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In cultivated plants, shoot morphology is an important factor that influences crop economic value. However, the effects of gene expression patterns on shoot morphology are not clearly understood. In this study, the molecular mechanism behind shoot morphology (including leaf, stem, and node) was analyzed using RNA sequencing to compare weedy (creeper) and cultivar (stand) growth types obtained in F7 derived from a cross of wild and cultivated soybeans. A total of 12,513 (in leaves), 14,255 (in stems), and 11,850 (in nodes) differentially expressed genes were identified among weedy and cultivar soybeans. Comparative transcriptome and expression analyses revealed 22 phytohormone-responsive genes. We found that GIBBERELLIN 2-OXIDASE 8 (GA2ox), SPINDLY (SPY), FERONIA (FER), AUXIN RESPONSE FACTOR 8 (ARF8), CYTOKININ DEHYDROGENASE-1 (CKX1), and ARABIDOPSIS HISTIDINE KINASE-3 (AHK3), which are crucial phytohormone response genes, were mainly regulated in the shoot of weedy and cultivar types. These results indicate that interactions between phytohormone signaling genes regulate shoot morphology in weedy and cultivar growth type plants. Our study provides insights that are useful for breeding and improving crops to generate high-yield soybean varieties.
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Globally, the cultivation area of genetically modified (GM) crops is increasing dramatically. Despite their well-known benefits, they may also pose many risks to agriculture and the environment. Among the various GM crops, GM rapeseed (Brassica napus L.) is widely cultivated, mainly for oil production. At the same time, B. napus possesses a number of characteristics, including the ability to form feral populations and act as small-seeded weeds, and has a high potential for hybridization with other species. In this review, we provide an overview of the commercialization, approval status, and cultivation of GM rapeseed, as well as the status of the feral rapeseed populations. In addition, we highlight the case studies on the unintentional environmental release of GM rapeseed during transportation in several countries. Previous studies suggest that the main reason for the unintentional release is seed spillage during transport/importing of rapeseed in both GM rapeseed-cultivating and -non-cultivating countries. Despite the fact that incidents of unintentional release have been recorded often, there have been no reports of serious detrimental consequences. However, since rapeseed has a high potential for hybridization, the possibilities of gene flow within the genus, especially with B. rapa, are relatively significant, and considering their weedy properties, effective management methods are needed. Hence, we recommend that specific programs be used for the effective monitoring of environmental releases of GM rapeseed as well as management to avoid environmental and agricultural perturbations.
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Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.
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Plantas Modificadas Genéticamente/fisiología , Espectroscopía Infrarroja Corta , Productos Agrícolas/fisiología , AlimentosRESUMEN
Bacterial communities in rhizosphere and root nodules have significant contributions to the growth and productivity of the soybean (Glycine max (L.) Merr.). In this report, we analyzed the physiological properties and dynamics of bacterial community structure in rhizosphere and root nodules at different growth stages using BioLog EcoPlate and high-throughput sequencing technology, respectively. The BioLog assay found that the metabolic capability of rhizosphere is in increasing trend in the growth of soybeans as compared to the bulk soil. As a result of the Illumina sequencing analysis, the microbial community structure of rhizosphere and root nodules was found to be influenced by the variety and growth stage of the soybean. At the phylum level, Actinobacteria were the most abundant in rhizosphere at all growth stages, followed by Alphaproteobacteria and Acidobacteria, and the phylum Bacteroidetes showed the greatest change. But, in the root nodules Alphaproteobacteria were dominant. The results of the OTU analysis exhibited the dominance of Bradyrhizobium during the entire stage of growth, but the ratio of non-rhizobial bacteria showed an increasing trend as the soybean growth progressed. These findings revealed that bacterial community in the rhizosphere and root nodules changed according to both the variety and growth stages of soybean in the field.
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Bacterias , Glycine max , Nodulación de la Raíz de la Planta , Raíces de Plantas , Rizosfera , Microbiología del Suelo , Bacterias/clasificación , Bacterias/crecimiento & desarrollo , Raíces de Plantas/crecimiento & desarrollo , Raíces de Plantas/microbiología , Glycine max/crecimiento & desarrollo , Glycine max/microbiologíaRESUMEN
Isoflavones are ecophysiologically active secondary metabolites derived from the phenylpropanoid pathway. They were mostly found in leguminous plants, especially in the pea family. Isoflavones play a key role in plant-environment interactions and act as phytoalexins also having an array of health benefits to the humans. According to epidemiological studies, a high intake of isoflavones-rich diets linked to a lower risk of hormone-related cancers, osteoporosis, menopausal symptoms, and cardiovascular diseases. These characteristics lead to the significant advancement in the studies on genetic and metabolic engineering of isoflavones in plants. As a result, a number of structural and regulatory genes involved in isoflavone biosynthesis in plants have been identified and characterized. Subsequently, they were engineered in various crop plants for the increased production of isoflavones. Furthermore, with the advent of high-throughput technologies, the regulation of isoflavone biosynthesis gains attention to increase or decrease the level of isoflavones in the crop plants. In the review, we begin with the role of isoflavones in plants, environment, and its benefits in human health. Besides, the main theme is to discuss the updated research progress in metabolic engineering of isoflavones in other plants species and regulation of production of isoflavones in soybeans.
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In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.