An annotation database for chemicals of emerging concern in exposome research.
An annotation database for chemicals of emerging concern in exposome research.
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DOI：
10.1016/j.envint.2021.106511
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发表时间：
2021-03
影响因子：
11.8
通讯作者：
J. Meijer;M. Lamoree;T. Hamers;J. Antignac;S. Hutinet;L. Debrauwer;A. Covaci;Carolin Huber;M. Krauss;D. Walker;Emma L. Schymanski;R. Vermeulen;J. Vlaanderen
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文献类型：
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作者：
J. Meijer;M. Lamoree;T. Hamers;J. Antignac;S. Hutinet;L. Debrauwer;A. Covaci;Carolin Huber;M. Krauss;D. Walker;Emma L. Schymanski;R. Vermeulen;J. Vlaanderen
背景新出现关注的化学品（CEC）包括一组非常广泛的化学品，这些化学品被怀疑对健康产生不利影响，但可获得的信息非常有限。通过使用全面的注释数据库，色谱技术结合高分辨率质谱（HRMS）可用于CEC的非靶向筛选和检测。建立一个数据库集中在注释的CEC在人体samples.ObjectivesThis研究将提供新的洞察到广泛的CEC在humans.ObjectivesThis研究描述了一种方法的聚集和策展的注释数据库（CEC屏幕）的CEC在人体生物samples.MethodsThe方法包括三个主要部分。首先，将来自各种来源的CEC化合物清单汇总，删除重复和无机化合物。随后，通过结构标准化来创建“MS就绪”和“QSAR就绪”SMILES，以及计算精确质量（单一同位素和加合物）和分子式来管理该列表。第二步包括I相代谢物的模拟。第三步也是最后一步包括计算与理化性质、环境归宿、毒性和吸收、分布、代谢、排泄（ADME）过程相关的QSAR预测，并从美国环保局CompTox Chemicals Dashboard.ResultsAll CEC screen数据库和属性文件公开（DOI：https：//doi.org/10.5281/zenodo.3956586）检索信息。总共有145，284个条目来自各种CEC数据源。在消除重复和策展之后，管道产生了70，397个独特的“MS就绪”结构和66，071个独特的QSAR就绪结构，对应于69，526个CAS号。I相代谢物的模拟产生了306，279种独特的代谢物。可以对64，684个QSAR就绪结构进行QSAR预测，而在69，526次查询中，从CompTox化学品仪表板中检索了59，739个CAS编号的信息。CECscreen被纳入thein silicofractionation方法MetFrag.DiscussionThe CECscreen数据库可用于优先注释非靶向HRMS中测量的CEC，便于大规模检测人类样本中的CEC，用于疑难杂症研究。CEC的大规模检测可以通过将本数据库与包含CEC（代谢物）和元数据测量的资源整合来进一步改进，进一步扩展到计算机和实验（例如，MassBank）生成MS/MS光谱，并开发能够在测量的化学特征中使用相关模式的生物信息学方法。
BackgroundChemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans.ObjectivesThis study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples.MethodsThe approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create “MS-ready” and “QSAR-ready” SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard.ResultsAll CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586). In total, 145,284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70,397 unique “MS-ready” structures and 66,071 unique QSAR-ready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in thein silicofragmentation approach MetFrag.DiscussionThe CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towardsin silicoand experimental (e.g.,MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features.