MedElevator Войти в исследования
← Поиск публикаций

DOI 10.1016/j.neuron.2020.11.002

Медицинская библиотека · Neuron · 2020

Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer's Disease.

Wang Minghui, Li Aiqun, Sekiya Michiko, Beckmann Noam D, Quan Xiuming, Schrode Nadine, Fernando Michael B, Yu Alex, Zhu Li, Cao Jiqing, Lyu Liwei, Horgusluoglu Emrin, Wang Qian, Guo Lei, Wang Yuan-Shuo, Neff Ryan, Song Won-Min, Wang Erming, Shen Qi, Zhou Xianxiao, Ming Chen, Ho Seok-Man, Vatansever Sezen, Kaniskan H Ümit, Jin Jian, Zhou Ming-Ming, Ando Kanae, Ho Lap, Slesinger Paul A, Yue Zhenyu, Zhu Jun, Katsel Pavel, Gandy Sam, Ehrlich Michelle E, Fossati Valentina, Noggle Scott, Cai Dongming, Haroutunian Vahram, Iijima Koichi M, Schadt Eric, Brennand Kristen J, Zhang Bin

СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАПоддержка правительства СШАПеревод пока не подготовлен

Аннотация

To identify the molecular mechanisms and novel therapeutic targets of late-onset Alzheimer's Disease (LOAD), we performed an integrative network analysis of multi-omics profiling of four cortical areas across 364 donors with varying cognitive and neuropathological phenotypes. Our analyses revealed thousands of molecular changes and uncovered neuronal gene subnetworks as the most dysregulated in LOAD. ATP6V1A was identified as a key regulator of a top-ranked neuronal subnetwork, and its role in disease-related processes was evaluated through CRISPR-based manipulation in human induced pluripotent stem cell-derived neurons and RNAi-based knockdown in Drosophila models. Neuronal impairment and neurodegeneration caused by ATP6V1A deficit were improved by a repositioned compound, NCH-51. This study provides not only a global landscape but also detailed signaling circuits of complex molecular interactions in key brain regions affected by LOAD, and the resulting network models will serve as a blueprint for developing next-generation therapeutic agents against LOAD.

Для цитирования: Wang Minghui, Li Aiqun, Sekiya Michiko, Beckmann Noam D, Quan Xiuming, Schrode Nadine, Fernando Michael B, Yu Alex, Zhu Li, Cao Jiqing, Lyu Liwei, Horgusluoglu Emrin, Wang Qian, Guo Lei, Wang Yuan-Shuo, Neff Ryan, Song Won-Min, Wang Erming, Shen Qi, Zhou Xianxiao, Ming Chen, Ho Seok-Man, Vatansever Sezen, Kaniskan H Ümit, Jin Jian, Zhou Ming-Ming, Ando Kanae, Ho Lap, Slesinger Paul A, Yue Zhenyu, Zhu Jun, Katsel Pavel, Gandy Sam, Ehrlich Michelle E, Fossati Valentina, Noggle Scott, Cai Dongming, Haroutunian Vahram, Iijima Koichi M, Schadt Eric, Brennand Kristen J, Zhang Bin. Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer's Disease.. Neuron. 2020. DOI: 10.1016/j.neuron.2020.11.002.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.