The Soybean Rpp3 Gene Encodes a TIR-NBS-LRR Protein that Confers Resistance to Phakopsora pachyrhizi.

Soybean rust is an economically significant disease caused by the fungus Phakopsora pachyrhizi that negatively impacts soybean (Glycine max [L.] Merr.) production throughout the world. Susceptible plants infected by P. pachyrhizi develop tan-colored lesions on the leaf surface that give rise to funnel-shaped uredinia as the disease progresses. While most soybean germplasm is susceptible, seven genetic loci (Rpp1 to Rpp7) that provide race-specific resistance to P. pachyrhizi (Rpp) have been identified. Rpp3 was first discovered and characterized in the soybean accession PI 462312 (Ankur), and it was also determined to be one of two Rpp genes present in PI 506764 (Hyuuga). Genetic crosses with PI 506764 were later used to fine-map the Rpp3 locus to a 371-kb region on chromosome 6. The corresponding region in the susceptible Williams 82 (Wm82) reference genome contains several homologous nucleotide binding site-leucine rich repeat (NBS-LRR) genes. To identify Rpp3, we designed oligonucleotide primers to amplify Rpp3 candidate (Rpp3C) NBS-LRR genes at this locus from PI 462312, PI 506764, and Wm82 using polymerase chain reaction (PCR). Five Rpp3C genes were identified in both Rpp3-resistant soybean lines, and co-silencing these genes compromised resistance to P. pachyrhizi. Gene expression analysis and sequence comparisons of the Rpp3C genes in PI 462312 and PI 506764 suggest that a single candidate gene, Rpp3C3, is responsible for Rpp3-mediated resistance. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.

A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database.

SoyBase, a USDA genetic and genomics database, holds professionally curated soybean genetic and genomic data, which is integrated and made accessible to researchers and breeders. The site holds several reference genome assemblies, as well as genetic maps, thousands of mapped traits, expression and epigenetic data, pedigree information, and extensive variant and genotyping data sets. SoyBase displays include genetic, genomic, and epigenetic maps of the soybean genome. Gene expression data is presented in the genome viewer as heat maps and pictorial and tabular displays in gene report pages. Millions of sequence variants have been added, representing variations across various collections of cultivars. This variant data is explorable using new interactive tools to visualize the distribution of those variants across the genome, between selected accessions. SoyBase holds several reference-quality soybean genome assemblies, accessible via various query tools and browsers, including a new visualization system for exploring the soybean pan-genome. SoyBase also serves as a nexus of announcements pertinent to the greater soybean research community. The database also includes a soybean-specific anatomic and biochemical trait ontology. The database can be accessed at https://soybase.org.

Dissection of canopy layer-specific genetic control of leaf angle in Sorghum bicolor by RNA sequencing.

BACKGROUND: Leaf angle is an important plant architecture trait, affecting plant density, light interception efficiency, photosynthetic rate, and yield. The "smart canopy" model proposes more vertical leaves in the top plant layers and more horizontal leaves in the lower canopy, maximizing conversion efficiency and photosynthesis. Sorghum leaf arrangement is opposite to that proposed in the "smart canopy" model, indicating the need for improvement. Although leaf angle quantitative trait loci (QTL) have been previously reported, only the Dwarf3 (Dw3) auxin transporter gene, colocalizing with a major-effect QTL on chromosome 7, has been validated. Additionally, the genetic architecture of leaf angle across canopy layers remains to be elucidated. RESULTS: This study characterized the canopy-layer specific transcriptome of five sorghum genotypes using RNA sequencing. A set of 284 differentially expressed genes for at least one layer comparison (FDR < 0.05) co-localized with 69 leaf angle QTL and were consistently identified across genotypes. These genes are involved in transmembrane transport, hormone regulation, oxidation-reduction process, response to stimuli, lipid metabolism, and photosynthesis. The most relevant eleven candidate genes for layer-specific angle modification include those homologous to genes controlling leaf angle in rice and maize or genes associated with cell size/expansion, shape, and cell number. CONCLUSIONS: Considering the predicted functions of candidate genes, their potential undesirable pleiotropic effects should be further investigated across tissues and developmental stages. Future validation of proposed candidates and exploitation through genetic engineering or gene editing strategies targeted to collar cells will bring researchers closer to the realization of a "smart canopy" sorghum.

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.

Interaction between Rag genes results in a unique synergistic transcriptional response that enhances soybean resistance to soybean aphids.

BACKGROUND: Pyramiding different resistance genes into one plant genotype confers enhanced resistance at the phenotypic level, but the molecular mechanisms underlying this effect are not well-understood. In soybean, aphid resistance is conferred by Rag genes. We compared the transcriptional response of four soybean genotypes to aphid feeding to assess how the combination of Rag genes enhanced the soybean resistance to aphid infestation. RESULTS: A strong synergistic interaction between Rag1 and Rag2, defined as genes differentially expressed only in the pyramid genotype, was identified. This synergistic effect in the Rag1/2 phenotype was very evident early (6 h after infestation) and involved unique biological processes. However, the response of susceptible and resistant genotypes had a large overlap 12 h after aphid infestation. Transcription factor (TF) analyses identified a network of interacting TF that potentially integrates signaling from Rag1 and Rag2 to produce the unique Rag1/2 response. Pyramiding resulted in rapid induction of phytochemicals production and deposition of lignin to strengthen the secondary cell wall, while repressing photosynthesis. We also identified Glyma.07G063700 as a novel, strong candidate for the Rag1 gene. CONCLUSIONS: The synergistic interaction between Rag1 and Rag2 in the Rag1/2 genotype can explain its enhanced resistance phenotype. Understanding molecular mechanisms that support enhanced resistance in pyramid genotypes could facilitate more directed approaches for crop improvement.

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.

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.

Dynamic gene expression changes in response to micronutrient, macronutrient, and multiple stress exposures in soybean.

Preserving crop yield is critical for US soybean production and the global economy. Crop species have been selected for increased yield for thousands of years with individual lines selected for improved performance in unique environments, constraints not experienced by model species such as Arabidopsis. This selection likely resulted in novel stress adaptations, unique to crop species. Given that iron deficiency is a perennial problem in the soybean growing regions of the USA and phosphate deficiency looms as a limitation to global agricultural production, nutrient stress studies in crop species are critically important. In this study, we directly compared whole-genome expression responses of leaves and roots to iron (Fe) and phosphate (Pi) deficiency, representing a micronutrient and macronutrient, respectively. Conducting experiments side by side, we observed soybean responds to both nutrient deficiencies within 24 h. While soybean responds largely to -Fe deficiency, it responds strongly to Pi resupply. Though the timing of the responses was different, both nutrient stress signals used the same molecular pathways. Our study is the first to demonstrate the speed and diversity of the soybean stress response to multiple nutrient deficiencies. We also designed the study to examine gene expression changes in response to multiple stress events. We identified 865 and 3375 genes that either altered their direction of expression after a second stress exposure or were only differentially expressed after a second stress event. Understanding the molecular underpinnings of these responses in crop species could have major implications for improving stress tolerance and preserving yield.

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.

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.

Visualization methods for differential expression analysis.

BACKGROUND: Despite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users. RESULTS: In this paper, we 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an integral component of differential expression analysis. We use public RNA-seq datasets to show that our new visualization tools can detect normalization issues, differential expression designation problems, and common analysis errors. We also show that our new visualization tools can identify genes of interest in ways undetectable with models. Our R package "bigPint" includes the plotting tools introduced in this paper, many of which are unique additions to what is currently available. The "bigPint" website is located at https://lindsayrutter.github.io/bigPint and contains short vignette articles that introduce new users to our package, all written in reproducible code. CONCLUSIONS: We emphasize that interactive graphics should be an indispensable component of modern RNA-seq analysis, which is currently not the case. This paper and its corresponding software aim to persuade 1) users to slightly modify their differential expression analyses by incorporating statistical graphics into their usual analysis pipelines, 2) developers to create additional complex and interactive plotting methods for RNA-seq data, possibly using lessons learned from our open-source codes. We hope our work will serve a small part in upgrading the RNA-seq analysis world into one that more wholistically extracts biological information using both models and visuals.

Rpp1 Encodes a ULP1-NBS-LRR Protein That Controls Immunity to Phakopsora pachyrhizi in Soybean.

Phakopsora pachyrhizi is the causal agent of Asian soybean rust. Susceptible soybean plants infected by virulent isolates of P. pachyrhizi are characterized by tan-colored lesions and erumpent uredinia on the leaf surface. Germplasm screening and genetic analyses have led to the identification of seven loci, Rpp1 to Rpp7, that provide varying degrees of resistance to P. pachyrhizi (Rpp). Two genes, Rpp1 and Rpp1b, map to the same region on soybean chromosome 18. Rpp1 is unique among the Rpp genes in that it confers an immune response (IR) to avirulent P. pachyrhizi isolates. The IR is characterized by a lack of visible symptoms, whereas resistance provided by Rpp1b to Rpp7 results in red-brown foliar lesions. Rpp1 maps to a region spanning approximately 150 kb on chromosome 18 between markers Sct_187 and Sat_064 in L85-2378 (Rpp1), an isoline developed from Williams 82 and PI 200492 (Rpp1). To identify Rpp1, we constructed a bacterial artificial chromosome library from soybean accession PI 200492. Sequencing of the Rpp1 locus identified three homologous nucleotide binding site-leucine rich repeat (NBS-LRR) candidate resistance genes between Sct_187 and Sat_064. Each candidate gene is also predicted to encode an N-terminal ubiquitin-like protease 1 (ULP1) domain. Cosilencing of the Rpp1 candidates abrogated the immune response in the Rpp1 resistant soybean accession PI 200492, indicating that Rpp1 is a ULP1-NBS-LRR protein and plays a key role in the IR.

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.

The MAPK Kinase Kinase GmMEKK1 Regulates Cell Death and Defense Responses.

MAPK signaling pathways play critical roles in plant immunity. Here, we silenced multiple genes encoding MAPKs using virus-induced gene silencing mediated by Bean pod mottle virus to identify MAPK genes involved in soybean (Glycine max) immunity. Surprisingly, a strong hypersensitive response (HR) cell death was observed when soybean MAPK KINASE KINASE1 (GmMEKK1), a homolog of Arabidopsis (Arabidopsis thaliana) MEKK1, was silenced. The HR was accompanied by the overaccumulation of defense signaling molecules, salicylic acid (SA) and hydrogen peroxide. Genes involved in primary metabolism, translation/transcription, photosynthesis, and growth/development were down-regulated in GmMEKK1-silenced plants, while the expression of defense-related genes was activated. Accordingly, GmMEKK1-silenced plants were more resistant to downy mildew (Peronospora manshurica) and Soybean mosaic virus compared with control plants. Silencing GmMEKK1 reduced the activation of GmMPK6 but enhanced the activation of GmMPK3 in response to flg22 peptide. Unlike Arabidopsis MPK4, GmMPK4 was not activated by either flg22 or SA. Interestingly, transient overexpression of GmMEKK1 in Nicotiana benthamiana also induced HR. Our results indicate that GmMEKK1 plays both positive and negative roles in immunity and appears to differentially activate downstream MPKs by promoting GmMPK6 activation but suppressing GmMPK3 activation in response to flg22. The involvement of GmMPK4 kinase activity in cell death and in flg22- or SA-triggered defense responses in soybean requires further investigation.

Leveraging RNA-Seq to Characterize Resistance to Brown Stem Rot and the Rbs3 Locus in Soybean.

Brown stem rot, caused by the fungus Phialophora gregata, reduces soybean yield by up to 38%. Although three dominant resistance loci have been identified (Rbs1 to Rbs3), the gene networks responsible for pathogen recognition and defense remain unknown. Further, identification and characterization of resistant and susceptible germplasm remains difficult. We conducted RNA-Seq of infected and mock-infected leaf, stem, and root tissues of a resistant (PI 437970, Rbs3) and susceptible (Corsoy 79) genotype. Combining historical mapping data with genotype expression differences allowed us to identify a cluster of receptor-like proteins that are candidates for the Rbs3 resistance gene. Reads mapping to the Rbs3 locus were used to identify potential novel single-nucleotide polymorphisms within candidate genes that could improve phenotyping and breeding efficiency. Comparing responses to infection revealed little overlap in differential gene expression between genotypes or tissues. Gene networks associated with defense, DNA replication, and iron homeostasis are hallmarks of resistance to P. gregata. This novel research demonstrates the utility of combining contrasting genotypes, gene expression, and classical genetic studies to characterize complex disease resistance loci.

Transcriptome response of Lolium arundinaceum to its fungal endophyte Epichloë coenophiala.

Tall fescue (Lolium arundinaceum) is one of the primary forage and turf grasses in temperate regions of the world. A number of favourable characteristics of tall fescue are enhanced by its seed-transmissible fungal symbiont (endophyte) Epichloë coenophiala. Our approach was to assemble the tall fescue transcriptome, then identify differentially expressed genes (DEGs) for endophyte-symbiotic (E+) vs endophyte-free (E-) clones in leaf blades, pseudostems, crowns and roots. RNA-seq reads were used to construct a tall fescue reference transcriptome and compare gene expression profiles. Over all tissues examined, 478 DEGs were identified between the E+ and E- clones for at least one tissue (more than two-fold; P < 0.0001, 238 E+ > E- and 240 E- > E+), although no genes were differentially expressed in all four tissues. Gene ontology (GO) terms, GO:0010200 (response to chitin), GO:0002679 (respiratory burst during defence response) and GO:0035556 (intracellular signal transduction) were significantly overrepresented among 25 E- > E+ DEGs in leaf blade, and a number of other DEGs were associated with defence and abiotic response. In particular, endophyte effects on various WRKY transcription factors may have implications for symbiotic stability, endophyte distribution in the plant, or defence against pathogens.

The Medicago sativa gene index 1.2: a web-accessible gene expression atlas for investigating expression differences between Medicago sativa subspecies.

BACKGROUND: Alfalfa (Medicago sativa L.) is the primary forage legume crop species in the United States and plays essential economic and ecological roles in agricultural systems across the country. Modern alfalfa is the result of hybridization between tetraploid M. sativa ssp. sativa and M. sativa ssp. falcata. Due to its large and complex genome, there are few genomic resources available for alfalfa improvement. RESULTS: A de novo transcriptome assembly from two alfalfa subspecies, M. sativa ssp. sativa (B47) and M. sativa ssp. falcata (F56) was developed using Illumina RNA-seq technology. Transcripts from roots, nitrogen-fixing root nodules, leaves, flowers, elongating stem internodes, and post-elongation stem internodes were assembled into the Medicago sativa Gene Index 1.2 (MSGI 1.2) representing 112,626 unique transcript sequences. Nodule-specific and transcripts involved in cell wall biosynthesis were identified. Statistical analyses identified 20,447 transcripts differentially expressed between the two subspecies. Pair-wise comparisons of each tissue combination identified 58,932 sequences differentially expressed in B47 and 69,143 sequences differentially expressed in F56. Comparing transcript abundance in floral tissues of B47 and F56 identified expression differences in sequences involved in anthocyanin and carotenoid synthesis, which determine flower pigmentation. Single nucleotide polymorphisms (SNPs) unique to each M. sativa subspecies (110,241) were identified. CONCLUSIONS: The Medicago sativa Gene Index 1.2 increases the expressed sequence data available for alfalfa by ninefold and can be expanded as additional experiments are performed. The MSGI 1.2 transcriptome sequences, annotations, expression profiles, and SNPs were assembled into the Alfalfa Gene Index and Expression Database (AGED) at http://plantgrn.noble.org/AGED/ , a publicly available genomic resource for alfalfa improvement and legume research.

Comprehensive characterization and RNA-Seq profiling of the HD-Zip transcription factor family in soybean (Glycine max) during dehydration and salt stress.

BACKGROUND: The homeodomain leucine zipper (HD-Zip) transcription factor family is one of the largest plant specific superfamilies, and includes genes with roles in modulation of plant growth and response to environmental stresses. Many HD-Zip genes are characterized in Arabidopsis (Arabidopsis thaliana), and members of the family are being investigated for abiotic stress responses in rice (Oryza sativa), maize (Zea mays), poplar (Populus trichocarpa) and cucumber (Cucmis sativus). Findings in these species suggest HD-Zip genes as high priority candidates for crop improvement. RESULTS: In this study we have identified members of the HD-Zip gene family in soybean cv. 'Williams 82', and characterized their expression under dehydration and salt stress. Homology searches with BLASTP and Hidden Markov Model guided sequence alignments identified 101 HD-Zip genes in the soybean genome. Phylogeny reconstruction coupled with domain and gene structure analyses using soybean, Arabidopsis, rice, grape (Vitis vinifera), and Medicago truncatula homologues enabled placement of these sequences into four previously described subfamilies. Of the 101 HD-Zip genes identified in soybean, 88 exist as whole-genome duplication-derived gene pairs, indicating high retention of these genes following polyploidy in Glycine ~13 Mya. The HD-Zip genes exhibit ubiquitous expression patterns across 24 conditions that include 17 tissues of soybean. An RNA-Seq experiment performed to study differential gene expression at 0, 1, 6 and 12 hr soybean roots under dehydration and salt stress identified 20 differentially expressed (DE) genes. Several of these DE genes are orthologs of genes previously reported to play a role under abiotic stress, implying conservation of HD-Zip gene functions across species. Screening of HD-Zip promoters identified transcription factor binding sites that are overrepresented in the DE genes under both dehydration and salt stress, providing further support for the role of HD-Zip genes in abiotic stress responses. CONCLUSIONS: We provide a thorough description of soybean HD-Zip genes, and identify potential candidates with probable roles in dehydration and salt stress. Expression profiles generated for all soybean genes, under dehydration and salt stress, at four time points, will serve as an important resource for the soybean research community, and will aid in understanding plant responses to abiotic stress.

Identification of candidate genes involved in early iron deficiency chlorosis signaling in soybean (Glycine max) roots and leaves.

BACKGROUND: Iron is an essential micronutrient for all living things, required in plants for photosynthesis, respiration and metabolism. A lack of bioavailable iron in soil leads to iron deficiency chlorosis (IDC), causing a reduction in photosynthesis and interveinal yellowing of leaves. Soybeans (Glycine max (L.) Merr.) grown in high pH soils often suffer from IDC, resulting in substantial yield losses. Iron efficient soybean cultivars maintain photosynthesis and have higher yields under IDC-promoting conditions than inefficient cultivars. RESULTS: To capture signaling between roots and leaves and identify genes acting early in the iron efficient cultivar Clark, we conducted a RNA-Seq study at one and six hours after replacing iron sufficient hydroponic media (100 µM iron(III) nitrate nonahydrate) with iron deficient media (50 µM iron(III) nitrate nonahydrate). At one hour of iron stress, few genes were differentially expressed in leaves but many were already changing expression in roots. By six hours, more genes were differentially expressed in the leaves, and a massive shift was observed in the direction of gene expression in both roots and leaves. Further, there was little overlap in differentially expressed genes identified in each tissue and time point. CONCLUSIONS: Genes involved in hormone signaling, regulation of DNA replication and iron uptake utilization are key aspects of the early iron-efficiency response. We observed dynamic gene expression differences between roots and leaves, suggesting the involvement of many transcription factors in eliciting rapid changes in gene expression. In roots, genes involved iron uptake and development of Casparian strips were induced one hour after iron stress. In leaves, genes involved in DNA replication and sugar signaling responded to iron deficiency. The differentially expressed genes (DEGs) and signaling components identified here represent new targets for soybean improvement.