Wang M, Li A, Sekiya M, Beckmann ND, Quan X, Schrode N, Fernando MB, Yu A, Zhu L, Cao J, Lyu L, Horgusluoglu E, Wang Q, Guo L, Wang YS, Neff R, Song WM, Wang E, Shen Q, Zhou X, Ming C, Ho SM, Vatansever S, Kaniskan HÜ, Jin J, Zhou MM, Ando K, Ho L, Slesinger PA, Yue Z, Zhu J, Katsel P, Gandy S, Ehrlich ME, Fossati V, Noggle S, Cai D, Haroutunian V, Iijima KM, Schadt E, Brennand KJ, Zhang B. (2020) Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease. Neuron [Epub ahead of print]. [abstract]
Researchers from Mount Sinai and the National Center for Geriatrics and Gerontology in Japan have identified new molecular mechanisms driving late-onset Alzheimer’s Disease (LOAD), as well as a promising therapeutic candidate for treatment, according to a study in the journal Neuron. LOAD is the most prevalent form of dementia among people over age 65, a progressive and irreversible brain disorder affecting more than 5.5 million people in the U.S., and the sixth leading cause of death.
Previous genetic and genome-wide association studies (GWAS) have identified some genetic mutations associated with LOAD, but the causal variants of the disease have remained uncharacterized. To explore the molecular mechanisms driving the pathogenesis of LOAD, the Mount Sinai-led team performed an integrative network biology analysis of a whole genome and RNA sequencing dataset from multiple cortical brain regions of hundreds of donors, both healthy and with LOAD. This work revealed thousands of molecular changes and uncovered numerous neuron-specific gene subnetworks dysregulated in LOAD.
From that investigation researchers predicted that ATP6V1A, a protein-coding gene, plays a major role in a critical signaling pathway in the brain, and that its deficit could be traced to LOAD. That linkage was evaluated using two methods: a CRISPR-based technique to manipulate ATP6V1A levels in donor-matched brain cells in vitro, and in RNAi-based knockdown in transgenic Drosophila models, meaning that genetic material is artificially introduced into fly models and specific genes are effectively silenced to study the effects. Indeed, the knockdown of ATP6V1A worsened LOAD-related neurodegeneration in both models.
Just as significantly, researchers predicted that a drug compound, NCH-51, could normalize the dysregulated genes in LOAD, including ATP6V1A, and demonstrated that NCH-51 dramatically improved the neuronal and neurodegenerative effects of the ATP6V1A deficit in both model systems. Specifically, the CRISPR-based experiment using human induced pluripotent stem cells (hiPSC) demonstrated that repression of ATP6V1A, particularly in combination with β-amyloid — a key neuropathological hallmark of AD — dramatically impacted neuronal function. “The human-based system we created proved to be a promising way to model the mechanisms underlying risk and progression in diseases like LOAD where living tissues are not available,” observed Kristen Brennand, PhD, Associate Professor, Genetics and Genomic Sciences, Mount Sinai, and co-author of the study.
The Drosophila experiments were also revealing, demonstrating that ATP6V1A deficit exacerbated both β-amyloid-mediated toxicity and tau-mediated axon degeneration.
Source – Mount Sinai
Wang M, Li A, Sekiya M, Beckmann ND, Quan X, Schrode N, Fernando MB, Yu A, Zhu L, Cao J, Lyu L, Horgusluoglu E, Wang Q, Guo L, Wang YS, Neff R, Song WM, Wang E, Shen Q, Zhou X, Ming C, Ho SM, Vatansever S, Kaniskan HÜ, Jin J, Zhou MM, Ando K, Ho L, Slesinger PA, Yue Z, Zhu J, Katsel P, Gandy S, Ehrlich ME, Fossati V, Noggle S, Cai D, Haroutunian V, Iijima KM, Schadt E, Brennand KJ, Zhang B. (2020) Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease. Neuron [Epub ahead of print]. [abstract]
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Researchers from Mount Sinai and the National Center for Geriatrics and Gerontology in Japan have identified new molecular mechanisms driving late-onset Alzheimer’s Disease (LOAD), as well as a promising therapeutic candidate for treatment, according to a study in the journal Neuron. LOAD is the most prevalent form of dementia among people over age 65, a progressive and irreversible brain disorder affecting more than 5.5 million people in the U.S., and the sixth leading cause of death.
Previous genetic and genome-wide association studies (GWAS) have identified some genetic mutations associated with LOAD, but the causal variants of the disease have remained uncharacterized. To explore the molecular mechanisms driving the pathogenesis of LOAD, the Mount Sinai-led team performed an integrative network biology analysis of a whole genome and RNA sequencing dataset from multiple cortical brain regions of hundreds of donors, both healthy and with LOAD. This work revealed thousands of molecular changes and uncovered numerous neuron-specific gene subnetworks dysregulated in LOAD.
From that investigation researchers predicted that ATP6V1A, a protein-coding gene, plays a major role in a critical signaling pathway in the brain, and that its deficit could be traced to LOAD. That linkage was evaluated using two methods: a CRISPR-based technique to manipulate ATP6V1A levels in donor-matched brain cells in vitro, and in RNAi-based knockdown in transgenic Drosophila models, meaning that genetic material is artificially introduced into fly models and specific genes are effectively silenced to study the effects. Indeed, the knockdown of ATP6V1A worsened LOAD-related neurodegeneration in both models.
Just as significantly, researchers predicted that a drug compound, NCH-51, could normalize the dysregulated genes in LOAD, including ATP6V1A, and demonstrated that NCH-51 dramatically improved the neuronal and neurodegenerative effects of the ATP6V1A deficit in both model systems. Specifically, the CRISPR-based experiment using human induced pluripotent stem cells (hiPSC) demonstrated that repression of ATP6V1A, particularly in combination with β-amyloid — a key neuropathological hallmark of AD — dramatically impacted neuronal function. “The human-based system we created proved to be a promising way to model the mechanisms underlying risk and progression in diseases like LOAD where living tissues are not available,” observed Kristen Brennand, PhD, Associate Professor, Genetics and Genomic Sciences, Mount Sinai, and co-author of the study.
The Drosophila experiments were also revealing, demonstrating that ATP6V1A deficit exacerbated both β-amyloid-mediated toxicity and tau-mediated axon degeneration.
Source – Mount Sinai
Wang M, Li A, Sekiya M, Beckmann ND, Quan X, Schrode N, Fernando MB, Yu A, Zhu L, Cao J, Lyu L, Horgusluoglu E, Wang Q, Guo L, Wang YS, Neff R, Song WM, Wang E, Shen Q, Zhou X, Ming C, Ho SM, Vatansever S, Kaniskan HÜ, Jin J, Zhou MM, Ando K, Ho L, Slesinger PA, Yue Z, Zhu J, Katsel P, Gandy S, Ehrlich ME, Fossati V, Noggle S, Cai D, Haroutunian V, Iijima KM, Schadt E, Brennand KJ, Zhang B. (2020) Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease. Neuron [Epub ahead of print]. [abstract]
Related Posts
POND-seq enables non-destructive RNA sequencing in living cells
Worm’s radical transformation shows metamorphosis can change the functions of cells
New method allows scientists to follow gene activity over time in the same cells
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data
MiRQuery – a user-friendly web app for the interactive analysis and visualization of microRNA sequencing data
RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease
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