An expanded database of high-resolution MS/MS spectra for lichen-derived natural products.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Joe Bracegirdle, John A Elix, Udayangani Mawalagedera, Yit-Heng Chooi, Cécile Gueidan
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Abstract

The history of lichen compound identification has long relied on techniques such as spot tests and TLC, which have been surpassed in sensitivity and accuracy by modern metabolomic techniques such as high-resolution MS/MS. In 2019, Olivier-Jimenez et al. released the Lichen DataBase (LDB), a library containing the Q-TOF MS/MS spectra of 251 metabolites on the MetaboLights and GNPS platforms, that has been widely used for the identification of lichen-derived unknowns. To increase the compound coverage, we have generated the Orbitrap MS/MS spectra of a further 534 lichen-derived compounds from the metabolite library of Jack Elix, housed at the CANB herbarium (Canberra, Australia). This included 399 unique metabolites that are not in the LDB, bringing the total number combined to 650. Technical validation was achieved by investigating the compounds in three Australian lichen extracts using the Library Search and Molecular Networking tools on the GNPS platform. This update provides a much larger database for lichen compound identification, which we envisage will allow refining the lichen chemotaxonomy framework and contribute to compound discovery.

Abstract Image

Abstract Image

Abstract Image

地衣衍生的天然产物的高分辨率MS/MS光谱扩展数据库。
地衣化合物鉴定的历史长期依赖于斑点试验和薄层色谱等技术,其灵敏度和准确性已被现代代谢组学技术(如高分辨率MS/MS)所超越。2019年,Olivier-Jimenez等人发布了地衣数据库(Lichen DataBase, LDB),该数据库包含251种代谢物在代谢产物和GNPS平台上的Q-TOF MS/MS谱,已被广泛用于地衣衍生未知物的鉴定。为了增加化合物的覆盖率,我们从澳大利亚堪培拉CANB植物标本馆的Jack Elix代谢物文库中提取了534种地衣衍生化合物的Orbitrap MS/MS谱。其中包括399种不在LDB中的独特代谢物,使总数达到650种。利用GNPS平台上的文库检索和分子网络工具对三种澳大利亚地衣提取物中的化合物进行了研究,获得了技术验证。这次更新为地衣化合物鉴定提供了一个更大的数据库,我们设想这将允许改进地衣化学分类框架,并有助于化合物的发现。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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