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Fig. 5 in Identification of iron-chelating phenolics contributing to seed coat coloration in soybeans (Glycine max (L.) Merr.) expressing aryloxyalkanoate dioxygenase-12

Fig. 5. UHPLC separation of color-enriched seed coat extracts from event DAS-411Ø4-7 and non-transgenic control with UV absorbance detection at 272 nm. Red trace, DAS-411Ø4-7; blue trace, non-transgenic control. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6 in Identification of iron-chelating phenolics contributing to seed coat coloration in soybeans (Glycine max (L.) Merr.) expressing aryloxyalkanoate dioxygenase-12

Fig. 6. LC-MS quantitation of genistin (brown) and genistein (blue) content of color-enriched seed coat fractions from event DAS-411Ø4-7 and non-transgenic control. Samples were either untreated in water, heated at 95 °C in water or heated at 95 °C in 2 N HCl. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in Identification of iron-chelating phenolics contributing to seed coat coloration in soybeans (Glycine max (L.) Merr.) expressing aryloxyalkanoate dioxygenase-12

Fig. 3. UV–visible absorption spectra of color-enriched fractions. (A) Absorbance spectra of 3 mg/mL color-enriched seed coat extracts (brown, event DAS-411Ø4-7; blue, non-transgenic control) dissolved in water (solid lines), or with the addition of 100 mM acetate (long dashes) or EDTA (short dashes). (B) Difference spectra of event DAS-411Ø4-7 (brown) and non-transgenic control (blue) samples dissolved in water or with 100 mM EDTA added. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Identification of iron-chelating phenolics contributing to seed coat coloration in soybeans (Glycine max (L.) Merr.) expressing aryloxyalkanoate dioxygenase-12

Fig. 2. Enrichment of seed coat coloration (SCC) from non-transgenic Maverick and event DAS-411Ø4-7 soybean seeds.

Fig. 3 in Glycine max (L.) Merr. (Soybean) metabolome responses to potassium availability

Fig. 3. Complete feature-based molecular network (FBMN) in global natural product social molecular networking (GNPS) of soybean trifoliate leaves and pod tissues, influenced by K+ availability. Nodes represent MS2 spectra and are connected based on spectral similarity defined (cosine score ≥ 0.8), matched fragment ion (5), and network TopK (10), encompassing 902 nodes and 1430 edges organised in 89 spectral molecular families. Large coloured nodes represent metabolites influ- enced by K+ nutrition identified by chemometrics models and representative chemical ontology.

Fig. 4 in Glycine max (L.) Merr. (Soybean) metabolome responses to potassium availability

Fig. 4. Isoflavonoids (isoflavones, coumestans and pterocarpans) and triterpenoid saponins (soyasaponins) as phytoalexins upregulated in soybean leaves under very low potassium availability.

Fig. 2 in Glycine max (L.) Merr. (Soybean) metabolome responses to potassium availability

Fig. 2. Soybean tissues representation and base peak intensity (BPI) mass chromatograms (UPLC-QToF-MSE) in negative ion mode (ESI-) displaying comparative metabolomic profile differences. The corresponding list of metabolites annotated in the chromatograms is available in Supplementary Data 1.

Fig. 1 in Glycine max (L.) Merr. (Soybean) metabolome responses to potassium availability

Fig. 1. Unsupervised and supervised chemometric models of UPLC-QTof-MSE data of soybeans under four soil K+ availability. These models allowed us to correlate the metabolomics data (404 and 221 molecular features to soybean leaves and pod tissues, respectively as X input) with ionomics (10 factors as Y input) coherently. (a) PCA-X&Y_A scatter plot of trifoliate leaves. (b) O2PLS-DA_B score plot highlighting the identified two clusters (C–I and C-II) of trifoliate leaves. (c) O2PLS-DA_B loading plot of trifoliate leaves, the loadings (factor) in the graph represents macro and micronutrients quantified by ICP-OES that contribute to the O2PLS-DA model. (d) PCA-X&Y_D scatter plot of soybean pod tissues. (e) O2PLS-DA_E score plot highlighting the identified four clusters (C–I, C-II, C-III, and C-IV) of soybean pod tissues. (f) O2PLS-DA_E loading plot of soybean pod tissues, the loadings (factor) in the graph represents nutrients quantified by ICP-OES that contribute to the O2PLS-DA model. List of abbreviations: LT – lower third leaves; MT – medium third leaves; UT– upper third leaves; IS - immature seeds; PV – pod valves; KVL – very low K+ availability; KL – low K+ availability; KM – medium K+ availability; KVH – very high K+ availability.

publicOct 2024View details →

dryad32/100

NBS-LRR PacBio sequencing from a Glycine max diversity panel

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opencc-zeroDec 2016View details →

Data from: Signatures of soft sweeps across the Dt1 locus underlying determinate growth habit in soybean [Glycine max (L.) Merr.]

Determinate growth habit is an agronomically important trait associated with domestication in soybean. Previous studies have demonstrated that the emergence of determinacy is correlated with artificial selection on four non-synonymous mutations in the Dt1 gene. To better understand the signatures of the soft sweeps across the Dt1 locus and track the origins of the determinate alleles, we examined patterns of nucleotide variation in Dt1 and the surrounding genomic region of approximately 800 kb. Four local, asymmetrical hard sweeps on four determinate alleles, sized approximately 660, 120, 220 and 150 kb were identified, which constitute the soft sweeps for the adaptation. These variable-sized sweeps substantially reflected the strength and timing of selection, and indicated that the selection on the alleles had been completed rapidly within half a century. Statistics of EHH, iHS, H12 and H2/H1 based on haplotype data had the power to detect the soft sweeps, revealing distinct signatures of extensive long-range LD and haplotype homozygosity, and multiple frequent adaptive haplotypes. A haplotype network constructed for Dt1 and a phylogenetic tree based on its extended haplotype block implied independent sources of the adaptive alleles through de novo mutations or rare standing variation in quick succession during the selective phase, strongly supporting multiple origins of the determinacy. We propose that the adaptation of soybean determinacy is guided by a model of soft sweeps and that this model might be indispensable during crop domestication or evolution.

opencc-zeroDec 2013View details →

Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

opencc-zeroApr 2022View details →

Cloning and functional characterization of a Type 3 Diacylglycerol Acyltransferasegene (GmDGAT3-2) from soybean (Glycine max L.)

Diacylglycerol acyltransferases (DGAT) function as the key rate-limiting enzymes in de novo biosynthesis of triacylglycerol (TAG) by transferring an acyl group from acyl-CoA to sn-3 of diacylglycerol (DAG) to form TAG. Here, two members of type 3 DGAT gene family, GmDGAT3-1 and GmDGAT3-2, were identified from soybean (Glycine max) genome. Both of them were predicted to encode soluble cytosolic proteins containing the typical thioredoxin-like ferredoxin domain. Quantitative PCR analysis revealed that GmDGAT3-2 expression was much higher than GmDGAT3-1's in various soybean tissues such as leaves, flowers and seeds. Functional complementation assay using TAG-deficient yeast (Saccharomyces cerevisiae) mutant H1246 demonstrated that GmDGAT3-2 fully restored TAG biosynthesis in the yeast and preferentially incorporated monounsaturated fatty acids (MUFAs), especially oleic acid (C18:1) into TAGs. This substrate specificity was further verified by feeding assays and in vitro enzyme activity characterization. Notably, transgenic tobacco (Nicotiana benthamiana) data showed that heterogeneous expression of GmDGAT3-2 resulted in significant increase of seed oil and C18:1 levels, but little change in contents of protein and starch compared to the EV-transformed tobacco plants. Taken together, GmDGAT3-2 displayed a strong enzymatic activity to catalyze TAG assembly with high substrate specificity for MUFAs, particularly C18:1, playing an important role in the cytosolic pathway of TAG synthesis in soybean. The present findings provide a scientific reference for improving oil yield and FA composition in soybean through gene modification, further expanding our knowledge of TAG biosynthesis and its regulatory mechanism in oilseeds.

opencc-by-4.0May 2024View details →

Structural equation models to interpret genome-wide association studies for morphological and productive traits in soybean [Glycine max (L.)]

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opencc-by-4.0Oct 2021View details →

Case study 13: Why is lentil (Lens culinaris) cultivation a success story in south-west Germany? Case study 14: Why is soybean (Glycine max) cultivation a success story in south-west Germany?

Case Study 13 Though lentils (Lens culinaris) are a traditional crop in many temperate European countries, the crop disappeared from European cropping systems in the mid-20th century. Lentils are still an important food in traditional and modern cuisine, and they currently gain importance based on the demand for vegetarian and vegan food. The revival of lentil growing and lentil consumption in parts of Germany for the last 10 years is an unprecedented story of success. We want to use this experience to push the re-introduction and expansion of lentil in modern farming all over Europe, particularly in those countries from where the crop has disappeared in the last decades. We seek to multiply the lentil acreage, to increase and stabilize yields, and to network European lentil growers. Due to agronomic reasons, growers cannot easily extent their lentil acreage, although there are many obvious benefits for the stability of agro-ecosystems such as the mixed cropping of lentils, diversification of crop rotations and N-fixation. If we want to keep lentil growing running and expanding, we have to understand these settings if growing should be promoted. On-farm research and data compilation from existing field trials will help to develop new and ecological sound techniques for lentil growing in modern farming systems. In a broad on-farm survey data will be collected for the first time to describe the impact of lentil growing on the agro-ecosystem and to determine and describe factors for successful lentil growing. This will improve the cropping system and the willingness of farmers to adopt lentil growing and thus increase the lentil acreage. Main challenges of lentil growing will be identifie in order to ease the adoption of lentil growing. The results will be a basis for decision-makers in European policy and society to develop tools for improving and maintaining ecological value of farmland, and for generally maintaining and improving agriculture in Europe, particularly in economically in underdeveloped regions. Lentil is exceptional in comparison to other pulses concerning their use: they are exclusively used for human consumption, their amount of anti-nutritive compounds is lowest among all pulses grown in Europe, grains can be consumed without complex processing, and consumers will directly benefit from the results of the project. Particularly the increasing trend of vegetarian or vegan diet can be satisfied easily and best with lentils. The overall outcome of the study would be(i)identification of agronomic key factors for successful lentil growing; (ii)stabilising and improving lentil growing based on(iii)to export the idea of lentil growing and improved cropping systems to other regions and countries in Europe based on (i)and(ii). Case Study 14 Several attempts have been made in the past to introduce soybean in temperate regions of Europe, which, however, did not lead to a long-lasting establishment of soybean cropping systems in agricultural practice. Now it seems there is a break-through in some regions of Germany, Eastern France, Austria and Switzerland, maybe due to climate change, the breeding and availability of adapted varieties (0,00,000-varieties), the demand for GMO free food and feed, and the demand for vegetarian/vegan food. The aim of the case study is to permanently integrate soybean in farming systems in temperate climates, to increase the stability and sustainability of soybean cropping systems, and thus to increase the soybean acreage in temperate climates in Europe. Currently, we assume that particularly organic farmers benefit from local soybean growing because of pest and disease risks field beans (Vicia faba) and peas (Pisum sativum) on the one hand, and the requirement of legumes for N-fixation in organic rotations on the other hand. In addition, local soy bean growing solves many problems related to the nutrition of monogastric animals in organic farming systems. For further expansion of soybean cropping in (temperate) Europe countries, the driving factors for soybean growing on practical farms (organic and conventional) have to be revealed in more detail. This approach should include experience from, and use contacts to, other European countries such as Austria. As rotational diseases and pests limit the percentage of soybean on farmland, new methods have to be developed to improve and stabilize the agro-ecosystems while growing soybean. On-farm surveys will reveal the status quo of agro-ecological impacts and agronomic success of soybean growing, with focus on major selected factors to improve the agro-ecological value and to increase yields and yield stability. Soybean could be well introduced in regions where maize-monocropping is dominant, such as the southern Rhine valley in Germany, and thus to increase crop diversity in compliance with the overall aims of European policy and public. The outcome of the soybean case study can finally be well combined with the lentil case study from the same stories of success but probably due to different reasons. Other crops which may be introduced in future into European agriculture will benefit from this knowledge and can be introduced more easily. The overall outcome of the study would be(i)identification of agronomic key factors for successful soybean growing in a temperate climate;(ii)stabilisation and improvement of soybean growing basing on (iii)to export the idea of soybean growing and improved cropping systems to other regions.

Fig. 7 in Identification of iron-chelating phenolics contributing to seed coat coloration in soybeans (Glycine max (L.) Merr.) expressing aryloxyalkanoate dioxygenase-12

Fig. 7. Absorbance spectra of genistin-iron complexes at pH 7 and pH 4.5.

Fig. 1 in Identification of iron-chelating phenolics contributing to seed coat coloration in soybeans (Glycine max (L.) Merr.) expressing aryloxyalkanoate dioxygenase-12

Fig. 1. Non-transgenic (A) and DAS-444Ø6-6 (B) whole soybean seeds.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov28/100

Effect of Intake of Glycin Max (L.) Merr. Peel Extract on Body Fat and Body Weight

ClinicalTrials.gov study NCT02108691. IPD Sharing: Not stated. Countries: 1. Publications: 0.

publicOct 2015View details →

Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)

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publicApr 2022View details →

Cloning and functional characterization of a Type 3 Diacylglycerol Acyltransferasegene (GmDGAT3-2) from soybean (Glycine max L.)

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