Mass Spectrometry-based Global Molecular Approaches and Computational Tools to Determine Phenotypic and Environmental Signatures of Endometriosis

摘要-项目2 大约10%的育龄妇女被诊断患有子宫内膜异位症，这是一种炎症性雌激素， 以子宫内膜组织在子宫外为特征的依赖性疾病。这可能低估了 频率.在缺乏这种疾病的分子生物标志物的情况下，诊断的“金标准”是组织学 通过侵入性外科手术（腹腔镜检查或剖腹手术）确认病变，这会延误诊断。 因此，项目2将使用基于质谱（MS）的全球方法来比较蛋白质组 来自患者或无疾病女性的在位子宫内膜增生性病变，目的是 鉴定能够更好地分层疾病表型的蛋白质生物标志物。此外，我们将 应用创新的计算方法将结果与多种分子谱（蛋白质， 环境化学物质[EC]和代谢物），这可以使新的诊断 战略布局这一实验策略反映了子宫内膜异位症显著改变了子宫内膜的蛋白质组， 受影响的细胞此外，EC与疾病有关。例如，朱迪斯和费舍尔博士报告说， 与EC暴露相关的子宫内膜异位症妇女组织蛋白质组（脂肪）的改变。与 关于其他小分子，最近的研究表明，组织代谢物的变化可能表现在血液中， 子宫内膜异位症患者因此，它们可能与疾病过程有关。我们的整体战略源于 事实上，在全球范围内，基于MS的分析正在改变研究人员解释复杂 疾病表型在这种情况下，特异性目标1将鉴定病变中的差异表达（DE）蛋白， 使用基于MS的方法对与子宫内膜异位症相关的在位样本和血清进行相关性分析， 量化特异性目标2将鉴定来自以下人群的库存血清样本中的代谢组学和代谢组学特征： 子宫内膜异位症患者与对照个体。具体目标3将应用基于机器学习的方法， 在这个项目中产生的组学数据集定义了子宫内膜异位症的表型分子特征， 可以帮助疾病分类和诊断。这些实验的主要意义在于， 正在重新定义子宫内膜异位症的景观，使用精确的医学方法。此外，我们的数据将 揭示了在子宫内膜异位症的异质性临床表现中起作用的效应物，这些效应物可以作为治疗的靶点。 诊断模式和治疗干预。如初步数据所示， 项目2团队在其他领域的拟议技术和计算策略方面拥有丰富的经验 contexts.关于创新，据我们所知，这是第一次多维度，多学科 子宫内膜异位症的治疗方法将采用先进的计算，基于机器学习的 无监督的方法来合并高阶数据集。我们相信，这些结果将使一个预测性的，非- 侵入性方法检测子宫内膜异位症并揭示潜在的生物标志物， 疾病和治疗目标。

ABSTRACT – PROJECT 2 Approximately 10% of reproductive-aged women are diagnosed with endometriosis, an inflammatory, estrogen- dependent disorder characterized by endometrial tissue outside the uterus. This is likely an underestimate of the frequency. In the absence of molecular biomarkers of this disease, the “gold standard” for diagnosis is histologic confirmation of the lesions via invasive surgical procedures (laparoscopy or laparotomy), which delays diagnosis. Accordingly, Project 2 will use mass spectrometry (MS)-based, global approaches to compare the proteomes of endometriotic lesions with eutopic endometrium from patients or women without disease with the goal of identifying protein biomarkers that enable better stratification of the disease phenotypes. Additionally, we will apply innovative computational methods to correlate the results with multiple molecular profiles (proteins, environmental chemicals [ECs] and metabolites) in patient and control sera, which could enable novel diagnostic strategies. This experimental strategy reflects the fact that endometriosis significantly alters the proteome of the affected cells. Also, ECs have been associated with the disease. For example, Drs. Giudice and Fisher reported alterations in the tissue proteome (fat) of women with endometriosis that correlate with EC exposures. With regard to other small molecules, recent studies suggest shifts in tissue metabolites may manifest in the blood of endometriosis patients. As such they may be linked to the disease process. Our overall strategy derives from the fact that MS-based analyses at a global level are transforming investigators' ability to explicate complex disease phenotypes. In this context, Specific Aim 1 will identify differentially expressed (DE) proteins in lesions, eutopic samples and sera that are associated with endometriosis using a MS-based approach for relative quantification. Specific Aim 2 will identify metabolomic and exposomic features in banked serum samples from endometriosis patients vs. control individuals. Specific Aim 3 will apply machine learning-based approaches to the -omic datasets generated in this project to define phenotypic molecular signatures of endometriosis that could aid in disease classification and diagnosis. The major significance of the proposed experiments is that we are redefining the landscape of endometriosis, using a precision medicine approach. Moreover, our data will reveal effectors with roles in the heterogeneous clinical manifestations of endometriosis that can be targets for diagnostic modalities and therapeutic interventions. As shown by the preliminary data, the members of the Project 2 team have extensive experience with the proposed technologies and computational strategies in other contexts. Regarding innovation, to our knowledge this is the first time that a multi-dimensional, multi-disciplinary approach to endometriosis will be pursued by using advanced computational, machine learning-based unsupervised methods to coalesce high order data sets. We believe the results will enable a predictive, non- invasive approach to detect endometriosis and reveal potential biomarkers, contributing factors underlying disease, and therapeutic targets.