Dataset Information

Evaluation on reprogramed biological processes in transgenic maize varieties using transcriptomics and metabolomics.

ABSTRACT: Genetic engineering (GM) has great potential to improve maize productivity, but rises some concerns on unintended effects, and equivalent as their comparators. There are some limitations through targeted analysis to detect the UE in genetically modified organisms in many previous studies. We here reported a case-study on the effects of introducing herbicides and insect resistance (HIR) gene cassette on molecular profiling (transcripts and metabolites) in a popular maize variety Zhengdan958 (ZD958) in China. We found that introducing HIR gene cassette bring a limited numbers of differential abundant genes (DAGs) or differential abundant metabolites (DAMs) between transgenic events and non-transgenic control. In contrast, averaged 10 times more DAGs and DAMs were observed when performed comparison under different growing environments in three different ecological regions of China than the numbers induced by gene effects. Major biological pathways relating to stress response or signaling transduction could explain somehow the effects of growing environments. We further compared two transgenic events mediated ZD958 (GM-ZD958) with either transgenic parent GM-Z58, and other genetic background nonGM-Z58, nonGM-ZD958, and Chang7-2. We found that the numbers of DAGs and DAMs between GM-ZD958 and its one parent maize variety, Z58 or GM-Z58 is equivalent, but not Chang7-2. These findings suggest that greater effects due to different genetic background on altered molecular profiling than gene modification itself. This study provides a case evidence indicating marginal effects of gene pleiotropic effects, and environmental effects should be emphasized.

SUBMITTER: Fu W

PROVIDER: S-EPMC7820507 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Evaluation on reprogramed biological processes in transgenic maize varieties using transcriptomics and metabolomics.

Fu Wei W Zhu Pengyu P Qu Mingnan M Zhi Wang W Zhang Yongjiang Y Li Feiwu F Zhu Shuifang S

Scientific reports 20210121 1

Genetic engineering (GM) has great potential to improve maize productivity, but rises some concerns on unintended effects, and equivalent as their comparators. There are some limitations through targeted analysis to detect the UE in genetically modified organisms in many previous studies. We here reported a case-study on the effects of introducing herbicides and insect resistance (HIR) gene cassette on molecular profiling (transcripts and metabolites) in a popular maize variety Zhengdan958 (ZD95 ...[more]

PMID: 33479482

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Project description:BACKGROUND: Maize is a major crop plant, grown for human and animal nutrition, as well as a renewable resource for bioenergy. When looking at the problems of limited fossil fuels, the growth of the world's population or the world's climate change, it is important to find ways to increase the yield and biomass of maize and to study how it reacts to specific abiotic and biotic stress situations. Within the OPTIMAS systems biology project maize plants were grown under a large set of controlled stress conditions, phenotypically characterised and plant material was harvested to analyse the effect of specific environmental conditions or developmental stages. Transcriptomic, metabolomic, ionomic and proteomic parameters were measured from the same plant material allowing the comparison of results across different omics domains. A data warehouse was developed to store experimental data as well as analysis results of the performed experiments. DESCRIPTION: The OPTIMAS Data Warehouse (OPTIMAS-DW) is a comprehensive data collection for maize and integrates data from different data domains such as transcriptomics, metabolomics, ionomics, proteomics and phenomics. Within the OPTIMAS project, a 44K oligo chip was designed and annotated to describe the functions of the selected unigenes. Several treatment- and plant growth stage experiments were performed and measured data were filled into data templates and imported into the data warehouse by a Java based import tool. A web interface allows users to browse through all stored experiment data in OPTIMAS-DW including all data domains. Furthermore, the user can filter the data to extract information of particular interest. All data can be exported into different file formats for further data analysis and visualisation. The data analysis integrates data from different data domains and enables the user to find answers to different systems biology questions. Finally, maize specific pathway information is provided. CONCLUSIONS: With OPTIMAS-DW a data warehouse for maize was established, which is able to handle different data domains, comprises several analysis results that will support researchers within their work and supports systems biological research in particular. The system is available at http://www.optimas-bioenergy.org/optimas_dw.

Project description:Disease lesion mimic (Les/les) mutants display disease-like spontaneous lesions in the absence of pathogen infection, implying the constitutive activation of defense responses. However, the genetic and biochemical bases underlying the activated defense responses in those mutants remain largely unknown. Here, we performed integrated transcriptomics and metabolomics analysis on three typical maize Les mutants Les4, Les10, and Les17 with large, medium, and small lesion size, respectively, thereby dissecting the activated defense responses at the transcriptional and metabolomic level. A total of 1,714, 4,887, and 1,625 differentially expressed genes (DEGs) were identified in Les4, Les10, and Les17, respectively. Among them, 570, 3,299, and 447 specific differentially expressed genes (SGs) were identified, implying a specific function of each LES gene. In addition, 480 common differentially expressed genes (CGs) and 42 common differentially accumulated metabolites (CMs) were identified in all Les mutants, suggesting the robust activation of shared signaling pathways. Intriguingly, substantial analysis of the CGs indicated that genes involved in the programmed cell death, defense responses, and phenylpropanoid and terpenoid biosynthesis were most commonly activated. Genes involved in photosynthetic biosynthesis, however, were generally repressed. Consistently, the dominant CMs identified were phenylpropanoids and flavonoids. In particular, lignin, the phenylpropanoid-based polymer, was significantly increased in all three mutants. These data collectively imply that transcriptional activation of defense-related gene expression; increase of phenylpropanoid, lignin, flavonoid, and terpenoid biosynthesis; and inhibition of photosynthesis are generalnatures associated with the lesion formation and constitutively activated defense responses in those mutants. Further studies on the identified SGs and CGs will shed new light on the function of each LES gene as well as the regulatory network of defense responses in maize.

Dataset Information

Evaluation on reprogramed biological processes in transgenic maize varieties using transcriptomics and metabolomics.

Publications

Evaluation on reprogramed biological processes in transgenic maize varieties using transcriptomics and metabolomics.

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