Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease

Zhou, Yingyue; Song, Wilbur M.; Andhey, Prabhakar S.; Swain, Amanda; Levy, Tyler; Miller, Kelly R.; Poliani, Pietro Luigi; Cominelli, Manuela; Grover, Shikha; Gilfillan, Susan; Cella, Marina; Ulland, Tyler K.; Zaitsev, Konstantin; Miyashita, Akinori; Ikeuchi, Takeshi; Sainouchi, Makoto; Kakita, Akiyoshi; Bennett, David A.; Schneider, Julie A.; Nichols, Michael R.; Beausoleil, Sean A.; Ulrich, Jason D.; Holtzman, David M.; Artyomov, Maxim N.; Colonna, Marco

doi:10.1038/s41591-019-0695-9

Cited by 692 publications

(851 citation statements)

References 62 publications

Supporting

Mentioning

773

Contrasting

Unclassified

Order By: Relevance

“…From the perspective of actual drug repurposing only the later compounds are useful. In other studies, gene expression changes have demonstrated increased interferon signaling in AD 65 and in ALS 66 brains. Recently, we have found that cytoplasmic dsRNA, a known activator of Type I interferon, is present in ALS brains with TDP-43 pathology.…”

Section: Discussionmentioning

confidence: 84%

Machine Learning Identifies Novel Candidates for Drug Repurposing in Alzheimer’s Disease

Rodriguez

Hug

Todorov

et al. 2020

Preprint

View full text Add to dashboard Cite

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. Repurposing can yield a useful therapeutic and also accelerate proof of concept studies that ultimately lead to a new molecular entity. We present a novel machine learning framework, DRIAD (Drug Repurposing In AD), that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD was validated on gene lists known to be associated with AD from other studies and subsequently applied to evaluate lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs were inspected for common trends among their nominal molecular targets and their "off-targets", revealing a high prevalence of kinases from the Janus (JAK), Unc-51-like (ULK) and NIMA-related (NEK) families. These kinase families are known to modulate pathways related to innate immune signaling, autophagy, and microtubule formation and function, suggesting possible disease-modifying mechanisms of action. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be evaluated in a clinical trial.

show abstract

Section: Discussionmentioning

confidence: 84%

Machine Learning Identifies Novel Candidates for Drug Repurposing in Alzheimer’s Disease

Rodriguez

Hug

Todorov

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…41 Colonna's laboratory is also using scRNA-seq and single nucleus RNA sequencing (snRNA-seq) to understand the transcription patterns of other types of cells in the brain and how the TREM2 variants observed in patients with Alzheimer's disease affect microglial gene expression in humans. 42 He suggested that pharmacological activation of microglial receptors may be a therapeutic strategy for Alzheimer's disease.…”

Section: Targeting Microglial Activity Via Trem2mentioning

confidence: 99%

Alternatives to amyloid for Alzheimer's disease therapies—a symposium report

Cable¹,

Holtzman

Hyman

et al. 2020

Annals of the New York Academy of Sciences

Self Cite

View full text Add to dashboard Cite

For decades, Alzheimer's disease research has focused on amyloid as the primary pathogenic agent. This focus has driven the development of numerous amyloid‐targeting therapies; however, with one possible exception, none of these therapies have been effective in preventing or delaying cognitive decline in patients, and there are no approved disease‐modifying agents. It is becoming more apparent that alternative drug targets are needed to address this complex disease. An increased understanding of Alzheimer's disease pathology has highlighted the need to target the appropriate disease pathology at the appropriate time in the disease course. Preclinical and early clinical studies have focused on targets, including inflammation, tau, vascular health, and the microbiome. This report summarizes the presentations from a New York Academy of Sciences' one‐day symposium entitled “Alzheimer's Disease Therapeutics: Alternatives to Amyloid,” held on November 20, 2019.

show abstract

“…The increased expression is strongly associated with an increase in reactive A1 astrocytes in the EU LA. Reactive A1 astrocytes have been shown to cause neuronal stress and eventual cell death, and have been reported in patients with several neurodegenerative diseases including AD [25][26][27] . Future studies are needed to identify the mechanism producing this expression difference.…”

Section: Mainmentioning

confidence: 99%

IncreasedAPOEε4expression is associated with reactive A1 astrocytes and the difference in Alzheimer Disease risk from diverse ancestral backgrounds

Griswold

Celis

Bussies

et al. 2020

Preprint

View full text Add to dashboard Cite

up to 70 words)APOEε4 African local genomic ancestry (LA) confers less risk for Alzheimer disease (AD) relative to European LA (LA) carriers. Single nucleus RNA sequencing from AD-APOE4/4 frontal cortex found European LA carriers have a 1.45-fold greater APOEε4 expression (p< 1.8 E10 -313 ) and are associated with a unique A1 reactive astrocyte cluster. This suggests a potential mechanism for the increased risk for AD seen in European LA carriers of APOEε4.

show abstract

Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease

Cited by 692 publications

References 62 publications

Machine Learning Identifies Novel Candidates for Drug Repurposing in Alzheimer’s Disease

Machine Learning Identifies Novel Candidates for Drug Repurposing in Alzheimer’s Disease

Alternatives to amyloid for Alzheimer's disease therapies—a symposium report

IncreasedAPOEε4expression is associated with reactive A1 astrocytes and the difference in Alzheimer Disease risk from diverse ancestral backgrounds

Contact Info

Product

Resources

About