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阿片类药物暴露妊娠中的胎盘DNA甲基化谱及其与新生儿阿片类药物戒断综合征的关联。

Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome.

作者信息

Radhakrishna Uppala, Vishweswaraiah Sangeetha, Uppala Lavanya V, Szymanska Marta, Macknis Jacqueline, Kumar Sandeep, Saleem-Rasheed Fozia, Aydas Buket, Forray Ariadna, Muvvala Srinivas B, Mishra Nitish K, Guda Chittibabu, Carey David J, Metpally Raghu P, Crist Richard C, Berrettini Wade H, Bahado-Singh Ray O

机构信息

Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA.

Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA.

出版信息

Genomics. 2021 May;113(3):1127-1135. doi: 10.1016/j.ygeno.2021.03.006. Epub 2021 Mar 9.

Abstract

Opioid abuse during pregnancy can result in Neonatal Opioid Withdrawal Syndrome (NOWS). We investigated genome-wide methylation analyses of 96 placental tissue samples, including 32 prenatally opioid-exposed infants with NOWS who needed therapy (+Opioids/+NOWS), 32 prenatally opioid-exposed infants with NOWS who did not require treatment (+Opioids/-NOWS), and 32 prenatally unexposed controls (-Opioids/-NOWS, control). Statistics, bioinformatics, Artificial Intelligence (AI), including Deep Learning (DL), and Ingenuity Pathway Analyses (IPA) were performed. We identified 17 dysregulated pathways thought to be important in the pathophysiology of NOWS and reported accurate AI prediction of NOWS diagnoses. The DL had an AUC (95% CI) =0.98 (0.95-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS from the +Opioids/-NOWS group and AUCs (95% CI) =1.00 (1.0-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS versus control and + Opioids/-NOWS group versus controls. This study provides strong evidence of methylation dysregulation of placental tissue in NOWS development.

摘要

孕期阿片类药物滥用可导致新生儿阿片类药物戒断综合征(NOWS)。我们对96份胎盘组织样本进行了全基因组甲基化分析,其中包括32名产前暴露于阿片类药物且患有NOWS需要治疗的婴儿(+阿片类药物/+NOWS)、32名产前暴露于阿片类药物且患有NOWS但无需治疗的婴儿(+阿片类药物/-NOWS)以及32名产前未暴露的对照婴儿(-阿片类药物/-NOWS,对照)。进行了统计学、生物信息学、包括深度学习(DL)在内的人工智能(AI)以及通路分析(IPA)。我们确定了17条失调的通路,这些通路被认为在NOWS的病理生理学中很重要,并报告了对NOWS诊断的准确人工智能预测。对于区分NOWS与+阿片类药物/-NOWS组,深度学习的曲线下面积(AUC,95%置信区间)=0.98(0.95 - 1.0),灵敏度和特异性均为100%;对于区分NOWS与对照组以及+阿片类药物/-NOWS组与对照组,AUC(95%置信区间)=1.00(1.0 - 1.0),灵敏度和特异性均为100%。这项研究为NOWS发展过程中胎盘组织的甲基化失调提供了有力证据。

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