Genetic diversity and genomic strategies for improving drought and waterlogging tolerance in soybeans

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.

Root system architecture change in response to waterlogging stress in a 448 global collection of rapeseeds (Brassica napus L.)

MAIN CONCLUSIONS

A novel image-based screening method for precisely identifying genotypic variations in rapeseed RSA under waterlogging stress was developed. Five key root traits were confirmed as good indicators of waterlogging and might be employed in breeding, particularly when using the MFVW approach. Waterlogging is a vital environmental factor that has detrimental effects on the growth and development of rapeseed (Brassica napus L.). Plant roots suffer from hypoxia under waterlogging, which ultimately confers yield penalty. Therefore, it is crucially important to understand the genetic variation of root system architecture (RSA) in response to waterlogging stress to guide the selection of new tolerant cultivars with favorable roots. This research was conducted to investigate RSA traits using image-based screening techniques to better understand how RSA changes over time during waterlogging at the seedling stage. First, we performed a t-test by comparing the relative root trait value between four tolerant and four sensitive accessions. The most important root characteristics associated with waterlogging tolerance at 12 h are total root length (TRL), total root surface area (TRSA), total root volume (TRV), total number of tips (TNT), and total number of forks (TNF). The root structures of 448 rapeseed accessions with or without waterlogging showed notable genetic diversity, and all traits were generally restrained under waterlogging conditions, except for the total root average diameter. Additionally, according to the evaluation and integration analysis of 448 accessions, we identified that five traits, TRL, TRSA, TRV, TNT, and TNF, were the most reliable traits for screening waterlogging-tolerant accessions. Using analysis of the membership function value (MFVW) and D-value of the five selected traits, 25 extremely waterlogging-tolerant materials were screened out. Waterlogging significantly reduced RSA, inhibiting root growth compared to the control. Additionally, waterlogging increased lipid peroxidation, accompanied by a decrease in the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). This study effectively improves our understanding of the response of RSA to waterlogging. The image-based screening method developed in this study provides a new scientific guidance for quickly examining the basic RSA changes and precisely predicting waterlogging-tolerant rapeseed germplasms, thus expanding the genetic diversity of waterlogging-tolerant rapeseed germplasm available for breeding.

Microbiome structure variation and soybean's defense responses during flooding stress and elevated CO2

Introduction

With current trends in global climate change, both flooding episodes and higher levels of CO2 have been key factors to impact plant growth and stress tolerance. Very little is known about how both factors can influence the microbiome diversity and function, especially in tolerant soybean cultivars. This work aims to (i) elucidate the impact of flooding stress and increased levels of CO2 on the plant defenses and (ii) understand the microbiome diversity during flooding stress and elevated CO2 (eCO2).

Methods

We used next-generation sequencing and bioinformatic methods to show the impact of natural flooding and eCO2 on the microbiome architecture of soybean plants' below- (soil) and above-ground organs (root and shoot). We used high throughput rhizospheric extra-cellular enzymes and molecular analysis of plant defense-related genes to understand microbial diversity in plant responses during eCO2 and flooding.

Results

Results revealed that bacterial and fungal diversity was substantially higher in combined flooding and eCO2 treatments than in non-flooding control. Microbial diversity was soil>root>shoot in response to flooding and eCO2. We found that sole treatment of eCO2 and flooding had significant abundances of Chitinophaga, Clostridium, and Bacillus. Whereas the combination of flooding and eCO2 conditions showed a significant abundance of Trichoderma and Gibberella. Rhizospheric extra-cellular enzyme activities were significantly higher in eCO2 than flooding or its combination with eCO2. Plant defense responses were significantly regulated by the oxidative stress enzyme activities and gene expression of Elongation factor 1 and Alcohol dehydrogenase 2 in floodings and eCO2 treatments in soybean plant root or shoot parts.

Conclusion

This work suggests that climatic-induced changes in eCO2 and submergence can reshape microbiome structure and host defenses, essential in plant breeding and developing stress-tolerant crops. This work can help in identifying core-microbiome species that are unique to flooding stress environments and increasing eCO2.

Transcriptome Analysis of GmPUB20A Overexpressing and RNA-Interferencing Transgenic Hairy Roots Reveals Underlying Negative Role in Soybean Resistance to Cyst Nematode

Ubiquitination genes are key components of plant responses to biotic stress. GmPUB20A, a ubiquitination gene, plays a negative role in soybean resistance to soybean cyst nematode (SCN). In this study, we employed high-throughput sequencing to investigate transcriptional changes in GmPUB20A overexpressing and RNA-interfering transgenic hairy roots. Totally, 7661 differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that DEGs were significantly enriched in disease resistance and signal transduction pathways. In addition, silencing Glyma.15G021600 and Glyma.09G284700 by siRNA, the total number of nematodes was decreased by 33.48% and 27.47% than control plants, respectively. Further, GUS activity and reactive oxygen species (ROS) assays revealed that GmPUB20A, Glyma.15G021600, and Glyma.09G284700 respond to SCN parasitism and interfere with the accumulation of ROS in plant roots, respectively. Collectively, our study provides insights into the molecular mechanism of GmPUB20A in soybean resistance to SCN.

SoyDBean: a database for SNPs reconciliation by multiple versions of soybean reference genomes

Due to the development of sequence technology and decreased cost, many whole genome sequences have been obtained. As a result, extensive genetic variations have been discovered from many populations and germplasms to understand the genetic diversity of soybean (Glycine max [L.] Merr.). However, assessing the quality of variation is essential because the published variants were collected using different bioinformatic methods and parameters. Furthermore, despite the enhanced genome contiguity and more efficient filling of "N" stretches in the new reference genome, there remains a dearth of endeavors to verify the caliber of variations present in it. The primary goal of this research was to discern a dependable set of SNPs that can withstand reconciliation across multiple reference genomes. Additionally, the investigation aimed to reconfirm the variations through the utilization of numerous whole genome sequencing data obtained from publicly available databases. Based on the result, we created datasets that comprised the thoroughly verified SNP coordinates between the reference assemblies. The resulting "SoyDBean" database is now publicly accessible through the following URL: http://soydbean.plantprofile.net/ .

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant's stability under drought stresses. We built a genomic prediction model (MT RRM   model ) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MT RRM   models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MT RRM   model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.

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.

Genetic analysis of grapevine root system architecture and loci associated gene networks

Own-rooted grapevines and grapevine rootstocks are vegetatively propagated from cuttings and have an adventitious root system. Unraveling the genetic underpinnings of the adventitious root system architecture (RSA) is important for improving own-rooted and grafted grapevine sustainability for a changing climate. Grapevine RSA genetic analysis was conducted in an Vitis sp. 'VRS-F2' population. Nine root morphology, three total root system morphology, and two biomass traits that contribute to root anchorage and water and nutrient uptake were phenotyped. Quantitative trait loci (QTL) analysis was performed using a high density integrated GBS and rhAmpSeq genetic map. Thirty-one QTL were detected for eleven of the RSA traits (surface area, root volume, total root length, fresh weight, number of tips, forks or links, longest root and average root diameter, link length, and link surface area) revealing many small effects. Several QTL were colocated on chromosomes 1, 9, 13, 18, and 19. QTL with identical peak positions on chromosomes 1 or 13 were enriched for AP2-EREBP, AS2, C2C2-CO, HMG, and MYB transcription factors, and QTL on chromosomes 9 or 13 were enriched for the ALFIN-LIKE transcription factor and regulation of autophagy pathways. QTL modeling for individual root traits identified eight models explaining 13.2 to 31.8% of the phenotypic variation. 'Seyval blanc' was the grandparent contributing to the allele models that included a greater surface area, total root length, and branching (number of forks and links) traits promoting a greater root density. In contrast, V. riparia 'Manitoba 37' contributed the allele for greater average branch length (link length) and diameter, promoting a less dense elongated root system with thicker roots. LATERAL ORGAN BOUNDARY DOMAIN (LBD or AS2/LOB) and the PROTODERMAL FACTOR (PFD2 and ANL2) were identified as important candidate genes in the enriched pathways underlying the hotspots for grapevine adventitious RSA. The combined QTL hotspot and trait modeling identified transcription factors, cell cycle and circadian rhythm genes with a known role in root cell and epidermal layer differentiation, lateral root development and cortex thickness. These genes are candidates for tailoring grapevine root system texture, density and length in breeding programs.

Overexpression of lncRNA77580 Regulates Drought and Salinity Stress Responses in Soybean

Emerging evidence indicates that long non-coding RNAs (lncRNAs) play important roles in diverse biological processes. However, the biological functions of most plant lncRNAs are still unknown. We previously discovered a soybean abiotic-stress-related lncRNA, lncRNA77580, and cloned the entire full-length sequence. Here, in order to fully identify the function of lncRNA77580 in soybean stress response, we created transgenic soybean lines overexpressing lncRNA77580. Compared with the wild type, overexpression of lncRNA77580 enhances the drought tolerance of soybean. However, the transgenic plants exhibit increased sensitivity to high salinity at the seedling stage. We found that lncRNA77580 modulates the transcription of different gene sets during salt and drought stress response. Under water deficit at the reproductive stage, lncRNA77580 overexpression increases the seed yield by increasing the seed number per plant. These results provide insight into the role of lncRNA77580 in soybean stress response.

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.

Effects of irrigation on root growth and development of soybean: A 3-year sandy field experiment

Increasing the water use efficiency of crops is an important agricultural goal closely related to the root system -the primary plant organ for water and nutrient acquisition. In an attempt to evaluate the response of root growth and development of soybean to water supply levels, 200 genotypes were grown in a sandy field for 3 years under irrigated and non-irrigated conditions, and 14 root traits together with shoot fresh weight and plant height were investigated. Three-way ANOVA revealed a significant effect of treatments and years on growth of plants, accounting for more than 80% of the total variability. The response of roots to irrigation was consistent over the years as most root traits were improved by irrigation. However, the actual values varied between years because the growth of plants was largely affected by the field microclimatic conditions (i.e., temperature, sunshine duration, and precipitation). Therefore, the best linear unbiased prediction values for each trait were calculated using the original data. Principal component analysis showed that most traits contributed to principal component (PC) 1, whereas average diameter, the ratio of thin and medium thickness root length to total root length contributed to PC2. Subsequently, we focused on selecting genotypes that exhibited significant improvements in root traits under irrigation than under non-irrigated conditions using the increment (I-index) and relative increment (RI-index) indices calculated for all traits. Finally, we screened for genotypes with high stability and root growth over the 3 years using the multi-trait selection index (MTSI).Six genotypes namely, GmJMC130, GmWMC178, GmJMC092, GmJMC068, GmWMC075, and GmJMC081 from the top 10% of genotypes scoring MTSI less than the selection threshold of 7.04 and 4.11 under irrigated and non-irrigated conditions, respectively, were selected. The selected genotypes have great potential for breeding cultivars with improved water usage abilities, meeting the goal of water-saving agriculture.

Responses of root system architecture to water stress at multiple levels: A meta-analysis of trials under controlled conditions

Plants are exposed to increasingly severe drought events and roots play vital roles in maintaining plant survival, growth, and reproduction. A large body of literature has investigated the adaptive responses of root traits in various plants to water stress and these studies have been reviewed in certain groups of plant species at a certain scale. Nevertheless, these responses have not been synthesized at multiple levels. This paper screened over 2000 literatures for studies of typical root traits including root growth angle, root depth, root length, root diameter, root dry weight, root-to-shoot ratio, root hair length and density and integrates their drought responses at genetic and morphological scales. The genes, quantitative trait loci (QTLs) and hormones that are involved in the regulation of drought response of the root traits were summarized. We then statistically analyzed the drought responses of root traits and discussed the underlying mechanisms. Moreover, we highlighted the drought response of 1-D and 2-D root length density (RLD) distribution in the soil profile. This paper will provide a framework for an integrated understanding of root adaptive responses to water deficit at multiple scales and such insights may provide a basis for selection and breeding of drought tolerant crop lines.

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.

Biotechnological Advances to Improve Abiotic Stress Tolerance in Crops

The major challenges that agriculture is facing in the twenty-first century are increasing droughts, water scarcity, flooding, poorer soils, and extreme temperatures due to climate change. However, most crops are not tolerant to extreme climatic environments. The aim in the near future, in a world with hunger and an increasing population, is to breed and/or engineer crops to tolerate abiotic stress with a higher yield. Some crop varieties display a certain degree of tolerance, which has been exploited by plant breeders to develop varieties that thrive under stress conditions. Moreover, a long list of genes involved in abiotic stress tolerance have been identified and characterized by molecular techniques and overexpressed individually in plant transformation experiments. Nevertheless, stress tolerance phenotypes are polygenetic traits, which current genomic tools are dissecting to exploit their use by accelerating genetic introgression using molecular markers or site-directed mutagenesis such as CRISPR-Cas9. In this review, we describe plant mechanisms to sense and tolerate adverse climate conditions and examine and discuss classic and new molecular tools to select and improve abiotic stress tolerance in major crops.

Systematic Characterization of the OSCA Family Members in Soybean and Validation of Their Functions in Osmotic Stress

Since we discovered OSCA1, a hyperosmolarity-gated calcium-permeable channel that acted as an osmosensor in Arabidopsis, the OSCA family has been identified genome-wide in several crops, but only a few OSCA members' functions have been experimentally demonstrated. Osmotic stress seriously restricts the yield and quality of soybean. Therefore, it is essential to decipher the molecular mechanism of how soybean responds to osmotic stress. Here, we first systematically studied and experimentally demonstrated the role of OSCA family members in the osmotic sensing of soybean. Phylogenetic relationships, gene structures, protein domains and structures analysis revealed that 20 GmOSCA members were divided into four clades, of which members in the same cluster may have more similar functions. In addition, GmOSCA members in clusters III and IV may be functionally redundant and diverged from those in clusters I and II. Based on the spatiotemporal expression patterns, GmOSCA1.6, GmOSCA2.1, GmOSCA2.6, and GmOSCA4.1 were extremely low expressed or possible pseudogenes. The remaining 16 GmOSCA genes were heterologously overexpressed in an Arabidopsis osca1 mutant, to explore their functions. Subcellular localization showed that most GmOSCA members could localize to the plasma membrane (PM). Among 16 GmOSCA genes, only overexpressing GmOSCA1.1, GmOSCA1.2, GmOSCA1.3, GmOSCA1.4, and GmOSCA1.5 in cluster I could fully complement the reduced hyperosmolality-induced [Ca²⁺]_i increase (OICI) in osca1. The expression profiles of GmOSCA genes against osmotic stress demonstrated that most GmOSCA genes, especially GmOSCA1.1, GmOSCA1.2, GmOSCA1.3, GmOSCA1.4, GmOSCA1.5, GmOSCA3.1, and GmOSCA3.2, strongly responded to osmotic stress. Moreover, overexpression of GmOSCA1.1, GmOSCA1.2, GmOSCA1.3, GmOSCA1.4, GmOSCA1.5, GmOSCA3.1, and GmOSCA3.2 rescued the drought-hypersensitive phenotype of osca1. Our findings provide important clues for further studies of GmOSCA-mediated calcium signaling in the osmotic sensing of soybean and contribute to improving soybean drought tolerance through genetic engineering and molecular breeding.

A Nuclear Factor Y-B Transcription Factor, GmNFYB17, Regulates Resistance to Drought Stress in Soybean

Soybean is sensitive to drought stress, and increasing tolerance to drought stresses is an important target for improving the performance of soybean in the field. The genetic mechanisms underlying soybean's drought tolerance remain largely unknown. Via a genome-wide association study (GWAS) combined with linkage analysis, we identified 11 single-nucleotide polymorphisms (SNPs) and 22 quantitative trait locus (QTLs) that are significantly associated with soybean drought tolerance. One of these loci, namely qGI10-1, was co-located by GWAS and linkage mapping. The two intervals of qGI10-1 were differentiated between wild and cultivated soybean. A nuclear factor Y transcription factor, GmNFYB17, was located in one of the differentiated regions of qGI10-1 and thus selected as a candidate gene for further analyses. The analysis of 29 homologous genes of GmNFYB17 in soybean showed that most of the genes from this family were involved in drought stress. The over-expression of GmNFYB17 in soybean enhanced drought resistance and yield accumulation. The transgenic plants grew better than control under limited water conditions and showed a lower degree of leaf damage and MDA content but higher RWC, SOD activity and proline content compared with control. Moreover, the transgenic plants showed a fast-growing root system, especially regarding a higher root-top ratio and more branching roots and lateral roots. The better agronomic traits of yield were also found in GmNFYB17 transgenic plants. Thus, the GmNFYB17 gene was proven to positively regulate drought stress resistance and modulate root growth in soybean. These results provide important insights into the molecular mechanisms underlying drought tolerance in soybean.

Physiological and Transcriptomic Characterization of Sea-Wheatgrass-Derived Waterlogging Tolerance in Wheat

Waterlogging, causing hypoxia stress and nitrogen depletion in the rhizosphere, has been an increasing threat to wheat production. We developed a wheat-sea wheatgrass (SWG) amphiploid showing superior tolerance to waterlogging and low nitrogen. Validated in deoxygenated agar medium for three weeks, hypoxia stress reduced the dry matter of the wheat parent by 40% but had little effect on the growth of the amphiploid. To understand the underlying mechanisms, we comparatively analyzed the wheat-SWG amphiploid and its wheat parent grown in aerated and hypoxic solutions for physiological traits and root transcriptomes. Compared with its wheat parent, the amphiploid showed less magnitude in forming root porosity and barrier to radial oxygen loss, two important mechanisms for internal O₂ movement to the apex, and downregulation of genes for ethylene, lignin, and reactive oxygen species. In another aspect, however, hypoxia stress upregulated the nitrate assimilation/reduction pathway in amphiploid and induced accumulation of nitric oxide, a byproduct of nitrate reduction, in its root tips, and the amphiploid maintained much higher metabolic activity in its root system compared with its wheat parent. Taken together, our research suggested that enhanced nitrate assimilation and reduction and accumulation of nitric oxide play important roles in the SWG-derived waterlogging tolerance.

Characterization of Root System Architecture Traits in Diverse Soybean Genotypes Using a Semi-Hydroponic System

Phenotypic variation and correlations among root traits form the basis for selecting and breeding soybean varieties with efficient access to water and nutrients and better adaptation to abiotic stresses. Therefore, it is important to develop a simple and consistent system to study root traits in soybean. In this study, we adopted the semi-hydroponic system to investigate the variability in root morphological traits of 171 soybean genotypes popularized in the Yangtze and Huaihe River regions, eastern China. Highly diverse phenotypes were observed: shoot height (18.7-86.7 cm per plant with a median of 52.3 cm); total root length (208-1663 cm per plant with a median of 885 cm); and root mass (dry weight) (19.4-251 mg per plant with a median of 124 mg). Both total root length and root mass exhibited significant positive correlation with shoot mass (p ≤ 0.05), indicating their relationship with plant growth and adaptation strategies. The nine selected traits contributed to one of the two principal components (eigenvalues > 1), accounting for 78.9% of the total genotypic variation. Agglomerative hierarchical clustering analysis separated the 171 genotypes into five major groups based on these root traits. Three selected genotypes with contrasting root systems were validated in soil-filled rhizoboxes (1.5 m deep) until maturity. Consistent ranking of the genotypes in some important root traits at various growth stages between the two experiments indicates the reliability of the semi-hydroponic system in phenotyping root trait variability at the early growth stage in soybean germplasms.

Mycorrhizal fungi induced activation of tomato defense system mitigates Fusarium wilt stress

The fungus Fusarium oxysporum f. sp. lycopersici (FOL) is known to cause vascular wilt on tomato almost over the world. Inoculation of FOL reduced plant growth and increased wilt of tomato. The following study examined the possible role of arbuscular mycorrhizal fungi (AMF) consortium comprising of Rhizophagus intraradices, Funneliformis mosseae and Claroideoglomus etunicatum against FOL in tomato and explored in an inducing plant systemic defense. AMF inoculation reduced the wilt disease within vascular tissue and in vivo production of fusaric acid was observed which may be responsible in reduced wilting. FOL had an antagonistic effect on AMF colonization, reduced the number of spores, arbuscules and vesicles. AMF also inhibited the damage induced by Fusarium wilt through increasing chlorophyll contents along with the activity of phosphate metabolising enzymes (acid and alkaline phosphatases). Moreover, tomato plants with mycorrhizal inoculation showed an increase in the level of antioxidant enzymes including glutathione reductase, catalase, and etc. with an ultimate influence on the elimination of reactive oxygen species. Moreover, rise in phosphatase along with antioxidant enzymatic systems and enhanced photosynthetic performance contributed to induced resistance against FOL in tomato.

Impact of Genomic Prediction Model, Selection Intensity, and Breeding Strategy on the Long-Term Genetic Gain and Genetic Erosion in Soybean Breeding

Genomic-assisted breeding has become an important tool in soybean breeding. However, the impact of different genomic selection (GS) approaches on short- and long-term gains is not well understood. Such gains are conditional on the breeding design and may vary with a combination of the prediction model, family size, selection strategies, and selection intensity. To address these open questions, we evaluated various scenarios through a simulated closed soybean breeding program over 200 breeding cycles. Genomic prediction was performed using genomic best linear unbiased prediction (GBLUP), Bayesian methods, and random forest, benchmarked against selection on phenotypic values, true breeding values (TBV), and random selection. Breeding strategies included selections within family (WF), across family (AF), and within pre-selected families (WPSF), with selection intensities of 2.5, 5.0, 7.5, and 10.0%. Selections were performed at the F4 generation, where individuals were phenotyped and genotyped with a 6K single nucleotide polymorphism (SNP) array. Initial genetic parameters for the simulation were estimated from the SoyNAM population. WF selections provided the most significant long-term genetic gains. GBLUP and Bayesian methods outperformed random forest and provided most of the genetic gains within the first 100 generations, being outperformed by phenotypic selection after generation 100. All methods provided similar performances under WPSF selections. A faster decay in genetic variance was observed when individuals were selected AF and WPSF, as 80% of the genetic variance was depleted within 28-58 cycles, whereas WF selections preserved the variance up to cycle 184. Surprisingly, the selection intensity had less impact on long-term gains than did the breeding strategies. The study supports that genetic gains can be optimized in the long term with specific combinations of prediction models, family size, selection strategies, and selection intensity. A combination of strategies may be necessary for balancing the short-, medium-, and long-term genetic gains in breeding programs while preserving the genetic variance.

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.

The History and Diversity of Rice Domestication as Resolved From 1464 Complete Plastid Genomes

The plastid is an essential organelle in autotrophic plant cells, descending from free-living cyanobacteria and acquired by early eukaryotic cells through endosymbiosis roughly one billion years ago. It contained a streamlined genome (plastome) that is uniparentally inherited and non-recombinant, which makes it an ideal tool for resolving the origin and diversity of plant species and populations. In the present study, a large dataset was amassed by de novo assembling plastomes from 295 common wild rice (Oryza rufipogon Griff.) and 1135 Asian cultivated rice (Oryza sativa L.) accessions, supplemented with 34 plastomes from other Oryza species. From this dataset, the phylogenetic relationships and biogeographic history of O. rufipogon and O. sativa were reconstructed. Our results revealed two major maternal lineages across the two species, which further diverged into nine well supported genetic clusters. Among them, the Or-wj-I/II/III and Or-wi-I/II genetic clusters were shared with cultivated (percentage for each cluster ranging 54.9%∼99.3%) and wild rice accessions. Molecular dating, phylogeographic analyses and reconstruction of population historical dynamics indicated an earlier origin of the Or-wj-I/II genetic clusters from East Asian with at least two population expansions, and later origins of other genetic clusters from multiple regions with one or more population expansions. These results supported a single origin of japonica rice (mainly in Or-wj-I/II) and multiple origins of indica rice (in all five clusters) for the history of rice domestication. The massive plastomic data set presented here provides an important resource for understanding the history and evolution of rice domestication as well as a genomic resources for use in future breeding and conservation efforts.

Mutation of GmAITR Genes by CRISPR/Cas9 Genome Editing Results in Enhanced Salinity Stress Tolerance in Soybean

Breeding of stress-tolerant plants is able to improve crop yield under stress conditions, whereas CRISPR/Cas9 genome editing has been shown to be an efficient way for molecular breeding to improve agronomic traits including stress tolerance in crops. However, genes can be targeted for genome editing to enhance crop abiotic stress tolerance remained largely unidentified. We have previously identified abscisic acid (ABA)-induced transcription repressors (AITRs) as a novel family of transcription factors that are involved in the regulation of ABA signaling, and we found that knockout of the entire family of AITR genes in Arabidopsis enhanced drought and salinity tolerance without fitness costs. Considering that AITRs are conserved in angiosperms, AITRs in crops may be targeted for genome editing to improve abiotic stress tolerance. We report here that mutation of GmAITR genes by CRISPR/Cas9 genome editing leads to enhanced salinity tolerance in soybean. By using quantitative RT-PCR analysis, we found that the expression levels of GmAITRs were increased in response to ABA and salt treatments. Transfection assays in soybean protoplasts show that GmAITRs are nucleus proteins, and have transcriptional repression activities. By using CRISPR/Cas9 to target the six GmAITRs simultaneously, we successfully generated Cas9-free gmaitr36 double and gmaitr23456 quintuple mutants. We found that ABA sensitivity in these mutants was increased. Consistent with this, ABA responses of some ABA signaling key regulator genes in the gmaitr mutants were altered. In both seed germination and seedling growth assays, the gmaitr mutants showed enhanced salt tolerance. Most importantly, enhanced salinity tolerance in the mutant plants was also observed in the field experiments. These results suggest that mutation of GmAITR genes by CRISPR/Cas9 is an efficient way to improve salinity tolerance in soybean.

Transcriptome profiling of two Dactylis glomerata L. cultivars with different tolerance in response to submergence stress

Submergence is one of the environmental stresses that limit plant growth and development. Dactylis glomerata L. is an important cool-season forage grass globally. To investigate the genes related to submergence response and the molecular mechanism associated with submergence tolerance, the transcriptome of D. glomerata in response to waterlogging treatment was analyzed. RNA-sequencing was performed in two D. glomerata cultivars, submergence tolerant 'Dianbei' and submergence sensitive 'Anba'. A total of 50,045 unique genes matched the known proteins in the NCBI nr database by BLAST searches and 60.8% (30,418) of these genes were annotated with GO terms. Among these, 1395 genes only differentially expressed in 'Dianbei' and 18 genes shown different expression all the time were detected between the submergence tolerant 'Dianbei' and sensitive 'Anba'. Gene ontology (GO) and KEGG pathway enrichment analyses demonstrated that the DEGs were mainly implicated in oxidation-reduction system, nucleic acid binding transcription factor activity, and glycerol kinase activity. The D. glomerata assembled transcriptome provided substantial molecular resource for further genomic analysis of forage grasses in response to submergence stress. The significant difference in expression of specific unigenes may account for waterlogging tolerance or acclimation in the two different D. glomerata cultivars. This study provided new insights into the molecular basis of submergence tolerance in D. glomerata.

Biotechnological Perspectives of Omics and Genetic Engineering Methods in Alfalfa

For several decades, researchers are working to develop improved major crops with better adaptability and tolerance to environmental stresses. Forage legumes have been widely spread in the world due to their great ecological and economic values. Abiotic and biotic stresses are main factors limiting legume production, however, alfalfa (Medicago sativa L.) shows relatively high level of tolerance to drought and salt stress. Efforts focused on alfalfa improvements have led to the release of cultivars with new traits of agronomic importance such as high yield, better stress tolerance or forage quality. Alfalfa has very high nutritional value due to its efficient symbiotic association with nitrogen-fixing bacteria, while deep root system can help to prevent soil water loss in dry lands. The use of modern biotechnology tools is challenging in alfalfa since full genome, unlike to its close relative barrel medic (Medicago truncatula Gaertn.), was not released yet. Identification, isolation, and improvement of genes involved in abiotic or biotic stress response significantly contributed to the progress of our understanding how crop plants cope with these environmental challenges. In this review, we provide an overview of the progress that has been made in high-throughput sequencing, characterization of genes for abiotic or biotic stress tolerance, gene editing, as well as proteomic and metabolomics techniques bearing biotechnological potential for alfalfa improvement.

Immunoprofiling of Cell Wall Carbohydrate Modifications During Flooding-Induced Aerenchyma Formation in Fabaceae Roots

Understanding plant adaptation mechanisms to prolonged water immersion provides options for genetic modification of existing crops to create cultivars more tolerant of periodic flooding. An important advancement in understanding flooding adaptation would be to elucidate mechanisms, such as aerenchyma air-space formation induced by hypoxic conditions, consistent with prolonged immersion. Lysigenous aerenchyma formation occurs through programmed cell death (PCD), which may entail the chemical modification of polysaccharides in root tissue cell walls. We investigated if a relationship exists between modification of pectic polysaccharides through de-methyl esterification (DME) and the formation of root aerenchyma in select Fabaceae species. To test this hypothesis, we first characterized the progression of aerenchyma formation within the vascular stele of three different legumes-Pisum sativum, Cicer arietinum, and Phaseolus coccineus-through traditional light microscopy histological staining and scanning electron microscopy. We assessed alterations in stele morphology, cavity dimensions, and cell wall chemistry. Then we conducted an immunolabeling protocol to detect specific degrees of DME among species during a 48-hour flooding time series. Additionally, we performed an enzymatic pretreatment to remove select cell wall polymers prior to immunolabeling for DME pectins. We were able to determine that all species possessed similar aerenchyma formation mechanisms that begin with degradation of root vascular stele metaxylem cells. Immunolabeling results demonstrated DME occurs prior to aerenchyma formation and prepares vascular tissues for the beginning of cavity formation in flooded roots. Furthermore, enzymatic pretreatment demonstrated that removal of cellulose and select hemicellulosic carbohydrates unmasks additional antigen binding sites for DME pectin antibodies. These results suggest that additional carbohydrate modification may be required to permit DME and subsequent enzyme activity to form aerenchyma. By providing a greater understanding of cell wall pectin remodeling among legume species, we encourage further investigation into the mechanism of carbohydrate modifications during aerenchyma formation and possible avenues for flood-tolerance improvement of legume crops.

FvC5SD overexpression enhances drought tolerance in soybean by reactive oxygen species scavenging and modulating stress-responsive gene expression

KEY MESSAGE

Overexpression of FvC5SD improves drought tolerance in soybean. Drought stress is one of the most important abiotic stress factors that influence soybean crop quality and yield. Therefore, the creation of drought-tolerant soybean germplasm resources through genetic engineering technology is effective in alleviating drought stress. FvC5SD is a type of C-5 sterol desaturase gene that is obtained from the edible fungus Flammulina velutipes. This gene has good tolerance to the effects of stresses, including drought and low temperature, in yeast cells and tomato. In this study, we introduced the FvC5SD gene into the soybean variety Shennong9 through the Agrobacterium-mediated transformation of soybean to identify drought-tolerant transgenic soybean varieties. PCR, RT-PCR, and Southern blot analysis results showed that T-DNA was inserted into the soybean genome and stably inherited by the progeny. The ectopic expression of FvC5SD under the control of a CaMV 35S promoter in transgenic soybean plants enhanced the plant's tolerance to dehydration and drought. Under drought conditions, the transgenic plants accumulated lower levels of reactive oxygen species and exhibited higher activities and expression levels of enzymes and cell than wild-type soybean. iTRAQ analysis of the comparative proteomics showed that some exogenous genes coding either functional or regulatory proteins were induced in the transgenic lines under drought stress. FvC5SD overexpression can serve as a direct and efficient target in improving drought tolerance in soybean and may be an important biotechnological strategy for trait improvement in soybean and other crops.

Domesticating the Undomesticated for Global Food and Nutritional Security: Four Steps

Ensuring the food and nutritional demand of the ever-growing human population is a major sustainability challenge for humanity in this Anthropocene. The cultivation of climate resilient, adaptive and underutilized wild crops along with modern crop varieties is proposed as an innovative strategy for managing future agricultural production under the changing environmental conditions. Such underutilized and neglected wild crops have been recently projected by the Food and Agricultural Organization of the United Nations as ‘future smart crops’ as they are not only hardy, and resilient to changing climatic conditions, but also rich in nutrients. They need only minimal care and input, and therefore, they can be easily grown in degraded and nutrient-poor soil also. Moreover, they can be used for improving the adaptive traits of modern crops. The contribution of such neglected, and underutilized crops and their wild relatives to global food production is estimated to be around 115–120 billion US$ per annum. Therefore, the exploitation of such lesser utilized and yet to be used wild crops is highly significant for climate resilient agriculture and thereby providing a good quality of life to one and all. Here we provide four steps, namely: (i) exploring the unexplored, (ii) refining the unrefined traits, (iii) cultivating the uncultivated, and (iv) popularizing the unpopular for the sustainable utilization of such wild crops as a resilient strategy for ensuring food and nutritional security and also urge the timely adoption of suitable frameworks for the large-scale exploitation of such wild species for achieving the UN Sustainable Development Goals.

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.

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.

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.

Metabolomics and Transcriptomics in Legumes Under Phosphate Deficiency in Relation to Nitrogen Fixation by Root Nodules

Phosphate (Pi) deficiency is a critical environmental constraint that affects the growth and development of several legume crops that are usually cultivated in semi-arid regions and marginal areas. Pi deficiency is known to be a significant limitation for symbiotic nitrogen (N2) fixation (SNF), and variability in SNF is strongly interlinked with the concentrations of Pi in the nodules. To deal with Pi deficiency, plants trigger various adaptive responses, including the induction and secretion of acid phosphatases, maintenance of Pi homeostasis in nodules and other organs, and improvement of oxygen (O2) consumption per unit of nodule mass. These molecular and physiological responses can be observed in terms of changes in growth, photosynthesis, and respiration. In this mini review, we provide a brief introduction to the problem of Pi deficiency in legume crops. We then summarize the current understanding of how Pi deficiency is regulated in legumes by changes in the transcriptomes and metabolomes found in different plant organs. Finally, we will provide perspectives on future directions for research in this field.

Genetic Analysis of Flooding Tolerance in an Andean Diversity Panel of Dry Bean (Phaseolus vulgaris L.)

Climate change models predict temporal and spatial shifts in precipitation resulting in more frequent incidents of flooding, particularly in regions with poor soil drainage. In these flooding conditions, crop losses are inevitable due to exposure of plants to hypoxia and the spread of root rot diseases. Improving the tolerance of bean cultivars to flooding is crucial to minimize crop losses. In this experiment, we evaluated the phenotypic responses of 277 genotypes from the Andean Diversity Panel to flooding at germination and seedling stages. A randomized complete block design, with a split plot arrangement, was employed for phenotyping germination rate, total weight, shoot weight, root weight, hypocotyl length, SPAD index, adventitious root rate, and survival score. A subset of genotypes (n = 20) were further evaluated under field conditions to assess correlations between field and greenhouse data and to identify the most tolerant genotypes. A genome-wide association study (GWAS) was performed using ~203 K SNP markers to understand the genetic architecture of flooding tolerance in this panel. Survival scores between field and greenhouse data were significantly correlated (r = 0.55, P = 0.01). Subsequently, a subset of the most tolerant and susceptible genotypes were evaluated under pathogenic Pythium spp. pressure. This experiment revealed a potential link between flooding tolerance and Pythium spp. resistance. Several tolerant genotypes were identified that could be used as donor parents in breeding pipelines, especially ADP-429 and ADP-604. Based on the population structure analysis, a subpopulation consisting of 20 genotypes from the Middle American gene pool was detected that also possessed the highest root weight, hypocotyl length, and adventitious root development under flooding conditions. Genomic regions associated with flooding tolerance were identified including a region on Pv08/3.2 Mb, which is associated with germination rate and resides in vicinity of SnRK1.1, a central gene involved in response of plants to hypoxia. Furthermore, a QTL at Pv07/4.7 Mb was detected that controls survival score of seedlings under flooding conditions. The association of these QTL with the survivability traits including germination rate and survival score, indicates that these loci can be used in marker-assisted selection breeding to improve flooding tolerance in the Andean germplasm.

Dissecting the Genetic Basis of Local Adaptation in Soybean

Soybean (Glycine max) is the most widely grown oilseed in the world and is an important source of protein for both humans and livestock. Soybean is widely adapted to both temperate and tropical regions, but a changing climate demands a better understanding of adaptation to specific environmental conditions. Here, we explore genetic variation in a collection of 3,012 georeferenced, locally adapted landraces from a broad geographical range to help elucidate the genetic basis of local adaptation. We used geographic origin, environmental data and dense genome-wide SNP data to perform an environmental association analysis and discover loci displaying steep gradients in allele frequency across geographical distance and between landrace and modern cultivars. Our combined application of methods in environmental association mapping and detection of selection targets provide a better understanding of how geography and selection may have shaped genetic variation among soybean landraces. Moreover, we identified several important candidate genes related to drought and heat stress, and revealed important genomic regions possibly involved in the geographic divergence of soybean.

Genetic Architecture of Flooding Tolerance in the Dry Bean Middle-American Diversity Panel

Flooding is a devastating abiotic stress that endangers crop production in the twenty-first century. Because of the severe susceptibility of common bean (Phaseolus vulgaris L.) to flooding, an understanding of the genetic architecture and physiological responses of this crop will set the stage for further improvement. However, challenging phenotyping methods hinder a large-scale genetic study of flooding tolerance in common bean and other economically important crops. A greenhouse phenotyping protocol was developed to evaluate the flooding conditions at early stages. The Middle-American diversity panel (n = 272) of common bean was developed to capture most of the diversity exits in North American germplasm. This panel was evaluated for seven traits under both flooded and non-flooded conditions at two early developmental stages. A subset of contrasting genotypes was further evaluated in the field to assess the relationship between greenhouse and field data under flooding condition. A genome-wide association study using ~150 K SNPs was performed to discover genomic regions associated with multiple physiological responses. The results indicate a significant strong correlation (r > 0.77) between greenhouse and field data, highlighting the reliability of greenhouse phenotyping method. Black and small red beans were the least affected by excess water at germination stage. At the seedling stage, pinto and great northern genotypes were the most tolerant. Root weight reduction due to flooding was greatest in pink and small red cultivars. Flooding reduced the chlorophyll content to the greatest extent in the navy bean cultivars compared with other market classes. Races of Durango/Jalisco and Mesoamerica were separated by both genotypic and phenotypic data indicating the potential effect of eco-geographical variations. Furthermore, several loci were identified that potentially represent the antagonistic pleiotropy. The GWAS analysis revealed peaks at Pv08/1.6 Mb and Pv02/41 Mb that are associated with root weight and germination rate, respectively. These regions are syntenic with two QTL reported in soybean (Glycine max L.) that contribute to flooding tolerance, suggesting a conserved evolutionary pathway involved in flooding tolerance for these related legumes.