APOE4  exacerbates synapse loss and neurodegeneration in Alzheimer’s disease patient iPSC-derived cerebral organoids

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Abstract

APOE4 is the strongest genetic risk factor associated with late-onset Alzheimer’s disease (AD). To address the underlying mechanism, we develop cerebral organoid models using induced pluripotent stem cells (iPSCs) with APOE ε3/ε3 or ε4/ε4 genotype from individuals with either normal cognition or AD dementia. Cerebral organoids from AD patients carrying APOE ε4/ε4 show greater apoptosis and decreased synaptic integrity. While AD patient-derived cerebral organoids have increased levels of Aβ and phosphorylated tau compared to healthy subject-derived cerebral organoids, APOE4 exacerbates tau pathology in both healthy subject-derived and AD patient-derived organoids. Transcriptomics analysis by RNA-sequencing reveals that cerebral organoids from AD patients are associated with an enhancement of stress granules and disrupted RNA metabolism. Importantly, isogenic conversion of APOE4 to APOE3 attenuates the APOE4-related phenotypes in cerebral organoids from AD patients. Together, our study using human iPSC-organoids recapitulates APOE4-related phenotypes and suggests APOE4-related degenerative pathways contributing to AD pathogenesis.

Abstract

APOE4 is a strong genetic risk factor for late-onset Alzheimer’s disease. Here, the authors show that APOE4 is associated with AD features in hiPSCs-derived cerebral organoids. Isogenic conversion of APOE4 to APOE3 attenuates the AD-associated phenotype.

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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Robust enumeration of cell subsets from tissue expression profiles

Aaron Newman, Chih Long Liu, Michael Green … (2015)

We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).

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NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease

Clifford R. Jack, David Bennett, Kaj Blennow … (2018)

In 2011, the National Institute on Aging and Alzheimer’s Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer’s disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer’s Association to update and unify the 2011 guidelines. This unifying update is labeled a “research framework” because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer’s Association Research Framework, Alzheimer’s disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six-stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker-based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker-based research should not be considered a template for all research into age-related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β-amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.

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Author and article information

Contributors

Guojun Bu:

ORCID: http://orcid.org/0000-0003-3491-1016

bu.guojun@mayo.edu

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 2 November 2020

Publication date PMC-release: 2 November 2020

Publication date Collection: 2020

Volume: 11

Electronic Location Identifier: 5540

Affiliations

[1 ]GRID grid.417467.7, ISNI 0000 0004 0443 9942, Department of Neuroscience, , Mayo Clinic, ; Jacksonville, FL 32224 USA

[2 ]GRID grid.417467.7, ISNI 0000 0004 0443 9942, Center for Regenerative Medicine, Neuroregeneration Lab, , Mayo Clinic, ; Jacksonville, FL 32224 USA

[3 ]GRID grid.417467.7, ISNI 0000 0004 0443 9942, Department of Health Sciences Research, , Mayo Clinic, ; Jacksonville, FL 32224 USA

[4 ]GRID grid.417467.7, ISNI 0000 0004 0443 9942, Department of Neurology, , Mayo Clinic, ; Jacksonville, FL 32224 USA

[5 ]GRID grid.215654.1, ISNI 0000 0001 2151 2636, School of Biological and Health Systems Engineering, , Arizona State University, ; Tempe, AZ 85287 USA

Author information

Yu Yamazaki http://orcid.org/0000-0002-0806-4960

Chia-Chen Liu http://orcid.org/0000-0001-7203-3926

Yuka A. Martens http://orcid.org/0000-0002-5194-4269

Francis Shue http://orcid.org/0000-0003-1659-1952

Thanh Thanh Nguyen http://orcid.org/0000-0002-2281-6540

Guojun Bu http://orcid.org/0000-0003-3491-1016

Article

Publisher ID: 19264

DOI: 10.1038/s41467-020-19264-0

PMC ID: 7608683

PubMed ID: 33139712

SO-VID: 85db60b1-4097-423f-a778-d40a71d89cbc

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 27 December 2019

Date accepted : 2 October 2020

Funding

Funded by: FundRef https://doi.org/10.13039/100000957, Alzheimer’s Association;

Award ID: 2018-AARF-592302

Award Recipient : Jing Zhao

Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);

Award ID: RF1AG051504

Award ID: R37AG027924

Award ID: RF1AG046205

Award ID: RF1AG057181

Award Recipient : Guojun Bu

Funded by: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)

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ScienceOpen disciplines: Uncategorized

Keywords: mechanisms of disease,alzheimer's disease

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ScienceOpen disciplines: Uncategorized

Keywords: mechanisms of disease, alzheimer's disease

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APOE4 exacerbates synapse loss and neurodegeneration in Alzheimer’s disease patient iPSC-derived cerebral organoids

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Alternative approaches to Alzheimer's Disease

Most cited references 67

WGCNA: an R package for weighted correlation network analysis

Robust enumeration of cell subsets from tissue expression profiles

NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease

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