ダイズプロテオームデータベース：
電気泳動を基盤にした手法と質量分析計を基盤にした手法から得られたタンパク質データの統合〇坂田克己1)、 大柳一2)、 杉本和彦3)、 小松節子4)1）前橋工科大学、2)King Abdullah University of Science and Technology、3)次世代作物開発研究セ、4)筑波大学
Licensed under a Creative Commons表示4.0国際ライセンス(c)2017

Abstract
The Soybean Proteome Database (SPD) stores data on soybean proteins obtained with gel-based and gel-free proteomic techniques. The database was constructed to provide information
on proteins for functional analyses. The majority of the data is focused on soybean (Glycine max ‘Enrei’). The growth and yield of soybean are strongly affected by environmental stresses
such as flooding. The database was originally constructed using data on soybean proteins separated by two-dimensional polyacrylamide gel electrophoresis, which is a gel-based proteomictechnique. Since 2015, the database has been expanded to incorporate data obtained by label-free mass spectrometry-based quantitative proteomics, which is a gel-free proteomic technique.
Here, the portions of the database consisting of gel-free proteomic data are described. The gel-free proteomic database contains 39,212 proteins identified in 63 sample sets, such astemporal and organ-specific samples of soybean plants grown under flooding stress or non-stressed conditions. In addition, data on organellar proteins identified in mitochondria, nuclei, andendoplasmic reticulum are stored. Furthermore, the database integrates multiple omics data such as genomics, transcriptomics, metabolomics, and proteomics. The SPD database isaccessible at http://proteome.dc.affrc.go.jp/Soybean/.

Introduction
Soybean is an important food crop that is rich in vegetable oil and protein, as well as phytochemicals such as isoflavones and phenolic compounds. Soybean has a complex genome
because multiple neopolyploid speciation events are superimposed on the paleopolyploid genome. To provide information on proteins for functional analyses, the Soybean Proteome
Database (SPD) was originally constructed using data for soybean proteins separated by two-dimensional polyacrylamide gel electrophoresis, which is classified as a gel-based proteomictechnique (Ohyanagi et al., 2012). In addition, the data were integrated with the information from transcriptomics, proteomics, and metabolomics (Sakata et al., 2009). The database has
been improved by incorporation of data from label-free mass spectrometry (MS)-based quantitative proteomics, which is classified as a gel-free proteomic technique. This technique isdirectly applied to peptide samples after digestion of proteins for liquid chromatography (LC)-MS. In this report, the portions of the SPD derived from gel-free proteomics are described.
Although soybean is a widely cultivated crop, its growth and yield are markedly affected by adverse environmental conditions, especially flooding stress. The SPD also stores information on
the flooding-responsive proteins, which were analyzed in temporal and organ-specific protein profiles of soybean grown in the presence or absence of flooding stress in the present study(Komatsu et al., 2017).

Fig. 1. Web presentation of SPD.
After opening the Soybean Proteome Database homepage(http://proteome.dc.affrc.go.jp/Soybean/), the lefthand menu includes
options for “Gel-based Proteomics” and “Gel-free Proteomics”. When
“Gel-free Proteomics” is selected, images of soybean plants (A) and alist of organs or organelles over a sequence of time points (B) aredisplayed. The “Enter Search keyword” box allows the user to searchfor protein information and “Download all data in spread sheet style” isused to download all data (C).

Fig. 4. Application of SPD for functional categorization of proteinsin the soybean root identified in a time-dependent manner.
The Soybean Proteome Database stores data for organs of soybean
plants grown under flooding stress with time course manner. Using
these data, a set of 228 proteins significantly changed in abundance inthe root under flooding stress, compared with those of non-treatedsoybean, were detected and analyzed using MapMan bin codes. The
proteins used in this analysis are listed in Table in Ref 3.

Fig. 2. How to use search result.
The explanation for “How to use search result” is in the bottom of firstpage of “Gel-free Proteomics Database for Soybean Proteins”. (1)Search data, (2) Save “Result” as a text file, and then (3) Open the file“Result cgi,txt” with Microsoft Excel.

Fig. 3. Example of a search for soybean protein information in SPD.
When an image of a specific soybean plant or an organ or organelle isselected, a list of proteins is displayed. The data tables and search
results are compatible with Microsoft Excel spreadsheet. The result filecan be downloaded onto a personal computer, and dragged/dropped
the file into a spreadsheet. When one of the proteins is selected,
information on its chromosomal position from DAIZUbase(http://daizu.dna.affrc.go.jp/) is shown.

Fig. 5. Application of SPD for cluster analysis of significantly
changed proteins in soybean during exposure to flooding stress.
The same set of 228 proteins used for functional categorization was
subjected to cluster analysis using a centroid linkage method based ona Euclidean distance metric.

Fig. 6. Application of SPD for in silico protein-protein interaction
analysis of proteins with significant changes in abundance. A setof 228 proteins that changed in abundance under flooding stresscompared with the starting point of treatment, which was 2-day-old
soybean seedlings, were analyzed for in silico protein-protein
interactions using the S-system differential equation.

Conclusion

References

1. SPD stores data obtained from both gel-based and gel-free proteomic techniques.
2. SPD stores different organs of soybean grown without or with stress in a time-dependent
manner.
3. Organellar proteins identified in mitochondria, nuclei, and endoplasmic reticulum are stored
4. Based on functional analyses, these data are useful for analyses of biological mechanisms insoybean.

1) Sakata, K., Ohyanagi, H., Nobori, H., Nakamura, T., Hashiguchi, A., Nanjo, Y., Mikami,Y., Yunokawa, H., Komatsu, S. Soybean proteome database: a data resource for plantdifferential omics. J. Proteome Res. 8, 3539-3548 (2009).
2) Ohyanagi, H., Sakata, K., Komatsu, S. Soybean Proteome Database 2012: update on
the comprehensive data repository for soybean proteomics. Front. Plant Sci. 3, 110 (2012).
3) Komatsu S, Wang X, Yin X, Nanjo Y, Ohyanagi H, Sakata K. Integration ofgel-based and gel-free proteomic data for functional analysis of proteins throughSoybean Proteome Database. J Proteomics 163:52-66 (2017).

http://proteome.dc.affrc.go.jp/Soybean/