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人类头皮的功能代谢组学:一个用于……的代谢生态位

Functional metabolomics of the human scalp: a metabolic niche for .

作者信息

Nothias Louis-Félix, Schmid Robin, Garlet Allison, Cameron Hunter, Leoty-Okombi Sabrina, André-Frei Valérie, Fuchs Regine, Dorrestein Pieter C, Ternes Philipp

机构信息

Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, California, USA.

Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA.

出版信息

mSystems. 2024 Feb 20;9(2):e0035623. doi: 10.1128/msystems.00356-23. Epub 2024 Jan 11.

Abstract

Although metabolomics data acquisition and analysis technologies have become increasingly sophisticated over the past 5-10 years, deciphering a metabolite's function from a description of its structure and its abundance in a given experimental setting is still a major scientific and intellectual challenge. To point out ways to address this "data to knowledge" challenge, we developed a functional metabolomics strategy that combines state-of-the-art data analysis tools and applied it to a human scalp metabolomics data set: skin swabs from healthy volunteers with normal or oily scalp (Sebumeter score 60-120, = 33; Sebumeter score > 120, = 41) were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), yielding four metabolomics data sets for reversed phase chromatography (C18) or hydrophilic interaction chromatography (HILIC) separation in electrospray ionization (ESI) + or - ionization mode. Following our data analysis strategy, we were able to obtain increasingly comprehensive structural and functional annotations, by applying the Global Natural Product Social Networking (M. Wang, J. J. Carver, V. V. Phelan, L. M. Sanchez, et al., Nat Biotechnol 34:828-837, 2016, https://doi.org/10.1038/nbt.3597), SIRIUS (K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, et al., Nat Methods 16:299-302, 2019, https://doi.org/10.1038/s41592-019-0344-8), and MicrobeMASST (S. ZuffaS, R. Schmid, A. Bauermeister, P. W, P. Gomes, et al., bioRxiv:rs.3.rs-3189768, 2023, https://doi.org/10.21203/rs.3.rs-3189768/v1) tools. We finally combined the metabolomics data with a corresponding metagenomic sequencing data set using MMvec (J. T. Morton, A. A. Aksenov, L. F. Nothias, J. R. Foulds, et. al., Nat Methods 16:1306-1314, 2019, https://doi.org/10.1038/s41592-019-0616-3), gaining insights into the metabolic niche of one of the most prominent microbes on the human skin, .IMPORTANCESystems biology research on host-associated microbiota focuses on two fundamental questions: which microbes are present and how do they interact with each other, their host, and the broader host environment? Metagenomics provides us with a direct answer to the first part of the question: it unveils the microbial inhabitants, e.g., on our skin, and can provide insight into their functional potential. Yet, it falls short in revealing their active role. Metabolomics shows us the chemical composition of the environment in which microbes thrive and the transformation products they produce. In particular, untargeted metabolomics has the potential to observe a diverse set of metabolites and is thus an ideal complement to metagenomics. However, this potential often remains underexplored due to the low annotation rates in MS-based metabolomics and the necessity for multiple experimental chromatographic and mass spectrometric conditions. Beyond detection, prospecting metabolites' functional role in the host/microbiome metabolome requires identifying the biological processes and entities involved in their production and biotransformations. In the present study of the human scalp, we developed a strategy to achieve comprehensive structural and functional annotation of the metabolites in the human scalp environment, thus diving one step deeper into the interpretation of "omics" data. Leveraging a collection of openly accessible software tools and integrating microbiome data as a source of functional metabolite annotations, we finally identified the specific metabolic niche of , one of the key players of the human skin microbiome.

摘要

尽管在过去5到10年里,代谢组学数据采集和分析技术变得越来越复杂,但从给定实验环境中代谢物的结构描述及其丰度来解读其功能,仍然是一项重大的科学和智力挑战。为了指出应对这一“数据到知识”挑战的方法,我们开发了一种功能代谢组学策略,该策略结合了最先进的数据分析工具,并将其应用于人类头皮代谢组学数据集:对来自头皮正常或油性的健康志愿者的皮肤拭子(皮脂计评分60 - 120,n = 33;皮脂计评分> 120,n = 41)进行液相色谱 - 串联质谱(LC - MS/MS)分析,产生了四个用于反相色谱(C18)或亲水相互作用色谱(HILIC)分离的代谢组学数据集,采用电喷雾电离(ESI)+或 - 电离模式。按照我们的数据分析策略,通过应用全球天然产物社交网络(M. Wang,J. J. Carver,V. V. Phelan,L. M. Sanchez等,《自然生物技术》34:828 - 837,2016,https://doi.org/10.1038/nbt.3597)、天狼星(K. Dührkop,M. Fleischauer,M. Ludwig,A. A. Aksenov等,《自然方法》16:299 - 302,2019,https://doi.org/10.1038/s41592 - 019 - 0344 - 8)和微生物质谱分析(S. ZuffaS,R. Schmid,A. Bauermeister,P. W,P. Gomes等,bioRxiv:rs.3.rs - 3189768,2023,https://doi.org/10.21203/rs.3.rs - 3189768/v1)工具,我们能够获得越来越全面的结构和功能注释。我们最终使用MMvec(J. T. Morton,A. A. Aksenov,L. F. Nothias,J. R. Foulds等,《自然方法》16:1306 - 1314,2019,https://doi.org/10.1038/s41592 - 019 - 0616 - 3)将代谢组学数据与相应的宏基因组测序数据集相结合,深入了解了人类皮肤上最突出的微生物之一的代谢生态位。

重要性

对宿主相关微生物群的系统生物学研究聚焦于两个基本问题

存在哪些微生物以及它们如何相互作用、与宿主以及更广泛的宿主环境相互作用?宏基因组学为问题的第一部分提供了直接答案:它揭示了微生物群落,例如我们皮肤上的微生物群落,并能洞察它们的功能潜力。然而,它在揭示它们的活跃作用方面存在不足。代谢组学向我们展示了微生物生存环境的化学成分以及它们产生的转化产物。特别是,非靶向代谢组学有潜力观察到各种各样的代谢物,因此是宏基因组学的理想补充。然而,由于基于质谱的代谢组学注释率较低以及需要多种实验色谱和质谱条件,这种潜力往往未得到充分探索。除了检测之外,探寻代谢物在宿主/微生物组代谢组中的功能作用需要识别参与其产生和生物转化的生物学过程和实体。在本项关于人类头皮的研究中,我们开发了一种策略,以实现对人类头皮环境中代谢物的全面结构和功能注释,从而更深入地解读“组学”数据。利用一系列可公开获取的软件工具,并将微生物组数据作为功能性代谢物注释的来源进行整合,我们最终确定了人类皮肤微生物组的关键参与者之一的特定代谢生态位。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1aa8/10878091/3e3faf93b0c2/msystems.00356-23.f001.jpg

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