Dataset Information

A logical network-based drug-screening platform for Alzheimer's disease representing pathological features of human brain organoids.

ABSTRACT: Developing effective drugs for Alzheimer's disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood-brain barrier-permeable FDA-approved drugs. Our study provides a strategy for precision medicine through the convergence of mathematical modeling and a miniature pathological brain model using iCOs.

SUBMITTER: Park JC

PROVIDER: S-EPMC7804132 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A logical network-based drug-screening platform for Alzheimer's disease representing pathological features of human brain organoids.

Park Jong-Chan JC Jang So-Yeong SY Lee Dongjoon D Lee Jeongha J Kang Uiryong U Chang Hongjun H Kim Haeng Jun HJ Han Sun-Ho SH Seo Jinsoo J Choi Murim M Lee Dong Young DY Byun Min Soo MS Yi Dahyun D Cho Kwang-Hyun KH Mook-Jung Inhee I

Nature communications 20210112 1

Developing effective drugs for Alzheimer's disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood- ...[more]

PMID: 33436582

Similar Datasets

Project description:Despite the massive investment in Alzheimer's disease (AD), there are still no disease-modifying treatments (DMTs) for AD. One major reason is attributed to the limitation of clinical "one-size-fits-all" approach, since the same AD treatment solely based on clinical diagnosis was unlikely to achieve good clinical efficacy. In recent years, computational approaches based on multiomics data have provided an unprecedented opportunity for drug discovery since they can substantially lower the costs and boost the efficiency. In this study, we intended to identify potential drug candidates for different pathological stages of AD by computationally repurposing Food and Drug Administration (FDA) approved drugs. First, we assembled gene expression data from three different AD pathological stages, which include mild cognitive impairment (MCI) and early and late stages of AD (EAD, LAD). We next quantified the network distances between drug target networks and AD modules by utilizing a network proximity approach, and identified 193 candidates that possessed significant associations with AD. After searching for previous literature evidence, 63 out of 193 (32.6%) predicted drugs were demonstrated to exert therapeutic effects on AD. We further explored the novel mechanism of action (MOA) for these drug candidates by determining the specific brain cells they might function on based on AD patient single cell transcriptomic data. Additionally, we selected several promising candidates that could cross the blood brain barrier together with confirmed neuroprotective effects, and subsequently determined the antioxidative activity of these compounds. Experimental results showed that azathioprine decreased the reactive oxygen species (ROS) and malondialdehyde (MDA) levels and improved the superoxide dismutase (SOD) activity in APP-SH-SY5Y cells. Finally, we deciphered the potential MOA of azathioprine against AD via network analysis and validated several apoptosis-related proteins (Caspase 3, Cleaved Caspase 3, Bax, Bcl2) through western blotting. In summary, this study presented an effective computational strategy utilizing omics data for AD drug repurposing, which provides a new perspective for drug discovery and development.

Project description:Alzheimer's disease (AD) pathologies were discovered in the accessible neurosensory retina. However, their exact nature and topographical distribution, particularly in the early stages of functional impairment, and how they relate to disease progression in the brain remain largely unknown. To better understand the pathological features of AD in the retina, we conducted an extensive histopathological and biochemical investigation of postmortem retina and brain tissues from 86 human donors. Quantitative examination of superior and inferior temporal retinas from mild cognitive impairment (MCI) and AD patients compared to those with normal cognition (NC) revealed significant increases in amyloid β-protein (Aβ42) forms and novel intraneuronal Aβ oligomers (AβOi), which were closely associated with exacerbated retinal macrogliosis, microgliosis, and tissue atrophy. These pathologies were unevenly distributed across retinal layers and geometrical areas, with the inner layers and peripheral subregions exhibiting most pronounced accumulations in the MCI and AD versus NC retinas. While microgliosis was increased in the retina of these patients, the proportion of microglial cells engaging in Aβ uptake was reduced. Female AD patients exhibited higher levels of retinal microgliosis than males. Notably, retinal Aβ42, S100 calcium-binding protein B+ macrogliosis, and atrophy correlated with severity of brain Aβ pathology, tauopathy, and atrophy, and most retinal pathologies reflected Braak staging. All retinal biomarkers correlated with the cognitive scores, with retinal Aβ42, far-peripheral AβOi and microgliosis displaying the strongest correlations. Proteomic analysis of AD retinas revealed activation of specific inflammatory and neurodegenerative processes and inhibition of oxidative phosphorylation/mitochondrial, and photoreceptor-related pathways. This study identifies and maps retinopathy in MCI and AD patients, demonstrating the quantitative relationship with brain pathology and cognition, and may lead to reliable retinal biomarkers for noninvasive retinal screening and monitoring of AD.

Dataset Information

A logical network-based drug-screening platform for Alzheimer's disease representing pathological features of human brain organoids.

Publications

A logical network-based drug-screening platform for Alzheimer's disease representing pathological features of human brain organoids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets