본 연구는 환경교육용 보드게임 개발에 관한 연구다. 이를 위해 문헌 고찰과 전문가 인터뷰를 실시하여 핵심 메커니즘과 용어를 도출하였다. 또한, 타당도와 신뢰도 검증을 위하여 델파이 조사를 진행하였다. 연구 결과 9개 항목에 대하여 합의가 이뤄졌으며, 이를 기반으로 ‘수풀로 메이커’ 프로토타입을 제작하고 플레이테스트 를 진행하였다. 그 결과, 환경에 대한 관심, 생태복원의 개념, 탄소 중립에 대한 이해를 보여, 연구의 초기 목 적을 달성한 것으로 나타났다. 본 연구로 인해 개발된‘수풀로 메이커’는 아동과 청소년의 환경교육 교재의 역할 뿐만 아니라, 환경에 대한 깊이 있는 이해를 견인하고, 더 나아가 실천양식 변화의 잠재성을 키우는 역 할에 기여할 수 있을 것으로 기대한다.

Cereal seeds, sorghum, foxtail millet, hog millet, adlay, and corn are traditionally used as health assistant as well as energy supplying food in Korea. While beneficial phytochemicals to human have revealed in cereals, the information on peptides from cereals is far less accumulated than major reserve protein. Here, we analyzed peptide profiles using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) in cereal seeds for construction of peptide information and attempted to develop peptide biomarkers for cereal identification. To optimize the analysis condition of SELDI-TOF MS, the effect of dilution factor on binding affinity to protein chips was tested using CM10 and Q10 arrays. Peptide clusters were significantly different at the level of 0.01 p-value. Peak spectra were the most stable in 1:50 of dilution factor in both chip arrays. Numbers of detected peak of 5 cereal seeds were 131 in CM10 and 74 in Q10 array. Each cereal was grouped as a cluster and well discriminated into different cluster in the level of 0.01 p-value. Numbers of potentially identified peptide biomarkers are 11, 13, 9, 5 and 12 in sorghum, foxtail millet, hog millet, adlay and corn, respectively. This study demonstrates that each cereal seed have own distinguishable specific peptides although their function are not identified yet in this study. In addition, the proteomic profiling using SELDI-TOF MS techniques could be a useful and powerful tool to discover peptide biomarker for discrimination and assess crop species, especially under 20 kDa.

Discovery, identification, and informatics of low molecular weight peptide are extensively rising in the field of proteomics research. In this study, we analyzed protein profiles to discover peptide based biomarker for twelve different soybean seeds with three different agronomic types using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). For optimization of SELDI-TOF MS in soybean seed proteome analysis, four different extraction buffers were tested with urea solubilization buffer, thiourea/urea solubilization buffer, phenol extraction buffer, and modified trichloroacetic acid (TCA)/acetone precipitation/urea solubilization extraction buffer. Two different type of ProteinChip arrays, cation exchange (CM10) and anion exchange (Q10), applied to profile peptides. Among the four different extraction buffers, phenol extraction was selected to protein extraction methodology. Numbers of detected peak cluster in twelve soybean seeds were 125 at CM10 and 90 at Q10 array in the mass range from 2 to 40 kDa. Among them, 82 peak clusters at CM10 and 33 peak clusters at Q10 array showed significantly different peak clusters at p<0.00004 (CM10) and p<0.00005 (Q10) among twelve different soybean cultivars. Moreover, 29 peak clusters at CM10 and 17 peak clusters at Q10 array were detected in all cultivars as an ‘universally existed peptide’. In comparison with three different agronomic types, total of 55 peak clusters (CM10) and 23 peak clusters (Q10) were significantly different peak clusters at p<0.00004 and p<0.0001, respectively. In these probability levels, soybean seeds were well discriminated into different cultivar and different type with each other. Also we could find several specific peptide biomarkers for agronomic type.