基于质谱技术的蚕豆(Phaseolus vulgaris L.)种子精子层肽组数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-07 DOI:10.1038/s41597-024-04044-y
Chandrodhay Saccaram, Céline Brosse, Boris Collet, Delphine Sourdeval, Tracy François, Benoît Bernay, Massimiliano Corso, Loïc Rajjou
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引用次数: 0

摘要

精囊层是围绕萌发种子的动态微环境,由种子渗出的天然化合物与种子相关微生物群落之间复杂的相互作用所形成。虽然人们知道植物渗出的肽会影响微生物群的多样性,但对种子渗出的肽却知之甚少。在这项研究中,我们首次利用生长在两个不同产区的八种基因型的蚕豆(Phaseolus vulgaris)种子描述了精子层的肽组特征。一项非靶向的 LC-MS/MS 肽组分析揭示了精子层中来自 414 个芸豆前体蛋白的 3,258 个肽段。这一全面的肽组数据集为了解精囊中普通豆类种子渗出肽的特征提供了宝贵的信息。它可用于鉴定具有潜在抗菌或其他生物活性的多肽,从而加深我们对精子贮藏层中种子渗出多肽功能作用的了解。
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A mass spectrometry-based peptidomic dataset of the spermosphere in common bean (Phaseolus vulgaris L.) seeds.

The spermosphere, a dynamic microenvironment surrounding germinating seeds, is shaped by the complex interactions between natural compounds exuded by seeds and seed-associated microbial communities. While peptides exuded by plants are known to influence microbiota diversity, little is known about those specifically exuded by seeds. In this study, we characterised the peptidome profile of the spermosphere for the first time using seeds from eight genotypes of common bean (Phaseolus vulgaris) grown in two contrasting production regions. An untargeted LC-MS/MS peptidomic analysis revealed 3,258 peptides derived from 414 precursor proteins of common bean in the spermosphere. This comprehensive peptidomic dataset provides valuable insights into the characteristics of peptides exuded by common bean seeds in the spermosphere. It can be used to identify peptides with potential antimicrobial or other biological activities, advancing our understanding of the functional roles of seed-exuded peptides in the spermosphere.

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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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