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Metabolomics in Rice Improvement

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Applications of Bioinformatics in Rice Research

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Abstract

Metabolomics is the analysis of micro-biomolecules related to the metabolic processes of a living organism. It has a strong connection between the genotype and phenotype of an organism. Metabolomics is like other “omics” technologies and can handle large-scale, complex, and dynamic databases. As a result, various data processing techniques are needed to retrieve biologically relevant knowledge from a living organism. Those data processing workflows of metabolomics research are typically complicated and require many phases or steps. This chapter will address the application and role of metabolites in the growth, development, and produce chemical compounds for defense against biotic and abiotic stress of rice and other crops. Hence, it is important to know the metabolic process of plants under different environmental conditions. Thus, metabolomic techniques may be used to elucidate the roles of unknown genes by using natural variations and mutations in target plants. These metabolomic methods could be useful in crop breeding, where important plant traits like taste, yield, and grain yield quality are strongly linked to metabolic conditions. Various data analysis techniques are used for metabolomics studies and metabolomics experiments. These methods can identify specific metabolites that help to improve stress resistance and disease resistance. We also discuss the several available computational techniques and tools that can assist in the biological interpretation of metabolomics data. We also introduce emerging methods for designing genome-scale metabolic models to analyze cellular metabolism.

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Abbreviations

2D:

Two-dimensional

AAA:

Aromatic amino acids

ADH:

Arogenate dehydrogenase

BL-SOM:

Batch-learning self-organizing map

CE:

Capillary electrophoresis

CE-MS:

Capillary electrophoresis-mass spectrometry

CT:

Cold tolerance

DAD:

Diode array detector

DIMS:

Direct infusion mass spectrometry

FT-ICR:

Fourier transform ion cyclotron resonance

GABA:

Gluconeogenesis-aminobutyric acid

GC-MS:

Gas chromatography-mass spectrometry

GM:

Genetically modified

HPTLC:

High-performance thin-layer chromatography

HRMS:

High-resolution mass spectrometry

LC:

Liquid chromatography

LC-MS:

Liquid chromatography-mass spectrometry

LT:

Low-temperature

MS:

Mass-spectrometry

NMR:

Nuclear magnetic resonance spectroscopy

PCA:

Principal component analysis

PGPR:

Plant growth-promoting rhizobacteria

PLS-DA:

Partial least squares discriminant analysis

SAR:

Systemic acquired resistance

TOF:

Time-of-flight

UPLC:

Ultra-performance liquid chromatography

WRC:

World Rice Core

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Authors and Affiliations

  1. District Headquarter Hospital, Ganjam, Odisha, India

    Pallabi Pati

  2. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Ravindra Donde, S. Sabarinathan, Gayatri Gouda & Manoj Kumar Gupta

  3. Department of Seed Science and Technology, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India

    S. Sabarinathan

  4. Department of Zoology, Khallikote Autonomous College, Ganjam, Odisha, India

    Sushil Kumar Rathore

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  1. Pallabi Pati
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  2. Ravindra Donde
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  3. S. Sabarinathan
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  4. Gayatri Gouda
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  5. Manoj Kumar Gupta
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  6. Sushil Kumar Rathore
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  1. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Manoj Kumar Gupta

  2. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Lambodar Behera

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Pati, P., Donde, R., Sabarinathan, S., Gouda, G., Gupta, M.K., Rathore, S.K. (2021). Metabolomics in Rice Improvement. In: Gupta, M.K., Behera, L. (eds) Applications of Bioinformatics in Rice Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-3997-5_4

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