• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.12-27
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• 2004

Thanks to spectacular advances in the techniques for identifying proteins separated by two-dimensional electrophoresis and in methods for large-scale analysis of proteome variations, proteomics is becoming an essential methodology in various fields of plant sciences. Plant proteomics would be most useful when combined with other functional genomics tools and approaches. A combination of microarray and proteomics analysis will indicate whether gene regulation is controlled at the level of transcription or translation and protein accumulation. In this review, we described the catalogues of the rice proteome which were constructed in our program, and functional characterization of some of these proteins was discussed. Mass-spectrometry is a most prevalent technique to identify rapidly a large of proteins in proteome analysis. However, the conventional Western blotting/sequencing technique us still used in many laboratories. As a first step to efficiently construct protein data-file in proteome analysis of major cereals, we have analyzed the N-terminal sequences of 100 rice embryo proteins and 70 wheat spike proteins separated by two-dimensional electrophoresis. Edman degradation revealed the N-terminal peptide sequences of only 31 rice proteins and 47 wheat proteins, suggesting that the rest of separated protein spots are N-terminally blocked. To efficiently determine the internal sequence of blocked proteins, we have developed a modified Cleveland peptide mapping method. Using this above method, the internal sequences of all blocked rice proteins (i. e., 69 proteins) were determined. Among these 100 rice proteins, thirty were proteins for which homologous sequence in the rice genome database could be identified. However, the rest of the proteins lacked homologous proteins. This appears to be consistent with the fact that about 30% of total rice cDNA have been deposited in the database. Also, the major proteins involved in the growth and development of rice can be identified using the proteome approach. Some of these proteins, including a calcium-binding protein that fumed out to be calreticulin, gibberellin-binding protein, which is ribulose-1,5-bisphosphate carboxylase/oxygenase activate in rice, and leginsulin-binding protein in soybean have functions in the signal transduction pathway. Proteomics is well suited not only to determine interaction between pairs of proteins, but also to identify multisubunit complexes. Currently, a protein-protein interaction database for plant proteins (http://genome .c .kanazawa-u.ac.jp/Y2H)could be a very useful tool for the plant research community. Recently, we are separated proteins from grain filling and seed maturation in rice to perform ESI-Q-TOF/MS and MALDI-TOF/MS. This experiment shows a possibility to easily and rapidly identify a number of 2-DE separated proteins of rice by ESI-Q-TOF/MS and MALDI-TOF/MS. Therefore, the Information thus obtained from the plant proteome would be helpful in predicting the function of the unknown proteins and would be useful in the plant molecular breeding. Also, information from our study could provide a venue to plant breeder and molecular biologist to design their research strategies precisely.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.