The role of microglia in major depressive disorder

严重抑郁障碍(MDD)是一种常见的精神疾病，影响着3亿多人 全世界。抑郁症是美国退伍军人中最常见的精神疾病，增加了他们患抑郁症的风险 住院和死亡。此外，退伍军人的抑郁症患病率明显高于一般人。 美国成年人口。现有的治疗方法对许多MDD患者无效，迫切需要 为了更好地了解MDD的生物学基础，以开发新的治疗策略。 遗传对MDD的贡献率约为40%，最近大规模的GWAS发现了102个显著的基因座 与MDD风险有关。然而，就像在其他复杂的疾病中一样，大多数MDD风险基因座位于 基因组的非编码区，并被预测改变基因调节元件(GREs； 例如增强剂或促进剂)。此外，GRE对基因表达的影响通常依赖于 细胞和组织类型，这使得理解GWAs风险基因座的功能影响成为一项挑战。连接 具有表达数量性状基因座(EQTL)和/或带有GREs表观遗传图谱的疾病风险变异 相关组织/细胞类型在其他精神疾病中产生了令人敬畏的结果，并为MDD带来了希望。 此外，通过大规模平行报道对eQTL/增强子活性进行了系统的实验验证 分析(MPRAs)提供了一个强大的平台来破译疾病风险变异的功能影响。 到目前为止，许多MDD的神经生物学研究都集中在神经元功能障碍上。 然而，多项研究表明，小胶质细胞(MG)--大脑的免疫细胞--与这种疾病的发病机制有关 无序。我们的初步数据显示，MDD的遗传风险反映在血液中基因表达的变化上- 衍生的髓系细胞。由于这些细胞和MG的转录有很大的重叠，这些数据 提示MG可能在MDD易感性中起重要作用。然而，大规模的基因表达 纯化的MG中的数据无法检验这一假设。 我们的首要目标是识别MDD中的MG功能障碍，并将这种功能障碍与遗传因素联系起来 通过整合MDD GWAS、MG eQTL和有效增强子的表观遗传学图谱，易患MDD 在MG。在Aim1中，我们将使用我们的新的荧光激活核分类(FANS)协议(它允许 从尸检的人脑中分离MG)以产生纯化的MG的高质量基因表达数据 来自大量MDD病例和对照(N~300)。重要的是，该队列包括来自 美国退伍军人。我们还将对MG患者的基因表达进行病例对照比较，包括 测试在退伍军人和非退伍军人中MDD的变化是否不同，在AIM2中，我们将使用基因表达数据 从Aim 1映射MG eQTL，并将这些eQTL与MDD Gwas的发现集成在一起 计算方法。这些分析将揭开MDD风险变异对基因的功能影响 在MG中表达，并将识别候选因果eSNP，这些eSNP将在Aim 3中进行功能验证。最后， 在Aim3中，我们将使用MPRA来验证由假定的增强子携带的候选因果eSNP 和/或MG的eQTL区域。MPRAs将在类似MG的细胞中进行，这些细胞将从人类分化而来 诱导的多能干细胞。 总的来说，拟议的研究将导致发现MDD风险变异和受影响的基因 它们很可能是MDD的病因，并在MG细胞中特异地发挥作用，为开发新的 治疗方法正在接近。这些研究对美国退伍军人尤其重要，因为抑郁症的激增- 退伍军人中的相关自杀事件需要开发新的MDD治疗策略。

Major depressive disorder (MDD) is a common mental illness that affects more than 300 million people worldwide. Depression is the most prevalent mental illness among U.S. veterans and increases their risk of hospitalization and death. Also, the prevalence of depression in veterans is significantly higher than in general U.S. adult population. Available treatments are ineffective for many MDD patients, and there is an urgent need to obtain a better understanding of biological bases of MDD in order to develop novel therapeutic strategies. Genetic contribution to MDD is ~40%, and recent large-scale GWAS discovered 102 significant loci that are linked to MDD risk. However, as in other complex disorders, the majority of the MDD risk loci reside within non-coding regions of the genome and are predicted to alter the activity of gene regulatory elements (GREs; e.g., enhancers or promoters). In addition, the effect of a GRE on gene expression is frequently dependent on the cell and tissue type, making it a challenge to understand the functional impact of GWAS risk loci. Linking the disease risk variants with expression quantitative trait loci (eQTLs) and/or with epigenetic maps of GREs in relevant tissues/cell types yielded formidable results in other psychiatric diseases and holds promise for MDD. In addition, a systematic experimental validation of eQTLs/enhancers’ activity via massively parallel reporter assays (MPRAs) provides a powerful platform to decipher the functional impact of the disease risk variants. To date, many neurobiological studies of MDD have been concentrated on neuronal dysfunction. However, multiple studies implicate microglia (MG)—the immune cells of the brain—to the pathogenesis of this disorder. Our preliminary data shows that MDD genetic risk is reflected in gene expression changes in the blood- derived myeloid cells. Because of the substantial overlap in transcriptomes of these cells and MG, these data suggest that MG might play an important role in susceptibility to MDD. However, large-scale gene expression data in purified MG are not available to test this hypothesis. Our overarching goal is to discern MG dysfunction in MDD as well as to link this dysfunction to genetic predisposition to MDD by integrating MDD GWAS, MG eQTLs, and epigenetic maps of enhancers that are active in MG. In Aim1, we will use our novel fluorescence-activated nuclei sorting (FANS) protocol (which allows the separation of MG from autopsied human brain) to generate high-quality gene expression data in MG purified from a large cohort of MDD cases and controls (N~300). Importantly, the cohort includes specimens from the U.S. veteran population. We will also perform a case-control comparison of gene expression in MG, including testing if alterations in MDD are different in veterans vs. non-veterans, In Aim2, we will use gene expression data from Aim 1 to map MG eQTLs, and will integrate these eQTLs with MDD GWAS findings using rigorous computational approaches. These analyses will unravel the functional implications of MDD risk variants on gene expression in MG, and will identify candidate causal eSNPs that will be functionally validated in Aim 3. Finally, in Aim3, we will use MPRAs to validate the candidate causal eSNPs that are harbored by putative enhancers and/or eQTL regions in MG. MPRAs will be performed in MG-like cells that will be differentiated from human induced pluripotent stem cells. Collectively, the proposed studies will lead to the discovery of MDD risk variants and the affected genes that are likely to be causal for MDD and act specifically in MG cells, paving the way for the development of novel treatment approaches. These studies are especially important for U.S. veterans, as the surge of depression- related suicides among veterans necessitate the development of novel therapeutic strategies for MDD.