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%的育龄妇女被诊断出患有子宫内膜异位症，一种炎性雌激素-- 以子宫外的子宫内膜组织为特征的依赖性疾病。这很可能低估了 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(腹腔镜术或开腹术)确认病变，这会延误诊断。 因此，项目2将使用基于质谱仪(MS)的全球方法来比较蛋白质组 有在位内膜的子宫内膜异位症的患者或妇女的目标是 确定能够更好地对疾病表型进行分层的蛋白质生物标记物。此外，我们还将 应用创新的计算方法将结果与多个分子图谱(蛋白质， 患者和对照血清中的环境化学物质[ECs]和代谢物)，这可能使新的诊断成为可能 战略。这一实验策略反映了这样一个事实，即子宫内膜异位症显著改变了 受影响的细胞。此外，内皮细胞与这种疾病有关。例如，Giudice和Fisher博士报告说 子宫内膜异位症患者组织蛋白质组(脂肪)的改变与EC暴露相关。使用 关于其他小分子，最近的研究表明，组织代谢物的变化可能出现在 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，全球范围内基于MS的分析正在改变研究人员解释复杂事物的能力 疾病表型。在此背景下，特定目标1将识别病变中差异表达(DE)蛋白， 用MS为基础的方法检测与子宫内膜异位症相关的在位样本和血清 量化。特定目标2将在来自中国的库存血清样本中识别代谢组和暴露组特征 子宫内膜异位症患者与对照组。特定目标3将应用基于机器学习的方法来 在这个项目中生成的基因组数据集定义了子宫内膜异位症的表型分子特征， 有助于疾病的分类和诊断。拟议中的实验的主要意义在于我们 正在使用精准医学方法重新定义子宫内膜异位症的图景。此外，我们的数据将 揭示在子宫内膜异位症不同临床表现中所起作用的效应因子，可作为靶点 诊断方式和治疗干预。初步数据显示，该委员会的成员 项目2团队在其他领域的建议技术和计算策略方面拥有丰富的经验 上下文。关于创新，据我们所知，这是第一次多维度、多学科的 将通过使用基于机器学习的高级计算来探索治疗子宫内膜异位症的方法 合并高阶数据集的非监督方法。我们相信这一结果将使我们能够预测、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、潜在的致病因素 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.