Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, Texas, United States of America.
Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, United States of America.
PLoS Comput Biol. 2022 Feb 17;18(2):e1009800. doi: 10.1371/journal.pcbi.1009800. eCollection 2022 Feb.
Alcohol Use Disorder (AUD) is a chronic, relapsing syndrome diagnosed by a heterogeneous set of behavioral signs and symptoms. There are no laboratory tests that provide direct objective evidence for diagnosis. Microarray and RNA-Seq technologies enable genome-wide transcriptome profiling at low costs and provide an opportunity to identify biomarkers to facilitate diagnosis, prognosis, and treatment of patients. However, access to brain tissue in living patients is not possible. Blood contains cellular and extracellular RNAs that provide disease-relevant information for some brain diseases. We hypothesized that blood gene expression profiles can be used to diagnose AUD. We profiled brain (prefrontal cortex, amygdala, and hypothalamus) and blood gene expression levels in C57BL/6J mice using RNA-seq one week after chronic intermittent ethanol (CIE) exposure, a mouse model of alcohol dependence. We found a high degree of preservation (rho range: [0.50, 0.67]) between blood and brain transcript levels. There was small overlap between blood and brain DEGs, and considerable overlap of gene networks perturbed after CIE related to cell-cell signaling (e.g., GABA and glutamate receptor signaling), immune responses (e.g., antigen presentation), and protein processing / mitochondrial functioning (e.g., ubiquitination, oxidative phosphorylation). Blood gene expression data were used to train classifiers (logistic regression, random forest, and partial least squares discriminant analysis), which were highly accurate at predicting alcohol dependence status (maximum AUC: 90.1%). These results suggest that gene expression profiles from peripheral blood samples contain a biological signature of alcohol dependence that can discriminate between CIE and Air subjects.
酒精使用障碍（AUD）是一种慢性、复发性综合征，通过一组异质的行为迹象和症状来诊断。目前没有实验室测试可以提供直接的客观证据来进行诊断。微阵列和 RNA-Seq 技术可以以较低的成本进行全基因组转录组分析，并提供识别生物标志物的机会，以促进患者的诊断、预后和治疗。然而，无法获得活体患者的脑组织。血液中含有细胞和细胞外 RNA，可以为一些脑部疾病提供与疾病相关的信息。我们假设血液中的基因表达谱可用于诊断 AUD。我们使用 RNA-Seq 对 C57BL/6J 小鼠在慢性间歇性乙醇（CIE）暴露一周后的大脑（前额叶皮层、杏仁核和下丘脑）和血液基因表达水平进行了分析，CIE 是一种酒精依赖的小鼠模型。我们发现血液和大脑转录水平之间具有高度的保存性（rho 范围：[0.50, 0.67]）。血液和大脑差异表达基因之间的重叠较小，而 CIE 后与细胞间信号转导（例如 GABA 和谷氨酸受体信号转导）、免疫反应（例如抗原呈递）和蛋白质加工/线粒体功能（例如泛素化、氧化磷酸化）相关的基因网络则存在相当大的重叠。我们使用血液基因表达数据来训练分类器（逻辑回归、随机森林和偏最小二乘判别分析），这些分类器在预测酒精依赖状态方面具有很高的准确性（最大 AUC：90.1%）。这些结果表明，外周血样中的基因表达谱包含酒精依赖的生物学特征，可以区分 CIE 和 Air 组。
