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利用“菌肽组学”估算微生物群落中生物的相对生物量。

Estimating relative biomasses of organisms in microbiota using "phylopeptidomics".

机构信息

Laboratoire Innovations technologiques pour la Détection et le Diagnostic (Li2D), Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRAE, F-30207, Bagnols-sur-Cèze, France.

Laboratory "Innovative technologies for Detection and Diagnostics", DRF-Li2D, CEA-Marcoule, BP 17171, F-30200, Bagnols-sur-Cèze, France.

出版信息

Microbiome. 2020 Mar 6;8(1):30. doi: 10.1186/s40168-020-00797-x.

Abstract

BACKGROUND

There is an important need for the development of fast and robust methods to quantify the diversity and temporal dynamics of microbial communities in complex environmental samples. Because tandem mass spectrometry allows rapid inspection of protein content, metaproteomics is increasingly used for the phenotypic analysis of microbiota across many fields, including biotechnology, environmental ecology, and medicine.

RESULTS

Here, we present a new method for identifying the biomass contribution of any given organism based on a signature describing the number of peptide sequences shared with all other organisms, calculated by mathematical modeling and phylogenetic relationships. This so-called "phylopeptidomics" principle allows for the calculation of the relative ratios of peptide-specified taxa by the linear combination of such signatures applied to an experimental metaproteomic dataset. We illustrate its efficiency using artificial mixtures of two closely related pathogens of clinical interest, and with more complex microbiota models.

CONCLUSIONS

This approach paves the way to a new vision of taxonomic changes and accurate label-free quantitative metaproteomics for fine-tuned functional characterization. Video abstract.

摘要

背景

对于开发快速而稳健的方法来量化复杂环境样本中微生物群落的多样性和时间动态,存在着重要的需求。由于串联质谱允许快速检查蛋白质含量,因此元蛋白质组学越来越多地用于包括生物技术、环境生态学和医学在内的许多领域的微生物群落的表型分析。

结果

在这里,我们提出了一种基于描述与所有其他生物体共享的肽序列数量的特征来识别任何给定生物体的生物量贡献的新方法,该特征通过数学建模和系统发育关系计算得出。这种所谓的“phylopeptidomics”原理允许通过将这种特征应用于实验元蛋白质组数据集的线性组合来计算肽指定分类群的相对比例。我们使用两种密切相关的具有临床意义的病原体的人工混合物以及更复杂的微生物群落模型来说明其效率。

结论

这种方法为分类变化和准确的无标签定量元蛋白质组学开辟了新的视野,可用于精细的功能特征描述。视频摘要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fdd/7060547/b70fc7e05e2e/40168_2020_797_Fig1_HTML.jpg

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