The role of microglia in major depressive disorder

重度抑郁症（MDD）是一种常见的精神疾病，影响超过3亿人 国际吧抑郁症是美国退伍军人中最普遍的精神疾病，并增加了他们患上抑郁症的风险 住院和死亡。此外，退伍军人抑郁症的患病率明显高于一般人群 美国成人人口。现有的治疗方法对许多MDD患者无效，迫切需要 更好地了解MDD的生物学基础，以开发新的治疗策略。 MDD的遗传贡献约为40%，最近的大规模GWAS发现了102个重要的基因座， 与MDD风险有关。然而，与其他复杂疾病一样，大多数MDD风险位点位于 基因组的非编码区，并被预测改变基因调控元件（GRES; 例如，在一个实施例中，增强子或启动子）。此外，GRE对基因表达的影响通常取决于 细胞和组织类型，这使得了解GWAS风险位点的功能影响成为一个挑战。连接 具有表达数量性状基因座（eQTL）和/或具有GRES表观遗传图谱的疾病风险变异体， 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，通过大规模平行报告基因系统地验证了eQTL/增强子的活性， 多克隆抗体分析（MPRAs）提供了一个强大的平台来解读疾病风险变体的功能影响。 迄今为止，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-与此发病机制有关。 disorder.我们的初步数据显示，MDD遗传风险反映在血液中的基因表达变化中- 衍生的骨髓细胞。由于这些细胞和MG的转录组的大量重叠，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MG的数据不能用于检验该假设。 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和增强子的表观遗传图谱， 在MG。在Aim 1中，我们将使用我们的新荧光激活核分选（FANS）协议（允许细胞核的细胞核分选）。 从尸检的人脑中分离MG）以在纯化的MG中产生高质量的基因表达数据 来自MDD病例和对照的大队列（N~300）。重要的是，该队列包括来自 美国老百姓。我们还将对MG的基因表达进行病例对照比较，包括 测试MDD的改变是否在退伍军人与非退伍军人中不同，在Aim 2中，我们将使用基因表达数据 从Aim 1开始绘制MG eQTL，并将这些eQTL与MDD GWAS结果整合，使用严格的 计算方法。这些分析将揭示MDD风险变异对基因表达的功能影响。 在MG中的表达，并将鉴定将在目的3中功能验证的候选因果eSNP。最后， 在Aim 3中，我们将使用MPRA来验证假定增强子所包含的候选因果eSNP 和/或eQTL区域。MPRA将在MG样细胞中进行，MG样细胞将从人MG样细胞分化为人MG样细胞。 诱导多能干细胞 总的来说，拟议的研究将导致发现MDD风险变体和受影响的基因 可能是MDD的原因，并在MG细胞中特异性发挥作用，为开发新的 治疗方法。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.