ABSTRACT Understanding the metabolic responses of the plant to a devastating foliar disease, soybean rust, caused by Phakopsora pachyrhizi, will assist in development of cultivars resistant to soybean rust. In this study, differences in phenolic metabolism were analyzed between inoculated and noninoculated plants using two susceptible and three resistant soybean genotypes with known resistance genes. Rust infection resulted in increased accumulation of isoflavonoids and flavonoids in leaves of all soybean genotypes tested. Although the soybean phytoalexin glyceollin was not detected in leaves of uninfected plants, accumulation of this compound at marked levels occurred in rust-infected leaves, being substantially higher in genotypes with a red-brown resistant reaction. In addition, there was inhibition of P. pachyrhizi spore germination by glyceollin, formononetin, quercetin, and kaempferol. However, there was no correlation between concentrations of flavonoids quercetin and kaempferol and rust-induced isoflavonoid formononetin in soybean leaves and rust resistance. Lignin synthesis also increased in all inoculated soybean genotypes whereas there was no significant difference in all noninoculated soybean genotypes. Cell wall lignification was markedly higher in inoculated resistant lines compared with inoculated susceptible lines, indicating a possible protective role of lignin in rust infection development.
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http://dx.doi.org/10.1094/PHYTO-99-12-1412 | DOI Listing |
Front Plant Sci
December 2024
United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, Fuchu, Japan.
Hairy vetch ( Roth), a leguminous plant with nitrogen-fixing ability, is used as a cover crop and has the potential to suppress weeds and plant diseases. The microbial composition, particularly fungal endophytes, which may be related to the beneficial functions of this crop, has not been previously studied. In this study, we analyzed the diversity and function of culturable fungal endophytes associated with hairy vetch from eight locations across Japan.
View Article and Find Full Text PDFWorld J Microbiol Biotechnol
December 2024
Laboratory of Biodiversity and Valorization of Arid Areas Bioresources (BVBAA), Faculty of Sciences of Gabes, University of Gabes Erriadh, 6072, Zrig, Tunisia.
In this study, we investigated various chromosomal and symbiotic markers in 40 bacterial strains that nodulate an invasive alien plant, Acacia salicina Lindl. in Tunisia. Our findings showed that the native rhizobia associated to A.
View Article and Find Full Text PDFSci Rep
December 2024
Limagrain Brazil S.A., Jataí, Goiás, Brazil.
This study investigates the effectiveness of high-throughput phenotyping (HTP) using RGB images from unmanned aerial vehicles (UAVs) to assess vegetation indices (VIs) in different soybean pure lines. The VIs were accessed at various stages of crop development and correlated with agronomic performance traits. The field research was conducted in the experimental area of the Mato Grosso do Sul Foundation, Brazil, with 60 soybean pure lines.
View Article and Find Full Text PDFBMC Plant Biol
December 2024
Key Laboratory of Biology and Genetics Improvement of Soybean, Zhongshan Biological Breeding Laboratory (ZSBBL), State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Agriculture, Ministry of Agriculture, National Innovation Platform for Soybean Breeding and Industry-Education Integration, Nanjing Agricultural University, Nanjing, 210095, China.
Background: Vegetable soybean is an important legume vegetable. High sucrose content is a significant quality characteristic of vegetable soybean that influences consumers' taste. However, the genetic basis of sucrose content in vegetable soybean is currently unclear.
View Article and Find Full Text PDFFront Artif Intell
December 2024
School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, United States.
The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. Genotype-environment interactions introduce large variations in crop-climate responses, and are hard to factor in to breeding programs. Data-driven approaches, particularly those based on machine learning, can help guide breeding efforts by factoring in genotype-environment interactions when making yield predictions.
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