Three-Dimensional Time-Lapse Analysis Reveals Multiscale Relationships in Maize Root Systems with Contrasting Architectures.

Understanding how an organism's phenotypic traits are conditioned by genetic and environmental variation is a central goal of biology. Root systems are one of the most important but poorly understood aspects of plants, largely due to the three-dimensional (3D), dynamic, and multiscale phenotyping challenge they pose. A critical gap in our knowledge is how root systems build in complexity from a single primary root to a network of thousands of roots that collectively compete for ephemeral, heterogeneous soil resources. We used time-lapse 3D imaging and mathematical modeling to assess root system architectures (RSAs) of two maize (Zea mays) inbred genotypes and their hybrid as they grew in complexity from a few to many roots. Genetically driven differences in root branching zone size and lateral branching densities along a single root, combined with differences in peak growth rate and the relative allocation of carbon resources to new versus existing roots, manifest as sharply distinct global RSAs over time. The 3D imaging of mature field-grown root crowns showed that several genetic differences in seedling architectures could persist throughout development and across environments. This approach connects individual and system-wide scales of root growth dynamics, which could eventually be used to predict genetic variation for complex RSAs and their functions.

The Quantitative Genetic Control of Root Architecture in Maize.

Roots remain an underexplored frontier in plant genetics despite their well-known influence on plant development, agricultural performance and competition in the wild. Visualizing and measuring root structures and their growth is vastly more difficult than characterizing aboveground parts of the plant and is often simply avoided. The majority of research on maize root systems has focused on their anatomy, physiology, development and soil interaction, but much less is known about the genetics that control quantitative traits. In maize, seven root development genes have been cloned using mutagenesis, but no genes underlying the many root-related quantitative trait loci (QTLs) have been identified. In this review, we discuss whether the maize mutants known to control root development may also influence quantitative aspects of root architecture, including the extent to which they overlap with the most recent maize root trait QTLs. We highlight specific challenges and anticipate the impacts that emerging technologies, especially computational approaches, may have toward the identification of genes controlling root quantitative traits.

Sugar release and growth of biofuel crops are improved by downregulation of pectin biosynthesis.

Cell walls in crops and trees have been engineered for production of biofuels and commodity chemicals, but engineered varieties often fail multi-year field trials and are not commercialized. We engineered reduced expression of a pectin biosynthesis gene (Galacturonosyltransferase 4, GAUT4) in switchgrass and poplar, and find that this improves biomass yields and sugar release from biomass processing. Both traits were maintained in a 3-year field trial of GAUT4-knockdown switchgrass, with up to sevenfold increased saccharification and ethanol production and sixfold increased biomass yield compared with control plants. We show that GAUT4 is an α-1,4-galacturonosyltransferase that synthesizes homogalacturonan (HG). Downregulation of GAUT4 reduces HG and rhamnogalacturonan II (RGII), reduces wall calcium and boron, and increases extractability of cell wall sugars. Decreased recalcitrance in biomass processing and increased growth are likely due to reduced HG and RGII cross-linking in the cell wall.

How can we harness quantitative genetic variation in crop root systems for agricultural improvement?

Root systems are a black box obscuring a comprehensive understanding of plant function, from the ecosystem scale down to the individual. In particular, a lack of knowledge about the genetic mechanisms and environmental effects that condition root system growth hinders our ability to develop the next generation of crop plants for improved agricultural productivity and sustainability. We discuss how the methods and metrics we use to quantify root systems can affect our ability to understand them, how we can bridge knowledge gaps and accelerate the derivation of structure-function relationships for roots, and why a detailed mechanistic understanding of root growth and function will be important for future agricultural gains.

Biolistic transformation of elite genotypes of switchgrass (Panicum virgatum L.).

Transformation of elite switchgrass (Panicum virgatum L.) genotypes would facilitate the characterization of genes related to cell wall recalcitrance to saccharification. However, transformation of explants from switchgrass plants has remained difficult. Therefore, the objective of this study was to develop a biolistic transformation protocol for elite genotypes. Three switchgrass genotypes (ST1, ST2, and AL2) were previously selected for tissue culture responsiveness. One genotype, SA37, was selected for further use due to its improved formation of callus amenable to transformation. Various medium sets were compared and a previously published medium set provided cultures with >96 % embryogenic callus, and data on transient and stable gene expression of RFP were used to optimize biolistic parameters, and further validate the switchgrass (PvUbi1) promoter. SA37 proved to be the most transformable, whereas eight transgenic calli on average were recovered per bombardment of 20 calli (40 % efficiency) when using a three-day day preculture step, 0.6 M osmotic adjustment medium, 4,482 kPa rupture disks and 0.4 µm gold particles which traveled 9 cm before hitting the target callus tissue. Regenerability was high, especially for ST2, for which it is possible to recover on average over 400 plants per half-gram callus tissue. It is now possible to routinely and efficiently engineer elite switchgrass genotypes using biolistic transformation.