GmGLU1 and GmRR4 contribute to iron deficiency tolerance in soybean

Iron deficiency chlorosis (IDC) is a form of abiotic stress that negatively impacts soybean yield. In a previous study, we demonstrated that the historical IDC quantitative trait locus (QTL) on soybean chromosome Gm03 was composed of four distinct linkage blocks, each containing candidate genes for IDC tolerance. Here, we take advantage of virus-induced gene silencing (VIGS) to validate the function of three high-priority candidate genes, each corresponding to a different linkage block in the Gm03 IDC QTL. We built three single-gene constructs to target GmGLU1 (GLUTAMATE SYNTHASE 1, Glyma.03G128300), GmRR4 (RESPONSE REGULATOR 4, Glyma.03G130000), and GmbHLH38 (beta Helix Loop Helix 38, Glyma.03G130400 and Glyma.03G130600). Given the polygenic nature of the iron stress tolerance trait, we also silenced the genes in combination. We built two constructs targeting GmRR4+GmGLU1 and GmbHLH38+GmGLU1. All constructs were tested on the iron-efficient soybean genotype Clark grown in iron-sufficient conditions. We observed significant decreases in soil plant analysis development (SPAD) measurements using the GmGLU1 construct and both double constructs, with potential additive effects in the GmRR4+GmGLU1 construct. Whole genome expression analyses (RNA-seq) revealed a wide range of affected processes including known iron stress responses, defense and hormone signaling, photosynthesis, and cell wall structure. These findings highlight the importance of GmGLU1 in soybean iron stress responses and provide evidence that IDC is truly a polygenic trait, with multiple genes within the QTL contributing to IDC tolerance. Finally, we conducted BLAST analyses to demonstrate that the Gm03 IDC QTL is syntenic across a broad range of plant species.

Virus induced gene silencing confirms oligogenic inheritance of brown stem rot resistance in soybean

Brown Stem Rot (BSR), caused by the soil borne fungal pathogen Phialophora gregata, can reduce soybean yields by as much as 38%. Previous allelism studies identified three Resistant to brown stem Rot genes (Rbs1, Rbs2, and Rbs3), all mapping to large, overlapping regions on soybean chromosome 16. However, recent fine-mapping and genome wide association studies (GWAS) suggest Rbs1, Rbs2, and Rbs3 are alleles of a single Rbs locus. To address this conflict, we characterized the Rbs locus using the Williams82 reference genome (Wm82.a4.v1). We identified 120 Receptor-Like Proteins (RLPs), with hallmarks of disease resistance receptor-like proteins (RLPs), which formed five distinct clusters. We developed virus induced gene silencing (VIGS) constructs to target each of the clusters, hypothesizing that silencing the correct RLP cluster would result in a loss of resistance phenotype. The VIGS constructs were tested against P. gregata resistant genotypes L78-4094 (Rbs1), PI 437833 (Rbs2), or PI 437970 (Rbs3), infected with P. gregata or mock infected. No loss of resistance phenotype was observed. We then developed VIGS constructs targeting two RLP clusters with a single construct. Construct B1a/B2 silenced P. gregata resistance in L78-4094, confirming at least two genes confer Rbs1-mediated resistance to P. gregata. Failure of B1a/B2 to silence resistance in PI 437833 and PI 437970 suggests additional genes confer BSR resistance in these lines. To identify differentially expressed genes (DEGs) responding to silencing, we conducted RNA-seq of leaf, stem and root samples from B1a/B2 and empty vector control plants infected with P. gregata or mock infected. B1a/B2 silencing induced DEGs associated with cell wall biogenesis, lipid oxidation, the unfolded protein response and iron homeostasis and repressed numerous DEGs involved in defense and defense signaling. These findings will improve integration of Rbs resistance into elite germplasm and provide novel insights into fungal disease resistance.

Coupling VIGS with Short- and Long-Term Stress Exposure to Understand the Fiskeby III Iron Deficiency Stress Response

Yield loss due to abiotic stress is an increasing problem in agriculture. Soybean is a major crop for the upper Midwestern United States and calcareous soils exacerbate iron deficiency for growers, resulting in substantial yield losses. Fiskeby III is a soybean variety uniquely resistant to a variety of abiotic stresses, including iron deficiency. Previous studies identified a MATE transporter (Glyma.05G001700) associated with iron stress tolerance in Fiskeby III. To understand the function of this gene in the Fiskeby III response to iron deficiency, we coupled its silencing using virus-induced gene silencing with RNAseq analyses at two timepoints. Analyses of these data confirm a role for the MATE transporter in Fiskeby III iron stress responses. Further, they reveal that Fiskeby III induces transcriptional reprogramming within 24 h of iron deficiency stress, confirming that like other soybean varieties, Fiskeby III is able to quickly respond to stress. However, Fiskeby III utilizes novel genes and pathways in its iron deficiency response. Identifying and characterizing these genes and pathways in Fiskeby III provides novel targets for improving abiotic stress tolerance in elite soybean lines.

Comparing Early Transcriptomic Responses of 18 Soybean (Glycine max) Genotypes to Iron Stress

Iron deficiency chlorosis (IDC) is an abiotic stress that negatively affects soybean (Glycine max [L.] Merr.) production. Much of our knowledge of IDC stress responses is derived from model plant species. Gene expression, quantitative trait loci (QTL) mapping, and genome-wide association studies (GWAS) performed in soybean suggest that stress response differences exist between model and crop species. Our current understanding of the molecular response to IDC in soybeans is largely derived from gene expression studies using near-isogenic lines differing in iron efficiency. To improve iron efficiency in soybeans and other crops, we need to expand gene expression studies to include the diversity present in germplasm collections. Therefore, we collected 216 purified RNA samples (18 genotypes, two tissue types [leaves and roots], two iron treatments [sufficient and deficient], three replicates) and used RNA sequencing to examine the expression differences of 18 diverse soybean genotypes in response to iron deficiency. We found a rapid response to iron deficiency across genotypes, most responding within 60 min of stress. There was little evidence of an overlap of specific differentially expressed genes, and comparisons of gene ontology terms and transcription factor families suggest the utilization of different pathways in the stress response. These initial findings suggest an untapped genetic potential within the soybean germplasm collection that could be used for the continued improvement of iron efficiency in soybean.

Mining Fiskeby III and Mandarin (Ottawa) Expression Profiles to Understand Iron Stress Tolerant Responses in Soybean

The soybean (Glycine max L. merr) genotype Fiskeby III is highly resistant to a multitude of abiotic stresses, including iron deficiency, incurring only mild yield loss during stress conditions. Conversely, Mandarin (Ottawa) is highly susceptible to disease and suffers severe phenotypic damage and yield loss when exposed to abiotic stresses such as iron deficiency, a major challenge to soybean production in the northern Midwestern United States. Using RNA-seq, we characterize the transcriptional response to iron deficiency in both Fiskeby III and Mandarin (Ottawa) to better understand abiotic stress tolerance. Previous work by our group identified a quantitative trait locus (QTL) on chromosome 5 associated with Fiskeby III iron efficiency, indicating Fiskeby III utilizes iron deficiency stress mechanisms not previously characterized in soybean. We targeted 10 of the potential candidate genes in the Williams 82 genome sequence associated with the QTL using virus-induced gene silencing. Coupling virus-induced gene silencing with RNA-seq, we identified a single high priority candidate gene with a significant impact on iron deficiency response pathways. Characterization of the Fiskeby III responses to iron stress and the genes underlying the chromosome 5 QTL provides novel targets for improved abiotic stress tolerance in soybean.

Gene Expression Responses to Sequential Nutrient Deficiency Stresses in Soybean

Throughout the growing season, crops experience a multitude of short periods of various abiotic stresses. These stress events have long-term impacts on plant performance and yield. It is imperative to improve our understanding of the genes and biological processes underlying plant stress tolerance to mitigate end of season yield loss. The majority of studies examining transcriptional changes induced by stress focus on single stress events. Few studies have been performed in model or crop species to examine transcriptional responses of plants exposed to repeated or sequential stress exposure, which better reflect field conditions. In this study, we examine the transcriptional profile of soybean plants exposed to iron deficiency stress followed by phosphate deficiency stress (-Fe-Pi). Comparing this response to previous studies, we identified a core suite of genes conserved across all repeated stress exposures (-Fe-Pi, -Fe-Fe, -Pi-Pi). Additionally, we determined transcriptional response to sequential stress exposure (-Fe-Pi) involves genes usually associated with reproduction, not stress responses. These findings highlight the plasticity of the plant transcriptome and the complexity of unraveling stress response pathways.

Examining Short-Term Responses to a Long-Term Problem: RNA-Seq Analyses of Iron Deficiency Chlorosis Tolerant Soybean

Iron deficiency chlorosis (IDC) is a global crop production problem, significantly impacting yield. However, most IDC studies have focused on model species, not agronomically important crops. Soybean is the second largest crop grown in the United States, yet the calcareous soils across most of the upper U.S. Midwest limit soybean growth and profitability. To understand early soybean iron stress responses, we conducted whole genome expression analyses (RNA-sequencing) of leaf and root tissue from the iron efficient soybean (Glycine max) cultivar Clark, at 30, 60 and 120 min after transfer to iron stress conditions. We identified over 10,000 differentially expressed genes (DEGs), with the number of DEGs increasing over time in leaves, but decreasing over time in roots. To investigate these responses, we clustered our expression data across time to identify suites of genes, their biological functions, and the transcription factors (TFs) that regulate their expression. These analyses reveal the hallmarks of the soybean iron stress response (iron uptake and homeostasis, defense, and DNA replication and methylation) can be detected within 30 min. Furthermore, they suggest root to shoot signaling initiates early iron stress responses representing a novel paradigm for crop stress adaptations.

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches

BACKGROUND

Iron (Fe) is an essential micronutrient for plant growth and development. Iron deficiency chlorosis (IDC), caused by calcareous soils or high soil pH, can limit iron availability, negatively affecting soybean (Glycine max) yield. This study leverages genome-wide association study (GWAS) and a genome-wide epistatic study (GWES) with previous gene expression studies to identify regions of the soybean genome important in iron deficiency tolerance.

RESULTS

A GWAS and a GWES were performed using 460 diverse soybean PI lines from 27 countries, in field and hydroponic iron stress conditions, using more than 36,000 single nucleotide polymorphism (SNP) markers. Combining this approach with available RNA-sequencing data identified significant markers, genomic regions, and novel genes associated with or responding to iron deficiency. Sixty-nine genomic regions associated with IDC tolerance were identified across 19 chromosomes via the GWAS, including the major-effect quantitative trait locus (QTL) on chromosome Gm03. Cluster analysis of significant SNPs in this region deconstructed this historically prominent QTL into four distinct linkage blocks, enabling the identification of multiple candidate genes for iron chlorosis tolerance. The complementary GWES identified SNPs in this region interacting with nine other genomic regions, providing the first evidence of epistatic interactions impacting iron deficiency tolerance.

CONCLUSIONS

This study demonstrates that integrating cutting edge genome wide association (GWA), genome wide epistasis (GWE), and gene expression studies is a powerful strategy to identify novel iron tolerance QTL and candidate loci from diverse germplasm. Crops, unlike model species, have undergone selection for thousands of years, constraining and/or enhancing stress responses. Leveraging genomics-enabled approaches to study these adaptations is essential for future crop improvement.

Using RNA-seq to characterize responses to 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitor herbicide resistance in waterhemp (Amaranthus tuberculatus)

BACKGROUND

Waterhemp (Amaranthus tuberculatus (Moq.) J.D. Sauer) is a problem weed commonly found in the Midwestern United States that can cause crippling yield losses for both maize (Zea mays L.) and soybean (Glycine max L. Merr). In 2011, 4-hydroxyphenylpyruvate-dioxygenase (HPPD, EC 1.13.11.27) inhibitor herbicide resistance was first reported in two waterhemp populations. Since the discovery of HPPD-herbicide resistance, studies have identified the mechanism of resistance and described the inheritance of the herbicide resistance. However, no studies have examined genome-wide gene expression changes in response to herbicide treatment in herbicide resistant and susceptible waterhemp.

RESULTS

We conducted RNA-sequencing (RNA-seq) analyses of two waterhemp populations (HPPD-herbicide resistant and susceptible), from herbicide-treated and mock-treated leaf samples at three, six, twelve, and twenty-four hours after treatment (HAT). We performed a de novo transcriptome assembly using all sample sequences. Following assessments of our assembly, individual samples were mapped to the de novo transcriptome allowing us to identify transcripts specific to a genotype, herbicide treatment, or time point. Our results indicate that the response of HPPD-herbicide resistant and susceptible waterhemp genotypes to HPPD-inhibiting herbicide is rapid, established as soon as 3 hours after herbicide treatment. Further, there was little overlap in gene expression between resistant and susceptible genotypes, highlighting dynamic differences in response to herbicide treatment. In addition, we used stringent analytical methods to identify candidate single nucleotide polymorphisms (SNPs) that distinguish the resistant and susceptible genotypes.

CONCLUSIONS

The waterhemp transcriptome, herbicide-responsive genes, and SNPs generated in this study provide valuable tools for future studies by numerous plant science communities. This collection of resources is essential to study and understand herbicide effects on gene expression in resistant and susceptible weeds. Understanding how herbicides impact gene expression could allow us to develop novel approaches for future herbicide development. Additionally, an increased understanding of the prolific traits intrinsic in weed success could lead to crop improvement.

An RNA-Seq based gene expression atlas of the common bean

BACKGROUND

Common bean (Phaseolus vulgaris) is grown throughout the world and comprises roughly 50% of the grain legumes consumed worldwide. Despite this, genetic resources for common beans have been lacking. Next generation sequencing, has facilitated our investigation of the gene expression profiles associated with biologically important traits in common bean. An increased understanding of gene expression in common bean will improve our understanding of gene expression patterns in other legume species.

RESULTS

Combining recently developed genomic resources for Phaseolus vulgaris, including predicted gene calls, with RNA-Seq technology, we measured the gene expression patterns from 24 samples collected from seven tissues at developmentally important stages and from three nitrogen treatments. Gene expression patterns throughout the plant were analyzed to better understand changes due to nodulation, seed development, and nitrogen utilization. We have identified 11,010 genes differentially expressed with a fold change ≥ 2 and a P-value < 0.05 between different tissues at the same time point, 15,752 genes differentially expressed within a tissue due to changes in development, and 2,315 genes expressed only in a single tissue. These analyses identified 2,970 genes with expression patterns that appear to be directly dependent on the source of available nitrogen. Finally, we have assembled this data in a publicly available database, The Phaseolus vulgaris Gene Expression Atlas (Pv GEA), http://plantgrn.noble.org/PvGEA/ . Using the website, researchers can query gene expression profiles of their gene of interest, search for genes expressed in different tissues, or download the dataset in a tabular form.

CONCLUSIONS

These data provide the basis for a gene expression atlas, which will facilitate functional genomic studies in common bean. Analysis of this dataset has identified genes important in regulating seed composition and has increased our understanding of nodulation and impact of the nitrogen source on assimilation and distribution throughout the plant.

An RNA-Seq transcriptome analysis of orthophosphate-deficient white lupin reveals novel insights into phosphorus acclimation in plants

Phosphorus, in its orthophosphate form (P(i)), is one of the most limiting macronutrients in soils for plant growth and development. However, the whole-genome molecular mechanisms contributing to plant acclimation to P(i) deficiency remain largely unknown. White lupin (Lupinus albus) has evolved unique adaptations for growth in P(i)-deficient soils, including the development of cluster roots to increase root surface area. In this study, we utilized RNA-Seq technology to assess global gene expression in white lupin cluster roots, normal roots, and leaves in response to P(i) supply. We de novo assembled 277,224,180 Illumina reads from 12 complementary DNA libraries to build what is to our knowledge the first white lupin gene index (LAGI 1.0). This index contains 125,821 unique sequences with an average length of 1,155 bp. Of these sequences, 50,734 were transcriptionally active (reads per kilobase per million reads ≥ 3), representing approximately 7.8% of the white lupin genome, using the predicted genome size of Lupinus angustifolius as a reference. We identified a total of 2,128 sequences differentially expressed in response to P(i) deficiency with a 2-fold or greater change and P ≤ 0.05. Twelve sequences were consistently differentially expressed due to P(i) deficiency stress in three species, Arabidopsis (Arabidopsis thaliana), potato (Solanum tuberosum), and white lupin, making them ideal candidates to monitor the P(i) status of plants. Additionally, classic physiological experiments were coupled with RNA-Seq data to examine the role of cytokinin and gibberellic acid in P(i) deficiency-induced cluster root development. This global gene expression analysis provides new insights into the biochemical and molecular mechanisms involved in the acclimation to P(i) deficiency.

An integrative approach to genomic introgression mapping

Near-isogenic lines (NILs) are valuable genetic resources for many crop species, including soybean (Glycine max). The development of new molecular platforms promises to accelerate the mapping of genetic introgressions in these materials. Here, we compare some existing and emerging methodologies for genetic introgression mapping: single-feature polymorphism analysis, Illumina GoldenGate single nucleotide polymorphism (SNP) genotyping, and de novo SNP discovery via RNA-Seq analysis of next-generation sequence data. We used these methods to map the introgressed regions in an iron-inefficient soybean NIL and found that the three mapping approaches are complementary when utilized in combination. The comparative RNA-Seq approach offers several additional advantages, including the greatest mapping resolution, marker depth, and de novo marker utility for downstream fine-mapping analysis. We applied the comparative RNA-Seq method to map genetic introgressions in an additional pair of NILs exhibiting differential seed protein content. Furthermore, we attempted to optimize the comparative RNA-Seq approach by assessing the impact of sequence depth, SNP identification methodology, and post hoc analyses on SNP discovery rates. We conclude that the comparative RNA-Seq approach can be optimized with sufficient sampling and by utilizing a post hoc correction accounting for gene density variation that controls for false discoveries.