Novel App knock-in mouse model shows key features of amyloid pathology and reveals profound metabolic dysregulation of microglia
Background Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those...
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| Published in | Molecular neurodegeneration Vol. 17; no. 1; pp. 41 - 29 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
11.06.2022
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1750-1326 1750-1326 |
| DOI | 10.1186/s13024-022-00547-7 |
Cover
| Abstract | Background
Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain.
Methods
We engineered a novel
App
knock-in mouse model (
App
SAA
) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse
App
gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous
App
SAA
mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used
in situ
imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and
in vivo
brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays.
Results
Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The
App
SAA
knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged.
Discussion
Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology. |
|---|---|
| AbstractList | Background
Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain.
Methods
We engineered a novel
App
knock-in mouse model (
App
SAA
) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse
App
gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous
App
SAA
mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used
in situ
imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and
in vivo
brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays.
Results
Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The
App
SAA
knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged.
Discussion
Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology. Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. We engineered a novel App knock-in mouse model (App.sup.SAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-[beta] pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous App.sup.SAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar A[beta] content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays. Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular A[beta] content. The App.sup.SAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged. Our findings demonstrate that fibrillar A[beta] in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology. Abstract Background Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. Methods We engineered a novel App knock-in mouse model (App SAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous App SAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays. Results Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The App SAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged. Discussion Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology. Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. We engineered a novel App knock-in mouse model (App ) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous App mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays. Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The App knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged. Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology. Background Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. Methods We engineered a novel App knock-in mouse model (App.sup.SAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-[beta] pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous App.sup.SAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar A[beta] content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays. Results Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular A[beta] content. The App.sup.SAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged. Discussion Our findings demonstrate that fibrillar A[beta] in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology. Keywords: Neuritic plaques, Vascular amyloid, Neurodegeneration, Astrogliosis, Phagocytic microglia, Lipid dyshomeostasis Background Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. Methods We engineered a novel App knock-in mouse model (AppSAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous AppSAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays. Results Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The AppSAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged. Discussion Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology. Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain.BACKGROUNDGenetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain.We engineered a novel App knock-in mouse model (AppSAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous AppSAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays.METHODSWe engineered a novel App knock-in mouse model (AppSAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous AppSAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays.Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The AppSAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged.RESULTSLeveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The AppSAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged.Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology.DISCUSSIONOur findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology. |
| ArticleNumber | 41 |
| Audience | Academic |
| Author | Thomsen, Elliot Ha, Connie Yulyaningsih, Ernie Lin, Karin Solanoy, Hilda Nguyen, Hoang Willem, Michael Suh, Jung H. Barnett, Daniel Pizzo, Michelle E. Lopez, Isabel Chau, Roni Di Paolo, Gilbert Liang, Chun-chi Calvert, Meredith Davis, Sonnet Khoury, Nathalie Earr, Timothy K. Zatcepin, Artem Sasner, Michael Palop, Jorge J. Harris, Julie A. Xia, Dan Dugas, Jason Lewcock, Joseph W. Kunze, Lea Astarita, Giuseppe Miller, Stephanie Haass, Christian Scearce-Levie, Kimberly Thorne, Robert G. Garceau, Dylan Brendel, Matthias Zimmer, Till S. DeVos, Sarah L. Orr, Anna G. Gnörich, Johannes Wind, Karin Sanchez, Pascal E. Shen, Kevin Hummel, Selina Masoud, Shababa T. Zuchero, Joy Yu Whitesell, Jennifer D. Sandmann, Thomas Lianoglou, Steve Leung, Amy Wing-Sze Lin, Chia-Ching |
| Author_xml | – sequence: 1 givenname: Dan surname: Xia fullname: Xia, Dan organization: Denali Therapeutics, Inc – sequence: 2 givenname: Steve surname: Lianoglou fullname: Lianoglou, Steve organization: Denali Therapeutics, Inc – sequence: 3 givenname: Thomas surname: Sandmann fullname: Sandmann, Thomas organization: Denali Therapeutics, Inc – sequence: 4 givenname: Meredith surname: Calvert fullname: Calvert, Meredith organization: Denali Therapeutics, Inc – sequence: 5 givenname: Jung H. surname: Suh fullname: Suh, Jung H. organization: Denali Therapeutics, Inc – sequence: 6 givenname: Elliot surname: Thomsen fullname: Thomsen, Elliot organization: Denali Therapeutics, Inc – sequence: 7 givenname: Jason surname: Dugas fullname: Dugas, Jason organization: Denali Therapeutics, Inc – sequence: 8 givenname: Michelle E. surname: Pizzo fullname: Pizzo, Michelle E. organization: Denali Therapeutics, Inc – sequence: 9 givenname: Sarah L. surname: DeVos fullname: DeVos, Sarah L. organization: Denali Therapeutics, Inc – sequence: 10 givenname: Timothy K. surname: Earr fullname: Earr, Timothy K. organization: Denali Therapeutics, Inc – sequence: 11 givenname: Chia-Ching surname: Lin fullname: Lin, Chia-Ching organization: Denali Therapeutics, Inc – sequence: 12 givenname: Sonnet surname: Davis fullname: Davis, Sonnet organization: Denali Therapeutics, Inc – sequence: 13 givenname: Connie surname: Ha fullname: Ha, Connie organization: Denali Therapeutics, Inc – sequence: 14 givenname: Amy Wing-Sze surname: Leung fullname: Leung, Amy Wing-Sze organization: Denali Therapeutics, Inc – sequence: 15 givenname: Hoang surname: Nguyen fullname: Nguyen, Hoang organization: Denali Therapeutics, Inc – sequence: 16 givenname: Roni surname: Chau fullname: Chau, Roni organization: Denali Therapeutics, Inc – sequence: 17 givenname: Ernie surname: Yulyaningsih fullname: Yulyaningsih, Ernie organization: Denali Therapeutics, Inc – sequence: 18 givenname: Isabel surname: Lopez fullname: Lopez, Isabel organization: Denali Therapeutics, Inc – sequence: 19 givenname: Hilda surname: Solanoy fullname: Solanoy, Hilda organization: Denali Therapeutics, Inc – sequence: 20 givenname: Shababa T. surname: Masoud fullname: Masoud, Shababa T. organization: Denali Therapeutics, Inc – sequence: 21 givenname: Chun-chi surname: Liang fullname: Liang, Chun-chi organization: Denali Therapeutics, Inc – sequence: 22 givenname: Karin surname: Lin fullname: Lin, Karin organization: Denali Therapeutics, Inc – sequence: 23 givenname: Giuseppe surname: Astarita fullname: Astarita, Giuseppe organization: Denali Therapeutics, Inc – sequence: 24 givenname: Nathalie surname: Khoury fullname: Khoury, Nathalie organization: Denali Therapeutics, Inc – sequence: 25 givenname: Joy Yu surname: Zuchero fullname: Zuchero, Joy Yu organization: Denali Therapeutics, Inc – sequence: 26 givenname: Robert G. surname: Thorne fullname: Thorne, Robert G. organization: Denali Therapeutics, Inc., Department of Pharmaceutics, University of Minnesota – sequence: 27 givenname: Kevin surname: Shen fullname: Shen, Kevin organization: Gladstone Institute of Neurological Disease, Department of Neurology, University of California – sequence: 28 givenname: Stephanie surname: Miller fullname: Miller, Stephanie organization: Gladstone Institute of Neurological Disease, Department of Neurology, University of California – sequence: 29 givenname: Jorge J. surname: Palop fullname: Palop, Jorge J. organization: Gladstone Institute of Neurological Disease, Department of Neurology, University of California – sequence: 30 givenname: Dylan surname: Garceau fullname: Garceau, Dylan organization: The Jackson Lab – sequence: 31 givenname: Michael surname: Sasner fullname: Sasner, Michael organization: The Jackson Lab – sequence: 32 givenname: Jennifer D. surname: Whitesell fullname: Whitesell, Jennifer D. organization: Allen Institute for Brain Science – sequence: 33 givenname: Julie A. surname: Harris fullname: Harris, Julie A. organization: Allen Institute for Brain Science – sequence: 34 givenname: Selina surname: Hummel fullname: Hummel, Selina organization: German Center for Neurodegenerative Diseases (DZNE) Munich, Department of Nuclear Medicine, University Hospital of Munich – sequence: 35 givenname: Johannes surname: Gnörich fullname: Gnörich, Johannes organization: German Center for Neurodegenerative Diseases (DZNE) Munich, Department of Nuclear Medicine, University Hospital of Munich – sequence: 36 givenname: Karin surname: Wind fullname: Wind, Karin organization: German Center for Neurodegenerative Diseases (DZNE) Munich, Department of Nuclear Medicine, University Hospital of Munich – sequence: 37 givenname: Lea surname: Kunze fullname: Kunze, Lea organization: German Center for Neurodegenerative Diseases (DZNE) Munich, Department of Nuclear Medicine, University Hospital of Munich – sequence: 38 givenname: Artem surname: Zatcepin fullname: Zatcepin, Artem organization: German Center for Neurodegenerative Diseases (DZNE) Munich, Department of Nuclear Medicine, University Hospital of Munich – sequence: 39 givenname: Matthias surname: Brendel fullname: Brendel, Matthias organization: German Center for Neurodegenerative Diseases (DZNE) Munich, Department of Nuclear Medicine, University Hospital of Munich – sequence: 40 givenname: Michael surname: Willem fullname: Willem, Michael organization: Department of Nuclear Medicine, University Hospital of Munich – sequence: 41 givenname: Christian surname: Haass fullname: Haass, Christian organization: German Center for Neurodegenerative Diseases (DZNE) Munich, Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig- Maximilians-Universität, Munich Cluster for Systems Neurology (SyNergy) – sequence: 42 givenname: Daniel surname: Barnett fullname: Barnett, Daniel organization: Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Neuroscience Graduate Program, Weill Cornell Medicine – sequence: 43 givenname: Till S. surname: Zimmer fullname: Zimmer, Till S. organization: Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine – sequence: 44 givenname: Anna G. surname: Orr fullname: Orr, Anna G. organization: Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Neuroscience Graduate Program, Weill Cornell Medicine – sequence: 45 givenname: Kimberly surname: Scearce-Levie fullname: Scearce-Levie, Kimberly organization: Denali Therapeutics, Inc – sequence: 46 givenname: Joseph W. surname: Lewcock fullname: Lewcock, Joseph W. organization: Denali Therapeutics, Inc – sequence: 47 givenname: Gilbert surname: Di Paolo fullname: Di Paolo, Gilbert organization: Denali Therapeutics, Inc – sequence: 48 givenname: Pascal E. orcidid: 0000-0002-2620-5165 surname: Sanchez fullname: Sanchez, Pascal E. email: sanchez@dnli.com organization: Denali Therapeutics, Inc |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35690868$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1073/pnas.1511003112 10.1038/s41590-018-0212-1 10.1016/j.neuroimage.2017.10.006 10.1523/JNEUROSCI.5171-13.2014 10.1093/bioinformatics/bti270 10.1016/j.neubiorev.2020.02.012 10.1016/j.cell.2017.05.018 10.1038/nn.3697 10.1093/bioinformatics/btp053 10.1038/s41593-019-0566-1 10.1073/pnas.1510329112 10.1186/gb-2010-11-2-r14 10.1002/jnr.24029 10.1016/j.neurobiolaging.2020.07.018 10.1083/jcb.201709069 10.1016/j.neurobiolaging.2016.04.011 10.1038/s41593-019-0433-0 10.1093/nar/gkv412 10.1016/j.nlm.2016.07.001 10.1016/j.neuint.2020.104798 10.1016/j.celrep.2017.09.039 10.1038/s41586-019-1195-2 10.1093/brain/awaa098 10.2967/jnumed.112.114660 10.1093/nar/gky1055 10.1186/s13024-020-00393-5 10.1038/nn.4492 10.15252/emmm.201911227 10.1093/bioinformatics/bts635 10.1016/j.stem.2015.08.001 10.1016/j.neuron.2019.12.007 10.3389/fnins.2016.00045 10.1093/bioinformatics/bts034 10.1186/s12868-019-0496-6 10.1093/nar/28.1.27 10.1016/j.exger.2006.05.016 10.1093/nar/gkv007 10.3389/fgene.2014.00088 10.1186/1471-2105-15-182 10.1002/cpns.81 10.1016/0197-4580(93)90041-9 10.1016/j.cell.2017.07.023 10.1016/j.nbd.2020.104976 10.1093/bioinformatics/btt656 10.1016/j.dadm.2019.08.002 10.1523/JNEUROSCI.1860-14.2014 10.1038/s41593-020-0650-6 10.15252/embj.201797397 10.1016/j.neurobiolaging.2010.03.008 10.1016/j.celrep.2019.03.099 10.1021/bm401874j 10.1016/S1389-0344(01)00067-3 10.1093/hmg/9.18.2589 10.1016/j.neuron.2020.09.029 10.1016/j.cell.2012.02.046 10.1016/j.cell.2021.08.002 10.1016/j.arr.2016.02.003 10.1093/jnen/61.9.797 10.1126/science.aan4183 10.7717/peerj.453 10.1523/JNEUROSCI.1202-06.2006 10.1186/2051-5960-1-60 10.1038/s41583-018-0054-8 10.2967/jnumed.107.045385 10.1038/nrn2274 10.1038/nature21029 10.1002/dvg.22938 10.1038/s41573-020-0065-9 10.1126/science.274.5284.99 10.1084/jem.20202717 10.1038/s41467-021-22624-z 10.1038/s41586-020-1968-7 10.1016/j.cell.2020.04.007 10.1038/s41467-021-23111-1 10.1038/s41593-020-00783-4 10.1186/gb-2014-15-2-r29 10.1002/cne.24555 10.1038/nmeth.3252 10.1186/s13024-017-0231-7 10.1126/scitranslmed.abe5640 10.1016/j.neuron.2016.05.018 10.1016/S1474-4422(16)00070-3 10.1186/1742-2094-11-3 10.1038/ng0892-345 10.1007/s00259-018-4039-7 10.1038/s41591-021-01456-w 10.1016/j.neuron.2018.02.029 10.1002/ca.980080612 10.1016/j.cell.2020.06.038 10.1016/j.celrep.2019.07.060 10.1001/jamaneurol.2016.6117 10.1172/JCI77983 10.1109/TPAMI.2006.233 10.1212/WNL.58.12.1791 10.1074/jbc.M111.274142 10.1093/brain/awn312 10.2967/jnumed.114.141416 10.1016/j.celrep.2020.107843 10.1002/ana.410400512 10.1186/s13024-021-00473-0 10.1038/s41467-017-01289-7 10.1016/j.celrep.2017.12.066 10.1093/gerona/glt136 10.1038/nm1295-1291 10.1038/nmeth.2019 10.1016/j.immuni.2017.08.008 10.1001/jamaneurol.2015.3037 10.1007/s00401-010-0787-6 10.3233/JAD-2007-11113 10.1084/jem.20151948 |
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| References | SA Liddelow (547_CR69) 2017; 541 DJ McCarthy (547_CR42) 2009; 25 A Dobin (547_CR37) 2013; 29 S Kumar-Singh (547_CR56) 2000; 9 T Wu (547_CR70) 2019; 28 PK Vemula (547_CR95) 2020; 140 MD Young (547_CR44) 2010; 11 PL Poliani (547_CR23) 2015; 125 E Masliah (547_CR67) 1996; 40 Y Sakakibara (547_CR6) 2019; 20 J Luo (547_CR94) 2014; 15 F Kosel (547_CR80) 2020; 112 CP Jacob (547_CR66) 2007; 11 E Andersson (547_CR101) 2020; 95 HA Born (547_CR111) 2014; 34 J Gotz (547_CR3) 2018; 19 K Hsiao (547_CR28) 1996; 274 M Bacioglu (547_CR102) 2016; 91 S Burgold (547_CR59) 2011; 121 M Karlstetter (547_CR76) 2014; 11 ER Zimmer (547_CR110) 2017; 20 L Mosconi (547_CR108) 2008; 49 CEG Leyns (547_CR87) 2019; 22 C Murphy-Royal (547_CR65) 2017; 95 WE Klunk (547_CR60) 2002; 61 M Ashburner (547_CR45) 2000; 25 C Escartin (547_CR63) 2021; 24 ME Ritchie (547_CR53) 2015; 43 RB Chan (547_CR72) 2012; 287 H Mathys (547_CR19) 2019; 570 A Grubman (547_CR13) 2021; 12 JC Whitehead (547_CR29) 2014; 69 R Liu (547_CR39) 2015; 43 BA Friedman (547_CR12) 2018; 22 547_CR68 A Alzheimer (547_CR92) 1995; 8 TK Ulland (547_CR43) 2017; 170 Q Wang (547_CR32) 2020; 181 CM Sandiego (547_CR77) 2015; 112 N Mattsson (547_CR99) 2017; 74 S Krasemann (547_CR15) 2017; 47 JT Leek (547_CR52) 2012; 28 X Xiang (547_CR20) 2021; 13 J Schindelin (547_CR35) 2012; 9 L Mosconi (547_CR107) 2007; 42 K Schlepckow (547_CR30) 2020; 12 DR Thal (547_CR61) 2002; 58 DV Hansen (547_CR9) 2018; 217 R Adalbert (547_CR62) 2009; 132 T Logan (547_CR88) 2021; 184 A Myers (547_CR4) 2019; 89 T Saito (547_CR7) 2014; 17 C Haass (547_CR58) 1995; 1 WC Kreisl (547_CR78) 2016; 44 C Li (547_CR93) 2017; 8 WM Song (547_CR11) 2018; 19 H Oakley (547_CR26) 2006; 26 TA Pascoal (547_CR79) 2021; 27 547_CR89 M Deussing (547_CR50) 2018; 165 H Sasaguri (547_CR5) 2017; 36 H Zetterberg (547_CR100) 2016; 73 K Srinivasan (547_CR18) 2020; 31 Y Zhang (547_CR71) 2014; 34 A Masuda (547_CR83) 2016; 135 AA Nugent (547_CR22) 2020; 105 W Huber (547_CR48) 2015; 12 K Kamino (547_CR55) 1992; 51 H Keren-Shaul (547_CR14) 2017; 169 M Citron (547_CR57) 1993; 14 S van der Walt (547_CR34) 2014; 2 A Rominger (547_CR51) 2013; 54 ECB Johnson (547_CR82) 2020; 15 C Sala Frigerio (547_CR17) 2019; 27 547_CR10 547_CR97 F Overhoff (547_CR49) 2016; 10 F Koentgen (547_CR25) 2016; 54 J Marschallinger (547_CR90) 2020; 23 SJ Webster (547_CR81) 2014; 5 T Kato (547_CR106) 2016; 30 S van Veen (547_CR96) 2020; 578 AR Gafson (547_CR98) 2020; 143 BJ Andreone (547_CR91) 2020; 23 WT Chen (547_CR8) 2020; 182 JL Jankowsky (547_CR27) 2001; 17 Y Liao (547_CR38) 2014; 30 L Verret (547_CR84) 2012; 149 CW Law (547_CR40) 2014; 15 H Jiang (547_CR36) 2014; 15 LK Hamilton (547_CR74) 2015; 17 B Olsson (547_CR103) 2016; 15 GKMJ Smyth (547_CR41) 2005; 21 Y Wang (547_CR75) 2016; 213 C Claes (547_CR73) 2021; 16 IM Nasrallah (547_CR109) 2014; 55 D Baglietto-Vargas (547_CR113) 2021; 12 K Scearce-Levie (547_CR1) 2020; 19 L Cantuti-Castelvetri (547_CR21) 2018; 359 JL Jankowsky (547_CR2) 2017; 12 547_CR112 SM Neuner (547_CR24) 2020; 143 AV Tzingounis (547_CR64) 2007; 8 J Arbizu (547_CR105) 2018; 45 The Gene Ontology C (547_CR47) 2019; 47 H Mathys (547_CR16) 2017; 21 JD Whitesell (547_CR31) 2019; 527 M Kanehisa (547_CR46) 2000; 28 AL Benedet (547_CR104) 2019; 11 L Grady (547_CR33) 2006; 28 M Mullan (547_CR54) 1992; 1 S Gowrishankar (547_CR86) 2015; 112 H Kalimo (547_CR85) 2013; 1 |
| References_xml | – volume: 112 start-page: 12468 issue: 40 year: 2015 ident: 547_CR77 publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.1511003112 – volume: 19 start-page: 1048 issue: 10 year: 2018 ident: 547_CR11 publication-title: Nat Immunol doi: 10.1038/s41590-018-0212-1 – ident: 547_CR89 – volume: 165 start-page: 83 year: 2018 ident: 547_CR50 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.10.006 – volume: 34 start-page: 3826 issue: 11 year: 2014 ident: 547_CR111 publication-title: J Neurosci doi: 10.1523/JNEUROSCI.5171-13.2014 – volume: 21 start-page: 2067 issue: 9 year: 2005 ident: 547_CR41 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti270 – volume: 112 start-page: 634 year: 2020 ident: 547_CR80 publication-title: Neurosci Biobehav Rev doi: 10.1016/j.neubiorev.2020.02.012 – volume: 169 start-page: 1276 issue: 7 year: 2017 ident: 547_CR14 publication-title: Cell doi: 10.1016/j.cell.2017.05.018 – volume: 17 start-page: 661 issue: 5 year: 2014 ident: 547_CR7 publication-title: Nat Neurosci doi: 10.1038/nn.3697 – volume: 25 start-page: 765 issue: 6 year: 2009 ident: 547_CR42 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp053 – volume: 23 start-page: 194 issue: 2 year: 2020 ident: 547_CR90 publication-title: Nat Neurosci doi: 10.1038/s41593-019-0566-1 – volume: 112 start-page: E3699 issue: 28 year: 2015 ident: 547_CR86 publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.1510329112 – volume: 11 start-page: R14 issue: 2 year: 2010 ident: 547_CR44 publication-title: Genome Biol doi: 10.1186/gb-2010-11-2-r14 – volume: 95 start-page: 2140 issue: 11 year: 2017 ident: 547_CR65 publication-title: J Neurosci Res doi: 10.1002/jnr.24029 – volume: 95 start-page: 143 year: 2020 ident: 547_CR101 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2020.07.018 – volume: 217 start-page: 459 issue: 2 year: 2018 ident: 547_CR9 publication-title: J Cell Biol doi: 10.1083/jcb.201709069 – volume: 44 start-page: 53 year: 2016 ident: 547_CR78 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2016.04.011 – volume: 22 start-page: 1217 issue: 8 year: 2019 ident: 547_CR87 publication-title: Nat Neurosci doi: 10.1038/s41593-019-0433-0 – volume: 43 issue: 15 year: 2015 ident: 547_CR39 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkv412 – volume: 135 start-page: 73 year: 2016 ident: 547_CR83 publication-title: Neurobiol Learn Mem doi: 10.1016/j.nlm.2016.07.001 – volume: 140 year: 2020 ident: 547_CR95 publication-title: Neurochem Int doi: 10.1016/j.neuint.2020.104798 – volume: 21 start-page: 366 issue: 2 year: 2017 ident: 547_CR16 publication-title: Cell Rep doi: 10.1016/j.celrep.2017.09.039 – volume: 570 start-page: 332 issue: 7761 year: 2019 ident: 547_CR19 publication-title: Nature doi: 10.1038/s41586-019-1195-2 – volume: 143 start-page: 1975 issue: 7 year: 2020 ident: 547_CR98 publication-title: Brain doi: 10.1093/brain/awaa098 – volume: 54 start-page: 1127 issue: 7 year: 2013 ident: 547_CR51 publication-title: J Nucl Med doi: 10.2967/jnumed.112.114660 – volume: 47 start-page: D330 issue: D1 year: 2019 ident: 547_CR47 publication-title: Nucleic Acids Res doi: 10.1093/nar/gky1055 – volume: 15 start-page: 53 issue: 1 year: 2020 ident: 547_CR82 publication-title: Mol Neurodegener doi: 10.1186/s13024-020-00393-5 – volume: 20 start-page: 393 issue: 3 year: 2017 ident: 547_CR110 publication-title: Nat Neurosci doi: 10.1038/nn.4492 – volume: 12 issue: 4 year: 2020 ident: 547_CR30 publication-title: EMBO Mol Med doi: 10.15252/emmm.201911227 – volume: 29 start-page: 15 issue: 1 year: 2013 ident: 547_CR37 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts635 – volume: 17 start-page: 397 issue: 4 year: 2015 ident: 547_CR74 publication-title: Cell Stem Cell doi: 10.1016/j.stem.2015.08.001 – volume: 105 start-page: 837 issue: 5 year: 2020 ident: 547_CR22 publication-title: Neuron doi: 10.1016/j.neuron.2019.12.007 – volume: 10 start-page: 45 year: 2016 ident: 547_CR49 publication-title: Front Neurosci doi: 10.3389/fnins.2016.00045 – volume: 28 start-page: 882 issue: 6 year: 2012 ident: 547_CR52 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts034 – volume: 20 start-page: 13 issue: 1 year: 2019 ident: 547_CR6 publication-title: BMC Neurosci doi: 10.1186/s12868-019-0496-6 – volume: 28 start-page: 27 issue: 1 year: 2000 ident: 547_CR46 publication-title: Nucleic Acids Res doi: 10.1093/nar/28.1.27 – volume: 42 start-page: 129 issue: 1-2 year: 2007 ident: 547_CR107 publication-title: Exp Gerontol doi: 10.1016/j.exger.2006.05.016 – volume: 43 issue: 7 year: 2015 ident: 547_CR53 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkv007 – volume: 5 start-page: 88 year: 2014 ident: 547_CR81 publication-title: Front Genet doi: 10.3389/fgene.2014.00088 – volume: 15 start-page: 182 year: 2014 ident: 547_CR36 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-15-182 – volume: 89 issue: 1 year: 2019 ident: 547_CR4 publication-title: Curr Protoc Neurosci doi: 10.1002/cpns.81 – volume: 14 start-page: 571 issue: 6 year: 1993 ident: 547_CR57 publication-title: Neurobiol Aging doi: 10.1016/0197-4580(93)90041-9 – volume: 170 start-page: 649 issue: 4 year: 2017 ident: 547_CR43 publication-title: Cell doi: 10.1016/j.cell.2017.07.023 – volume: 143 year: 2020 ident: 547_CR24 publication-title: Neurobiol Dis doi: 10.1016/j.nbd.2020.104976 – volume: 30 start-page: 923 issue: 7 year: 2014 ident: 547_CR38 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btt656 – volume: 11 start-page: 679 year: 2019 ident: 547_CR104 publication-title: Alzheimers Dement (Amst) doi: 10.1016/j.dadm.2019.08.002 – volume: 34 start-page: 11929 issue: 36 year: 2014 ident: 547_CR71 publication-title: J Neurosci doi: 10.1523/JNEUROSCI.1860-14.2014 – volume: 23 start-page: 927 issue: 8 year: 2020 ident: 547_CR91 publication-title: Nat Neurosci doi: 10.1038/s41593-020-0650-6 – volume: 36 start-page: 2473 issue: 17 year: 2017 ident: 547_CR5 publication-title: EMBO J doi: 10.15252/embj.201797397 – ident: 547_CR68 doi: 10.1016/j.neurobiolaging.2010.03.008 – volume: 27 start-page: 1293 issue: 4 year: 2019 ident: 547_CR17 publication-title: Cell Rep doi: 10.1016/j.celrep.2019.03.099 – volume: 15 start-page: 1985 issue: 6 year: 2014 ident: 547_CR94 publication-title: Biomacromolecules doi: 10.1021/bm401874j – volume: 17 start-page: 157 issue: 6 year: 2001 ident: 547_CR27 publication-title: Biomol Eng doi: 10.1016/S1389-0344(01)00067-3 – volume: 9 start-page: 2589 issue: 18 year: 2000 ident: 547_CR56 publication-title: Hum Mol Genet doi: 10.1093/hmg/9.18.2589 – ident: 547_CR10 doi: 10.1016/j.neuron.2020.09.029 – volume: 149 start-page: 708 year: 2012 ident: 547_CR84 publication-title: Cell doi: 10.1016/j.cell.2012.02.046 – volume: 184 start-page: 4651 issue: 18 year: 2021 ident: 547_CR88 publication-title: Cell doi: 10.1016/j.cell.2021.08.002 – volume: 30 start-page: 73 year: 2016 ident: 547_CR106 publication-title: Ageing Res Rev doi: 10.1016/j.arr.2016.02.003 – volume: 61 start-page: 797 issue: 9 year: 2002 ident: 547_CR60 publication-title: J Neuropathol Exp Neurol doi: 10.1093/jnen/61.9.797 – volume: 359 start-page: 684 issue: 6376 year: 2018 ident: 547_CR21 publication-title: Science doi: 10.1126/science.aan4183 – volume: 2 year: 2014 ident: 547_CR34 publication-title: PeerJ doi: 10.7717/peerj.453 – volume: 26 start-page: 10129 issue: 40 year: 2006 ident: 547_CR26 publication-title: J Neurosci doi: 10.1523/JNEUROSCI.1202-06.2006 – volume: 1 start-page: 60 year: 2013 ident: 547_CR85 publication-title: Acta Neuropathol Commun doi: 10.1186/2051-5960-1-60 – volume: 19 start-page: 583 issue: 10 year: 2018 ident: 547_CR3 publication-title: Nat Rev Neurosci doi: 10.1038/s41583-018-0054-8 – volume: 49 start-page: 390 issue: 3 year: 2008 ident: 547_CR108 publication-title: J Nucl Med doi: 10.2967/jnumed.107.045385 – volume: 8 start-page: 935 issue: 12 year: 2007 ident: 547_CR64 publication-title: Nat Rev Neurosci doi: 10.1038/nrn2274 – volume: 541 start-page: 481 issue: 7638 year: 2017 ident: 547_CR69 publication-title: Nature doi: 10.1038/nature21029 – volume: 54 start-page: 326 issue: 6 year: 2016 ident: 547_CR25 publication-title: Genesis doi: 10.1002/dvg.22938 – volume: 19 start-page: 447 issue: 7 year: 2020 ident: 547_CR1 publication-title: Nat Rev Drug Discov doi: 10.1038/s41573-020-0065-9 – volume: 274 start-page: 99 issue: 5284 year: 1996 ident: 547_CR28 publication-title: Science doi: 10.1126/science.274.5284.99 – ident: 547_CR97 doi: 10.1084/jem.20202717 – volume: 25 start-page: 25 issue: 1 year: 2000 ident: 547_CR45 publication-title: The Gene Ontology Consortium. Nat Genet – volume: 12 start-page: 2421 issue: 1 year: 2021 ident: 547_CR113 publication-title: Nat Commun doi: 10.1038/s41467-021-22624-z – volume: 578 start-page: 419 issue: 7795 year: 2020 ident: 547_CR96 publication-title: Nature doi: 10.1038/s41586-020-1968-7 – volume: 181 start-page: 936 issue: 4 year: 2020 ident: 547_CR32 publication-title: Cell doi: 10.1016/j.cell.2020.04.007 – volume: 12 start-page: 3015 issue: 1 year: 2021 ident: 547_CR13 publication-title: Nat Commun doi: 10.1038/s41467-021-23111-1 – volume: 24 start-page: 312 issue: 3 year: 2021 ident: 547_CR63 publication-title: Nat Neurosci doi: 10.1038/s41593-020-00783-4 – volume: 15 start-page: R29 issue: 2 year: 2014 ident: 547_CR40 publication-title: Genome Biol doi: 10.1186/gb-2014-15-2-r29 – volume: 527 start-page: 2122 issue: 13 year: 2019 ident: 547_CR31 publication-title: J Comp Neurol doi: 10.1002/cne.24555 – volume: 12 start-page: 115 issue: 2 year: 2015 ident: 547_CR48 publication-title: Nat Methods doi: 10.1038/nmeth.3252 – volume: 12 start-page: 89 issue: 1 year: 2017 ident: 547_CR2 publication-title: Mol Neurodegener doi: 10.1186/s13024-017-0231-7 – volume: 13 start-page: eabe5640 issue: 615 year: 2021 ident: 547_CR20 publication-title: Sci Transl Med doi: 10.1126/scitranslmed.abe5640 – volume: 91 start-page: 56 issue: 1 year: 2016 ident: 547_CR102 publication-title: Neuron doi: 10.1016/j.neuron.2016.05.018 – volume: 15 start-page: 673 issue: 7 year: 2016 ident: 547_CR103 publication-title: Lancet Neurol doi: 10.1016/S1474-4422(16)00070-3 – volume: 11 start-page: 3 year: 2014 ident: 547_CR76 publication-title: J Neuroinflammation doi: 10.1186/1742-2094-11-3 – volume: 1 start-page: 345 issue: 5 year: 1992 ident: 547_CR54 publication-title: Nat Genet doi: 10.1038/ng0892-345 – volume: 45 start-page: 1497 issue: 9 year: 2018 ident: 547_CR105 publication-title: Eur J Nucl Med Mol Imaging doi: 10.1007/s00259-018-4039-7 – volume: 27 start-page: 1592 issue: 9 year: 2021 ident: 547_CR79 publication-title: Nat Med doi: 10.1038/s41591-021-01456-w – ident: 547_CR112 doi: 10.1016/j.neuron.2018.02.029 – volume: 51 start-page: 998 issue: 5 year: 1992 ident: 547_CR55 publication-title: Am J Hum Genet – volume: 8 start-page: 429 issue: 6 year: 1995 ident: 547_CR92 publication-title: Clin Anat doi: 10.1002/ca.980080612 – volume: 182 start-page: 976 issue: 4 year: 2020 ident: 547_CR8 publication-title: Cell doi: 10.1016/j.cell.2020.06.038 – volume: 28 start-page: 2111 issue: 8 year: 2019 ident: 547_CR70 publication-title: Cell Rep doi: 10.1016/j.celrep.2019.07.060 – volume: 74 start-page: 557 issue: 5 year: 2017 ident: 547_CR99 publication-title: JAMA Neurol doi: 10.1001/jamaneurol.2016.6117 – volume: 125 start-page: 2161 issue: 5 year: 2015 ident: 547_CR23 publication-title: J Clin Invest doi: 10.1172/JCI77983 – volume: 28 start-page: 1768 issue: 11 year: 2006 ident: 547_CR33 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2006.233 – volume: 58 start-page: 1791 issue: 12 year: 2002 ident: 547_CR61 publication-title: Neurology doi: 10.1212/WNL.58.12.1791 – volume: 287 start-page: 2678 issue: 4 year: 2012 ident: 547_CR72 publication-title: J Biol Chem doi: 10.1074/jbc.M111.274142 – volume: 132 start-page: 402 issue: Pt 2 year: 2009 ident: 547_CR62 publication-title: Brain doi: 10.1093/brain/awn312 – volume: 55 start-page: 2003 issue: 12 year: 2014 ident: 547_CR109 publication-title: J Nucl Med doi: 10.2967/jnumed.114.141416 – volume: 31 issue: 13 year: 2020 ident: 547_CR18 publication-title: Cell Rep doi: 10.1016/j.celrep.2020.107843 – volume: 40 start-page: 759 issue: 5 year: 1996 ident: 547_CR67 publication-title: Ann Neurol doi: 10.1002/ana.410400512 – volume: 16 start-page: 50 issue: 1 year: 2021 ident: 547_CR73 publication-title: Mol Neurodegener doi: 10.1186/s13024-021-00473-0 – volume: 8 start-page: 1257 issue: 1 year: 2017 ident: 547_CR93 publication-title: Nat Commun doi: 10.1038/s41467-017-01289-7 – volume: 22 start-page: 832 issue: 3 year: 2018 ident: 547_CR12 publication-title: Cell Rep doi: 10.1016/j.celrep.2017.12.066 – volume: 69 start-page: 621 issue: 6 year: 2014 ident: 547_CR29 publication-title: J Gerontol A Biol Sci Med Sci doi: 10.1093/gerona/glt136 – volume: 1 start-page: 1291 issue: 12 year: 1995 ident: 547_CR58 publication-title: Nat Med doi: 10.1038/nm1295-1291 – volume: 9 start-page: 676 issue: 7 year: 2012 ident: 547_CR35 publication-title: Nat Methods doi: 10.1038/nmeth.2019 – volume: 47 start-page: 566 issue: 3 year: 2017 ident: 547_CR15 publication-title: Immunity doi: 10.1016/j.immuni.2017.08.008 – volume: 73 start-page: 60 issue: 1 year: 2016 ident: 547_CR100 publication-title: JAMA Neurol doi: 10.1001/jamaneurol.2015.3037 – volume: 121 start-page: 327 issue: 3 year: 2011 ident: 547_CR59 publication-title: Acta Neuropathol doi: 10.1007/s00401-010-0787-6 – volume: 11 start-page: 97 issue: 1 year: 2007 ident: 547_CR66 publication-title: J Alzheimers Dis doi: 10.3233/JAD-2007-11113 – volume: 213 start-page: 667 issue: 5 year: 2016 ident: 547_CR75 publication-title: J Exp Med doi: 10.1084/jem.20151948 |
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Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of transformative... Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for... Background Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative... Background Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of transformative... Abstract Background Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of... |
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| SubjectTerms | Advertising executives Alzheimer Disease - genetics Alzheimer Disease - metabolism Alzheimer's disease Amyloid beta-Peptides - metabolism Amyloid beta-Protein Precursor - genetics Amyloid beta-Protein Precursor - metabolism Amyloid precursor protein Amyloidosis - metabolism Animal models Animals Astrogliosis Biomarkers Biomedical and Life Sciences Biomedicine Brain - metabolism Brain research Cerebrospinal fluid Cloning Coding Disease Models, Animal Energy metabolism Enzymes Gene expression Genetic aspects Genetic engineering Genetically modified organisms Homologous recombination Hyperactivity Imaging systems Immune response Instrument industry Laboratories Lipid dyshomeostasis Lipid metabolism Lipids Medical research Medicine, Experimental Metabolic pathways Metabolism Metabolites Metabolomics Mice Mice, Transgenic Microglia Microglia - metabolism Molecular Medicine Mutation Neuritic plaques Neurodegeneration Neurodegenerative diseases Neuroimaging Neurology Neuronal-glial interactions Neurosciences Pathology PET imaging Phagocytic microglia Phenotypes Plaque, Amyloid - pathology Positron emission tomography Protein engineering Receptors, GABA - metabolism Research Article Rodents Senile plaques Vascular amyloid β-Amyloid |
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| Title | Novel App knock-in mouse model shows key features of amyloid pathology and reveals profound metabolic dysregulation of microglia |
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