A Multi-Omic Platform for Polycystic Ovary Syndrome Characterization and Management

项目摘要/摘要 多囊卵巢综合征(PCOS)是女性最常见的内分泌疾病 生育年龄。由于多囊卵巢综合征的复杂性质，诊断往往被延误， 治疗和自我管理是有限的。随着最近在高吞吐量方面的发展，下一步- 代和代谢组学技术，现在有一个独特的机会来利用成本- 有效的多组学方法研究多囊卵巢综合征患者。 在这项申请中，我们建议将这些技术应用于检查患有多囊卵巢综合征的女性， 开发具有成本效益、可扩展、集成性和高度精确度的平台。我们的多- 组学平台(FemBio)将整合患者的医疗记录、基因组学、微生物组 (鸟枪式元基因组学)，和代谢组学，以创建患有和 没有多囊卵巢综合征。这种方法有可能提供更全面的 多囊卵巢综合征的特征，以导致识别改进的诊断标志物，并 允许发现新的治疗靶点。此外，我们还提出了一种预期的 观察性队列研究，包括全基因组测序、鸟枪式元基因组 肠道微生物组、粪便代谢组学、血清和尿液激素水平的测序 多囊卵巢综合征患者和健康对照组的临床指标。 这个平台(FemBio)将使人们能够更广泛地访问最新的分子分析、多个 面向普通公众的组织量化和计算建模，目前是 缺乏。反过来，这将改善多囊卵巢综合征的特性、长期管理、 并为数百万患有这种疾病的女性开辟了潜在的新治疗选择 无序性，以及作为一个大型的带注释的多组体数据集，可以帮助PCOS领域 更广泛地说。

Project Summary/Abstract Polycystic ovarian syndrome (PCOS) is the most common endocrine disorder in women of reproductive age. Due to the complex nature of PCOS, diagnosis is often delayed and options for treatment and self-management are limited. With recent developments in high-throughput, next- generation and metabolomics technologies, there is now a unique opportunity to utilize a cost- effective, multi-omic approach to study individuals with PCOS. In this application, we propose to apply these technologies to examine women with PCOS, developing a platform that is cost-effective, scalable, integrative, and highly accurate. Our multi- omic platform (FemBio) will integrate patient’s medical records, genomics, microbiome (shotgun metagenomics), and metabolomics to create a molecular profile of women with and without PCOS. This approach has the potential to provide a more comprehensive characterization of PCOS, to lead to the identification of improved diagnostic markers, and to allow for the discovery of novel targets for treatment. Moreover, we propose a prospective observational cohort study including whole genome sequencing, shotgun metagenomic sequencing of the gut microbiome, fecal metabolomics, serum and urine hormone levels, and clinical indices in a sample of individuals with PCOS and a healthy control group. This platform (FemBio) will enable broader accessibility to the latest in molecular assays, multi- omic quantification, and computational modeling for the general public, which currently is lacking. In turn, this will improve options for PCOS characterization, long-term management, and open potentially new treatment options for the millions of women suffering from this disorder, as well as serve as a large, annotated multi-omic data set that can help the PCOS field more broadly.