Mood State Blood Biomarkers: A Discovery-Based Approach

描述(由申请人提供)： 摘要：到目前为止，还没有针对情绪障碍的临床实验室血液测试。鉴于情绪障碍的复杂性质，目前依赖于患者对症状的自我报告和临床医生对患者面谈的印象是利用现有治疗方式提供尽可能好的护理以及开发包括新药在内的新的和改进的治疗方法的速度限制步骤。我们提出了一种方法，并为其提供了原则证据，以帮助识别情绪状态的状态血液生物标记物。这些生物标志物可以作为客观的临床实验室检测的基础。我们的方法是基于人类和动物模型数据的整合，作为减少每种方法固有的假阳性并帮助识别真正的生物标记分子的一种方式。我们建议测量被诊断为双相情感障碍的患者的新鲜血液样本中的基因表达差异，这些患者在抽血时情绪评分较低，与那些情绪评分较高的患者相比。然后，我们将把我们的人类血液基因表达数据与人类尸检数据、人类遗传连锁/关联数据和动物模型基因表达数据(使用我们小组研究的两个不同动物模型的数据，一个是药物基因组学，一个是转基因动物模型)整合在一起，作为一种称为融合功能基因组学的方法，作为交叉验证和优先排序结果的贝叶斯策略。在这个项目的初步研究中，我们已经确定了一系列情绪状态的高概率血液候选生物标记基因，值得进一步研究。在我们的情绪状态候选生物标志物基因列表中，我们有五个与髓鞘形成有关的基因(MBP、Edg2、Mag、PMP22和UGt8)，以及六个与生长因子信号转导有关的基因(FGFR1、Fzd3、erbB3、Igfbp4、Igfbp6和Ptprm)。所有这些基因之前都有证据表明，在人类死后大脑中，情绪障碍受试者的表达存在差异。根据十个顶级候选生物标志物(五个用于高情绪，五个用于低情绪)开发的预测分数在两个独立的队列中显示了对高情绪和低情绪状态的敏感性和特异性。我们的初步研究表明，血液生物标记物可能为了解大脑功能和疾病状态提供了一个出人意料的信息窗口，特别是在情绪障碍方面。这一激动人心的初步工作需要在更大的独立队列中仔细扩展和复制，并与正常对照进行比较，以得出更多的规范数据，这也是我们在这个项目中建议做的。 公共卫生相关性： 对退伍军人医疗保健的潜在影响：通过生物标记物分析对情绪状态进行更客观的评估，将导致对受精神障碍影响的退伍军人进行更有针对性的治疗，主要涉及情绪失调，如双相情感障碍或抑郁症，其次，如创伤后应激障碍，疗效更好，副作用减少。这将对患者的健康、福祉、安全、生活质量和独立功能产生影响，并减少住院和总体医疗成本。此外，生物标记物分析可能有助于评估治疗反应、复发风险以及早期干预努力，以防止易感个体疾病的全面发展。

DESCRIPTION (provided by applicant): Abstract: There are to date no clinical laboratory blood tests for mood disorders. Given the complex nature of mood disorders, the current reliance on patient self-report of symptoms and the clinician's impression on interview of patient is a rate limiting step in delivering the best possible care with existing treatment modalities, as well as in developing new and improved treatment approaches, including new medications. We propose, and provide proof of principle for, an approach to help identify state blood biomarkers for mood state. Such biomarkers can serve as a basis for objective clinical laboratory tests. Our approach is based on the integration of human and animal model data, as a way of reducing the false-positives inherent in each approach and helping identify true biomarker molecules. We propose to measure gene expression differences in fresh blood samples from subjects with DIGS- diagnosed bipolar disorders that have low mood scores vs. those that have high mood scores at the time of the blood draw. We will then integrate our human blood gene expression data with human postmortem data, human genetic linkage/association data and animal model gene expression data (using data from two different animal models studied in our group, one pharmacogenomic and one transgenic), an approach called Convergent Functional Genomics, as a Bayesian strategy for cross-validating and prioritizing findings. In pilot studies for this project, we have identified a series of high probability blood candidate biomarker genes for mood state that deserve future scrutiny. Topping our list of candidate biomarker genes for mood state we have five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signaling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6, and Ptprm). All of these genes have prior evidence of differential expression in human postmortem brains from mood disorder subjects. A predictive score developed based on a panel of ten top candidate biomarkers (five for high mood, five for low mood) shows sensitivity and specificity for high mood and low mood states, in two independent cohorts. Our preliminary studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state, specifically in regards to mood disorders. This exciting preliminary work needs to be carefully extended and replicated in a larger independent cohort, as well as compared to normal controls to derive additional normative data, which is what we propose to do in this project. PUBLIC HEALTH RELEVANCE: Potential Impact on Veterans Health Care: A more objective assessment of mood state with biomarker profiling will lead to more targeted treatments for veterans affected by psychiatric disorders involving mood dysregulation primarily, such as in bipolar disorder or depression, and secondarily, such as in PTSD, with improved efficacy and decreased side-effects. This will have an impact on patient health, well- being, safety, quality of life, and independent functioning, as well as decrease hospitalizations and overall health-care costs. Moreover, biomarker profiling may help with assessing response to treatment, risk of relapse, and early intervention efforts to prevent the full-blown development of illness in susceptible individuals.