The AD Knowledge Portal: A Repository for Multi‐Omic Data on Alzheimer's Disease and Aging

Greenwood, Anna K.; Montgomery, Kelsey S.; Kauer, Nicole; Woo, Kara; Leanza, Zoë; Poehlman, William L.; Gockley, Jake; Sieberts, Solveig K.; Bradic, Ljubomir; Logsdon, Benjamin A.; Peters, Mette A.; Omberg, Larsson; Mangravite, Lara M.

doi:10.1002/cphg.105

Cited by 56 publications

(54 citation statements)

References 4 publications

(6 reference statements)

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“…We retrieved six blood AD datasets (GSE63060, GSE63061, GSE85426, GSE4229, ADNI, and ROSMAP [ 20 , 21 ]), six brain AD datasets (GSE33000, GSE84422-GPL96, GSE84422-GPL97, GSE118553, GSE132903, and GSE5281), 11 blood CVD datasets (GSE60993, GSE20681, GSE59867, GSE90074, GSE34198, GSE12288, GSE20680, GSE9820, GSE62646, GSE66360, GSE7638), three heart CVD datasets (GSE57338, GSE1869, and GSE5406), one fat CVD dataset (GSE64554), and one carotid plaque CVD dataset (GSE43292). Most datasets were obtained from the Gene Expression Omnibus database [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

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Identification of Disease-Related Genes That Are Common between Alzheimer’s and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis

Lee

2021

Biomedicines

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Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.

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Section: Methodsmentioning

confidence: 99%

“…Pathways with an FDR-corrected p -value < 0.05 were defined as significantly enriched pathways. From MSigDB [ 9 ], the pathway information, including KEGG [ 8 ] and Gene Ontology [ 21 ], were obtained.…”

Section: Methodsmentioning

confidence: 99%

Identification of Disease-Related Genes That Are Common between Alzheimer’s and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis

Lee

2021

Biomedicines

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“…program is working towards this goal through generation and examination of diverse data including multi-omics profiling of different modalities and across relevant tissues, where all data generated through the AMP-AD initiative is rapidly shared through the AD Knowledge Portal (https://adknowledgeportal.org; [6]).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Molecular Atlas of Alzheimer’s Disease

Wörheide

Krumsiek

Nataf

et al. 2021

Preprint

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INTRODUCTION: Embedding single-omics disease associations into the wider context of multi-level molecular changes in Alzheimer's disease (AD) remains one central challenge in AD research. METHODS: Results from numerous AD-specific omics studies from AMP-AD, NIAGADS, and other initiatives were integrated into a comprehensive network resource and complemented with molecular associations from large-scale population-based studies to provide a global view on AD. RESULTS: We present the AD Atlas, an online resource (www.adatlas.org) integrating over 20 large studies providing disease-relevant information on 20,353 protein-coding genes, 8,615 proteins, 997 metabolites and 31 AD-related phenotypes. Multiple showcases demonstrate the utility of this resource for contextualization of AD research results and subsequent downstream analyses, such as drug repositioning approaches. DISCUSSION: By providing a global view on multi-omics results through a user-friendly interface, the AD Atlas enables the formulation of molecular hypotheses and retrieval of clinically relevant insights that can be validated in follow-up analyses or experiments.

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“…In this work, we are leveraging the multi-dimensional, well characterized and high quality genomic, pathological and clinical data from the Accelerating Medicines Project for Alzheimer’s Disease (AMP-AD) program (Hodes and Buckholtz, 2016) and applying the latest deep learning framework to identity pseudo-temporal trajectories in transcriptomic space and the underlying gene signatures for AD progression. As a major component of the AMP-AD program, the Target Discovery and Preclinical Validation Project brings together different organizations to collect and analyze multidimensional molecular data (genomic, transcriptomic, epigenomic, proteomic) from more than 2,000 human brains and peripheral tissues from multiple AD cohorts (Greenwood et al ., 2020). Using the RNA-seq data from dorsolateral prefrontal cortex (DLPFC) region in the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (Bennett et al ., 2012a; Bennett et al ., 2012b), a deep learning model was first trained to perform joint supervised classification between the two termini of the disease continuum (AD and control diagnosis group) to achieve the maximum separation.…”

Section: Introductionmentioning

confidence: 99%

“…As a major component of the AMP-AD program, the Target Discovery and Preclinical Validation Project brings together different organizations to collect and analyze multidimensional molecular data (genomic, transcriptomic, epigenomic, proteomic) from more than 2,000 human brains and peripheral tissues from multiple AD cohorts. 17 Using the RNA-seq data from dorsolateral prefrontal cortex (DLPFC) region in the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort, 18,19 we first trained a deep learning model to perform supervised classification between the two termini of the disease continuum (AD and control diagnosis group). The goal is to achieve the maximum separation of neuropathologically confirmed cases and controls.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer’s disease

Wang

Readhead

Chen

et al. 2021

Preprint

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Brain tissue gene expression from donors with and without Alzheimer's disease (AD) have been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyze RNA-seq data from 1,114 brain donors from the AMP-AD consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles, and clinical severity in multiple AD dementia populations. Starting from the cross-sectional data in the ROSMAP cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors AD progression. A severity index (SI) was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this dataset, SIs were found to be very closely correlated with all the AD neuropathology biomarkers (R ~ 0.5, p < 1e-11) and global cognitive function (R = -0.68, p < 2.2e-16). We then applied the model to additional transcriptomic datasets from different brain regions (MAYO, n = 266; MSBB, n = 214), and observed that the model remained significantly predictive (p < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with AD co-expression regulatory networks, resolving two discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with AD, other age-related neurological disorders and controls; and revealed the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyze heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like AD.

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The AD Knowledge Portal: A Repository for Multi‐Omic Data on Alzheimer's Disease and Aging

Cited by 56 publications

References 4 publications

Identification of Disease-Related Genes That Are Common between Alzheimer’s and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis

Identification of Disease-Related Genes That Are Common between Alzheimer’s and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis

An Integrated Molecular Atlas of Alzheimer’s Disease

Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer’s disease

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