl (glycogenolysis), S1pr1 (immune modulation), and Sod2 (anti-oxidant). Only the pan-injury-UP mouse signature was clearly present in some human neurodegenerative transcriptomic datasets.

Conclusions

Acute and chronic CNS injuries lead to distinct astrocyte gene expression programs beyond their common astrocyte reaction signature. However, caution should be taken when extrapolating astrocyte transcriptomic findings from mouse models to human diseases.

A meta-analysis of gene expression data highlights synaptic dysfunction in the hippocampus of brains with Alzheimer's disease

Since the world population is ageing, dementia is going to be a growing concern. Alzheimer’s disease is the most common form of dementia. The pathogenesis of Alzheimer’s disease is extensively studied, yet unknown remains. Therefore, we aimed to extract new knowledge from existing data. We analysed about 2700 upregulated genes and 2200 downregulated genes from three studies on the CA1 of the hippocampus of brains with Alzheimer’s disease. We found that only the calcium signalling pathway enriched by 48 downregulated genes was consistent between all three studies. We predicted miR-129 to target nine out of 48 genes. Then, we validated miR-129 to regulate six out of nine genes in HEK cells. We noticed that four out of six genes play a role in synaptic plasticity. Finally, we confirmed the upregulation of miR-129 in the hippocampus of brains of rats with scopolamine-induced amnesia as a model of Alzheimer’s disease. We suggest that future research should investigate the possible role of miR-129 in synaptic plasticity and Alzheimer’s disease. This paper presents a novel framework to gain insight into potential biomarkers and targets for diagnosis and treatment of diseases.

Increased mitochondrial calcium levels associated with neuronal death in a mouse model of Alzheimer's disease

Mitochondria contribute to shape intraneuronal Ca2+ signals. Excessive Ca2+ taken up by mitochondria could lead to cell death. Amyloid beta (Aβ) causes cytosolic Ca2+ overload, but the effects of Aβ on mitochondrial Ca2+ levels in Alzheimer's disease (AD) remain unclear. Using a ratiometric Ca2+ indicator targeted to neuronal mitochondria and intravital multiphoton microscopy, we find increased mitochondrial Ca2+ levels associated with plaque deposition and neuronal death in a transgenic mouse model of cerebral β-amyloidosis. Naturally secreted soluble Aβ applied onto the healthy brain increases Ca2+ concentration in mitochondria, which is prevented by blockage of the mitochondrial calcium uniporter. RNA-sequencing from post-mortem AD human brains shows downregulation in the expression of mitochondrial influx Ca2+ transporter genes, but upregulation in the genes related to mitochondrial Ca2+ efflux pathways, suggesting a counteracting effect to avoid Ca2+ overload. We propose lowering neuronal mitochondrial Ca2+ by inhibiting the mitochondrial Ca2+ uniporter as a novel potential therapeutic target against AD.

Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer's disease from multi-dimensional genomic data

The effective and accurate diagnosis of Alzheimer's disease (AD), especially in the early stage (i.e., mild cognitive impairment (MCI)) remains a big challenge in AD research. So far, multiple biomarkers have been associated with AD diagnosis and progression. However, most of the existing research only utilized single modality data for diagnostic biomarker identification, which did not take the advantages of multi-modal data that provide comprehensive and complementary information at multiple levels into consideration. In this paper, we integrate multi-modal genomic data from postmortem AD brains (i.e., mRNA, miRNA and epigenomic data) and propose a hyper-graph based sparse canonical correlation analysis (HGSCCA) method to extract the most correlated multi-modal biomarkers associated with AD and MCI. Specifically, our model utilizes the sparse canonical correlation analysis framework (SCCA), which aims at finding the best linear projections for each input modality so that the strongest correlation within the selected features of multi-dimensional genomic data can be captured. In addition, with the consideration of high-order relationships among different subjects, we also introduce a hyper-graph-based regularization term that will lead to the selection of more discriminative biomarkers. To evaluate the effectiveness of the proposed method, we conduct the experiments on the well-known AD cohort study, The Religious Orders Study and Memory and Aging Project (ROSMAP) dataset, and the results show that our method can not only identify meaningful biomarkers for the diagnosis AD disease, but also achieve superior classification performance than the comparing methods.

Characterization of the 18 kDa translocator protein (TSPO) expression in post-mortem normal and Alzheimer's disease brains

The 18 kDa translocator protein (TSPO) is a widely used target for microglial PET imaging radioligands, but its expression in post-mortem normal and diseased human brain is not well described. We aimed at characterizing the TSPO expression in human control (CTRL) and Alzheimer's disease (AD) brains. Specifically, we sought to: (1) define the cell type(s) expressing TSPO; (2) compare tspo mRNA and TSPO levels between AD and CTRL brains; (3) correlate TSPO levels with quantitative neuropathological measures of reactive glia and AD neuropathological changes; and (4) investigate the effects of the TSPO rs6971 SNP on tspo mRNA and TSPO levels, glial responses and AD neuropathological changes. We performed quantitative immunohistochemistry and Western blot in post-mortem brain samples from CTRL and AD subjects, as well as analysis of publicly available mouse and human brain RNA-Seq datasets. We found that: (1) TSPO is expressed not just in microglia, but also in astrocytes, endothelial cells and vascular smooth muscle cells; (2) there is substantial overlap of tspo mRNA and TSPO levels between AD and CTRL subjects and in TSPO levels between temporal neocortex and white matter in both groups; (3) TSPO cortical burden does not correlate with the burden of activated microglia or reactive astrocytes, Aβ plaques or neurofibrillary tangles, or the cortical thickness; (4) the TSPO rs6971 SNP does not significantly impact tspo mRNA or TSPO levels, the magnitude of glial responses, the cortical thickness, or the burden of AD neuropathological changes. These results could inform ongoing efforts toward the development of reactive glia-specific PET radioligands.

The Major Risk Factors for Alzheimer's Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques

Gene expression profiles of more than 10,000 individual microglial cells isolated from cortex and hippocampus of male and female AppNL-G-F mice over time demonstrate that progressive amyloid-β accumulation accelerates two main activated microglia states that are also present during normal aging. Activated response microglia (ARMs) are composed of specialized subgroups overexpressing MHC type II and putative tissue repair genes (Dkk2, Gpnmb, and Spp1) and are strongly enriched with Alzheimer's disease (AD) risk genes. Microglia from female mice progress faster in this activation trajectory. Similar activated states are also found in a second AD model and in human brain. Apoe, the major genetic risk factor for AD, regulates the ARMs but not the interferon response microglia (IRMs). Thus, the ARMs response is the converging point for aging, sex, and genetic AD risk factors.