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

基于质谱的全局分子方法和计算工具来确定子宫内膜异位症的表型和环境特征

• 批准号: 10699969
• 负责人: SUSAN J. FISHER
• 金额: $ 38.28万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10699969
• 关键词: Affect; Age; Animal Model; Biological Markers; Biology; Blood; Cell physiology; Cells; Chlorinated Hydrocarbons; Classification; Clinical; Clinical Data; Complex; Computing Methodologies; Data; Data Analytics; Data Set; Diagnosis; Diagnostic; Dimensions; Dioxins; Disease; Disease stratification; Endometrial; Enzyme-Linked Immunosorbent Assay; Estrogens; Fatty acid glycerol esters; Frequencies; Genetic; Goals; Histologic; Individual; Infertility; Inflammatory; Ions; Laparoscopy; Laparotomy; Lesion; Link; Machine Learning; Maps; Mass Spectrum Analysis; Methods; Modality; Molecular; Molecular Profiling; Onset of illness; Operative Surgical Procedures; Pain; Pain Disorder; Pathway interactions; Patients; Phenotype; Play; Polychlorinated Biphenyls; Process; Proteins; Proteome; Proteomics; Recurrence; Reporting; Research; Research Personnel; Risk; Risk Estimate; Role; Sampling; Serology; Serum; Severity of illness; Techniques; Technology; Testing; Therapeutic Intervention; Time; Tissue Sample; Tissues; Training; Uterine cavity; Uterus; Woman; aged; clinical phenotype; community engagement; comparison control; computerized tools; data integration; diagnosis standard; diagnostic biomarker; diagnostic strategy; differential expression; disease classification; disease diagnosis; disease diagnostic; disease phenotype; disorder subtype; effective therapy; endometriosis; environmental chemical; environmental chemical exposure; epidemiology study; eutopic endometrium; experience; experimental study; genetic association; innovation; interdisciplinary approach; ketogentic; member; metabolomics; molecular marker; molecular phenotype; multidisciplinary; multiple omics; novel; novel diagnostics; novel marker; phenotypic data; phthalates; potential biomarker; precision medicine; predictive signature; protein biomarkers; reproductive; small molecule; symptomatology; systematic review; targeted treatment; therapeutic target; tool

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.

摘要 - 项目2 大约10％的生殖妇女被诊断为子宫内膜异位症，一种炎症，雌激素 依赖性疾病以子宫外的子宫内膜组织为特征。这可能是对 频率。在没有该疾病的分子生物标志物的情况下，用于诊断的“金标准”是组织学 通过侵入性外科手术（腹腔镜或剖腹手术）确认病变，这会延迟诊断。 根据，项目2将使用基于质谱（MS）的全局方法来比较蛋白质组 来自患者或没有疾病的妇女的配置子宫内膜的子宫内膜损伤，目的 鉴定蛋白质生物标志物，可以更好地分层疾病表型。此外，我们会的 应用创新的计算方法将结果与多个分子曲线相关联（蛋白质， 患者和对照血清中的环境化学物质[ECS]和代谢产物），这可以实现新颖的诊断 策略。这种实验策略反映了以下事实：子宫内膜异位症显着改变了 受影响的细胞。此外，EC与该疾病有关。例如，博士。 Giudice和Fisher报道 子宫内膜异位症女性组织蛋白质组（脂肪）的改变，与EC暴露有关。和 关于其他小分子，最近的研究表明，组织代谢产物的转移可能体现在 子宫内膜异位症患者。因此，它们可能与疾病过程有关。我们的整体策略来自 在全球层面基于MS的分析的事实正在改变研究者的解释能力 疾病表型。在这种情况下，特定目标1将在病变中识别不同表达的（DE）蛋白， 与子宫内膜异位症相关的配置样品和血清，使用基于MS的方法 定量。特定的目标2将在库的血清样品中识别代谢组和外胚层特征 子宫内膜异位症患者与对照个体。特定目标3将应用基于机器学习的方法 该项目中生成的 - 摩尼克数据集，以定义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。提出的实验的主要意义是我们 使用精确医学方法重新定义子宫内膜异位症的景观。而且，我们的数据将 在子宫内膜异位症的异质临床表现中揭示了作用的作用，这可能是 诊断方式和治疗干预措施。如初步数据所示， 项目2团队在其他的技术和计算策略方面具有丰富的经验 contexts.Neinovation，据我们所知，这是第一次是多维，多学科的 子宫内膜异位症方法将通过使用高级计算，基于机器学习的方法来追求 无监督的方法合并高阶数据集。我们认为结果将使预测性，非 侵入性检测子宫内膜异位症并揭示潜在的生物标志物，造成潜在因素的潜在因素 疾病和治疗靶标。