SEQUENCES INVOLVED IN PLANT YIELD AND METHODS OF USING

Nucleic acid sequences involved in plant yield are provided, as are methods of using such nucleic acid sequences

Cytokinin-mediated source ⁄sink modifications improve drought tolerance and increase grain yield in rice under water-stress

Drought is the major environmental factor limiting crop productivity worldwide. We hypothesized that it is possible to enhance drought tolerance by delaying stress-induced senescence through the stress-induced synthesis of cytokinins in crop-plants. We generated transgenic rice (Oryza sativa) plants expressing an isopentenyltransferase (IPT) gene driven by PSARK, a stress- and maturation-induced promoter. Plants were tested for drought tolerance at two yield-sensitive developmental stages: pre- and post-anthesis. Under both treatments, the transgenic rice plants exhibited delayed response to stress with significantly higher grain yield (GY) when compared to wild-type plants. Gene expression analysis revealed a significant shift in expression of hormone-associated genes in the transgenic plants. During water-stress (WS), PSARK::IPT plants displayed increased expression of brassinosteroid-related genes and repression of jasmonate-related genes. Changes in hormone homeostasis were associated with resource(s) mobilization during stress. The transgenic plants displayed differential expression of genes encoding enzymes associated with hormone synthesis and hormone-regulated pathways. These changes and associated hormonal crosstalk resulted in the modification of source ⁄ sink relationships and a stronger sink capacity of the PSARK::IPT plants during WS. As a result, the transgenic plants had higher GY with improved quality (nutrients and starch content)

Utilizing trait networks and structural equation models as tools to interpret multi‑trait genome‑wide association studies

Background: Plant breeders seek to develop cultivars with maximal agronomic value, which is often assessed using numerous, often genetically correlated traits. As intervention on one trait will affect the value of another, breeding decisions should consider the relationships among traits in the context of putative causal structures (i.e., trait networks). While multi-trait genome-wide association studies (MTM-GWAS) can infer putative genetic signals at the multivariate scale, standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated. We extended the scope of MTM-GWAS by incorporating trait network structures into GWAS using structural equation models (SEM-GWAS). Here, we illustrate the utility of SEM-GWAS using a digital metric for shoot biomass, root biomass, water use, and water use efficiency in rice. Results: A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects acting on a trait into direct and indirect effects. Using this novel approach, we show that for most QTL associated with water use, total SNP effects were driven by genetic effects acting directly on water use rather that genetic effects originating from upstream traits. Conversely, total SNP effects for water use efficiency were largely due to indirect effects originating from the upstream trait, projected shoot area. Conclusions: We describe a robust framework that can be applied to multivariate phenotypes to understand the interrelationships between complex traits. This framework provides novel insights into how QTL act within a phenotypic network that would otherwise not be possible with conventional multi-trait GWAS approaches. Collectively, these results suggest that the use of SEM may enhance our understanding of complex relationships among agronomic traits

Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splines

Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions

Transmission Routes of the Microbiome and Resistome from Manure to Soil and Lettuce

The land application of animal manure can introduce manure microbiome and resistome to croplands where food crops are grown. The objective of this study was to characterize the microbiome and resistome on and in the leaves of lettuce grown in manured soil and identify the main transmission routes of microbes and antibiotic resistance genes (ARGs) from soil to the episphere and endosphere of lettuce. Shotgun metagenomic results show that manure application significantly altered the composition of the microbiome and resistome of surface soil. SourceTracker analyses indicate that manure and original soil were the main source of the microbiome and resistome of the surface soil and rhizosphere soil, respectively. Manure application altered the microbiome and resistome in the episphere of lettuce (ADONIS p \u3c 0.05), and surface soil accounted for ~81% of the microbes and ~62% of the ARGs in episphere. Manure application had limited impacts on the microbiome and resistome in the endosphere (ADONIS p \u3e 0.05). Our results show that manure-borne microbes and ARGs reached the episphere primarily through surface soil and some epiphytic microbes and ARGs further entered the endosphere. Our findings can inform the development of pre- and postharvest practices to minimize the transmission of manure-borne resistome from food crops to consumers

Cytokinin-mediated source ⁄sink modifications improve drought tolerance and increase grain yield in rice under water-stress

Drought is the major environmental factor limiting crop productivity worldwide. We hypothesized that it is possible to enhance drought tolerance by delaying stress-induced senescence through the stress-induced synthesis of cytokinins in crop-plants. We generated transgenic rice (Oryza sativa) plants expressing an isopentenyltransferase (IPT) gene driven by PSARK, a stress- and maturation-induced promoter. Plants were tested for drought tolerance at two yield-sensitive developmental stages: pre- and post-anthesis. Under both treatments, the transgenic rice plants exhibited delayed response to stress with significantly higher grain yield (GY) when compared to wild-type plants. Gene expression analysis revealed a significant shift in expression of hormone-associated genes in the transgenic plants. During water-stress (WS), PSARK::IPT plants displayed increased expression of brassinosteroid-related genes and repression of jasmonate-related genes. Changes in hormone homeostasis were associated with resource(s) mobilization during stress. The transgenic plants displayed differential expression of genes encoding enzymes associated with hormone synthesis and hormone-regulated pathways. These changes and associated hormonal crosstalk resulted in the modification of source ⁄ sink relationships and a stronger sink capacity of the PSARK::IPT plants during WS. As a result, the transgenic plants had higher GY with improved quality (nutrients and starch content)

Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

Genome-wide association mapping identifies quantitative trait loci (QTL) that influence the mean differences between the marker genotypes for a given trait. While most loci influence the mean value of a trait, certain loci, known as variance heterogeneity QTL (vQTL) determine the variability of the trait instead of the mean trait value (mQTL). In the present study, we performed a variance heterogeneity genome-wide association study (vGWAS) for grain cadmium (Cd) concentration in bread wheat. We used double generalized linear model and hierarchical generalized linear model to identify vQTL associated with grain Cd. We identified novel vQTL regions on chromosomes 2A and 2B that contribute to the Cd variation and loci that affect both mean and variance heterogeneity (mvQTL) on chromosome 5A. In addition, our results demonstrated the presence of epistatic interactions between vQTL and mvQTL, which could explain variance heterogeneity. Overall, we provide novel insights into the genetic architecture of grain Cd concentration and report the first application of vGWAS in wheat. Moreover, our findings indicated that epistasis is an important mechanism underlying natural variation for grain Cd concentration

A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum

Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360◦ view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 \u3e 0.91 for individual leaf area; R2 \u3e 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D

Transient Heat Stress During Early Seed Development Primes Germination and Seedling Establishment in Rice

Rice yield is highly sensitive to increased temperature. Given the trend of increasing global temperatures, this sensitivity to higher temperatures poses a challenge for achieving global food security. Early seed development in rice is highly sensitive to unfavorable environmental conditions. Heat stress (HS) during this stage decreases seed size and fertility, thus reducing yield. Here, we explore the transgenerational phenotypic consequences of HS during early seed development on seed viability, germination, and establishment. To elucidate the impact of HS on the developmental events in post-zygotic rice seeds, we imposed moderate (35°C) and severe (39°C) HS treatments initiated 1 day after fertilization and maintained for 24, 48, or 72 h. The transient HS treatments altered the initiation of endosperm (ED) cellularization, seed size and/or the duration of spikelet ripening. Notably, seeds exposed to 24 and 48 h moderate HS exhibited higher germination rate compared to seeds derived from plants grown under control or severe HS. A short-term HS resulted in altered expression of Gibberellin (GA) and ABA biosynthesis genes during early seed development, and GA and ABA levels and starch content at maturity. The increased germination rate after 24 of moderate HS could be due to altered ABA sensitivity and/or increased starch level. Our findings on the impact of transient HS on hormone homeostasis provide an experimental framework to elucidate the underlying molecular and metabolic pathways

Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification

Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments