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

Abstract

Heterosis 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 F 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.

Genotype-by-environment interactions affecting heterosis in maize

“…There is widespread interest in developing methods to characterize the molecular basis of heterosis, and to predict hybrid performance to increase the efficiency of hybrid breeding programs. Researchers have attempted to utilize genomic sequence [ 8 ], RNA expression levels of genes [ 2 , 9 , 10 ], sRNAs [ 11 , 12 ], proteomic [ 13 ], and metabolomic [ 8 , 14 ] data to predict or dissect heterosis [ 15 ]. While relationships have been identified using each of these data types, no data type is able to completely predict hybrid performance individually [ 16 ].…”

Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize

“…Metabolomic data of roots (R)of all parent lines were quantified as described in de Abreu e Lima et al (2017). In short, for each of the two replicates per line, 10 seedlings were grown in climate chambers.…”

“…Profiling resulted in 284 metabolites. After raw data were normalized (van den Berg et al 2006), BLUEs and repeatabilities (w 2 ) for metabolite levels were obtained as detailed in de Abreu e Lima et al (2017) and Westhues et al (2017). Passing a repeatability threshold set to 0.3 was required in both heterotic groups, resulting in 148 root metabolites for further analyses.…”

Low‐temperature tolerance of the Antarctic species Deschampsia antarctica: A complex metabolic response associated with nutrient remobilization

“…Nowadays, plant ecophysiology could benefit from the use of the new “omics” technologies to further decipher the mechanisms driving plant responses under field conditions (Flexas & Gago, ). Metabolomics offer the biochemical basis to understand the physiological plant responses and their underlying metabolic pathways that finally can promote the generation of new biotechnological tools (Obata & Fernie, ; de Abreu e Lima et al, ). In addition, statistical modelling has been used in systems biology to identify molecular markers (e.g., genes, proteins, and metabolites) that are predictive of complex physiological traits in multiple environments and/or species.…”

“…In addition, statistical modelling has been used in systems biology to identify molecular markers (e.g., genes, proteins, and metabolites) that are predictive of complex physiological traits in multiple environments and/or species. Such associations between molecular markers and traits can in turn be inferred with modern statistical techniques, like high‐dimensional regularized regressions (de Abreu e Lima et al, ; Li et al, ; Omranian, Eloundou‐Mbebi, Mueller‐Roeber, & Nikoloski, ; Wen et al, ). Recently, several studies showed the link between in vivo gas exchange and primary metabolism through statistical modelling (Gago et al, , ; Lima et al, ; Clemente‐Moreno, Omranian, et al, ).…”