Integrating transcriptomic and metabolomic analysis in roots of wild soybean seedlings in response to low-phosphorus stress

Li, Mingxia; Zhou, Jing; Lang, Xianyu; Han, Dayong; Hu, Yongjun; Ding, Yao; Wang, Guangye; Guo, Jixun; Shi, Lianxuan

doi:10.3389/fpls.2022.1006806

Cited by 7 publications

(4 citation statements)

References 52 publications

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“…Compared to ordinary wild soybean, seedlings of barren-tolerant wild soybean are more tolerant to phosphorus stress. It absorbs more phosphorus by increasing root length and MYB61 is the key transcription factor for this resistance [ 36 ]. Rice MYB61 may influence the efficiency of crop nitrogen utilization, which in turn affects rice growth and yield [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of a Comprehensive Gene Co-Expression Network Associated with Autotetraploid Potato (Solanum tuberosum L.) Development Using WGCNA Analysis

Li,

Wang,

Wang

2023

Genes

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The formation and development of potato tissues and organs is a complex process regulated by a variety of genes and environmental factors. The regulatory mechanisms underlying the growth and development are still unclear. In this work, we aimed to explore the changes in gene expression patterns and genetic characteristics of potato tissues throughout different developmental stages. To achieve this, we used autotetraploid potato JC14 as an experimental subject to analyze the transcriptome of the root, stem, and leaf at the seedling, tuber formation, and tuber expansion stages. The results revealed thousands of differentially expressed genes, predominantly involved in defense response and carbohydrate metabolism according to KEGG pathway enrichment analysis. Weighted gene co-expression network analysis (WGCNA) revealed a total of 12 co-expressed gene modules, with 4 modules showing the highest correlation with potato stem development. By calculating the connectivity of genes within the module, hub genes were identified, and functional annotations were subsequently performed. A total of 40 hub genes from the four modules were identified, and their functions were found to be related to carbohydrate metabolism, defense response, and transcription factors. These findings provide important insights for further understanding of the molecular regulation and genetic mechanisms involved in potato tissue development.

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Section: Discussionmentioning

confidence: 99%

Identification of a Comprehensive Gene Co-Expression Network Associated with Autotetraploid Potato (Solanum tuberosum L.) Development Using WGCNA Analysis

Li,

Wang,

Wang

2023

Genes

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“…Sugarcane MYBs play an important role in stem development and various stress responses, among which MYB61 positively responds to drought stress [35]. Seedlings of barren-tolerant wild soybean are more adaptive to phosphorus stress than common wild soybean, which absorb more phosphorus by increasing root length, and MYB61 is a key transcription factor for resistance to low phosphorus stress in barren-tolerant wild soybean [36]. Rice MYB61 may regulate the efficiency of crop nitrogen usage, which in turn affects rice growth and yield [37].…”

Section: Discussionmentioning

confidence: 99%

WGCNA Identifies a Comprehensive Gene Co-Expression Network Associated with Development in autotetraploid potato tissues

Li¹,

Wang²,

Wang³

2023

Preprint

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The formation and development of potato tissues and organs is a complex process regulated by a variety of genes and environmental factors. However, the regulatory mechanisms underlying the growth and development are still unclear. In this study, we used autotetraploid potato JC14 as experimental subject to analyze the transcriptome of root, stem and leaf at seedling, tuber formation and tuber expansion stages to explore the spatio-temporal expression pattern of genes and genetic development characteristics. The results identified thousands of differentially expressed genes and KEGG pathway enrichment analysis showed that these genes were mainly involved in defense response and carbohydrate metabolism pathways. A total of 12 co-expressed Gene modules were identified by Weighted Gene Co-expression Network Analysis (WGCNA), and 4 modules were screened out with the highest correlation with potato stem developmental traits. Core genes in the network were further investigated and functionally annotated by computing the connectivity of genes within the module. The results unveiled number of hub genes in stems at different developmental stages, including carbohydrate metabolism related genes, the defense response related genes, and transcription factors. These findings provide important leads for further understanding of the molecular regulation and genetic mechanisms of potato tissue development.

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“…S6. In order to further characterize differences between significant DEGs and DEMs among soybeans from various regions, Spearman correlation coefficient analysis was applied [61]. Hierarchical clustering (Fig.…”

Section: Integration Of Metabolomic and Transcriptomic Analysismentioning

confidence: 99%

Classification of soybeans from different habitats based on metabolomic–transcriptomic integration

Wang,

Zheng,

Wang

et al. 2024

Appl Biol Chem

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Soybeans are a significant agricultural product in China, with certain geographical locations often yielding higher quality, and thus more expensive, soybean crops. In this study, metabolomics and transcriptomics analyses were conducted on soybean samples from nine regions in Heilongjiang and Liaoning Provinces using untargeted liquid chromatography–mass spectrometry (LC–MS) and Illumina sequencing technologies. The primary objective was to devise an effective and unbiased method for determining the geographical origin of each soybean variety to mitigate potential fraudulent practices. Through multidimensional and unidimensional analyses, successful identification of differentially expressed metabolites (DEMs) and differentially expressed genes (DEGs) was achieved, yielding statistically significant outcomes. Integration of the metabolomics and transcriptomics datasets facilitated the construction of a correlation network model capable of distinguishing soybeans originating from different geographical locations, leading to the identification of significant biomarkers exemplifying noteworthy distinctions. To validate the feasibility of this method in practical applications, partial least squares discriminant analysis was employed to differentiate soybean samples from the nine regions. The results convincingly showcased the applicability and reliability of this approach in accurately pinpointing the geographical origin of soybeans. Distinguishing itself from prior research in soybean traceability, this study incorporates an integrated analysis of metabolomics and transcriptomics data, thereby unveiling biomarkers that offer a more precise differentiation of soybean traits across distinct regions, thereby bridging a critical research gap within the soybean traceability domain. This innovative dual-data integration analysis methodology is poised to enhance the accuracy of soybean traceability tools and lay a new foundation for future agricultural product identification research.

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Integrating transcriptomic and metabolomic analysis in roots of wild soybean seedlings in response to low-phosphorus stress

Cited by 7 publications

References 52 publications

Identification of a Comprehensive Gene Co-Expression Network Associated with Autotetraploid Potato (Solanum tuberosum L.) Development Using WGCNA Analysis

Identification of a Comprehensive Gene Co-Expression Network Associated with Autotetraploid Potato (Solanum tuberosum L.) Development Using WGCNA Analysis

WGCNA Identifies a Comprehensive Gene Co-Expression Network Associated with Development in autotetraploid potato tissues

Classification of soybeans from different habitats based on metabolomic–transcriptomic integration

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