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Metabolic robustness in young roots underpins a predictive model of maize hybrid performance in the field

doi: 10.1111/tpj.13495
pmid: 28122143
SummaryHeterosis has been extensively exploited for yield gain in maize (Zea mays L.). Here we conducted a comparative metabolomics‐based analysis of young roots from in vitro germinating seedlings and from leaves of field‐grown plants in a panel of inbred lines from the Dent and Flint heterotic patterns as well as selected F1 hybrids. We found that metabolite levels in hybrids were more robust than in inbred lines. Using state‐of‐the‐art modeling techniques, the most robust metabolites from roots and leaves explained up to 37 and 44% of the variance in the biomass from plants grown in two distinct field trials. In addition, a correlation‐based analysis highlighted the trade‐off between defense‐related metabolites and hybrid performance. Therefore, our findings demonstrated the potential of metabolic profiles from young maize roots grown under tightly controlled conditions to predict hybrid performance in multiple field trials, thus bridging the greenhouse–field gap.
- Max Planck Society Germany
- University of Hohenheim Germany
- Max Planck Institute of Neurobiology Germany
Plant Leaves, Seedlings, Hybridization, Genetic, Metabolomics, Biomass, Plant Roots, Zea mays
Plant Leaves, Seedlings, Hybridization, Genetic, Metabolomics, Biomass, Plant Roots, Zea mays
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).29 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
