Biological psychiatry · 2024
Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
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.
Sullivan Kyle A, Kainer David, Lane Matthew, Cashman Mikaela, Miller J Izaak, Garvin Michael R, Townsend Alice, Quach Bryan C, Willis Caryn, Kruse Peter, Gaddis Nathan C, Mathur Ravi, Corradin Olivia, Maher Brion S, Scacheri Peter C, Sanchez-Roige Sandra, Palmer Abraham A, Troiani Vanessa, Chesler Elissa J, Kember Rachel L, Kranzler Henry R, Justice Amy C, Xu Ke, Aouizerat Bradley E, Hancock Dana B, Johnson Eric O, Jacobson Daniel A, VA Million Veteran Program
Аннотация
BACKGROUND: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed.
METHODS: To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals.
RESULTS: We identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. Some of the identified drugs are approved to treat other substance use disorders or depression.
CONCLUSIONS: Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.
METHODS: To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals.
RESULTS: We identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. Some of the identified drugs are approved to treat other substance use disorders or depression.
CONCLUSIONS: Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.