A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize.

The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms makes the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype-phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic, and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein coexpression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified 15 loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein coexpression network, and a reprogramming of the genetic control of the abundance of many proteins, including those involved in stress response. Colocalizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the coexpression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.

Maize adaptation across temperate climates was obtained via expression of two florigen genes.

Expansion of the maize growing area was central for food security in temperate regions. In addition to the suppression of the short-day requirement for floral induction, it required breeding for a large range of flowering time that compensates the effect of South-North gradients of temperatures. Here we show the role of a novel florigen gene, ZCN12, in the latter adaptation in cooperation with ZCN8. Strong eQTLs of ZCN8 and ZCN12, measured in 327 maize lines, accounted for most of the genetic variance of flowering time in platform and field experiments. ZCN12 had a strong effect on flowering time of transgenic Arabidopsis thaliana plants; a path analysis showed that it directly affected maize flowering time together with ZCN8. The allelic composition at ZCN QTLs showed clear signs of selection by breeders. This suggests that florigens played a central role in ensuring a large range of flowering time, necessary for adaptation to temperate areas.

High-throughput phenotyping reveals differential transpiration behaviour within the banana wild relatives highlighting diversity in drought tolerance.

Crop wild relatives, the closely related species of crops, may harbour potentially important sources of new allelic diversity for (a)biotic tolerance or resistance. However, to date, wild diversity is only poorly characterized and evaluated. Banana has a large wild diversity but only a narrow proportion is currently used in breeding programmes. The main objective of this study was to evaluate genotype-dependent transpiration responses in relation to the environment. By applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential. We characterized and evaluated six (sub)species and discerned four phenotypic clusters. Significant differences were observed in leaf area, cumulative transpiration and transpiration efficiency. We confirmed a general stomatal-driven 'isohydric' drought avoidance behaviour, but discovered genotypic differences in the onset and intensity of stomatal closure. We pinpointed crucial genotype-specific soil water potentials when drought avoidance mechanisms were initiated and when stress kicked in. Differences between (sub)species were dependent on environmental conditions, illustrating the need for high-throughput dynamic phenotyping, modelling and validation. We conclude that the banana wild relatives contain useful drought tolerance traits, emphasising the importance of their conservation and potential for use in breeding programmes.

Physiological and genetic control of transpiration efficiency in African rice, Oryza glaberrima Steud.

Improving crop water use efficiency, the amount of carbon assimilated as biomass per unit of water used by a plant, is of major importance as water for agriculture becomes scarcer. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand remain unknown. These traits were measured in 2019 in a panel of 147 African rice (Oryza glaberrima) genotypes known to be potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly contrasted genotypes in terms of biomass accumulation and transpiration confirmed these associations and suggested that root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency, and its residuals, with links to genes involved in water transport and cell wall patterning. Our data suggest that root-shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.

Wild Wheat Rhizosphere-Associated Plant Growth-Promoting Bacteria Exudates: Effect on Root Development in Modern Wheat and Composition.

Diazotrophic bacteria isolated from the rhizosphere of a wild wheat ancestor, grown from its refuge area in the Fertile Crescent, were found to be efficient Plant Growth-Promoting Rhizobacteria (PGPR), upon interaction with an elite wheat cultivar. In nitrogen-starved plants, they increased the amount of nitrogen in the seed crop (per plant) by about twofold. A bacterial growth medium was developed to investigate the effects of bacterial exudates on root development in the elite cultivar, and to analyze the exo-metabolomes and exo-proteomes. Altered root development was observed, with distinct responses depending on the strain, for instance, with respect to root hair development. A first conclusion from these results is that the ability of wheat to establish effective beneficial interactions with PGPRs does not appear to have undergone systematic deep reprogramming during domestication. Exo-metabolome analysis revealed a complex set of secondary metabolites, including nutrient ion chelators, cyclopeptides that could act as phytohormone mimetics, and quorum sensing molecules having inter-kingdom signaling properties. The exo-proteome-comprised strain-specific enzymes, and structural proteins belonging to outer-membrane vesicles, are likely to sequester metabolites in their lumen. Thus, the methodological processes we have developed to collect and analyze bacterial exudates have revealed that PGPRs constitutively exude a highly complex set of metabolites; this is likely to allow numerous mechanisms to simultaneously contribute to plant growth promotion, and thereby to also broaden the spectra of plant genotypes (species and accessions/cultivars) with which beneficial interactions can occur.

Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System.

Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. The open-source Phenotyping Hybrid Information System (PHIS) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved. Its ontology-driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases. It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi-source and multi-scale data, but also because it is based on 10 yr of trial and error in our groups.

Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection.

Breeders select for yield, thereby indirectly selecting for traits that contribute to it. We tested if breeding has affected a range of traits involved in plant architecture and light interception, via the analysis of a panel of 60 maize hybrids released from 1950 to 2015. This was based on novel traits calculated from reconstructions derived from a phenotyping platform. The contribution of these traits to light interception was assessed in virtual field canopies composed of 3D plant reconstructions, with a model tested in a real field. Two categories of traits had different contributions to genetic progress. (a) The vertical distribution of leaf area had a high heritability and showed a marked trend over generations of selection. Leaf area tended to be located at lower positions in the canopy, thereby improving light penetration and distribution in the canopy. This potentially increased the carbon availability to ears, via the amount of light absorbed by the intermediate canopy layer. (b) Neither the horizontal distribution of leaves in the relation to plant rows nor the response of light interception to plant density showed appreciable trends with generations. Hence, among many architectural traits, the vertical distribution of leaf area was the main indirect target of selection.

To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

Based on case studies, we discuss the extent to which genome-wide association studies (GWAS) are affected by outlier plants, i.e. those deviating from the expected distribution on a multi-criteria basis. Using a raw dataset consisting of daily measurements of leaf area, biomass, and plant height for thousands of plants, we tested three different cleaning methods for their effects on genetic analyses. No-cleaning resulted in the highest number of dubious quantitative trait loci, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false-positives (with no-cleaning and/or a low threshold for minor allele frequency) and the risk of missing interesting rare alleles. Cleaning can lower the risk of the latter by making it possible to choose a higher threshold in GWAS.

Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies.

Multi-genotype canopies are frequent in phenotyping experiments and are of increasing interest in agriculture. Radiation interception efficiency (RIE) and radiation use efficiency (RUE) have low heritabilities in such canopies. We propose a revised Monteith equation that identifies environmental and genetic components of RIE and RUE. An environmental term, a component of RIE, characterizes the effect of the presence or absence of neighbours on light interception. The ability of a given plant to compete with its neighbours is then identified, which accounts for the genetic variability of RIE of plants having similar leaf areas. This method was used in three experiments in a phenotyping platform with 765 plants of 255 maize hybrids. As expected, the heritability of the environmental term was near zero, whereas that of the competitiveness term increased with phenological stage, resulting in the identification of quantitative trait loci. In the same way, RUE was dissected as an effect of intercepted light and a genetic term. This approach was used for predicting the behaviour of individual genotypes in virtual multi-genotype canopies. A large effect of competitiveness was observed in multi-genotype but not in single-genotype canopies, resulting in a bias for genotype comparisons in breeding fields.

Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand.

Stomatal conductance is central for the trades-off between hydraulics and photosynthesis. We aimed at deciphering its genetic control and that of its responses to evaporative demand and water deficit, a nearly impossible task with gas exchanges measurements. Whole-plant stomatal conductance was estimated via inversion of the Penman-Monteith equation from data of transpiration and plant architecture collected in a phenotyping platform. We have analysed jointly 4 experiments with contrasting environmental conditions imposed to a panel of 254 maize hybrids. Estimated whole-plant stomatal conductance closely correlated with gas-exchange measurements and biomass accumulation rate. Sixteen robust quantitative trait loci (QTLs) were identified by genome wide association studies and co-located with QTLs of transpiration and biomass. Light, vapour pressure deficit, or soil water potential largely accounted for the differences in allelic effects between experiments, thereby providing strong hypotheses for mechanisms of stomatal control and a way to select relevant candidate genes among the 1-19 genes harboured by QTLs. The combination of allelic effects, as affected by environmental conditions, accounted for the variability of stomatal conductance across a range of hybrids and environmental conditions. This approach may therefore contribute to genetic analysis and prediction of stomatal control in diverse environments.

High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform.

Light interception and radiation-use efficiency (RUE) are essential components of plant performance. Their genetic dissections require novel high-throughput phenotyping methods. We have developed a suite of methods to evaluate the spatial distribution of incident light, as experienced by hundreds of plants in a glasshouse, by simulating sunbeam trajectories through glasshouse structures every day of the year; the amount of light intercepted by maize (Zea mays) plants via a functional-structural model using three-dimensional (3D) reconstructions of each plant placed in a virtual scene reproducing the canopy in the glasshouse; and RUE, as the ratio of plant biomass to intercepted light. The spatial variation of direct and diffuse incident light in the glasshouse (up to 24%) was correctly predicted at the single-plant scale. Light interception largely varied between maize lines that differed in leaf angles (nearly stable between experiments) and area (highly variable between experiments). Estimated RUEs varied between maize lines, but were similar in two experiments with contrasting incident light. They closely correlated with measured gas exchanges. The methods proposed here identified reproducible traits that might be used in further field studies, thereby opening up the way for large-scale genetic analyses of the components of plant performance.

Genetic variation in a grapevine progeny (Vitis vinifera L. cvs Grenache×Syrah) reveals inconsistencies between maintenance of daytime leaf water potential and response of transpiration rate under drought.

In the face of water stress, plants evolved with different abilities to limit the decrease in leaf water potential, notably in the daytime (ΨM). So-called isohydric species efficiently maintain high ΨM, whereas anisohydric species cannot prevent ΨM from dropping as soil water deficit develops. The genetic and physiological origins of these differences in (an)isohydric behaviours remain to be clarified. This is of particular interest within species such as Vitis vinifera L. where continuous variation in the level of isohydry has been observed among cultivars. With this objective, a 2 year experiment was conducted on the pseudo-F1 progeny from a cross between the two widespread cultivars Syrah and Grenache using a phenotyping platform coupled to a controlled-environment chamber. Potted plants of all the progeny were analysed for ΨM, transpiration rate, and soil-to-leaf hydraulic conductance, under both well-watered and water deficit conditions. A high genetic variability was found for all the above traits. Four quantitative trait loci (QTLs) were detected for ΨM under water deficit conditions, and 28 other QTLs were detected for the different traits in either condition. Genetic variation in ΨM maintenance under water deficit weakly correlated with drought-induced reduction in transpiration rate in the progeny, and QTLs for both traits did not completely co-localize. This indicates that genetic variation in the control of ΨM under water deficit was not due simply to variation in transpiration sensitivity to soil drying. Possible origins of the diversity in (an)isohydric behaviours in grapevine are discussed on the basis of concurrent variations in soil-to-leaf hydraulic conductance and stomatal control of transpiration.

Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images.

BACKGROUND: Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. RESULTS: First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. CONCLUSIONS: We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.

Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field.

Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.

High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge.

Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding community from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and comparable to genomic selection. Despite the fact that the two methodological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome), they both consider the targeted traits (e.g. grain yield, growth, phenology, plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e. physiological) putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.

PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time.

BACKGROUND: High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS: We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data.

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.

Dual Δ¹³C/δ¹8O response to water and nitrogen availability and its relationship with yield in field-grown durum wheat.

The combined use of stable carbon and oxygen isotopes in plant matter is a tool of growing interest in cereal crop management and breeding, owing to its relevance for assessing the photosynthetic and transpirative performance under different growing conditions including water and N regimes. However, this method has not been applied to wheat grown under real field conditions. Here, plant growth, grain yield (GY) and the associated agronomic components, carbon isotope discrimination (Δ¹³C) plus oxygen isotope composition (δ¹8O) as well as leaf and canopy gas exchange were measured in field-grown wheat subjected to different water and N availabilities. Water limitation was the main factor affecting yield, leaf and canopy gas exchange and Δ¹³C and δ¹8O, whereas N had a smaller effect on such traits. The combination of Δ¹³C and δ¹8O gave a clear advantage compared with gas exchange measurements, as it provides information on the instantaneous and the long-term plant photosynthetic and transpirative performance and are less labour intensive than gas exchange measurements. In addition, the combination of plant Δ¹³C and δ¹8O predicted differences in GY and related agronomical parameters, providing agronomists and breeders with integrative traits for selecting crop management practices and/or genotypes with better performance under water-limiting and N-limiting conditions.

Does ear C sink strength contribute to overcoming photosynthetic acclimation of wheat plants exposed to elevated CO2?

Wheat plants (Triticum durum Desf., cv. Regallo) were grown in the field to study the effects of contrasting [CO(2)] conditions (700 versus 370 µmol mol(-1)) on growth, photosynthetic performance, and C management during the post-anthesis period. The aim was to test whether a restricted capacity of sink organs to utilize photosynthates drives a loss of photosynthetic capacity in elevated CO(2). The ambient (13)C/(12)C isotopic composition (δ(13)C) of air CO(2) was changed from -10.2‰ in ambient [CO(2)] to -23.6‰ under elevated [CO(2)] between the 7th and the 14th days after anthesis in order to study C assimilation and partitioning between leaves and ears. Elevated [CO(2)] had no significant effect on biomass production and grain filling, and caused an accumulation of C compounds in leaves. This was accompanied by up-regulation of phosphoglycerate mutase and ATP synthase protein content, together with down-regulation of adenosine diphosphate glucose pyrophosphatase protein. Growth in elevated [CO(2)] negatively affected Rubisco and Rubisco activase protein content and induced photosynthetic down-regulation. CO(2) enrichment caused a specific decrease in Rubisco content, together with decreases in the amino acid and total N content of leaves. The C labelling revealed that in flag leaves, part of the C fixed during grain filling was stored as starch and structural C compounds whereas the rest of the labelled C (mainly in the form of soluble sugars) was completely respired 48 h after the end of labelling. Although labelled C was not detected in the δ(13)C of ear total organic matter and respired CO(2), soluble sugar δ(13)C revealed that a small amount of labelled C reached the ear. The (12)CO(2) labelling suggests that during the beginning of post-anthesis the ear did not contribute towards overcoming flag leaf carbohydrate accumulation, and this had a consequent effect on protein expression and photosynthetic acclimation.

Is heterosis in maize mediated through better water use?

SUMMARY: *Heterosis increases yield potential and improves adaptation to stress in maize (Zea mays); however, the underlying mechanisms remain elusive. *A set of tropical inbred lines and their hybrids were grown in the field for 2 yr under three different water regimes. First-year plant water use was evaluated by measuring instantaneous traits (stomatal conductance (g(s)) and steady-state chlorophyll fluorescence (F(s))) in individual leaves together with time-integrative traits, which included mineral accumulation in the whole leaves of plants and oxygen isotope enrichment above source water (Delta(18)O) and carbon isotope discrimination (Delta(13)C) in the same pooled leaves and in mature kernels. Second-year water use was evaluated by measuring leaf temperature, g(s) and relative water content (RWC). *Within each growing condition, hybrids showed higher F(s), mineral accumulation, RWC, and lower leaf temperature, Delta(18)O and Delta(13)C than inbred lines. Therefore, hybrids had a better water status than inbred lines, regardless of the water conditions. Differences in grain yield across growing conditions were explained by differences in water-use traits, with hybrids and inbred lines following a common pattern. Within each growing condition, most variations in grain yield, between hybrids and inbred lines, were also explained by differences in plant water-use traits. *Heterosis in tropical maize seems to be mediated by improved water use, irrespective of the water conditions during growth.