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Genetic Architecture and Candidate Genes for Pubescence Length and Density and Its Relationship With Resistance to Common Cutworm in Soybean.

ABSTRACT: Soybean pubescence plays an important role in insect resistance, drought tolerance, and other stresses. Hence, a deep understanding of the molecular mechanism underlying pubescence is a prerequisite to a deeper understanding of insect resistance and drought tolerance. In the present study, quantitative trait loci (QTL) mapping of pubescence traits was performed using a high-density inter-specific linkage map of one recombinant inbred line (RIL) population, designated NJRINP. It was observed that pubescence length (PL) was negatively correlated with pubescence density (PD). A total of 10 and 9 QTLs distributed on six and five chromosomes were identified with phenotypic variance (PV) of 3.0-9.9% and 0.8-15.8% for PL and PD, respectively, out of which, eight and five were novel. Most decreased PL (8 of 10) and increased PD (8 of 9) alleles were from the wild soybean PI 342618B. Based on gene annotation, Protein ANalysis THrough Evolutionary Relationships and literature search, 21 and 12 candidate genes were identified related to PL and PD, respectively. In addition, Glyma.12G187200 from major QTLs qPL-12-1 and qPD-12-2, was identified as Ps (sparse pubescence) before, having an expression level of fivefold greater in NN 86-4 than in PI 342618B, hence it might be the candidate gene that is conferring both PL and PD. Based on gene expression and cluster analysis, three and four genes were considered as the important candidate genes of PL and PD, respectively. Besides, leaves with short and dense (SD) pubescence, which are similar to the wild soybean pubescence morphology, had the highest resistance to common cutworm (CCW) in soybean. In conclusion, the findings in the present study provide a better understanding of genetic basis and candidate genes information of PL and PD and the relationship with resistance to CCW in soybean.

SUBMITTER: Li Y

PROVIDER: S-EPMC8776989 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescence classes, and test a machine learning (ML) classification approach. A principal component analysis (PCA) on hyperspectral soybean plot data identified clusters related to pubescence classes, while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability. Aerial images from 2018, 2019, and 2020 were analyzed in this study. A 60-plot test (2019) of genotypes with known pubescence was used as reference data, while whole-field images from 2018, 2019, and 2020 were used to examine the broad applicability of the classification methodology. Two indices, a red/blue ratio and blue normalized difference vegetation index (blue NDVI), were effective at differentiating tawny and gray pubescence types in high-resolution imagery. A ML approach using a support vector machine (SVM) radial basis function (RBF) classifier was able to differentiate the gray and tawny types (83.1% accuracy and kappa = 0.740 on a pixel basis) on images where reference training data was present. The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel, indicating limitations of using aerial imagery for pubescence classification in some environmental conditions. High-throughput classification of gray and tawny pubescence types is possible using aerial imagery, but light tawny soybeans remain difficult to classify and may require training data from each field season.

Project description:Common cutworm (CCW; Spodoptera litura Fabricius) is a major leaf-feeding pest of soybean in Asia. The previous methods of measuring antixenosis against CCW using adult plant under field or net-room conditions were time-consuming, labor-intensive and precision-inferior. To solve the problems, this study aimed at (i) establishing a high-throughput phenotyping method for evaluating antixenosis against CCW at early seedling stage, (ii) using the procedure to evaluate the antixenosis of an insect-resistant versus -susceptible germplasm population (IRSGP), (iii) validating the proposed method through comparing the results with the historical phenotypic data and phenotyping-genotyping consistency data using PAV (presence/absence variation) markers linked with the identified loci CCW-1 and CCW-2, (iv) and evaluating the efficiency of the novel method through comparisons to the previous methods.A dynamic and efficient evaluation procedure characterized with using V1 stage soybean seedlings infested with third-instar larvae in a micro-net-room in greenhouse with damaged leaf percentage (DLP) as indicator was established and designated V1TMD method. The middle term testing stage is the best dates for identifying resistant and susceptible accessions. The results from the V1TMD method were relatively stable, precise and accurate in comparison with the previous method with the detected most resistant and susceptible accessions consistent to the previous results. The DLP values differentiated obviously to coincide with the resistant and susceptible alleles of the PAV markers Gm07PAV0595 and Gm07PAV0389 tightly linked to the two resistance-related loci, CCW-1 and CCW-2, respectively, in IRSGP. Thus V1TMD is a high-throughput phenotyping method with its estimated efficiency 12 times and period shortening 4 times of those of the previous method.A dynamic and efficient V1TMD method for testing antixenosis against CCW was established, with highly resistant and highly susceptible accessions as standard checks and DLP as indicator. The method is remarkably quick, highly reproducible, and capable of testing large samples, therefore, is a high-throughput phenotyping method.

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Genetic Architecture and Candidate Genes for Pubescence Length and Density and Its Relationship With Resistance to Common Cutworm in Soybean.

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