Faculty Opinions recommendation of TREM2 lipid sensing sustains the microglial response in an Alzheimer's disease model.

Author(s):  
William Seaman
Cell ◽  
2015 ◽  
Vol 160 (6) ◽  
pp. 1061-1071 ◽  
Author(s):  
Yaming Wang ◽  
Marina Cella ◽  
Kaitlin Mallinson ◽  
Jason D. Ulrich ◽  
Katherine L. Young ◽  
...  

2020 ◽  
Vol 217 (9) ◽  
Author(s):  
Shoutang Wang ◽  
Meer Mustafa ◽  
Carla M. Yuede ◽  
Santiago Viveros Salazar ◽  
Philip Kong ◽  
...  

TREM2 is a receptor for lipids expressed in microglia. The R47H variant of human TREM2 impairs ligand binding and increases Alzheimer’s disease (AD) risk. In mouse models of amyloid β (Aβ) accumulation, defective TREM2 function affects microglial response to Aβ plaques, exacerbating tissue damage, whereas TREM2 overexpression attenuates pathology. Thus, AD may benefit from TREM2 activation. Here, we examined the impact of an anti-human TREM2 agonistic mAb, AL002c, in a mouse AD model expressing either the common variant (CV) or the R47H variant of TREM2. Single-cell RNA-seq of microglia after acute systemic administration of AL002c showed induction of proliferation in both CV- and R47H-transgenic mice. Prolonged administration of AL002c reduced filamentous plaques and neurite dystrophy, impacted behavior, and tempered microglial inflammatory response. We further showed that a variant of AL002c is safe and well tolerated in a first-in-human phase I clinical trial and engages TREM2 based on cerebrospinal fluid biomarkers. We conclude that AL002 is a promising candidate for AD therapy.


2021 ◽  
Vol 218 (9) ◽  
Author(s):  
Yun Chen ◽  
Marco Colonna

Alzheimer’s disease (AD) is characterized by extracellular aggregates of amyloid β peptides, intraneuronal tau aggregates, and neuronal death. This pathology triggers activation of microglia. Because variants of genes expressed in microglia correlate with AD risk, microglial response to pathology plausibly impacts disease course. In mouse AD models, single-cell RNA sequencing (scRNA-seq) analyses delineated this response as progressive conversion of homeostatic microglia into disease-associated microglia (DAM); additional reactive microglial populations have been reported in other models of neurodegeneration and neuroinflammation. We review all of these microglial signatures, highlighting four fundamental patterns: DAM, IFN–microglia, MHC-II microglia, and proliferating microglia. We propose that all reported microglia populations are either just one or a combination, depending on the clustering strategy applied and the disease model. We further review single-nucleus RNA sequencing (snRNA-seq) data from human AD specimens and discuss reasons for parallels and discrepancies between human and mouse transcriptional profiles. Finally, we outline future directions for delineating the microglial impact in AD pathogenesis.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lishu Duan ◽  
Mufeng Hu ◽  
Joseph A. Tamm ◽  
Yelena Y. Grinberg ◽  
Fang Shen ◽  
...  

AbstractAlzheimer’s disease (AD) is a common neurodegenerative disease with poor prognosis. New options for drug discovery targets are needed. We developed an imaging based arrayed CRISPR method to interrogate the human genome for modulation of in vitro correlates of AD features, and used this to assess 1525 human genes related to tau aggregation, autophagy and mitochondria. This work revealed (I) a network of tau aggregation modulators including the NF-κB pathway and inflammatory signaling, (II) a correlation between mitochondrial morphology, respiratory function and transcriptomics, (III) machine learning predicted novel roles of genes and pathways in autophagic processes and (IV) individual gene function inferences and interactions among biological processes via multi-feature clustering. These studies provide a platform to interrogate underexplored aspects of AD biology and offer several specific hypotheses for future drug discovery efforts.


2014 ◽  
Vol 6 (3) ◽  
pp. 26 ◽  
Author(s):  
Shuang Wang ◽  
Yang Yu ◽  
Shuang Geng ◽  
Dongmei Wang ◽  
Li Zhang ◽  
...  

2013 ◽  
Vol 33 (30) ◽  
pp. 12208-12217 ◽  
Author(s):  
N. Cheng ◽  
L. Bai ◽  
E. Steuer ◽  
L. Belluscio

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