LipidBlast templates as flexible tools for creating new in-silico tandem mass spectral libraries.

Tandem mass spectral libraries (MS/MS) are usually built by acquiring experimentally measured mass spectra from chemical reference compounds. We here show the versatility of in-silico or computer generated tandem mass spectra that are directly obtained from compound structures. We use the freely available LipidBlast development software to generate 15 000 MS/MS spectra of the glucuronosyldiacylglycerol (GlcADG) lipid class, recently discovered for the first time in plants. The generation of such an in-silico MS/MS library for positive and negative ionization mode took 5 h development time, including the validation of the obtained mass spectra. Such libraries allow for high-throughput annotations of previously unknown glycolipids. The publicly available LipidBlast templates are universally applicable for the development of MS/MS libraries for novel lipid classes

シャカイ ノ ナカ ノ ヒョウゲン カツドウ ニ ヨル ニホンゴキョウイク ニ オケル ヒョウゲン カツドウ ノ カッセイカ

日本語教育において生まれた表現活動という概念は、社会における母語による言語活動を表現活動として捉え直すことによって、表現活動に必要とされる諸力についての新たなる考究を生み出す。表現活動においては、活動従事者の感受性、内発性、自己創作性、社会性、ライフワーク性が注目されるものとなり、表現活動の発展は、教育の世界と社会における双方向性のある活性化をもたらす可能性を持っている。研究ノー

Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics

<p>Abstract</p> <p>Background</p> <p>Increasing awareness of limitations to natural resources has set high expectations for plant science to deliver efficient crops with increased yields, improved stress tolerance, and tailored composition. Collections of representative varieties are a valuable resource for compiling broad breeding germplasms that can satisfy these diverse needs.</p> <p>Results</p> <p>Here we show that the untargeted high-coverage metabolomic characterization of such core collections is a powerful approach for studying the molecular backgrounds of quality traits and for constructing predictive metabolome-trait models. We profiled the metabolic composition of kernels from field-grown plants of the rice diversity research set using 4 complementary analytical platforms. We found that the metabolite profiles were correlated with both the overall population structure and fine-grained genetic diversity. Multivariate regression analysis showed that 10 of the 17 studied quality traits could be predicted from the metabolic composition independently of the population structure. Furthermore, the model of amylose ratio could be validated using external varieties grown in an independent experiment.</p> <p>Conclusions</p> <p>Our results demonstrate the utility of metabolomics for linking traits with quantitative molecular data. This opens up new opportunities for trait prediction and construction of tailored germplasms to support modern plant breeding.</p