Metabolomics is a rapidly advancing field of research that aims to understand the intricate metabolic pathways and networks within organisms. One intriguing area of investigation within metabolomics is the study of metabolic profiles in hybridized crops, such as the hybridization of rice plants. This emerging field, known as metabolomics-assisted plant breeding, holds great promise for improving crop productivity, enhancing nutritional value, and increasing stress tolerance in agricultural plants.
The hybridization of rice, one of the world's most important staple crops, offers an opportunity to explore the potential impact of metabolic changes on plant development, growth, and overall performance. Metabolomics approaches enable researchers to comprehensively analyze and quantify the small-molecule metabolites present in rice plants and their hybrids. By comparing the metabolic profiles of the parent plants with those of the hybrid offspring, researchers can gain valuable insights into the metabolic changes that occur during hybridization.
By studying the metabolites involved in primary and secondary metabolism, researchers can identify key metabolic pathways that are altered in the hybrid offspring. This knowledge can then be used to guide breeding programs towards developing rice varieties with improved traits, such as higher yields, enhanced nutritional content, or increased resistance to biotic and abiotic stresses. Metabolomics also provides a means to understand the underlying molecular mechanisms that contribute to these desirable traits.
Additionally, metabolomics-assisted plant breeding offers a non-invasive and high-throughput approach to select superior hybrid lines based on their metabolic profiles. This can significantly accelerate the breeding process by allowing researchers to screen a large number of hybrids and identify those with the most desirable metabolic traits.
Case. Hybrid rice metabolomics highlights biomarkers for predicting complex phenotypes (1)
Heterosis, also known as hybrid vigor, is a biological phenomenon commonly observed in the offspring of hybrid crosses (F1 generation), where they exhibit superior performance in terms of yield, biomass, growth rate, and fertility compared to their parents. This natural occurrence has been widely utilized to enhance global food production, but its underlying metabolic mechanisms remain unclear.
To investigate the metabolic level mechanisms of heterosis in rice and identify metabolic pathway biomarkers associated with yield heterosis, the research team employed a untargeted metabolomics approach coupled with machine learning methods. They mapped the metabolomic landscape of heterosis in rice and explored the potential application of metabolic pathway biomarkers in predicting yield heterosis.
Study Materials
Phenotypic data from 18 parents and 287 hybrid varieties were collected, and metabolic profiling was conducted on the seedlings of the parents.
Technical Approach
Identification of six agronomic trait-related metabolic analytes associated with heterosis.
Analysis of the correlation between heterosis-related metabolites and agronomic traits.
Analysis of metabolic pathways enriched in heterosis, depicting the metabolomic landscape of reproductive and nutritional trait heterosis in rice.
Prediction of yield heterosis based on enriched pathways.
Research Findings
1. Identification of heterosis-related metabolic analytes for six agronomic traits
To identify metabolic analytes associated with heterosis in rice, the authors first statistically analyzed the heterosis of six agronomic traits, including four yield-related traits (seed setting rate, 1000-grain weight, number of grains per panicle, and tiller number), individual yield, and one nutritional trait (plant height). Significant variations in the degree of heterosis were observed among different traits at both the individual and population levels. Subsequently, partial least squares regression analysis was conducted based on the metabolic analytes to screen for heterosis-related metabolites associated with different traits. The results indicated that, in terms of yield heterosis, seed setting rate and tiller number contributed more significantly compared to the number of grains per panicle and 1000-grain weight, based on the overlapping heterosis-related metabolic analytes.
2. Correlation among heterosis-related metabolic analytes for different traits
To investigate the correlation among heterosis-related metabolic analytes for the five reproductive traits and plant height, the authors conducted a correlation analysis. Based on the correlation coefficients, the heterosis of seed setting rate (R=0.72) and tiller number (R=0.66) contributed more significantly to yield heterosis compared to the heterosis of the number of grains per panicle (R=0.34) and 1000-grain weight (R=0.16). This suggests that overlapping metabolic analytes between traits determine the correlated patterns at the phenotypic level, and the four yield components and plant height contribute synergistically to yield heterosis to varying degrees.
3. Analysis of metabolic pathways enriched in heterosis
After conducting differential metabolic network analysis between high and low heterosis individuals, it was found that the significantly enriched metabolic pathways for yield heterosis mainly belonged to amino acid and carbohydrate metabolism, and these two pathways exhibited a negative correlation. Based on the analysis at the metabolite level, it was inferred that higher levels of amino acid metabolites and lower levels of carbohydrate metabolites were closely associated with a higher degree of yield heterosis. Subsequently, the metabolic landscape of reproductive and nutritional trait heterosis in rice was depicted based on the overlapping metabolic pathways between traits. The results indicated that the metabolic profiles of the significantly enriched pathways for the four yield components (especially amino acid and carbohydrate metabolism pathways) consistently exhibited a correlated pattern with the degree of yield heterosis. However, the metabolic profiles of the nutritional trait (plant height) showed an opposite relationship with the five reproductive traits.
4. Enriched metabolic pathways for predicting yield heterosis
Based on the metabolite levels in the significantly enriched metabolic pathways for yield heterosis, the authors conducted biomarker analysis by calculating the ratios of all pathway pairs. The results showed that when using ROC curve analysis on the ratio of the tyrosine metabolism pathway and sulfur metabolism pathway, the best model consisted of only 10 features, with an area under the curve (AUC) of 0.907 and a prediction accuracy of 0.827. This indicates the key role of the tyrosine metabolism pathway in yield heterosis and demonstrates that the prediction of yield heterosis can be achieved by using the metabolite levels of the tyrosine metabolism pathway. Furthermore, the study found that the performance of pathway biomarkers depends on the completeness and accuracy of pathway information. Subsequent validation experiments conducted on other hybrid populations confirmed the contribution of metabolite level variations in the tyrosine metabolism pathway to predicting yield heterosis, suggesting that the metabolite levels in significantly enriched pathways can predict yield heterosis in different environments and populations.
In this study, untargeted metabolomics and machine learning algorithms were employed to depict the metabolomic landscape of heterosis in rice and explore the potential application of metabolic pathway biomarkers in accurately predicting complex phenotypes. This research aims to provide new insights for assisting in breeding programs. By utilizing untargeted metabolomics, the study offers a comprehensive analysis of the metabolic profiles associated with heterosis in rice. Furthermore, through the application of machine learning algorithms, the study investigates the potential of using metabolic pathway biomarkers to achieve precise predictions of complex phenotypes. The findings of this research open up new avenues for utilizing metabolomics and machine learning in plant breeding programs.
Reference
- Dan, Zhiwu, et al. "The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes." Plant Physiology 187.2 (2021): 1011-1025.
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