Dataset Information

Proteomic Profile of Glyphosate-Resistant Soybean under Combined Herbicide and Drought Stress Conditions.

ABSTRACT: While some genetically modified (GM) plants have been targeted to confer tolerance to abiotic stressors, transgenes are impacted by abiotic stressors, causing adverse effects on plant physiology and yield. However, routine safety analyses do not assess the response of GM plants under different environmental stress conditions. In the context of climate change, the combination of abiotic stressors is a reality in agroecosystems. Therefore, the aim of this study was to analyze the metabolic cost by assessing the proteomic profiles of GM soybean varieties under glyphosate spraying and water deficit conditions compared to their non-transgenic conventional counterparts. We found evidence of cumulative adverse effects that resulted in the reduction of enzymes involved in carbohydrate metabolism, along with the expression of amino acids and nitrogen metabolic enzymes. Ribosomal metabolism was significantly enriched, particularly the protein families associated with ribosomal complexes L5 and L18. The interaction network map showed that the affected module representing the ribosome pathway interacts strongly with other important proteins, such as the chloro-plastic gamma ATP synthase subunit. Combined, these findings provide clear evidence for increasing the metabolic costs of GM soybean plants in response to the accumulation of stress factors. First, alterations in the ribosome pathway indicate that the GM plant itself carries a metabolic burden associated with the biosynthesis of proteins as effects of genetic transformation. GM plants also showed an imbalance in energy demand and production under controlled conditions, which was increased under drought conditions. Identifying the consequences of altered metabolism related to the interaction between plant transgene stress responses allows us to understand the possible effects on the ecology and evolution of plants in the medium and long term and the potential interactions with other organisms when these organisms are released in the environment.

SUBMITTER: Benevenuto RF

PROVIDER: S-EPMC8622064 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future.

Dataset Information

Proteomic Profile of Glyphosate-Resistant Soybean under Combined Herbicide and Drought Stress Conditions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets