A major natural genetic variation associated with root system architecture and plasticity improves waterlogging tolerance and yield in soybean

Integrating physiological and multi-omics methods to elucidate heat stress tolerance for sustainable rice production

Heat stress presents unique challenges compared to other environmental stressors, as predicting crop responses and understanding the mechanisms for heat tolerance are complex tasks. The escalating impact of devastating climate changes heightens the frequency and intensity of heat stresses, posing a noteworthy threat to global agricultural productivity, especially in rice-dependent regions of the developing world. Humidity has been demonstrated to negatively affect rice yields worldwide. Plants have evolved intricate biochemical adaptations, involving intricate interactions among genes, proteins, and metabolites, to counter diverse external signals and ensure their survival. Modern-omics technologies, encompassing transcriptomics, metabolomics, and proteomics, have revolutionized our comprehension of the intricate biochemical and cellular shifts that occur in stressed agricultural plants. Integrating these multi-omics approaches offers a comprehensive view of cellular responses to heat stress and other challenges, surpassing the insights gained from multi-omics analyses. This integration becomes vital in developing heat-tolerant crop varieties, which is crucial in the face of increasingly unpredictable weather patterns. To expedite the development of heat-resistant rice varieties, aiming at sustainability in terms of food production and food security globally, this review consolidates the latest peer-reviewed research highlighting the application of multi-omics strategies.

Genome-wide analysis of soybean hypoxia inducible gene domain containing genes: a functional investigation of GmHIGD3

The response of Hypoxia Inducible Gene Domain (HIGD) proteins to hypoxia plays a crucial role in plant development. However, the research on this gene family in soybean has been lacking. In this study, we aimed to identify and comprehensively analyze soybean HIGD genes using the Glycine max genome database. As a result, six GmHIGD genes were successfully identified, and their phylogeny, gene structures, and putative conserved motifs were analyzed in comparison to Arabidopsis and rice. Collinearity analysis indicated that the HIGD gene family in soybean has expanded to some extent when compared to Arabidopsis. Additionally, the cis-elements in the promoter regions of GmHIGD and the transcription factors potentially binding to these regions were identified. All GmHIGD genes showed specific responsiveness to submergence and hypoxic stresses. Expression profiling through quantitative real-time PCR revealed that these genes were significantly induced by PEG treatment in root tissue. Co-expressed genes of GmHIGD were primarily associated with oxidoreductase and dioxygenase activities, as well as peroxisome function. Notably, one of GmHIGD genes, GmHIGD3 was found to be predominantly localized in mitochondria, and its overexpression in Arabidopsis led to a significantly reduction in catalase activity compared to wild-type plants. These results bring new insights into the functional role of GmHIGD in terms of subcellular localization and the regulation of oxidoreductase activity.

Genetic diversity and candidate genes for transient waterlogging tolerance in mungbean at the germination and seedling stages

Mungbean [Vigna radiata var. radiata (L.) Wilczek] production in Asia is detrimentally affected by transient soil waterlogging caused by unseasonal and increasingly frequent extreme precipitation events. While mungbean exhibits sensitivity to waterlogging, there has been insufficient exploration of germplasm for waterlogging tolerance, as well as limited investigation into the genetic basis for tolerance to identify valuable loci. This research investigated the diversity of transient waterlogging tolerance in a mini-core germplasm collection of mungbean and identified candidate genes for adaptive traits of interest using genome-wide association studies (GWAS) at two critical stages of growth: germination and seedling stage (i.e., once the first trifoliate leaf had fully-expanded). In a temperature-controlled glasshouse, 292 genotypes were screened for tolerance after (i) 4 days of waterlogging followed by 7 days of recovery at the germination stage and (ii) 8 days of waterlogging followed by 7 days of recovery at the seedling stage. Tolerance was measured against drained controls. GWAS was conducted using 3,522 high-quality DArTseq-derived SNPs, revealing five significant associations with five phenotypic traits indicating improved tolerance. Waterlogging tolerance was positively correlated with the formation of adventitious roots and higher dry masses. FGGY carbohydrate kinase domain-containing protein was identified as a candidate gene for adventitious rooting and mRNA-uncharacterized LOC111241851, Caffeoyl-CoA O-methyltransferase At4g26220 and MORC family CW-type zinc finger protein 3 and zinc finger protein 2B genes for shoot, root, and total dry matter production. Moderate to high broad-sense heritability was exhibited for all phenotypic traits, including seed emergence (81%), adventitious rooting (56%), shoot dry mass (81%), root dry mass (79%) and SPAD chlorophyll content (70%). The heritability estimates, marker-trait associations, and identification of sources of waterlogging tolerant germplasm from this study demonstrate high potential for marker-assisted selection of tolerance traits to accelerate breeding of climate-resilient mungbean varieties.

Physio-biochemical and metabolomic responses of the woody plant Dalbergia odorifera to salinity and waterlogging

BACKGROUND

Trees have developed a broad spectrum of molecular mechanisms to counteract oxidative stress. Secondary metabolites via phenolic compounds emblematized the hidden bridge among plant kingdom, human health, and oxidative stress. Although studies have demonstrated that abiotic stresses can increase the production of medicinal compounds in plants, research comparing the efficiency of these stresses still needs to be explored. Thus, the present research paper provided an exhaustive comparative metabolomic study in Dalbergia odorifera under salinity (ST) and waterlogging (WL).

RESULTS

High ST reduced D. odorifera's fresh biomass compared to WL. While WL only slightly affected leaf and vein size, ST had a significant negative impact. ST also caused more significant damage to water status and leaflet anatomy than WL. As a result, WL-treated seedlings exhibited better photosynthesis and an up-regulation of nonenzymatic pathways involved in scavenging reactive oxygen species. The metabolomic and physiological responses of D. odorifera under WL and salinity ST stress revealed an accumulation of secondary metabolites by the less aggressive stress (WL) to counterbalance the oxidative stress. Under WL, more metabolites were more regulated compared to ST. ST significantly altered the metabolite profile in D. odorifera leaflets, indicating its sensitivity to salinity. WL synthesized more metabolites involved in phenylpropanoid, flavone, flavonol, flavonoid, and isoflavonoid pathways than ST. Moreover, the down-regulation of L-phenylalanine correlated with increased p-coumarate, caffeate, and ferulate associated with better cell homeostasis and leaf anatomical indexes under WL.

CONCLUSIONS

From a pharmacological and medicinal perspective, WL improved larger phenolics with therapeutic values compared to ST. Therefore, the data showed evidence of the crucial role of medical tree species' adaptability on ROS detoxification under environmental stresses that led to a significant accumulation of secondary metabolites with therapeutic value.

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning

Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crops, such as soybean and cotton, which has resulted in severe off-target dicamba exposure and substantial yield losses to non-tolerant crops. There is a strong demand for non-genetically engineered DT soybeans through conventional breeding selection. Public breeding programs have identified genetic resources that confer greater tolerance to off-target dicamba damage in soybeans. Efficient and high throughput phenotyping tools can facilitate the collection of a large number of accurate crop traits to improve the breeding efficiency. This study aimed to evaluate unmanned aerial vehicle (UAV) imagery and deep-learning-based data analytic methods to quantify off-target dicamba damage in genetically diverse soybean genotypes. In this research, a total of 463 soybean genotypes were planted in five different fields (different soil types) with prolonged exposure to off-target dicamba in 2020 and 2021. Crop damage due to off-target dicamba was assessed by breeders using a 1-5 scale with a 0.5 increment, which was further classified into three classes, i.e., susceptible (≥3.5), moderate (2.0 to 3.0), and tolerant (≤1.5). A UAV platform equipped with a red-green-blue (RGB) camera was used to collect images on the same days. Collected images were stitched to generate orthomosaic images for each field, and soybean plots were manually segmented from the orthomosaic images. Deep learning models, including dense convolutional neural network-121 (DenseNet121), residual neural network-50 (ResNet50), visual geometry group-16 (VGG16), and Depthwise Separable Convolutions (Xception), were developed to quantify crop damage levels. Results show that the DenseNet121 had the best performance in classifying damage with an accuracy of 82%. The 95% binomial proportion confidence interval showed a range of accuracy from 79% to 84% (p-value ≤ 0.01). In addition, no extreme misclassifications (i.e., misclassification between tolerant and susceptible soybeans) were observed. The results are promising since soybean breeding programs typically aim to identify those genotypes with 'extreme' phenotypes (e.g., the top 10% of highly tolerant genotypes). This study demonstrates that UAV imagery and deep learning have great potential to high-throughput quantify soybean damage due to off-target dicamba and improve the efficiency of crop breeding programs in selecting soybean genotypes with desired traits.

Plant responses to limited aeration: Advances and future challenges

Limited aeration that is caused by tissue geometry, diffusion barriers, high elevation, or a flooding event poses major challenges to plants and is often, but not exclusively, associated with low oxygen. These processes span a broad interest in the research community ranging from whole plant and crop responses, post-harvest physiology, plant morphology and anatomy, fermentative metabolism, plant developmental processes, oxygen sensing by ERF-VIIs, gene expression profiles, the gaseous hormone ethylene, and O₂ dynamics at cellular resolution. The International Society for Plant Anaerobiosis (ISPA) gathers researchers from all over the world contributing to understand the causes, responses, and consequences of limited aeration in plants. During the 14th ISPA meeting, major research progress was related to the evolution of O₂ sensing mechanisms and the intricate network that balances low O2 signaling. Here, the work moved beyond flooding stress and emphasized novel underexplored roles of low O2 and limited aeration in altitude adaptation, fruit development and storage, and the vegetative development of growth apices. Regarding tolerance towards flooding, the meeting stressed the relevance and regulation of developmental plasticity, aerenchyma, and barrier formation to improve internal aeration. Additional newly explored flood tolerance traits concerned resource balance, senescence, and the exploration of natural genetic variation for novel tolerance loci. In this report, we summarize and synthesize the major progress and future challenges for low O₂ and aeration research presented at the conference.

The intervention of classical and molecular breeding approaches to enhance flooding stress tolerance in soybean - An review

Abiotic stresses and climate changes cause severe loss of yield and quality of crops and reduce the production area worldwide. Flooding stress curtails soybean growth, yield, and quality and ultimately threatens the global food supply chain. Flooding tolerance is a multigenic trait. Tremendous research in molecular breeding explored the potential genomic regions governing flood tolerance in soybean. The most robust way to develop flooding tolerance in soybean is by using molecular methods, including quantitative trait loci (QTL) mapping, identification of transcriptomes, transcription factor analysis, CRISPR/Cas9, and to some extent, genome-wide association studies (GWAS), and multi-omics techniques. These powerful molecular tools have deepened our knowledge about the molecular mechanism of flooding stress tolerance. Besides all this, using conventional breeding methods (hybridization, introduction, and backcrossing) and other agronomic practices is also helpful in combating the rising flooding threats to the soybean crop. The current review aims to summarize recent advancements in breeding flood-tolerant soybean, mainly by using molecular and conventional tools and their prospects. This updated picture will be a treasure trove for future researchers to comprehend the foundation of flooding tolerance in soybean and cover the given research gaps to develop tolerant soybean cultivars able to sustain growth under extreme climatic changes.

Linkage analysis and residual heterozygotes derived near isogenic lines reveals a novel protein quantitative trait loci from a Glycine soja accession

Modern soybean [Glycine max (L.) Merr] cultivars have low overall genetic variation due to repeated bottleneck events that arose during domestication and from selection strategies typical of many soybean breeding programs. In both public and private soybean breeding programs, the introgression of wild soybean (Glycine soja Siebold and Zucc.) alleles is a viable option to increase genetic diversity and identify new sources for traits of value. The objectives of our study were to examine the genetic architecture responsible for seed protein and oil using a recombinant inbred line (RIL) population derived from hybridizing a G. max line ('Osage') with a G. soja accession (PI 593983). Linkage mapping identified a total of seven significant quantitative trait loci on chromosomes 14 and 20 for seed protein and on chromosome 8 for seed oil with LOD scores ranging from 5.3 to 31.7 for seed protein content and from 9.8 to 25.9 for seed oil content. We analyzed 3,015 single F4:9 soybean plants to develop two residual heterozygotes derived near isogenic lines (RHD-NIL) populations by targeting nine SNP markers from genotype-by-sequencing, which corresponded to two novel quantitative trait loci (QTL) derived from G. soja: one for a novel seed oil QTL on chromosome 8 and another for a novel protein QTL on chromosome 14. Single marker analysis and linkage analysis using 50 RHD-NILs validated the chromosome 14 protein QTL, and whole genome sequencing of RHD-NILs allowed us to reduce the QTL interval from ∼16.5 to ∼4.6 Mbp. We identified two genomic regions based on recombination events which had significant increases of 0.65 and 0.72% in seed protein content without a significant decrease in seed oil content. A new Kompetitive allele-specific polymerase chain reaction (KASP) assay, which will be useful for introgression of this trait into modern elite G. max cultivars, was developed in one region. Within the significantly associated genomic regions, a total of eight genes are considered as candidate genes, based on the presence of gene annotations associated with the protein or amino acid metabolism/movement. Our results provide better insights into utilizing wild soybean as a source of genetic diversity for soybean cultivar improvement utilizing native traits.

Hydrogen sulphide and nitric oxide mitigate the negative impacts of waterlogging stress on wheat (Triticum aestivum L.)

Nitric oxide (NO) and hydrogen sulphide (H₂ S) are important gaseous signalling molecules that regulate key physiochemical mechanisms of plants under environmental stresses. A number of attempts have been made to improve waterlogging tolerance in plants, but with limited success. Having said that, NO and H₂ S are vital signalling molecules, but their role in mitigating waterlogging effects on crop plants is not well established. We investigated the efficacy of exogenous NO and H₂ S to alleviate waterlogging effects in two wheat cultivars (Galaxy-2013 and FSD-2008). Waterlogging produced a noticeable reduction in plant growth, yield, chlorophyll, soluble sugars and free amino acids. Besides, waterlogging induced severe oxidative damage seen as higher cellular TBARS and H₂ O₂ content. Antioxidant enzyme activity increased together with a notable rise in Fe²⁺ and Mn²⁺ content. Proline content was higher in waterlogged plants compared with non-waterlogged plants. In contrast, waterlogging caused a substantial decline in endogenous levels of essential nutrients (K⁺ , Ca²⁺ and Mg²⁺ ). Waterlogged conditions led to Fe²⁺ and Mn²⁺ toxicity due to rapid reduction of Fe³⁺ and Mn³⁺ in the soil. Exogenous NO and H₂ S significantly protected plants from waterlogging effects by enhancing the oxidative defence and regulating nutritional status. Besides, the protective effects of exogenous NO were more prominent as compared with effects of H₂ S. Further, we did not study the effect of H₂ S and NO on photosynthetic attributes and expression of stress-related genes. Therefore, future studies should examine the effects of H₂ S and NO on wheat physiology and gene expression under waterlogging.

RNA-Seq Reveals Waterlogging-Triggered Root Plasticity in Mungbean Associated with Ethylene and Jasmonic Acid Signal Integrators for Root Regeneration

Global climate changes increase the frequency and intensity of heavy precipitation events, which result in flooding or soil waterlogging. One way to overcome these low-oxygen stresses is via modifying the plant root system to improve internal aeration. Here, we used a comparative RNA-seq based transcriptomic approach to elucidate the molecular mechanisms of waterlogging-triggered root plasticity in mungbean (Vigna radiata), a major grain legume cultivated in Asia. Two mungbean varieties with contrasting waterlogging tolerance due to the plasticity of the root system architecture were subjected to short-term and long-term waterlogging. Then, RNA-seq was performed. Genes highly expressed in both genotypes under short-term waterlogging are related to glycolysis and fermentation. Under long-term waterlogging, the expression of these genes was less induced in the tolerant variety, suggesting it had effectively adapted to waterlogging via enhancing root plasticity. Remarkably, under short-term waterlogging, the expression of several transcription factors that serve as integrators for ethylene and jasmonic acid signals controlling root stem cell development was highly upregulated only in the tolerant variety. Sequentially, root development-related genes were more expressed in the tolerant variety under long-term waterlogging. Our findings suggest that ethylene and jasmonic acids may contribute to waterlogging-triggered root plasticity by relaying environmental signals to reprogram root regeneration. This research provides the basis for the breeding and genetic engineering of waterlogging-tolerant crops in the future.

Physiology of Plant Responses to Water Stress and Related Genes: A Review

Drought and waterlogging seriously affect the growth of plants and are considered severe constraints on agricultural and forestry productivity; their frequency and degree have increased over time due to global climate change. The morphology, photosynthetic activity, antioxidant enzyme system and hormone levels of plants could change in response to water stress. The mechanisms of these changes are introduced in this review, along with research on key transcription factors and genes. Both drought and waterlogging stress similarly impact leaf morphology (such as wilting and crimping) and inhibit photosynthesis. The former affects the absorption and transportation mechanisms of plants, and the lack of water and nutrients inhibits the formation of chlorophyll, which leads to reduced photosynthetic capacity. Constitutive overexpression of 9-cis-epoxydioxygenase (NCED) and acetaldehyde dehydrogenase (ALDH), key enzymes in abscisic acid (ABA) biosynthesis, increases drought resistance. The latter forces leaf stomata to close in response to chemical signals, which are produced by the roots and transferred aboveground, affecting the absorption capacity of CO2, and reducing photosynthetic substrates. The root system produces adventitious roots and forms aerenchymal to adapt the stresses. Ethylene (ETH) is the main response hormone of plants to waterlogging stress, and is a member of the ERFVII subfamily, which includes response factors involved in hypoxia-induced gene expression, and responds to energy expenditure through anaerobic respiration. There are two potential adaptation mechanisms of plants (“static” or “escape”) through ETH-mediated gibberellin (GA) dynamic equilibrium to waterlogging stress in the present studies. Plant signal transduction pathways, after receiving stress stimulus signals as well as the regulatory mechanism of the subsequent synthesis of pyruvate decarboxylase (PDC) and alcohol dehydrogenase (ADH) enzymes to produce ethanol under a hypoxic environment caused by waterlogging, should be considered. This review provides a theoretical basis for plants to improve water stress tolerance and water-resistant breeding.

Poplar's Waterlogging Resistance Modeling and Evaluating: Exploring and Perfecting the Feasibility of Machine Learning Methods in Plant Science

Floods, as one of the most common disasters in the natural environment, have caused huge losses to human life and property. Predicting the flood resistance of poplar can effectively help researchers select seedlings scientifically and resist floods precisely. Using machine learning algorithms, models of poplar's waterlogging tolerance were established and evaluated. First of all, the evaluation indexes of poplar's waterlogging tolerance were analyzed and determined. Then, significance testing, correlation analysis, and three feature selection algorithms (Hierarchical clustering, Lasso, and Stepwise regression) were used to screen photosynthesis, chlorophyll fluorescence, and environmental parameters. Based on this, four machine learning methods, BP neural network regression (BPR), extreme learning machine regression (ELMR), support vector regression (SVR), and random forest regression (RFR) were used to predict the flood resistance of poplar. The results show that random forest regression (RFR) and support vector regression (SVR) have high precision. On the test set, the coefficient of determination (R²) is 0.8351 and 0.6864, the root mean square error (RMSE) is 0.2016 and 0.2780, and the mean absolute error (MAE) is 0.1782 and 0.2031, respectively. Therefore, random forest regression (RFR) and support vector regression (SVR) can be given priority to predict poplar flood resistance.

Physiological and Expressional Regulation on Photosynthesis, Starch and Sucrose Metabolism Response to Waterlogging Stress in Peanut

Waterlogging has negative effects on crop yield. Physiological and transcriptome data of two peanut cultivars [Zhongkaihua 1 (ZKH 1) and Huayu 39 (HY 39)] were studied under normal water supply and waterlogging stress for 5 or 10 days at the flowering stage. The results showed that the main stem height, the number of lateral branches, lateral branch length, and the stem diameter increased under waterlogging stress, followed by an increase in dry matter accumulation, which was correlated with the increase in the soil and plant analysis development (SPAD) and net photosynthetic rate (Pn) and the upregulation of genes related to porphyrin and chlorophyll metabolism and photosynthesis. However, the imbalance of the source-sink relationship under waterlogging was the main cause of yield loss, and waterlogging caused an increase in the sucrose and soluble sugar contents and a decrease in the starch content; it also decreased the activities of sucrose synthetase (SS) and sucrose phosphate synthetase (SPS), which may be due to the changes in the expression of genes related to starch and sucrose metabolism. However, the imbalance of the source-sink relationship led to the accumulation of photosynthate in the stems and leaves, which resulted in the decrease of the ratio of pod dry weight to total dry weight (PDW/TDW) and yield. Compared with ZKH 1, the PDW of HY 39 decreased more probably because more photosynthate accumulated in the stem and leaves of HY 39 and could not be effectively transported to the pod.

Prioritization and Evaluation of Flooding Tolerance Genes in Soybean [Glycine max (L.) Merr.]

Soybean [Glycine max (L.) Merr.] is one of the most important legume crops abundant in edible protein and oil in the world. In recent years there has been increasingly more drastic weather caused by climate change, with flooding, drought, and unevenly distributed rainfall gradually increasing in terms of the frequency and intensity worldwide. Severe flooding has caused extensive losses to soybean production and there is an urgent need to breed strong soybean seeds with high flooding tolerance. The present study demonstrates bioinformatics big data mining and integration, meta-analysis, gene mapping, gene prioritization, and systems biology for identifying prioritized genes of flooding tolerance in soybean. A total of 83 flooding tolerance genes (FTgenes), according to the appropriate cut-off point, were prioritized from 36,705 test genes collected from multidimensional genomic features linking to soybean flooding tolerance. Several validation results using independent samples from SoyNet, genome-wide association study, SoyBase, GO database, and transcriptome databases all exhibited excellent agreement, suggesting these 83 FTgenes were significantly superior to others. These results provide valuable information and contribution to research on the varieties selection of soybean.

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning

Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.

Soybean BARCSoySNP6K: An assay for soybean genetics and breeding research

The limited number of recombinant events in recombinant inbred lines suggests that for a biparental population with a limited number of recombinant inbred lines, it is unnecessary to genotype the lines with many markers. For genomic prediction and selection, previous studies have demonstrated that only 1000-2000 genome-wide common markers across all lines/accessions are needed to reach maximum efficiency of genomic prediction in populations. Evaluation of too many markers will not only increase the cost but also generate redundant information. We developed a soybean (Glycine max) assay, BARCSoySNP6K, containing 6000 markers, which were carefully chosen from the SoySNP50K assay based on their position in the soybean genome and haplotype block, polymorphism among accessions and genotyping quality. The assay includes 5000 single nucleotide polymorphisms (SNPs) from euchromatic and 1000 from heterochromatic regions. The percentage of SNPs with minor allele frequency >0.10 was 95% and 91% in the euchromatic and heterochromatic regions, respectively. Analysis of progeny from two large families genotyped with SoySNP50K versus BARCSoySNP6K showed that the position of the common markers and number of unique bins along linkage maps were consistent based on the SNPs genotyped with the two assays; however, the rate of redundant markers was dramatically reduced with the BARCSoySNP6K. The BARCSoySNP6K assay is proven as an excellent tool for detecting quantitative trait loci, genomic selection and assessing genetic relationships. The assay is commercialized by Illumina Inc. and being used by soybean breeders and geneticists and the list of SNPs in the assay is an ideal resource for SNP genotyping by targeted amplicon sequencing.

Review: Proteomic Techniques for the Development of Flood-Tolerant Soybean

Soybean, which is rich in protein and oil as well as phytochemicals, is cultivated in several climatic zones. However, its growth is markedly decreased by flooding stress, which is caused by climate change. Proteomic techniques were used for understanding the flood-response and -tolerant mechanisms in soybean. Subcellular proteomics has potential to elucidate localized cellular responses and investigate communications among subcellular components during plant growth and under stress stimuli. Furthermore, post-translational modifications play important roles in stress response and tolerance to flooding stress. Although many flood-response mechanisms have been reported, flood-tolerant mechanisms have not been fully clarified for soybean because of limitations in germplasm with flooding tolerance. This review provides an update on current biochemical and molecular networks involved in soybean tolerance against flooding stress, as well as recent developments in the area of functional genomics in terms of developing flood-tolerant soybeans. This work will expedite marker-assisted genetic enhancement studies in crops for developing high-yielding stress-tolerant lines or varieties under abiotic stress.

Root angle modifications by the DRO1 homolog improve rice yields in saline paddy fields

The root system architecture (RSA) of crops can affect their production, particularly in abiotic stress conditions, such as with drought, waterlogging, and salinity. Salinity is a growing problem worldwide that negatively impacts on crop productivity, and it is believed that yields could be improved if RSAs that enabled plants to avoid saline conditions were identified. Here, we have demonstrated, through the cloning and characterization of qSOR1 (quantitative trait locus for SOIL SURFACE ROOTING 1), that a shallower root growth angle (RGA) could enhance rice yields in saline paddies. qSOR1 is negatively regulated by auxin, predominantly expressed in root columella cells, and involved in the gravitropic responses of roots. qSOR1 was found to be a homolog of DRO1 (DEEPER ROOTING 1), which is known to control RGA. CRISPR-Cas9 assays revealed that other DRO1 homologs were also involved in RGA. Introgression lines with combinations of gain-of-function and loss-of-function alleles in qSOR1 and DRO1 demonstrated four different RSAs (ultra-shallow, shallow, intermediate, and deep rooting), suggesting that natural alleles of the DRO1 homologs could be utilized to control RSA variations in rice. In saline paddies, near-isogenic lines carrying the qSOR1 loss-of-function allele had soil-surface roots (SOR) that enabled rice to avoid the reducing stresses of saline soils, resulting in increased yields compared to the parental cultivars without SOR. Our findings suggest that DRO1 homologs are valuable targets for RSA breeding and could lead to improved rice production in environments characterized by abiotic stress.

Should Root Plasticity Be a Crop Breeding Target?

Root phenotypic plasticity has been proposed as a target for the development of more productive crops in variable environments. However, the plasticity of root anatomical and architectural responses to environmental cues is highly complex, and the consequences of these responses for plant fitness are poorly understood. We propose that root phenotypic plasticity may be beneficial in natural or low-input systems in which the availability of soil resources is spatiotemporally dynamic. Crop ancestors and landraces were selected with multiple stresses, competition, significant root loss and heterogenous resource distribution which favored plasticity in response to resource availability. However, in high-input agroecosystems, the value of phenotypic plasticity is unclear, since human management has removed many of these constraints to root function. Further research is needed to understand the fitness landscape of plastic responses including understanding the value of plasticity in different environments, environmental signals that induce plastic responses, and the genetic architecture of plasticity before it is widely adopted in breeding programs. Phenotypic plasticity has many potential ecological, and physiological benefits, but its costs and adaptive value in high-input agricultural systems is poorly understood and merits further research.

Genome-wide association study of drought tolerance and biomass allocation in wheat

Genome wide association studies (GWAS) are important in discerning the genetic architecture of complex traits such as biomass allocation for improving drought tolerance and carbon sequestration potential of wheat. The objectives of this study were to deduce the population structure and marker-trait association for biomass traits in wheat under drought-stressed and non-stressed conditions. A 100-wheat (Triticum aestivum L.) genotype panel was phenotyped for days to heading (DTH), days to maturity (DTM), shoot biomass (SB), root biomass (RB), root to shoot ratio (RS) and grain yield (GY). The panel was sequenced using 15,600 single nucleotide polymorphism (SNPs) markers and subjected to genetic analysis using the compressed mixed linear model (CMLM) at false discovery rate (FDR < 0.05). Population structure analysis revealed six sub-clusters with high membership ancestry coefficient of ≤0.65 to their assigned sub-clusters. A total of 75 significant marker-trait associations (MTAs) were identified with a linkage disequilibrium threshold of 0.38 at 5cM. Thirty-seven of the MTAs were detected under drought-stressed condition and 48% were on the B genome, where most quantitative trait loci (QTLs) for RB, SB and GY were previously identified. There were seven pleiotropic markers for RB and SB that may facilitate simultaneous selection. Thirty-seven putative candidate genes were mined by gene annotation on the IWGSC RefSeq 1.1. The significant MTAs observed in this study will be useful in devising strategies for marker-assisted breeding for simultaneous improvement of drought tolerance and to enhance C sequestration capacity of wheat.

Comparative Transcriptome Analysis of Waterlogging-Sensitive and Tolerant Zombi Pea (Vigna Vexillata) Reveals Energy Conservation and Root Plasticity Controlling Waterlogging Tolerance

Vigna vexillata (zombi pea) is an underutilized legume crop considered to be a potential gene source in breeding for abiotic stress tolerance. This study focuses on the molecular characterization of mechanisms controlling waterlogging tolerance using two zombi pea varieties with contrasting waterlogging tolerance. Morphological examination revealed that in contrast to the sensitive variety, the tolerant variety was able to grow, maintain chlorophyll, form lateral roots, and develop aerenchyma in hypocotyl and taproots under waterlogging. To find the mechanism controlling waterlogging tolerance in zombi pea, comparative transcriptome analysis was performed using roots subjected to short-term waterlogging. Functional analysis indicated that glycolysis and fermentative genes were strongly upregulated in the sensitive variety, but not in the tolerant one. In contrast, the genes involved in auxin-regulated lateral root initiation and formation were expressed only in the tolerant variety. In addition, cell wall modification, aquaporin, and peroxidase genes were highly induced in the tolerant variety under waterlogging. Our findings suggest that energy management and root plasticity play important roles in mitigating the impact of waterlogging in zombi pea. The basic knowledge obtained from this study can be used in the molecular breeding of waterlogging-tolerant legume crops in the future.

Quantifying Variation in Soybean Due to Flood Using a Low-Cost 3D Imaging System

Flood has an important effect on plant growth by affecting their physiologic and biochemical properties. Soybean is one of the main cultivated crops in the world and the United States is one of the largest soybean producers. However, soybean plant is sensitive to flood stress that may cause slow growth, low yield, small crop production and result in significant economic loss. Therefore, it is critical to develop soybean cultivars that are tolerant to flood. One of the current bottlenecks in developing new crop cultivars is slow and inaccurate plant phenotyping that limits the genetic gain. This study aimed to develop a low-cost 3D imaging system to quantify the variation in the growth and biomass of soybean due to flood at its early growth stages. Two cultivars of soybeans, i.e. flood tolerant and flood sensitive, were planted in plant pots in a controlled greenhouse. A low-cost 3D imaging system was developed to take measurements of plant architecture including plant height, plant canopy width, petiole length, and petiole angle. It was found that the measurement error of the 3D imaging system was 5.8% in length and 5.0% in angle, which was sufficiently accurate and useful in plant phenotyping. Collected data were used to monitor the development of soybean after flood treatment. Dry biomass of soybean plant was measured at the end of the vegetative stage (two months after emergence). Results show that four groups had a significant difference in plant height, plant canopy width, petiole length, and petiole angle. Flood stress at early stages of soybean accelerated the growth of the flood-resistant plants in height and the petiole angle, however, restrained the development in plant canopy width and the petiole length of flood-sensitive plants. The dry biomass of flood-sensitive plants was near two to three times lower than that of resistant plants at the end of the vegetative stage. The results indicate that the developed low-cost 3D imaging system has the potential for accurate measurements in plant architecture and dry biomass that may be used to improve the accuracy of plant phenotyping.

Characterization of the XTH Gene Family: New Insight to the Roles in Soybean Flooding Tolerance

Xyloglucan endotransglycosylases/hydrolases (XTHs) are a class of enzymes involved in the construction and remodeling of cellulose/xyloglucan crosslinks and play an important role in regulating cell wall extensibility. However, little is known about this class of enzymes in soybean. Here, 61 soybean XTH genes (GmXTHs) were identified and classified into three subgroups through comparative phylogenetic analysis. Genome duplication greatly contributed to the expansion of GmXTH genes in soybean. A conserved amino acid motif responsible for the catalytic activity was identified in all GmXTHs. Further expression analysis revealed that most GmXTHs exhibited a distinct organ-specific expression pattern, and the expression level of many GmXTH genes was significantly associated with ethylene and flooding stress. To illustrate a possible role of XTH genes in regulating stress responses, the ArabidopsisAtXTH31 gene was overexpressed in soybean. The generated transgenic plants exhibited improved tolerance to flooding stress, with a higher germination rate and longer roots/hypocotyls during the seedling stage and vegetative growth stages. In summary, our combined bioinformatics and gene expression pattern analyses suggest that GmXTH genes play a role in regulating soybean stress responses. The enhanced soybean flooding tolerance resulting from the expression of an Arabidopsis XTH also supports the role of XTH genes in regulating plant flooding stress responses.

Proteomic Analysis of Rapeseed Root Response to Waterlogging Stress

The overall health of a plant is constantly affected by the changing and hostile environment. Due to climate change and the farming pattern of rice (Oryza sativa) and rapeseed (Brassica napus L.), stress from waterlogging poses a serious threat to productivity assurance and the yield of rapeseed in China's Yangtze River basin. In order to improve our understanding of the complex mechanisms behind waterlogging stress and identify waterlogging-responsive proteins, we firstly conducted iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomic analysis of rapeseed roots under waterlogging treatments, for both a tolerant cultivar ZS9 and sensitive cultivar GH01. A total of 7736 proteins were identified by iTRAQ, of which several hundred showed different expression levels, including 233, 365, and 326 after waterlogging stress for 4H, 8H, and 12H in ZS9, respectively, and 143, 175, and 374 after waterlogging stress for 4H, 8H, and 12H in GH01, respectively. For proteins repeatedly identified at different time points, gene ontology (GO) cluster analysis suggested that the responsive proteins of the two cultivars were both enriched in the biological process of DNA-dependent transcription and the oxidation⁻reduction process, and response to various stress and hormone stimulus, while different distribution frequencies in the two cultivars was investigated. Moreover, overlap proteins with similar or opposite tendencies of fold change between ZS9 and GH01 were observed and clustered based on the different expression ratios, suggesting the two genotype cultivars exhibited diversiform molecular mechanisms or regulation pathways in their waterlogging stress response. The following qRT-PCR (quantitative real-time polymerase chain reaction) results verified the candidate proteins at transcription levels, which were prepared for further research. In conclusion, proteins detected in this study might perform different functions in waterlogging responses and would provide information conducive to better understanding adaptive mechanisms under environmental stresses.

Improving Flooding Tolerance of Crop Plants

A major problem of climate change is the increasing duration and frequency of heavy rainfall events. This leads to soil flooding that negatively affects plant growth, eventually leading to death of plants if the flooding persists for several days. Most crop plants are very sensitive to flooding, and dramatic yield losses occur due to flooding each year. This review summarizes recent progress and approaches to enhance crop resistance to flooding. Most experiments have been done on maize, barley, and soybean. Work on other crops such as wheat and rape has only started. The most promising traits that might enhance crop flooding tolerance are anatomical adaptations such as aerenchyma formation, the formation of a barrier against radial oxygen loss, and the growth of adventitious roots. Metabolic adaptations might be able to improve waterlogging tolerance as well, but more studies are needed in this direction. Reasonable approaches for future studies are quantitative trait locus (QTL) analyses or genome-wide association (GWA) studies in combination with specific tolerance traits that can be easily assessed. The usage of flooding-tolerant relatives or ancestral cultivars of the crop of interest in these experiments might enhance the chances of finding useful tolerance traits to be used in breeding.