【Objective】 Mining the key drought-resistant genes of maize, revealing its drought-resistant molecular mechanism, and providing genetic resources and theoretical guidance for the cultivation of new drought-resistant maize varieties.【Method】Transcriptome data combined with weighted gene co-expression network (WGCNA) and screening methods for physiological and biochemical indicators of drought resistance were used to identify ZmPAL genes associated with drought resistance and rewatering. Genome-wide analysis of the genes encoding PAL was performed using bioinformatics methods. Quantitative real-time fluorescence PCR (qRT-PCR) was used to detect the expression of ZmPAL genes under drought treatment conditions, as well as the expression characteristics of ZmPAL5 among different inbred lines and the expression patterns in different tissues. Finally, genetic transformation was used to analyze the drought resistance function of ZmPAL5 in maize, and the deletion-type Arabidopsis mutant was analyzed for drought resistance with the help of CRISPR/Cas9 technology for the PAL5 homologous gene.【Result】Nineteen maize ZmPAL genes were identified, six of which were clustered on chromosome 5 and encoded proteins that were mostly hydrophilic acidic proteins and relatively evolutionarily conserved in the PAL family of genes. The promoter region of ZmPAL genes contained a large number of cis-acting elements associated with hormonal and abiotic stress responses. Six core genes were identified, four of which were significantly up-regulated for expression after drought treatment. In particular, ZmPAL5 showed an 8.57-fold increase in expression after drought stress. The expression level of ZmPAL5 was found to be significantly higher in the drought-resistant inbred line Zheng 8713 than in the drought-sensitive inbred line B73 under both drought stress and rewatering treatments. Meanwhile, ZmPAL5, a constitutively expressed gene, showed a high level of expression in young stems. Overexpressed ZmPAL5 maize grew well under drought stress, and its relative water content, lignin, chlorophyll, soluble protein, proline content, and activities of superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase were 1.52, 1.49, 1.47, 1.43, 1.44, 1.41, 1.53, 1.41, and 1.35 times, but the malondialdehyde content was 0.65 times that of the wild type. The PAL5-deficient Arabidopsis mutant was sensitive to drought. Under drought stress, its physiological and biochemical indexes showed the opposite trend to those of overexpression of ZmPAL5 maize. 【Conclusion】 Six core genes (ZmPAL3, ZmPAL5, ZmPAL6, ZmPAL8, ZmPAL11, and ZmPAL13) were screened in response to drought stress, in which the expression of ZmPAL5 was positively correlated with drought resistance. ZmPAL5 positively regulated the drought resistance and resilience of the plant by influencing the content of osmotically regulated substances and antioxidant enzyme activities.
【Objective】 Phytic acid is one of the main anti-nutritional components in sesame. To explore the optimal conditions for efficient extraction of phytic acid from sesame seeds, establish a high-throughput detection method for phytic acid content, and apply it to the detection of phytic acid content variation in sesame population materials and the screening of low phytic acid germplasm resources, so as to promote the basic research of phytic acid in sesame and the breeding of sesame varieties with low phytic acid content. 【Method】Using 0.4 mol·L-1 HCl as the extraction solvent, single factor tests such as seed weight, crusher time, crusher frequency and extraction solution volume were carried out to extract phytic acid from sesame seeds, and the content of phytic acid was determined in a high-throughput manner using modified iron precipitation method. On the basis of the single factor test, the response surface experiment of Box-Behnken four factors and three levels was carried out. The quadratic polynomial regression equation model with phytic acid yield as the response value was established, and the response surface plot and contour plot were drawn. The main factors affecting phytic acid yield and the interaction between the factors were analyzed to determine the optimal extraction conditions for the detection of phytic acid content. Using this parameter condition, the phytic acid content of 200 sesame germplasm resources planted in two environments was determined to screen low phytic acid germplasm. 【Result】Analysis of variance (ANOVA) showed that the established regression model was highly significant (P<0.0001), the lack of fit was not significant (P>0.05), the equation fitted the test well, and this regression equation could be used to optimize the extraction of phytic acid from sesame seeds. The linear term of the four factors had a very significant effect on phytic acid yield, and the order of influence on the yield of phytic acid was seed weight>crusher time>crusher frequency>extract solution volume. The response surface analysis diagram showed that there were some interactions between seed weight and crusher time, seed weight and crusher frequency, crusher time and crusher frequency, crusher time and extraction solution volume, and crusher frequency and extraction solution volume. The optimal phytic acid extraction conditions optimized by single factor test and response surface design were as follows: seed weight of 30 mg, crusher time of 6.5 min, crusher frequency of 50 Hz, extraction solution volume of 435 μL. The predicted phytic acid yield under this condition was 14.03 mg·g-1, and the actual average phytic acid yield was 14.14 mg·g-1, the model prediction was very close to the actual value of the test. Three stable low phytic acid sesame germplasms were screened out from 200 germplasm resources, with an average content of 11.63 mg·g-1【Conclusion】 An efficient phytic acid extraction and detection technology for sesame seeds was established, which could significantly reduce the experimental time and the amount of samples used, and provided a feasible method for the high-throughput detection of phytic acid content in sesame seeds with good reproducibility and high accuracy.
【Objective】 The selection of low glycemic index potato varieties is not only essential for controlling blood glucose, reducing obesity, and maintaining oral health but also constitutes a crucial approach to meeting diverse consumer demands and enhancing potato production efficiency. This process provides a foundation for the breeding of low glycemic index potato varieties and the improvement of biological breeding methods.【Method】 Eight domestically and internationally cultivated potato varieties were employed as experimental materials. The study involved the examination of tuber agronomic traits, analysis of total starch, amylose, rapidly digestible starch, slowly digestible starch, resistant starch, soluble sugars, insoluble dietary fiber, soluble dietary fiber, and soluble protein content in tubers before and after steaming processing. Additionally, the investigation included the evaluation of the retention levels of these components and the post-processing tuber flavor quality and in vitro/vivo glycemic index.【Result】 Among the eight varieties, the yield ranged from 21.50 to 49.90 t·hm-2, with marketable yield percentages ranging from 60.04% to 90.21% and length-to-width ratios from 1.21 to 2.90. Sensory evaluation scores for flavor ranged from 64 to 73. Texture profile analysis results indicated that hardness ranged from 9.78 N to 19.97 N, adhesiveness from 0.44 mJ to 1.66 mJ, cohesiveness from 0.052 to 0.070, springiness from 0.51 to 1.02 mm, and chewiness from 0.28 to 1.38 mJ. Before and after steaming, the total starch content of eight potato varieties ranged from 67.07% to 76.72% dry weight (DW) and 57.69% to 67.40% DW, respectively. The range of amylose content was 5.36% to 19.23% DW and 5.43% to 6.83% DW, while rapidly digestible starch content varied from 1.18% to 8.23% DW and 14.31% to 28.56% DW. The range of slowly digestible starch content was 3.33% to 7.69% DW and 12.81% to 27.65% DW, and resistant starch content varied from 53.71% to 70.36% DW and 11.80% to 25.80% DW. Soluble sugar content ranged from 25.98 to 56.86 mg·g-1 DW and 11.38 to 50.24 mg·g-1 DW, while total dietary fiber content varied from 29.62% to 36.17% DW and 43.67% to 52.55% DW. Insoluble dietary fiber content ranged from 17.69% to 23.70% DW and 30.31% to 44.12% DW, and soluble dietary fiber content ranged from 11.07% to 18.48% DW and 7.37% to 14.09% DW. Soluble protein content varied from 42.26 to 64.14 mg·g-1 DW and 0.71 to 4.82 mg·g-1 DW. Following steaming, the total starch content of the eight varieties exhibited a range of -15.49% to -5.97%, with changes in amylose content ranging from -12.39% to 0.56%. The variations in rapidly digestible starch, slowly digestible starch, and resistant starch were in the ranges of 10.44% to 25.86%, 5.12% to 23.09%, and -56.8% to -29.88%, respectively. Soluble sugar content varied from -27.07% to 15.70%, while changes in insoluble dietary fiber and soluble dietary fiber ranged from 11.41% to 25.19% and -4.73% to 0.77%, respectively. Soluble protein content exhibited a range of -60.86% to -39.67%. Correlation analysis revealed a significant positive correlation between the glycemic index and tuber total starch and rapidly digestible starch content, while a significant negative correlation was observed with resistant starch and insoluble dietary fiber content. The glycemic index of the eight varieties ranged from 58.08 to 100.64 in vitro and from 57.80 to 92.47 in vivo【Conclusion】 Under potato breeding program, the in vitro glycemic index can replace the in vivo glycemic index as an alternative evaluation method. The content of tuber total starch, rapidly digestible starch, slowly digestible starch, resistant starch, and insoluble dietary fiber are key agronomic traits be considered in the breeding process of low glycemic index potato varieties. The Lucinda was identified as a low glycemic index potato variety with superior flavor quality after cooking processing.
【Objective】 The aim of this study was to investigate the characteristics of the transport and distribution of photosynthetic products and related gene expression levels during the grain filling period of the Nanjing series of super rice, as well as the differences with the control varieties, to summarize the physiological advantages of high-yield in the Nanjing series super rice, so as to provide the theoretical basis for the development of high-quality and high-yield japonica rice.【Method】Nanjing 5718, Nanjingjinggu, Nanjing 3908, and Nanjing 5055 were used as the research materials, with Huaidao 5 as the control. The photosynthetic rate of flag leaves, distribution and transportation of aboveground dry matter, and expression levels of photosynthetic product transport related genes at different stages of flag leaf and seed development were measured every 7 days during the booting stage, flowering stage, and flowering to maturity stage, and yield differences were statistically analyzed too.【Result】The yield and 1000-grain weight of the Nanjing series of super rice were higher than those of Huaidao 5, and its net photosynthetic rate of flag leaves was significantly higher than that in Huaidao 5 during the booting stage and 28 days after flowering. In terms of photosynthetic product transport, the stem and leaf dry weight, leaf output, output rate, and transport rate of the Nanjing series super rice were significantly higher than those of Huaidao 5 after flowering, with Nanjing 5718 having the highest leaf output and output rate. The expression of genes related to starch degradation and carbohydrate metabolism (OsSPS1, OsSUT2, and OsGWD1) in the flag leaves of Nanjing 5718 was initiated earlier than other varieties, and the highest expression level was also higher than other varieties. The SWEET gene in grains played an important role in early sucrose transport during grain filling, while the OsPK3, OsSUT1, and OsSUT2 genes played an important role in sugar transport and unloading during the middle and late filling stages. The OsAGPL2 and OsDPE1 genes played the important roles in starch synthesis during the middle and late filling stages. The expression levels of genes related to starch synthesis and sugar transport in grains of Nanjing 5718, Nanjingjinggu, and Nanjing 3908 were significantly higher than those of Huaidao 5 at different stages. 【Conclusion】 The higher yield of the Nanjing series of super rice was mainly characterized by the following characteristics in terms of material transport: a large accumulation of dry matter in stem, leaf and panicles, and a high transport rate of leaf and stem dry matter; the high expression levels of genes related to sucrose metabolism and transport in leaves were beneficial for the synthesis, loading, and transport of sucrose at the source end; the high expression levels of genes related to sucrose transport and starch synthesis in grains were conducive to the unloading of sucrose at the storage end and the synthesis of starch in grains.
【Objective】 Wheat grain phenotype parameters were tested after grains only must been threshed by combine, this process was time-consuming, laborious and complicate. Therefore, a method to test morphological parameters of wheat grains on spike based on improved Mask R-CNN was proposed in this research.【Method】Two sides front images of three varieties wheat spikes, including Zhenmai 25, Ningmai 13 and Longmai 88 (Early maturity variety), were collected, and then the image enhancement data set was constructed by using Gaussian filter, salt and pepper noise, and vertical flip image enhancement method. A method combined with deep learning and morphological processing for testing phenotype parameters of wheat grains on spike was proposed. Firstly, the improved Mask R-CNN network model for segmenting spike glume was constructed, which was based on feature extraction networks of ResNet and FNP, and the innovative components Coordinate Attention (CA) module, Aggregation module, and Halfconv module were integrated into it. And the glumes on spike image were accurately detected, located, segmented and counted by the improved Mask R-CNN network model. Secondly, five phenotype parameters of the glumes on spike were extracted by using morphologic processing method from the segmented mask image of wheat spike glume, and the linear correlation equations between the phenotype parameters of the glumes and the phenotype parameters of grains were established. Finally, the linear correlation equations between the phenotype parameters of glume and the phenotypic parameters of grain were used to predict the phenotype parameters of grain.【Result】(1) F1 score, average precision (AP) and recall rate of the optimal model for separating spike glume based on the improved Mask R-CNN network model were 91.12%, 94.13% and 88.30%, respectively, and the average consuming time for detecting a single image was 97 milliseconds, so the improved Mask R-CNN network model could quickly and accurately identify the glumes on the single wheat spike. The root-mean-square error and average relative error for segmenting spike grain by the model were 0.94 piece and 0.65%, respectively, so this showed that spike glumes were segmented precisely by the model. (2) The established linear correlation equations for length, thickness, area, circumference, and length-thickness ratio between wheat spike glume and actual grain were y=0.7258x, y=0.5166x, y=0.3748x, y=0.6756x and y=1.4085x, respectively, and the determination coefficient (R2) of the linear correlation equations all were greater than 0.85. (3) The above correlation equations were verified and phenotype parameters of grain were predicted by using the extracted phenotype parameter data of wheat spike glume, the root-mean-square errors and average relative error for length, thickness, area, circumference and length-thickness ratio between predicted values and actual values of wheat grains were 0.17 mm, 0.08 mm, 0.46 mm2, 0.33 mm, 0.12, and 0.02%, 0.02%, 0.02%, 0.03%, respectively. The determination coefficient (R2) for each phenotype parameter between the predicted data and the actual data was above 0.85, research results indicated that the proposed method in this study was feasible【Conclusion】 The method for testing phenotype parameters of wheat grains on spike based on improved Mask R-CNN was proposed in this research, and the phenotype parameters of wheat grain on spike could be predicted accurately and effectively by the phenotype parameters of wheat spike glume. This research provided a new method to extract rapidly and simply wheat grain phenotype parameters on spike.
