Multi-scale modelling predicts plant stem bending behaviour in response to wind to inform lodging resistance.

Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modelling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behaviour with the physical traits of the plants. To further investigate we created an independent finite-element simulation framework integrating our recently developed multi-scale material modelling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multi-scale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.

Multiscale characterization and micromechanical modeling of crop stem materials.

An essential prerequisite for the efficient biomechanical tailoring of crops is to accurately relate mechanical behavior to compositional and morphological properties across different length scales. In this article, we develop a multiscale approach to predict macroscale stiffness and strength properties of crop stem materials from their hierarchical microstructure. We first discuss the experimental multiscale characterization based on microimaging (micro-CT, light microscopy, transmission electron microscopy) and chemical analysis, with a particular focus on oat stems. We then derive in detail a general micromechanics-based model of macroscale stiffness and strength. We specify our model for oats and validate it against a series of bending experiments that we conducted with oat stem samples. In the context of biomechanical tailoring, we demonstrate that our model can predict the effects of genetic modifications of microscale composition and morphology on macroscale mechanical properties of thale cress that is available in the literature.

Relationships Among Arsenic-Related Traits, Including Rice Grain Arsenic Concentration and Straighthead Resistance, as Revealed by Genome-Wide Association.

There is global concern that rice grains and foods can contain harmful amounts of arsenic (As), motivating breeders to produce cultivars that restrict As accumulation in grains to protect human health. Arsenic is also toxic to plants, with straighthead disorder (StHD), causing panicle sterility, being observed in rice. The genetic variation in StHD resistance suggests that plants have evolved mechanisms that reduce As toxicity, possibly via regulation of As uptake, transport, or detoxification/sequestration. Because these mechanisms could also underlie the wide (3- to 100-fold) differences in grain As concentration (grain-As) observed among diverse rice genotypes, it was hypothesized that some genes reduce both grain-As content and StHD susceptibility and may be detectable as co-located StDH and As quantitative trait loci (QTL). We used a machine-learning Bayesian network approach plus high-resolution genome-wide association study (GWAS) to identify QTL for grain-As and StHD resistance within the USDA Rice Minicore Collection (RMC). Arsenic enters roots through phosphorus (P) and silica (Si) transporters, As detoxification involves sulfur (S), and cell signaling to activate stress tolerance mechanisms is impacted by Si, calcium (Ca), and copper (Cu). Therefore, concentrations of Si, P, S, Ca, and Cu were included in this study to elucidate physiological mechanisms underlying grain-As and StHD QTL. Multiple QTL (from 9 to 33) were identified for each of the investigated As-associated traits. Although the QTL for StHD, Si, and grain-As did not overlap as heavily as our hypothesis predicted (4/33 StHD and 4/15 As QTL co-located), they do provide useful guidance to future research. Furthermore, these are the first StHD and Si QTL to be identified using high-density mapping, resulting in their being mapped to shorter, more precise genomic regions than previously reported QTL. The candidate genes identified provide guidance for future research, such as gene editing or mutation studies to further investigate the role of antioxidants and ROS scavenging to StHD resistance, as indicated by candidate genes around the commonly reported qStHD8-2 QTL. Other genes indicated for future study for improving grain-As and StHD include several multidrug and toxic compound extrusion (MATE) genes, F-box genes, and NIPs not documented to date to transport As.

Mapping crown rust resistance at multiple time points in elite oat germplasm.

Crown rust, caused by Puccinia coronata f. sp. avenae Erikss., is the most important disease impacting cultivated oat (Avena sativa L.). Genetic resistance is the most desirable management strategy. The genetic architecture of crown rust resistance is not fully understood, and previous mapping investigations have mostly ignored temporal variation. A collection of elite oat lines sourced from oat breeding programs in the American Upper Midwest and Canada was genotyped using a high-density genotyping-by-sequencing system and evaluated for crown rust disease severity at multiple time points throughout the growing season in three disease nursery environments. Genome-wide association mapping was conducted for disease severity on each observation date of each trial, area under the disease progress curve for each trial, heading date for each trial, and area under the disease progress curve in a multi-environment model. Crown rust resistance quantitative trait loci (QTL) were detected on linkage groups Mrg05, Mrg12, Mrg15, Mrg18, Mrg20, and Mrg33. None of these QTL were coincident with a days-to-heading QTL detected on Mrg02. Only the QTL detected on Mrg15 was detected in multiple mapping models. The QTL on Mrg05, Mrg12, Mrg18, Mrg20, and Mrg33 were detected on only a single observation date and were not detected on observations just days before and after. This result uncovers the importance of temporal variation in mapping experiments which is usually ignored. It is possible that high density temporal data could be used to more precisely characterize the nature of plant resistance in other systems.