Soybean-SCN Battle: Novel Insight into Soybean's Defense Strategies against Heterodera glycines.

Soybean cyst nematode (SCN, Heterodera glycines, Ichinohe) poses a significant threat to global soybean production, necessitating a comprehensive understanding of soybean plants' response to SCN to ensure effective management practices. In this study, we conducted dual RNA-seq analysis on SCN-resistant Plant Introduction (PI) 437654, 548402, and 88788 as well as a susceptible line (Lee 74) under exposure to SCN HG type 1.2.5.7. We aimed to elucidate resistant mechanisms in soybean and identify SCN virulence genes contributing to resistance breakdown. Transcriptomic and pathway analyses identified the phenylpropanoid, MAPK signaling, plant hormone signal transduction, and secondary metabolite pathways as key players in resistance mechanisms. Notably, PI 437654 exhibited complete resistance and displayed distinctive gene expression related to cell wall strengthening, oxidative enzymes, ROS scavengers, and Ca2+ sensors governing salicylic acid biosynthesis. Additionally, host studies with varying immunity levels and a susceptible line shed light on SCN pathogenesis and its modulation of virulence genes to evade host immunity. These novel findings provide insights into the molecular mechanisms underlying soybean-SCN interactions and offer potential targets for nematode disease management.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits.

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components.

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

Quantitative trait loci and gene-specific markers associated with resistance to soybean cyst nematode HG type 2.5.7.

Soybean cyst nematode (SCN) is one of the most damaging soybean (Glycine max) pests worldwide. More than 95% of SCN-resistant commercial cultivars in North America are derived from a single source of resistance named PI 88788, and the widespread use of this source in the past three decades has led to the selection of virulent biotypes of SCN, such as HG (Heterodera glycines) type 2.5.7 that can overcome the PI 88788-type resistance. The objectives of this study were to identify quantitative trait loci (QTL) and candidate genes underlying the resistance to HG type 2.5.7 isolate and to measure the impact of the resistance factors on seed yield. To achieve the goals, a recombinant inbreed line (RIL) population was established from a cross between an SCN-susceptible high-yielding elite soybean cultivar, OAC Calypso, and the cultivar LD07-3419, resistant to SCN HG type 2.5.7. The RILs resistant to HG type 2.5.7 were identified using greenhouse bioassay tested for differentiation of resistant sources using Kompetitive Allele-Specific PCR (KASP) assay at rhg1 and Rhg4 loci and also for rhg1 copy number variation using TaqMan assay. The RILs were also genotyped using genotype-by-sequencing and three SCN-related QTL were identified on chromosomes 9, 12, and 18 using composite interval mapping. In addition, 31 genes involved in protein kinase activity were identified within QTL regions as potential causal candidate genes underlying the resistance. No significant correlation was found between seed yield and the resistance to SCN in the RIL population evaluated under non-SCN-infested environments. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01330-8.

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods.

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.

Effects of type I Diacylglycerol O-acyltransferase (DGAT1) genes on soybean (Glycine max L.) seed composition.

Type I Diacylglycerol acyltransferase (DGAT1) catalyzes the final step of the biosynthesis process of triacylglycerol (TAG), the major storage lipids in plant seeds, through the esterification of diacylglycerol (DAG). To characterize the function of DGAT1 genes on the accumulation of oil and other seed composition traits in soybean, transgenic lines were generated via trans-acting siRNA technology, in which three DGAT1 genes (Glyma.13G106100, Glyma.09G065300, and Glyma.17G053300) were downregulated. The simultaneous downregulation of the three isoforms in transgenic lines was found to be associated with the reduction of seed oil concentrations by up to 18 mg/g (8.3%), which was correlated with increases in seed protein concentration up to 42 mg/g (11%). Additionally, the downregulations also influenced the fatty acid compositions in the seeds of transgenic lines through increasing the level of oleic acid, up to 121 mg/g (47.3%). The results of this study illustrate the importance of DGAT1 genes in determining the seed compositions in soybean through the development of new potential technology for manipulating seed quality in soybean to meet the demands for its various food and industrial applications.

Genomic regions associated with important seed quality traits in food-grade soybeans.

BACKGROUND: The production of soy-based food products requires specific physical and chemical characteristics of the soybean seed. Identification of quantitative trait loci (QTL) associated with value-added traits, such as seed weight, seed protein and sucrose concentration, could accelerate the development of competitive high-protein soybean cultivars for the food-grade market through marker-assisted selection (MAS). The objectives of this study were to identify and validate QTL associated with these value-added traits in two high-protein recombinant inbred line (RIL) populations. RESULTS: The RIL populations were derived from the high-protein cultivar 'AC X790P' (49% protein, dry weight basis), and two high-yielding commercial cultivars, 'S18-R6' (41% protein) and 'S23-T5' (42% protein). Fourteen large-effect QTL (R2 > 10%) were identified associated with seed protein concentration. Of these QTL, seven QTL were detected in both populations, and eight of them were co-localized with QTL associated with either seed sucrose concentration or seed weight. None of the protein-related QTL was found to be associated with seed yield in either population. Sixteen candidate genes with putative roles in protein metabolism were identified within seven of these protein-related regions: qPro_Gm02-3, qPro_Gm04-4, qPro_Gm06-1, qPro_Gm06-3, qPro_Gm06-6, qPro_Gm13-4 and qPro-Gm15-3. CONCLUSION: The use of RIL populations derived from high-protein parents created an opportunity to identify four novel QTL that may have been masked by large-effect QTL segregating in populations developed from diverse parental cultivars. In total, we have identified nine protein QTL that were detected either in both populations in the current study or reported in other studies. These QTL may be useful in the curated selection of new soybean cultivars for optimized soy-based food products.

Anti-cancer activity of pegylated liposomal trans-anethole on breast cancer cell lines MCF-7 and T47D.

OBJECTIVE: To examine the role of liposomes for the encapsulation of drugs and their suitability for chemotherapy of breast cancer. RESULTS: Pegylated liposomal trans-anethole nanoparticles were synthesized through a reverse-phase evaporation technique. Nanoparticles were characterized in terms of mean diameter, size distribution, zeta potential, encapsulation and drug loading efficiency, drug release pattern and cytotoxicity effects. Size and zeta potential of pegylated nanoliposomal drug and blank pegylated nanoliposomal were 257 nm and -28 mV; 35.7 nm and -21 mV, respectively. Encapsulation and drug loading efficiency were 78 ± 2.5 and 2.3 ± 4.1 %, respectively. There was a 57 % release of trans-anethole from pegylated liposomal nanoparticles in 48 h. Compared to free drug, toxicological studies indicated around 9- and 8-fold cytotoxicity effect against MCF-7 and T47D cell lines respectively. CONCLUSIONS: PEG-liposomes provided a high stability and slow release of trans-anethole in two cancer cell lines.

Effects of nanoliposomal and pegylated nanoliposomal artemisinin in treatment of breast cancer.

This study is aimed to investigate the nanoliposomal artemisinin preparation, and its implementation on breast cancer cells. Side effects have been one of the common challenges of drug usage, as well as cancer treatment. In order to reduce such effects, nanotechnology has been a great help. Nanoliposomes are provided through reverse phase evaporation. In this method, certain proportions of phosphatidylcholine, cholesterol and artemisinin were mixed together. Besides, the obtained formulation was pegylated by using polyethylene glycol 2000 in order to increase its stability and solubility. The mean diameter of non-pegylated and pegylated liposomal artemisinin was determined by Zeta sizer system. The percent of drug released from liposome was performed by dialysis. The encapsulation efficiency of both formulations was estimated by spectrophotometry method. As a result, encapsulation and drug release of nanoliposomal formulation were more than the pegylation of the same formulation. In addition, this study indicated that cytotoxicity effect of pegylated nanoliposomal artemisinin was more, in comparison with nanoliposomal artemisinin.

Identification and cloning of putative water clarification genes of Moringa peregrina (Forssk.) Fiori in E. coli Xl1 blue cells.

BACKGROUND: Water purification processes include the use of chemical compounds despite the concern that they may induce diseases. An ecological solution to this dilemma can come from the use of plant seeds for this purpose. Moringa peregrina (Forssk.) Fiori seeds have water clarification ability. Therefore, the aim of this work was to look for certain water clarification genes in M. peregrina. MATERIALS AND METHODS: After preparation of M. peregrina callus, mRNA was extracted from these cells. After application of reverse transcriptase, the obtained cDNA (s) were used for PCR amplification of the desired genes using primers based on MO2.1 gene of Moringa oleifera. DNA amplification products were cloned in E. coli Xl1 blue cells and DNA sequences were compared with Mo1,2 gene in M. oleifera. RESULTS: We obtained 3 PCR products (approximately 200, 300, and 400 bps). CONCLUSION: After comparison of the sequences of 300bp band obtained from M. peregrina with Mo1,2 gene in M. oleifera, it seems that 300bp band is a good candidate to investigate regarding its potential flocculent activity.

Investigation of the effects of curcumin on serum cytokines in obese individuals: a randomized controlled trial.

BACKGROUND: Obesity is a disorder often accompanied by a heightened state of systemic inflammation and immunoactivation. The present randomized crossover trial aimed to investigate the efficacy of curcumin, a bioactive polyphenol with established anti-inflammatory and immunomodulatory effects, on the serum levels of a panel of cytokines and mediators in obese individuals. METHODS: Thirty obese individuals were randomized to receive curcumin at a daily dose of 1 g or a matched placebo for 4 weeks. Following a 2-week wash-out period, each group was assigned to the alternate treatment regimen for another 4 weeks. Serum samples were collected at the start and end of each study period. Serum levels of IL-1α, IL-1ß, IL-2, IL-4, IL-6, IL-8, IL-10, VEGF, IFNγ, EGF, MCP-1, and TNF α were measured using a multiplex Biochip Array Technology based method. RESULTS: Mean serum IL-1ß (P = 0.042), IL-4 (P = 0.008), and VEGF (P = 0.01) were found to be significantly reduced by curcumin therapy. In contrast, no significant difference was observed in the concentrations of IL-2, IL-6, IL-8, IL-10, IFNγ, EGF, and MCP-1. CONCLUSIONS: The findings of the present trial suggested that curcumin may exert immunomodulatory effects via altering the circulating concentrations of IL-1ß, IL-4, and VEGF.

A comparative proteome approach to decipher the mechanism of rice adaptation to phosphorous deficiency.

Mineral deficiency limits crop production in most soils and in Asia alone, about 50% of rice lands are phosphorous deficient. In an attempt to determine the mechanism of rice adaptation to phosphorous deficiency, changes in proteome patterns associated with phosphorous deficiency have been investigated. We analyzed the parental line Nipponbare in comparison to its near isogenic line (NIL6-4) carrying a major phosphorous uptake QTL (Pup1) on chromosome 12. Using 2-DE, the proteome pattern of roots grown under 1 and 100 microM phosphorous were compared. Out of 669 proteins reproducibly detected on root 2-DE gels, 32 proteins showed significant changes in the two genotypes. Of them, 17 proteins showed different responses in two genotypes under stress condition. MS resulted in identification of 26 proteins involved in major phosphorous deficiency adaptation pathways including reactive oxygen scavenging, citric acid cycle, signal transduction, and plant defense responses as well as proteins with unknown function. Our results highlighted a coordinated response in NIL in response to phosphorous deficiency which may confer higher adaptation to nutrient deficiency.