Plant Metabolic Network: A multi-species resource of plant metabolic information

Hawkins, Charles; Ginzburg, Daniel; Zhao, Kangmei; Dwyer, William; Xue, Bo; Xu, Angela; Rice, Sarah E.; Cole, Benjamin; Paley, Suzanne M.; Karp, Peter D.; Rhee, Seung Y.

doi:10.1101/2021.03.30.437738

Cited by 4 publications

(5 citation statements)

References 101 publications

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“…Integration of proteomic and transcriptomic profiles, systematic salt tolerance in cotton, and the alternative molecular network of N-deficiency stress in rice were revealed (Peng et al, 2018;Liang et al, 2021). In addition, the genomescale metabolic pathways integrated with other databases (eg., PMN) were constructed (Hawkins et al, 2021) (Table 1).…”

Section: Integration Of Other Multi-omics Approachesmentioning

confidence: 99%

Integration of multi-omics technologies for crop improvement: Status and prospects

Zhang

Zhang²,

Yu³

et al. 2022

Front. Bioinform.

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With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.

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Section: Integration Of Other Multi-omics Approachesmentioning

confidence: 99%

Integration of multi-omics technologies for crop improvement: Status and prospects

Zhang

Zhang²,

Yu³

et al. 2022

Front. Bioinform.

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“…Principal component analysis (PCA) and Heatmap were generated with the R package "FactoMineR" and "Pheatmap" respectively, after performing log transformation and Pareto scaling. Metabolites were mapped to metabolic pathways using the Plant Metabolic Network database (Hawkins et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Molecular mechanisms of resistance to Myzus persicae conferred by the peach Rm2 gene: A multi-omics view

et al. 2022

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The transcriptomic and metabolomic responses of peach to Myzus persicae infestation were studied in Rubira, an accession carrying the major resistance gene Rm2 causing antixenosis, and GF305, a susceptible accession. Transcriptome and metabolome showed both a massive reconfiguration in Rubira 48 hours after infestation while GF305 displayed very limited changes. The Rubira immune system was massively stimulated, with simultaneous activation of genes encoding cell surface receptors involved in pattern-triggered immunity and cytoplasmic NLRs (nucleotide-binding domain, leucine-rich repeat containing proteins) involved in effector-triggered immunity. Hypersensitive reaction featured by necrotic lesions surrounding stylet punctures was supported by the induction of cell death stimulating NLRs/helpers couples, as well as the activation of H2O2-generating metabolic pathways: photorespiratory glyoxylate synthesis and activation of the futile P5C/proline cycle. The triggering of systemic acquired resistance was suggested by the activation of pipecolate pathway and accumulation of this defense hormone together with salicylate. Important reduction in carbon, nitrogen and sulphur metabolic pools and the repression of many genes related to cell division and growth, consistent with reduced apices elongation, suggested a decline in the nutritional value of apices. Finally, the accumulation of caffeic acid conjugates pointed toward their contribution as deterrent and/or toxic compounds in the mechanisms of resistance.

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“…PMN also offers tools for pathway visualization and analysis. These tools can be used to understand the relationships between metabolites, pathways, and genes [55]. PMN is a valuable resource for researchers in a variety of fields, including plant biology, biochemistry, and medicine.…”

Section: Plant Metabolic Networkmentioning

confidence: 99%

“…PMN also offers tools for pathway visualization and analysis. These tools can be used to understand the relationships between metabolites, pathways, and genes [55].…”

Section: Metabolomics Databasesmentioning

confidence: 99%

Metabolomics of natural samples: A tutorial review on the latest technologies

Eshawu,

Ghalsasi

2023

J of Separation Science

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Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early‐career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early‐career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

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Plant Metabolic Network: A multi-species resource of plant metabolic information

Cited by 4 publications

References 101 publications

Integration of multi-omics technologies for crop improvement: Status and prospects

Integration of multi-omics technologies for crop improvement: Status and prospects

Molecular mechanisms of resistance to Myzus persicae conferred by the peach Rm2 gene: A multi-omics view

Metabolomics of natural samples: A tutorial review on the latest technologies

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