RootSlice-A novel functional-structural model for root anatomical phenotypes

Cortical cell size regulates root metabolic cost

It has been hypothesized that vacuolar occupancy in mature root cortical parenchyma cells regulates root metabolic cost and thereby plant fitness under conditions of drought, suboptimal nutrient availability, and increased soil mechanical impedance. However, the mechanistic role of vacuoles in reducing root metabolic cost was unproven. Here we provide evidence to support this hypothesis. We first show that root cortical cell size is determined by both cortical cell diameter and cell length. Significant genotypic variation for both cortical cell diameter (~1.1- to 1.5-fold) and cortical cell length (~ 1.3- to 7-fold) was observed in maize and wheat. GWAS and QTL analyses indicate cortical cell diameter and length are heritable and under independent genetic control. We identify candidate genes for both phenes. Empirical results from isophenic lines contrasting for cortical cell diameter and length show that increased cell size, due to either diameter or length, is associated with reduced root respiration, nitrogen content, and phosphorus content. RootSlice, a functional-structural model of root anatomy, predicts that an increased vacuolar: cytoplasmic ratio per unit cortical volume causes reduced root respiration and nutrient content. Ultrastructural imaging of cortical parenchyma cells with varying cortical diameter and cortical cell length confirms the in silico predictions and shows that an increase in cell size is correlated with increased vacuolar volume and reduced cytoplasmic volume. Vacuolar occupancy and its relationship with cell size merits further investigation as a phene for improving crop adaptation to edaphic stress.

Plant microphenotype: from innovative imaging to computational analysis

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.

Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize

Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. Given the investment required to characterize root anatomy, we present a machine-learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment that we used subsequently for genotype-environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial hydraulic conductance in varieties sourced from cooler, drier highland areas. We discuss our results in the context of previously described water-banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome-wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.

A role for fermentation in aerobic conditions as revealed by computational analysis of maize root metabolism during growth by cell elongation

The root is a well-studied example of cell specialisation, yet little is known about the metabolism that supports the transport functions and growth of different root cell types. To address this, we used computational modelling to study metabolism in the elongation zone of a maize lateral root. A functional-structural model captured the cell-anatomical features of the root and modelled how they changed as the root elongated. From these data, we derived constraints for a flux balance analysis model that predicted metabolic fluxes of the 11 concentric rings of cells in the root. We discovered a distinct metabolic flux pattern in the cortical cell rings, endodermis and pericycle (but absent in the epidermis) that involved a high rate of glycolysis and production of the fermentation end-products lactate and ethanol. This aerobic fermentation was confirmed experimentally by metabolite analysis. The use of fermentation in the model was not obligatory but was the most efficient way to meet the specific demands for energy, reducing power and carbon skeletons of expanding cells. Cytosolic acidification was avoided in the fermentative mode due to the substantial consumption of protons by lipid synthesis. These results expand our understanding of fermentative metabolism beyond that of hypoxic niches and suggest that fermentation could play an important role in the metabolism of aerobic tissues.

Large root cortical cells and reduced cortical cell files improve growth under suboptimal nitrogen in silico

Suboptimal nitrogen availability is a primary constraint to plant growth. We used OpenSimRoot, a functional-structural plant/soil model, to test the hypothesis that larger root cortical cell size (CCS), reduced cortical cell file number (CCFN), and their interactions with root cortical aerenchyma (RCA) and lateral root branching density (LRBD) are useful adaptations to suboptimal soil nitrogen availability in maize (Zea mays). Reduced CCFN increased shoot dry weight over 80%. Reduced respiration, reduced nitrogen content, and reduced root diameter accounted for 23%, 20%, and 33% of increased shoot biomass, respectively. Large CCS increased shoot biomass by 24% compared with small CCS. When simulated independently, reduced respiration and reduced nutrient content increased the shoot biomass by 14% and 3%, respectively. However, increased root diameter resulting from large CCS decreased shoot biomass by 4% due to an increase in root metabolic cost. Under moderate N stress, integrated phenotypes with reduced CCFN, large CCS, and high RCA improved shoot biomass in silt loam and loamy sand soils. In contrast, integrated phenotypes composed of reduced CCFN, large CCS and reduced lateral root branching density had the greatest growth in silt loam, while phenotypes with reduced CCFN, large CCS and high LRBD were the best performers in loamy sands. Our results support the hypothesis that larger CCS, reduced CCFN, and their interactions with RCA and LRBD could increase nitrogen acquisition by reducing root respiration and root nutrient demand. Phene synergisms may exist between CCS, CCFN, and LRBD. CCS and CCFN merit consideration for breeding cereal crops with improved nitrogen acquisition, which is critical for global food security.