【Objective】The objective of this study is to optimize the classification and discriminant method of maize heterotic groups, and provide guidance and reference for maize breeding practices.【Method】Solid-phase chips were used to genotype 60 waxy maize inbred lines, and high-quality SNP markers with different density were obtained through quality control. Population structure analysis and genetic distance clustering were used to classify the 60 waxy maize inbred lines into different groups, and the differences between different classification methods were compared. On this basis, random forest and support vector machine methods were used to sample and discriminate the results of different classification methods. Five-fold cross-validation was used for sampling, and the prediction accuracy of maize group classification based on different classification methods was compared.【Result】Using different quality control standards, 11 431 and 4 022 molecular markers were obtained, respectively. Based on these two molecular marker densities, 60 materials were divided into 5 and 4 clusters, respectively. When using 11 431 SNP markers, the population structure analysis and genetic distance clustering results showed that the intra-cluster sample consistency was 63.33%. When using 4 022 SNP markers for clustering, the intra-cluster sample consistency was 90.00%. The prediction accuracy results for discriminating maize inbred line clusters showed that the average prediction accuracy (91.43%) of Random Forest and Support Vector Machine using 4 022 markers were higher than that of 11 431 markers (86.25%). Among them, the highest prediction accuracy was achieved by Random Forest using 4 022 markers, with a prediction accuracy of 94.17%.【Conclusion】Clustering analysis ultimately divided 60 waxy maize inbred lines into 4 clusters. Sampling and cross-validation results using Random Forest and Support Vector Machine for cluster classification showed that Random Forest achieved higher prediction accuracy than Support Vector Machine.
【Objective】Seeking key indicators and methods for accurately characterize drought tolerance in sweet potato, and screening and identifying drought-tolerant sweet potato germplasm resources, to provide effective methods for the rapid and accurate identification of drought-tolerant sweet potato germplasm resources, and to provide material and theoretical basis for selection and breeding of high quality and drought-tolerant sweet potato varieties. 【Method】Fifty-four sweet potato germplasm resources were used as materials for drought stress experiments. By using two treatments including drought stress and control, and combining with drought pool cultivation experiment and field test, the effects of drought stress on the growth and development, physiological and biochemical characteristics, antioxidant metabolism, photosynthetic characteristics and yield of different sweet potato germplasm resources were investigated, the response characteristics of different sweet potato germplasm resources to drought were analyzed, and the effective indicators for drought tolerance evaluation in sweet potato were selected. The drought tolerance evaluation was preformed using principal component analysis, correlation analysis, direct evaluation of drought resistance coefficient, and calculation of comprehensive drought tolerance measurement value (D value) based on membership function, and the drought-tolerant sweet potato germplasm resources were screened and identified.【Result】The results obtained from the drought pool cultivation experiment showed the influences of drought treatment on the main stem length, aboveground fresh weight, underground dry weight and fresh weight of storage root were extremely significant (P<0.01), and eight drought-tolerant germplasm resources were screened based on cluster analysis of D values. In the field test, the main stem length, stem diameter, number of branches, leaf area index, leaf relative water content, total chlorophyll content, stomatal conductance, net photosynthetic rate, transpiration rate, intercellular carbon dioxide concentration, proline (Pro), malondialdehyde (MAD), peroxidase (POD), superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT) showed highly significant differences (P<0.01) under drought stress when compared with control. Through the establishment of regression models, it could be initially determined that eight indicators including the leaf area index, root tip, leaf POD, leaf APX, storage root Pro, storage root SOD, storage root CAT, and yield could be used as indicators for drought tolerance identification in sweet potato. XN18111-1, 20XN18-1, XN1834-11 and XN17104-46 were classified as drought-tolerant germplasm resources according to grading of drought resistance coefficient based on yield. The D values of XN18111-1, 20XN18-1 and XN1862-61 were over 0.6 and showed high drought tolerance based on comprehensive drought tolerance evaluation. 【Conclusion】Based on results of comprehensive drought tolerance evaluation in drought pool cultivation experiment, as well as the comprehensive drought tolerance evaluation and yield evaluation in field test, XN18111-1 and 20XN18-1 were finally identified as drought-tolerant germplasm resources, which can be used as drought-tolerant breeding materials or ideal resource materials for study on drought-tolerance mechanism in sweet potato.
【Objective】Based on the long-term experiment in the North China Plain (NCP), the differences in soil nutrient and aggregate nutrient distribution between diversified crops and wheat-maize rotation systems were investigated. Additionally, it provided a comprehensive evaluation of soil quality indices (SQI), offering a scientific basis for enhancing soil quality and productivity in the NCP. 【Method】Four diversified crop rotation systems were evaluated, including spring sweet potato-winter wheat-summer maize (Psw-WM), spring peanut-winter wheat-summer maize (Pns-WM), spring sorghum-winter wheat-summer maize (Ps-WM), with winter wheat-summer maize (WM-WM) serving as the control. The soil samples from the 0-40 cm depth were collected during the second rotation in 2022, at the flowering and harvesting stages of winter wheat. The soil enzymes activities, aggregate stability, organic matter, and concentrations of nitrogen, phosphorus, and potassium in soil and aggregates of different sizes (>2.00 mm, 0.50-2.00 mm, 0.25-0.50 mm, and <0.25 mm) were assessed. The SQI for each crop rotation system was then comprehensively evaluated. 【Result】Compared with WM-WM, the three other crop rotations increased soil inorganic nitrogen content. Psw-WM significantly enhanced organic matter in the 0-20 cm layer, total nitrogen in soil aggregates (>2.00 mm, 0-10 cm), and organic matter in soil aggregates (>2.00 mm and 0.50-2.00 mm, 0-10 cm), which also increased cellulase, catalase, and alkaline protease activities. Pns-WM improved organic matter in the 20-40 cm layer and available potassium in soil aggregates (0.25-0.50 mm and >2.00 mm, 10-20 cm), as well as organic matter in soil aggregates (0-10 cm, >2.00 mm and 10-20 cm, >0.50 mm), which also increased sucrase, urease, and alkaline protease activities. Psw-WM improved the stability of 0-10 cm soil aggregates, while Pns-WM improved the stability of 0-30 cm soil aggregates. Both Pns-WM and Psw-WM significantly improved the SQI, with Pns-WM showing a higher improvement than Psw-WM. The path analysis revealed that the average weight diameter (MWD) of aggregates was a direct and significant affecting SQI. It also had a significant indirect positive effect on SQI by influencing inorganic nitrogen. Additionally, the increased organic matter led to a higher proportion of large aggregates, which significantly affected SQI indirectly. 【Conclusion】Legume (peanut) and root crop (sweet potato) rotations with wheat-maize rotations could significantly improve soil quality and enhance the soil nutrient supply capacity in the NCP.
【Objective】Nitrogen is one of the essential nutrients for plant growth and development, and it plays an important role in strengthening chlorophyll synthesis in crops, enhancing plant resistance, and improving yield and quality. This study harnessed hyperspectral technology to swiftly, precisely, and non-invasively monitor nitrogen levels in pepper foliage throughout its growth cycle, delving into the correlation between leaf nitrogen content (LNC) and spectral reflectance characteristics. 【Method】The study was based on the hyperspectral data of pepper leaves collected from Guanzhuang Demonstration Base in Pepper Research Institute of Guizhou Academy of Agricultural Sciences in 2021. The research encompassed four pepper varieties (Qianjiao No. 8, Hongla No. 18, Layan 101, and Hong Global) and five different nitrogen fertilizer application rates (0, 120, 240, 360, and 480 kg·hm-2). The pepper leaf spectral data were processed, involving Multiple Scatter Correction (MSC), Savitzky-Golay (SG) and First Derivative (FD), followed by the selection of sensitive bands using Pearson correlation coefficient, Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS). Subsequently, three machine learning algorithms, such as Partial Least Squares Regression (PLSR), Random Forest (RF) and Radial Basis Function Neural Network (RBFNN), were employed to construct models for monitoring nitrogen levels in pepper leaves, to achieve the goals of enhancing agricultural production efficiency and accuracy, and realizing intelligent management and precise fertilization. 【Result】After preprocessing, the original spectra improved correlation coefficients significantly. Among these, the spectral data's inversion performance was notably superior after SG processing, with the effectiveness ranking as SG>FD>MSC>original spectra. Contrasting various band selection methods, the employing Pearson correlation coefficient for band selection resulted in bands being overly concentrated, leading to either redundant information or incomplete information extraction. While CARS algorithm selected bands across a broad range and in large quantities, its effectiveness was inferior to SPA due to containing more redundant information and noise. SPA-selected nitrogen content characteristic bands effectively reduced collinearity and redundant information, yielding the optimal model with the highest R⊃2; and the smallest RMSE. The performance of different modeling methods for pepper LNC estimation was as follows: RBFNN performed the best, followed by PLSR, with RF exhibiting the poorest performance. Among these, the SG-SPA-RBFNN combined model demonstrated the best inversion accuracy, with modeling results of R⊃2; =0.98 and RMSE =0.62, and validation results of R⊃2; =0.98 and RMSE =1.21, with an RPD of 3.08. RBFNN model excelled in handling high-dimensional spectral data, surpassing traditional PLSR and RF models. 【Conclusion】The hyperspectral reflectance characteristics were utilized to establish nitrogen content prediction models, which could effectively monitor nitrogen levels in pepper leaves, thereby enhancing agricultural management efficiency and providing the technical support for precise management and variable fertilization in pepper cultivation.
【Objective】Macro-transcriptome sequencing and small RNA sequencing are commonly used high-throughput sequencing techniques in virus identification. The objective of this study is to explore the application efficiency of macro-transcriptome sequencing and small RNA sequencing in the identification of emerging viruses in apples, analyze the impact of different tissue types on the identification results, and to provide a basis for the accurate diagnosis of apple virus diseases.【Method】The samples of apple peel and branch bark were collected from ‘Luli’ apple trees exhibiting novel viral symptoms in Shenzhou County of Hengshui City in August 2022. Total RNA was extracted, and macro-transcriptome libraries and small RNA libraries were constructed for high-throughput sequencing. Bioinformatic techniques and software were utilized to analyze and evaluate the sequencing data. Initially, the indicators from the high-throughput sequencing technique results were compared. Subsequently, a comprehensive evaluation of the effectiveness of each sequencing method was conducted using the analytic hierarchy process (AHP) and a 5-level grading system to calculate the weighted values of these indicators. Finally, RT-PCR was employed to validate the high-throughput sequencing results, and the genomic characteristics and phylogenetic relationships of the emerging viruses were analyzed.【Result】In terms of splicing effect, using the same tissue material, the macro-transcriptome sequencing outperformed small RNA sequencing. When the same technique was applied, the splicing effect for fruit peel tissue was better than that of branch bark. In terms of the number of virus species detected, macro-transcriptome sequencing identified the highest number of virus species in branch bark, including eight viruses: apple chlorotic leaf spot virus (ACLSV), apple stem pitting virus (ASPV), apple stem grooving virus (ASGV), apple necrotic mosaic virus (ApNMV), apple rubbery wood virus 2 (ARWV-2), apple green crinkle associated virus (AGCaV), citrus concave gum-associated virus (CCGaV), and citrus tatter leaf virus (CTLV). In contrast, small RNA sequencing technique detected the fewest virus types in branch bark. There were differences in virus types between fruit peels and branch barks detected through small RNA sequencing technique. When fruit peels were used as the detection target, both methods identified the same number of virus types. After comprehensively comparing the synthesis score of various indicators, the macro-transcriptome sequencing of bark samples scored the highest. The results of high-throughput sequencing were consistent with those obtained through RT-PCR. ARWV-2 and CCGaV were discovered for the first time in Hebei Province, and were designated as ARWV-2-HB and CCGaV-HB. The GenBank accession numbers for the coat protein (CP) gene of ARWV-2-HB and the movement protein (MP) and CP genes of CCGaV-HB are PQ095583, PQ095581, and PQ095582. The genomic sequences of both viruses showed over 96% identity to their respective representative isolates. Phylogenetic trees constructed based on the CP amino acid sequences of ARWV-2 and CCGaV revealed that ARWV-2-HB was most closely related to LYXS (MZ819711), while CCGaV-HB exhibited relatively close relationships with Mishima (MK940543), Gala (MK940542), Gala-BJ (OP820577), Fuji-BJ (OP556109), and AC1 (MH038043).【Conclusion】Using macro-transcriptome sequencing and small RNA sequencing techniques, the fruit peel and branch bark of the same ‘Luli’ apple tree were sequenced separately. Among two sequencing methods, the macro-transcriptome sequencing of branch bark showed the best sequencing effect, discovered the highest number of viruses and relatively complete viral genome sequences. When using small RNA sequencing, only a portion of virus types could be detected in both fruit peels and branch barks. Due to the differences in virus types detected from different tissue materials, it is recommended to test both tissue materials simultaneously. ARWV-2 and CCGaV were reported in Hebei Province in this study, and partial genomic sequences of ARWV-2-HB and CCGaV-HB were revealed, which enriching the genomic sequence information of ARWV-2 and CCGaV. Furthermore, the phylogenetic relationships of these two viruses with other representative isolates have been clarified.
【Objective】Mucin-like proteins are integral to the formation of salivary sheaths in Hemiptera insects. This research seeks to prepare a specific antibody targeting the Diaphorina citri mucin-like protein (DcMucin-like) and to employ immunofluorescent labeling to identify the feeding sites of D. citri, so as to provide a basis for the study of the biological functions of DcMucin-like.【Method】The salivary glands, midgut, ovaries, and testes of D. citri were dissected for analysis. Specific primers were designed based on the DcMucin-like sequence of the psyllid, and real-time fluorescent quantitative PCR was employed to assess the transcriptional level differences of DcMucin-like across various tissues. The DcMucin-like sequence, excluding the signal peptide, was amplified and subsequently inserted into the pET-28a vector to construct a recombinant plasmid. Following sequence verification, the plasmid was transformed into Rosetta expression strains. The expression of recombinant protein was induced using 0.5 mmol·L-1 IPTG at 37 ℃ with agitation for 8 h. The presence of the recombinant protein was confirmed via SDS-PAGE gel electrophoresis. Following large-scale bacterial culture, the cells were lysed, and the supernatant was subjected to purify using Ni-NTA affinity chromatography to obtain the antigen. This antigen was subsequently used to immunize rabbits five times. The resulting purified serum IgG yielded the DcMucin-like polyclonal antibody, whose specificity was assessed through Western blot analysis. Real-time fluorescent quantitative PCR and Western blot analyses were employed to compare the transcriptional and protein expression levels of DcMucin-like between healthy and Candidatus Liberibacter asiaticus (CLas) infected D. citri. The feeding sites of D. citri on citrus leaves post-ingestion were labeled using DcMucin-like polyclonal antibodies conjugated with fluorescein isothiocyanate (FITC). These feeding sites, along with the salivary sheaths of D. citri, were subsequently examined under a confocal microscope.【Result】Real-time fluorescent quantitative PCR analysis revealed that the DcMucin-like exhibited significantly elevated expression level in the salivary gland of D. citri compared to the midgut, ovary, and testis. Rosetta expression strains harboring the pET28a-DcMucin were induced with IPTG, resulting in the production of substantial quantities of recombinant protein in the supernatant of the bacterial lysate. The recombinant protein was utilized to immunize rabbits for the production of antiserum, from which purified IgG was subsequently employed to generate DcMucin-like polyclonal antibodies. Western blot analysis confirmed the successful acquisition of specific DcMucin-like polyclonal antibodies. Furthermore, DcMucin-like expression was found to be upregulated in D. citri response to CLas infection. The DcMucin-like (FITC) fluorescent antibody-labeled tissue sections of citrus leaves, following D. citri feeding, were examined using a confocal microscope. Specific FITC fluorescence signals were detected in proximity to the feeding sites, suggesting that DcMucin-like was released into plant tissues during D. citri feeding to participate in the formation of salivary sheaths.【Conclusion】DcMucin-like is highly expressed in the salivary glands of D. citri and exhibits upregulation in response to CLas infection. Specific polyclonal antibodies targeting the DcMucin-like salivary protein of D. citri were successfully generated, demonstrating high specificity. Additionally, it was confirmed that DcMucin-like was secreted into citrus plant tissues during D. citri feeding. These findings provide a foundational basis for further investigation into the role of DcMucin-like in the interactions among CLas, D. citri, and citrus plants.
【Objective】 This study was to clarify differences of zinc (Zn) concentration in wheat grain and flour and the corresponding affecting factors over major wheat production regions, with the purpose to provide the theoretical basis for improving the Zn nutritional quality of wheat grain in China. 【Method】During 2020-2021 and 2021-2022 wheat growing seasons, 421 wheat and soil samples were collected from major wheat production regions in 17 provinces and autonomous regions of China, to explore the relationship of Zn concentration in wheat grain, flour and bran with wheat yield, yield components and soil properties.【Result】The average Zn concentration of the wheat grain, flour and bran was 28.1, 10.8 and 60.6 mg·kg-1, respectively, with 94.8% of grain and 89.5% of flour samples could not meet with the recommended Zn concentration of 40 mg·kg-1 for grain and 15 mg·kg-1 for flour by nutritionists. The highest grain Zn concentration was observed in rice-wheat region (RW), followed by that in wheat-maize regions (MW) and dryland wheat region (DW), and the lowest was in spring-wheat region (SW). In rice-wheat region, the lower pH promoted the activation of soil Zn, and its availability was significantly higher than that in other regions, the lowered phosphorus fertilizer application rate was also conducive to Zn absorption and its translocation from root to the aboveground, and the average Zn concentration in wheat grains and flour was therefore as high as 31.5 and 12.2 mg·kg-1, respectively. In wheat-maize region, the soil fertility was higher, so that the yield was significantly greater than that in other wheat regions, resulting in relatively lower Zn concentrations in wheat grains and flour, which were 27.1 and 10.3 mg·kg-1, respectively. In dryland wheat region, the higher soil pH limited soil Zn availability and wheat Zn absorption, leading to the grain and flour Zn concentration being relatively lower as 26.5 and 10.1 mg·kg-1, respectively. In spring-wheat region, since the soil available Zn concentration was significantly lower than that in other wheat regions, which was not conducive to Zn absorption by wheat and its accumulation in grain, and therefore the Zn concentrations in grain and flour were the lowest as 24.6 and 9.4 mg·kg-1, respectively, while Zn concentration decreased significantly with the increase of 1000-grain weight.【Conclusion】 Therefore, in order to improve the Zn concentration of wheat grains and flour, it was not only necessary to improve the soil pH, available Zn level and reasonable nitrogen and phosphorus fertilizer application, but also jointly to optimize the yield components to improve the wheat yield and grain and flour Zn concentration.
