内源性类固醇激素高覆盖分析策略研究及其应用

The endogenous steroid hormones play key roles in regulating endocrine and immune systems. The metabolic abnormality in endogenous steroid hormones is highly associated with the occurrence and development of various diseases. Therefore, the investigation on steroidomics will be helpful to understand the key functions of steroid hormones played in different physiological and pathological processes. The current challenge in steroidomics is how to develop a sensitivity and coverage efficient method to detect the endogenous steroid hormones with different abundance. This project is intended to solve the problem from the aspects of sample preparation, sample analysis and compound identification. For sample preparation, firstly, a novel surface functionalized nanophase material of mSiO2 will be synthesized and used for selective enrichment and stable isotope labelled derivatization of commercial phenolic hydroxyl steroid hormones; and then a stable isotope labelled reagent will be synthesized for the chemical derivatization of commercial carbonyl steroid hormones. For sample analysis, a nanoLC-HRMS-based method will be developed for comprehensive analysis of 70+ commercial steroid hormone standards with and without stable isotope labelled chemical derivatization. The quantitative structure-retention relationship (QSRR) of will be constructed based on the nanoLC-MSn data of 70+ commercial steroid hormone standards. Besides, an in silico simulated steroid hormone database will be developed based on network databases. QSRR and in silico simulated steroid hormone database will be combined for identification of unknown and/or undetected but theoretical existed steroid hormone in complex biological samples. The combination of QSRR and in silico simulated database is helpful for peak identification especially for low abundant metabolites. Subsequently, an application study on hydroxyl and carbonyl steroid hormones will be carried out for serum and urine samples from patients with ovarian cancer and ovarian dropsy and the healthy controls on the basis of the workflow mentioned above. The application will be designed as discovery set and validation set. Differential steroid hormones screened in the discovery set will be further checked in the validation set by nanoLC-QQQ-MS as MRM acquisition mode. Such application study is promising to provide new evidence for early diagnosis and treatment of ovarian cancer. The performance of this project will provide research thoughts for studying important functional metabolites with low abundance.

类固醇激素参与内分泌及免疫等调节，其代谢异常与疾病的发生发展密切相关，对固醇类激素全面检测有助于研究其在不同疾病中的病理生理功能。内源性类固醇激素含量低，浓度差异大。不同丰度类固醇激素高灵敏高覆盖分析是类固醇激素代谢组学研究的巨大挑战。本项目拟通过研制同时富集和衍生羟基功能的表面活性基团修饰的介孔纳米材料，以及合成高羰基反应活性的同位素标记衍生化试剂，发展羟基和羰基类固醇激素的高灵敏、高特异性预处理新方法。通过构建纳升液相色谱联用高分辨质谱的分析系统实现不同丰度类固醇激素的高灵敏高覆盖分析检测；发展基于结构-保留规律及基于in silico代谢数据库的定性策略，提高低丰度代谢物的定性能力。在此基础上，开展卵巢癌类固醇激素代谢研究，寻找与卵巢癌发生发展密切相关的类固醇激素类差异代谢物，为卵巢癌的早期诊断和预后治疗提供新依据。本项目的实施为低丰度重要代谢物的分析提供了可供借鉴的研究思路。

类固醇激素是生物体内极为重要的一类生物功能小分子，在调节内分泌与免疫功能、维持第二性征和机体生长发育、调控基因转录等方面发挥着关键作用。类固醇激素代谢异常与疾病的发生发展密切相关，对类固醇激素全面检测有助于研究其在不同疾病中的病理生理功能。人体内类固醇激素含量低，个体水平差异大，且受生理周期影响。不同丰度类固醇激素高灵敏高覆盖分析是类固醇代谢组学分析研究面临的巨大难题。本项目围绕临床生物样本类固醇激素检测灵敏度不足、覆盖度有限等难题，从样品预处理、数据的获取及化合物结构鉴定着手，开展了系统的方法学研究和示范应用研究。发展了羰基和酚羟基类固醇激素的高灵敏预处理新方法，灵敏度最高提升4000倍，解决了低丰度类固醇激素分析检测的难题；针对不同的临床检测需求，研发了基于UPLC-MS/MS、基于microbore LC-MS/MS以及基于2D LC-HRMS的系列类固醇激素高灵敏高覆盖分析新方法，实现了临床生物样本中不同丰度类固醇激素代谢物的高灵敏高覆盖分析检测；发展了类固醇激素结构-保留规律模型并构建了基于代谢网络的in silico类固醇激素高覆盖数据库，极大提高了低丰度类固醇激素的定性能力；最后，将上述方法示范应用于卵巢癌和先天性肾上腺皮质增生症（CAH）患者血清类固醇激素代谢谱研究，发现了与卵巢癌发生发展密切相关的关键类固醇激素代谢物分子，为后续进一步研究卵巢癌的发生发展机制、早期诊断及预后研究提供了科学依据；发现了17-OHP和21-Deoxycortisol，11-Deoxycortisol和11-Deoxycorticosterone的代谢水平以及17-OHP/11-Deoxycortisol的比值可实现不同酶缺乏引起的CAH疾病亚型的有效诊断。本项目在实施过程中，已在国际期刊发表SCI论文12篇，获得中国发明专利授权4项，申请中国发明专利9项。辅助培养毕业博士研究生2人，在读博士生3名。