The composition of amylopectin is the determinant of rice eating quality under certain threshold of protein content and the ratio of amylose and amylopectin. In molecular biology level, the fine structure of amylopectin is determined by relative activities of starch branching enzyme (SBE), granule-bound starch synthase (GBSS), and soluble starch synthase (SSS) in rice grain under the same ADP-Glucose level. But the underlying mechanism of eating quality in molecular biology level remains unclear. This paper reports the differences on major parameters such as SNP and insertion-deletion sites, RNA expressions, and enzyme activities associated with eating quality of japonica varieties. Eight japonica rice varieties with significant differences in various eating quality parameters such as palatability and protein content were used in this experiment. Association analysis between nucleotide polymorphism and eating quality showed that S12 and S13 loci in SBE1, S55 in SSS1, S58 in SSS2A were significantly associated with apparent amylose content, alkali digestion value, setback viscosity, consistency viscosity, pasting temperature, which explained most of the variation in apparent amylose content, setback viscosity, and consistency viscosity; and explained almost all variations in alkali digestion value and pasting temperature. Thirty-five SNPs and insertion-deletions from SBE1, SBE3, GBSS1, SSS1, and SSS2A differentiated high or intermediate palatability rice varieties from low palatability rice varieties. Correlation analysis between enzyme activities and eating quality properties revealed that SBE25 and SSS15/W15 were positively correlated with palatability, whereas GBSS10 and GBSS15 were negatively correlated. Gene expressions showed that SBE1 and SBE3 expressions in high palatability varieties tended to be higher than middle and low palatability varieties. Collectively, SBE1, SBE3, SSS1, and SSS2A, especially SBE1 and SBE3 could improve eating quality, but GBSS1 decreased eating quality. The results indicated the possibility of developing high palatability cultivars through modification of key genes related to japonica rice eating quality formation in starch biosynthesis.

Sailila E. Abdula, Hye Jung Lee, Moo Geun Jee, Yu Jin Jung, Kwon Kyoo Kang, Ill Sup Nou, Sang-Bok Lee, Won-Ha Yang, and Yong-Gu Cho*. PBB 2013;1:33-48. http://dx.doi.org/

UDP-glucose 4-epimerase (UGE) catalyzes the reversible conversion of UDP-glucose to UDP-galactose. To understand the biological function of UGE from Brassica rapa, the gene BrUGE1 was cloned and introduced into the genome of wild type rice ‘Gopum’ using the Agrobacterium-mediated transformation method. Four lines which carried a single copy gene were selected and forwarded to T3 generation. Agronomic traits evaluation of the transgenic T3 lines (CB01, CB03, and CB06) under optimal field conditions revealed enriched biomass production particularly in panicle length, number of productive tillers, number of spikelets per panicle, and filled spikelets. These remarkably improved agronomic traits were ascribed to a higher photosynthetic rate complemented with higher CO2 assimilation. Transcripts of BrUGE1 in transgenic lines continuously accumulated at higher levels after the 20% PEG6000 treatment, implying its probable role in drought stress regulation. This was paralleled by rapid accumulation of soluble sugars which act as osmoprotectants, leading to delayed leaf rolling and drying. Our findings suggest the potential of BrUGE1 in improving rice growth performance under optimal and water deficit conditions.

Marjohn Niño, Joonki Kim, Hye Jung Lee, Sailila E. Abdula, Ill Sup Nou, Yong-Gu Cho. PBB 2014;2:97-109. http://dx.doi.org/

Rice (Oryza sativa L.) production in the Philippines remains highly vulnerable to recurrent outbreaks of the Rice Black Bug (RBB; Scotinophara coarctata F.) and White Stemborer (WSB; Scirpophaga innotata W.), two of the most destructive pests in Southeast Asian rice ecosystems. Classical economic threshold levels (ETLs) are difficult to estimate in smallholder settings due to the lack of cost–loss data, often leading to either delayed or excessive pesticide application. To address this, the present study developed an adaptive outbreak-forecasting framework that integrates the Number–Size (N–S) fractal model with machine learning (ML) classifiers to define and predict pest regime transitions. Seven years (2018–2024) of light-trap surveillance data from the Philippine Rice Research Institute–Midsayap Experimental Station were combined with daily climate variables from the NASA POWER database, including air temperature, humidity, precipitation, wind, soil moisture, and lunar phase. The N–S fractal model identified natural breakpoints in the log–log cumulative frequency of pest counts, yielding early-warning and severe-outbreak thresholds of 134 and 250 individuals for WSB and 575 and 11,383 individuals for RBB, respectively. Eight ML algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Balanced Bagging, LightGBM, XGBoost, and CatBoost were trained on variance-inflation-filtered climatic and temporal predictors. Among these, CatBoost achieved the highest predictive performance for WSB at the 94.3rd percentile (accuracy = 0.932, F1 = 0.545, ROC–AUC = 0.957), while Logistic Regression performed best for RBB at the 75.1st percentile (F1 = 0.520, ROC–AUC = 0.716). SHAP (SHapley Additive exPlanations) analysis revealed that outbreak probability increases under warm nighttime temperatures, high surface soil moisture, moderate humidity, and calm wind conditions, with lunar phase exerting additional modulation of nocturnal pest activity. The integrated fractal–ML approach thus provides a statistically defensible and ecologically interpretable basis for adaptive pest surveillance. It offers an early-warning system that supports data-driven integrated pest management (IPM), reduces unnecessary pesticide use, and strengthens climate resilience in Philippine rice ecosystems.