https://orcid.org/0000-0002-2150-7404
Figures
Abstract
Soybean is one of the most economically important crops worldwide. However, soybean yield can be substantially decreased by many diseases. Soybean genotypes could have different reactions to pathogen infection. As a first step toward investigating the biochemical basis of soybean resistance and susceptibility to disease, phytochemicals in the seeds of 52 soybean genotypes previously reported to have different reactions to diseases of soybean rust (SBR), Phomopsis seed decay (PSD), and purple seed stain (PSS) were analyzed. Using GC-MS, a total of 46 compounds were tentatively identified which included 11 chemical groups. Among those, the major group was esters, followed by carboxylic acid, ketone, and sugar moieties. Compounds having reported antioxidant, anti-microbial, and anti-inflammatory activities were also identified. UHPLC-DAD/MS analysis indicated that there were five major isoflavone components presented in the samples, including daidzin, glycitin, genistin, malonyldaidzin, and malonylglycitin. Isoflavones have been reported to play an important role in defense from plant pathogens. Although there was variance in the isoflavone content among soybean genotypes, those with the SBR resistance Rpp6 gene (PI 567102B, PI 567104B, PI 567129) consistently exhibited the highest concentrations of daidzin, glycitin, genistin, and malonyldaidzin. The SBR resistant genotype, PI 230970 (Rpp2) had the greatest amount of genistin. The SBR resistant genotype, PI 200456 (Rpp5) resistant genotype uniquely contained glycitein, a compound that was absent in the other 51 genotypes examined. A PSD-resistant genotype PI 424324B had nearly four times the amount of stigmasterol as PI 556625, which was susceptible to SBR, PSD, and PSS in our previous tests. Results of this study provide useful information for further investigation of the biochemical basis of soybean resistance to diseases. The results may also aid in selection of soybean lines for breeding for resistance to soybean rust and other diseases.
Citation: Li S, Wang M, Lee J (2024) Phytochemical profiling of soybean genotypes using GC-MS and UHPLC-DAD/MS. PLoS ONE 19(8): e0308489. https://doi.org/10.1371/journal.pone.0308489
Editor: Binod Bihari Sahu, National Institute of Technology Rourkela, INDIA
Received: April 9, 2024; Accepted: July 24, 2024; Published: August 15, 2024
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: This research was supported by the USDA-ARS Projects 6066-21220-014-000D, 6066-21220-016-00D (Crop Genetics Research Unit, ARS-USDA) and 6060-41000-015-000D (Natural Products Utilization Research Unit, ARS-USDA).
Competing interests: The authors have declared that no competing interests exist.
Introduction
Soybean (Glycine max (L.) Merr.) is one of the most economically important crops in the world. Although soybean is native to Asia, it was introduced into North America, Europe, and later into South and Central America, and eventually to other parts of the world [1]. World soybean production was 170 million metric tons (MMT) in 1960 and increased to 398.3 MMT in 2023 (https://ipad.fas.usda.gov/cropexplorer/cropview/commodityView.aspx?cropid=2222000). Soybean produced 70.86% of the global supply of plant-based protein meal and 28.88% of the plant-based oil in the 2020/2021 market year based on the Market View Data Base of the United Soybean Board [2]. Soybean is considered essential for global food security [3]. The seeds of soybean have 8.3 to 27.9% oil content and 34.1 to 56.8% protein content depending on the soybean varieties and cultivation conditions [4]. Studies indicate that soybeans and soy-based foods offer a plethora of health advantages, in addition to being among the most abundant and cost-effective protein sources available [5]. Soybean is ideal for human and animal nutrition [6], as well as for biodiesel production [7].
Global demand for soybean production has been significantly increasing. However, soybean production can be severely impacted by many diseases. Of more than 200 reported soybean pathogens, about 35 cause major economic impacts [8]. Soybean genotypes can have different reactions to pathogen infection [9, 10]. It has been reported that soybean contains numerous bioactive phytochemicals, which include, but are not limited to, phenolic acids, flavonoids, isoflavones, saponins, phytosterols, and sphingolipids [11–13]. Although soybean phytochemicals have a positive effect on the human immune system [14], their role in the soybean genotype’s response to pathogens has not been well-studied.
As a first step toward investigating the biochemical basis of soybean resistance and susceptibility to disease, phytochemicals in the seeds of 52 soybean genotypes previously reported to have different reactions to diseases of soybean rust (SBR) caused by Phakopsora pachyrhizi, Phomopsis seed decay (PSD) caused by Diaporthe longicolla, and purple seed stain caused (PSS) by Cercospora spp. were analyzed and their potential value against their causal pathogens were explored. SBR is one of the most devasting foliar diseases causing yield loss of up to 90% [8]. Both PSD and PSS are seed diseases reducing seed quality and seed lot grade for marketing purposes [8].
The specific objectives of this study were (i) to identify and classify/categorize compounds in soybean seeds using gas chromatography/mass spectrometry (GC-MS) analysis; (ii) to analyze isoflavones in soybean seeds using ultra-high performance liquid chromatography/diode-array detector-mass spectrometry (UHPLC/DAD-MS); and (iii) to profile phytochemicals among soybean genotypes. Overall, the results of this study provide useful information for further investigation of the biochemical basis of soybean resistance and susceptibility to disease. The results may also aid in selection of soybean genotypes for breeding for resistance to soybean diseases.
Material and methods
Plant materials
Seeds of fifty-two soybean genotypes originating from eight countries (Brazil, China, India, Indonesia, South Korea, Japan, USA, and Vietnam) were used in this study. The genotype’s name, geographical origin, and references related to the genotype’s reactions to three soybean pathogens (Phakopsora pachyrhizi, Diaporthe longicolla, Cercospora spp.) that cause soybean rust (SBR), Phomopsis seed decay (PSD), and purple seed stain (PSS), respectively are shown in Table 1. Seeds of these genotypes were obtained from the curators of the USDA-ARS GRIN (ars-grin.gov).
Chemicals and standards
Chemical structures of eight primary isoflavone compounds in soybeans are shown in Fig 1. Daidzin (> 96%), daidzein (> 98%), genistin (>99%), genistein (> 99%), glycitin (>99%), and glycitein > 99%) were purchased from Indofine (Township, NJ, USA). Malonyldaidzin (> 95%) was obtained from BOC Sciences (Shirley, NY, USA). Malonylgenistin (> 98%) was obtained from AvaChem Scientific (San Antonio, TX, USA). LC-MS grade methanol, acetonitrile, and formic acid were purchased from Sigma-Aldrich (St. Louis, MO, USA). Water was purified using a Milli-Q system (Millipore, Bedford, MA, USA).
Sample extraction method
Eight isoflavones were quantified from a total of 52 soybean seed samples. Solid soybean seeds were ground and homogenized to obtain a uniform matrix. For GC-MS analysis, 5 mL methanol was added to 100 mg of soybean seed powder and sonicated at room temperature for 60 min. For LC/DAD-MS analysis, 1.5 mL of 70% ethanol (EtOH, v/v) aqueous containing 0.1% formic acid was added to 100 mg of soybean seed powder, sonicated at room temperature for 30 min, and then centrifuged at 14,000 r/min for 10 min. The supernatant was filtered. The whole process was repeated two additional times, and then the volume was fixed to 5 mL in a volumetric flask. Both of the extraction solutions for GC-MS and LC-DAD/MS analyses were stored at 4ºC.
Gas Chromatography-Mass Spectrometry (GC-MS) analysis
The methanolic extracts of 52 soybean seed genotypes were analyzed using an Agilent 7890 GC (Agilent Technologies, Santa Clara, CA, USA) equipped with a 7693 autosampler. Separation was achieved on an Agilent DB-5MS ultra inert column (60 m x 0.25 mm x 0.25 μm). The helium carrier gas was set to constant flow mode at 1 mL/min. The inlet was held at 260ºC and was operated in split mode with a split ratio of 50:1. The GC oven temperature was ramped from 80ºC at 3ºC/min to 125ºC, programmed at 1ºC/min to 140ºC, held for 10 min at 140ºC, then ramped at 3ºC/min to 170ºC, and finally ramped at 8ºC/min to 280ºC, where it was held for 10 min. Triplicate injections of each sample were made with a volume of 1 μL. The peak area percent was calculated as an average of the three injections. The presence/absence of each compound was defined based on the detection of the compound in the triplicate injections with a threshold of S/N > 3:1.
The mass spectral detector was an Agilent 5977A quadrupole mass spectrometer operated in the full spectral acquisition mode. The mass spectrometer was equipped with an electron ionization source, which was operated with an electron voltage of 70 eV. The ion source, quadrupole, and transfer line temperatures were set to 230, 150, and 280ºC, respectively. Data were acquired using MassHunter Acquisition software (B.07006.2704).
Data analysis was performed using MassHunter Qualitative analysis software (B.07.00).
Compound identification involved a comparison of the spectra with the NIST database (Version 2.2) using a probability-based matching algorithm.
Ultra-high performance liquid-diode array detector/mass spectrometry (UHPLC-DAD/MS) analysis
Analysis of the isoflavone components in soybean seeds was performed on a 1290 Infinity series UHPLC system equipped with a diode array detector, binary pump, autosampler, and thermostatted column compartment. Separation was achieved using an Agilent ZORBAX Eclipse Plus C18 column (2.1 x 100 mm, 1.8 micron) maintained at 30ºC throughout the analysis. The mobile phase consisted of water (A) and acetonitrile (B) both containing 0.1% formic acid. The gradient elution was as follows: 0 min 10% B, 0–20 min 40% B, 20–25 min 100% B. A 5 min wash of 100% B followed each run, after which an equilibration period of 6 min with 10% B was completed. The eluent was pumped at a flow rate of 0.25 mL/min with the injection volume set at 2 μL. The DAD wavelength was set at 250 and 260 nm. The optimized wavelength for quantification of each compound was also evaluated [15]
The mass spectrometric analysis used for compound identification and confirmation was performed with an Agilent 6120 quadrupole mass spectrometry equipped with an ESI source using the following parameters: drying gas (N2) flow rate of 10 L/min and temperature 300ºC, nebulizer pressure 30 psi, sheath gas temperature 325ºC with a flow rate of 10 L/min, capillary voltage 3000 V, and fragmentor voltage 120 V. The data acquisition was controlled by Agilent MassHunter Acquisition Software (Ver. A.05.01) and data analysis was processed with MassHunter Qualitative Analysis and MassHunter Quantitative Analysis Software (Ver. B.10.0).
Eight standards of isoflavone components, including daidzin (1), glycitin (2), genistin (3), malonyldaidzin (4), malonylglycitin (5), daidzein (6), glycitein (7), and genistein (8) were used in the analysis. The individual stock solutions of the eight reference standards were prepared in 70% ethanol at 5 mg/mL (for those standards which have solubility issues, 70% ethanol with 10 drops of DMSO was used). These stock solutions were diluted to obtain the concentrations required for preparation of a standard working solution ranging from 0.5 to 625 μg/mL (625, 500, 400, 312, 200, 100, 75, 50, 25, 10, 5, and 0.5 μg/mL). Based on the calibration curve, the slope, intercept, and R2 values were calculated.
GC-MS data analysis
The percent peak area data obtained from GC-MS analysis was combined into different groups of compounds and then exported to SIMCA-P + 13.0 software (Umetrics AB, Umeå, Sweden). With the variables in the dataset being Pareto scaled, principal component analysis (PCA) was performed. The identification with the highest probability score was taken for each compound.
Results and discussion
GC-MS analysis
Using GC-MS analysis, a total of 46 compounds were identified based on NIST database search, retention time, and retention indices compared with literature data. A typical total ion chromatogram of one soybean genotype is shown in Fig 2. The tentative compound identification and classification results given in Table 2 and S1 Table indicated that the methanolic extract was mainly comprised of 11 chemical groups, including esters, carboxylic acids, ketones, sugar moieties, heterocyclic compounds, and phenolic compounds. Compounds having reported antioxidant, anti-microbial, and anti-inflammatory activities were identified in Table 2.
Phenolic compounds have been reported to be significantly associated with anti-microbial and antioxidant activities [16, 17]. Two phenolic compounds, 4-vinylguaiacol and 2,3-dimethoxyphenol, were identified. 4-Vinylguaiacol was found in three genotypes possessing soybean rust resistance gene: PI 417132 (Rpp3), PI 200487 and PI 200526 (Rpp5). Regarding another phenolic compound 2,3-dimethoxyphenol, it was detected in 19 soybean genotypes, which include 11 lines containing soybean rust resistance gene (either Rpp1, or Rpp3, Rpp5, Rpp6) and three lines susceptible to soybean rust, two lines resistant to Phomopsis seed decay and one line susceptible to Phomopsis seed decay (S1 Table). How soybean genotypes with different levels of resistance or susceptibility related to the presence of those compounds is uncertain. It is worth noting that no phenolic compounds were identified from soybean genotype PI 518671 (Williams 82), which is a well-known susceptible genotype to soybean rust [18], Phomopsis seed decay [19, 20], and purple seed stain [21].
Five carboxylic acids were identified in total. Notably, palmitic acid and oleic acid, both recognized for their anti-inflammatory properties [22, 23], were detected in all genotypes. Linoleic acid, which is known to possess anti-hyperlipidemic effects [24] was also detected in all samples. Another compound, linolenic acid, which has been reported to have anti-inflammatory activity, was absent in samples SBR3-SBR6 (PI 594538A, PI 594538B, PI 230970, PI 567025A) and SBR9-SBR10 (PI 605854B and PI 605891A). A PSD-resistant genotype, SBR50 (PI 424324B) had nearly four times the amount (6.71 mg/g) of stigmasterol in dried soybean sample as SBR46 (PI 51867), which had 3.06 mg/g of stigmasterol. This soybean line was susceptible to SBR, PSD, and PSS in our previous tests [18–21]. Another PSD resistant genotype, SBR50 (PI 549020), had 3.86 mg/g of stigmasterol. One unknown genotype, SBR41 (PI 471208), had the highest content of stigmasterol. Further study is needed to test if soybean line PI 471208 is resistant to PSD.
Chemometrics analysis using GC-MS data
In S1 Table, compounds present in various soybean samples were identified. Utilizing this information, we classified each compound into its respective group and aggregated them to produce S2 Table of Supplementary Material for principal component analysis (PCA). Findings from PCA revealed three principal components (PCs) as shown in Table 3 and Fig 3. These components collectively accounted for 89.87% of the total variance observed among the genotypes. For each main component, greater positive coefficients represented a significant factor. PC1 indicated 42.23% of total variance and was mainly composed of the phytochemical classes, carboxylic acid, sugar moiety, and ester. These classes exhibit a significant positive loading on PC1. Genotypes SBR53 (PI 549020), SBR12 (PI 417503), SBR47 (PI 556625), SBR24 (PI 506764), SBR43 (PI 567351B), and SBR51 (PI 458130) showed the most variability according to these components. PC2 illustrated 39.54% of the total variance and classes with higher scores were sugar moiety, aldehyde, and ketone. The genotypes that showed most variance based on this component were SBR47 (PI556625), SBR24 (PI 506764), SBR1 (PI 587880A), SBR8 (PI 567039), SBR10 (PI 605891A, and SBR11 (PI 605865B). PC3 accounted for 8.11% of the total variance, with ester, tocopherol, and heterocyclic compound exhibiting higher scores. Among genotypes, SBR42 (PI 606440A), SBR43 (PI 567351B), SBR16 (PI 606405), and SBR41 (PI 471208) displayed the greatest variability along this component. Based on these findings, it can be inferred that the most divergent genotypes were SBR24 (PI 506764), SBR47 (PI556625), and SBR43 (PI 567351B), as they exhibited positive loading in at least two out of the three components. The effective utilization of PCA minimized the number of variables required for cultivar classification, thereby enabling soybean researchers to establish more meaningful relationships between key soybean characteristics.
SBR #1-#36, #45, #50–51, and #53 were resistant soybean accessions; SBR #46-#49, and #52 were susceptible soybean accessions; and SBR #37-#44 were unknown.
UHPLC/DAD-MC analysis of isoflavone components
Five major isoflavone components, viz. daidzin (1), glycitin (2), genistin (3), malonyldaidzin (4), and malonylglycitin (5), were identified and quantified in this study. The structures of these compounds are shown in Fig 1. Chromatograms of the eight isoflavone reference standards and seed samples from different soybean genotypes are illustrated in Fig 4. Quantification results for each isoflavone component can be found in Table 4. While there is variability in the isoflavone content among soybean genotypes, the top five highest concentrations of daidzin, glycitin, genistin, and malonyldaidzin were observed in all three soybean genotypes containing the soybean rust Rpp6 gene (PI 567102B, PI 567104B, PI 567129). In previous analyses of soybean leaves, an increase accumulation of the isoflavonoids genistein and daidzein occurred in leaves after inoculation with the soybean rust causal pathogen P. pachyrhizi [28]. Those two isoflavones could be the key phytochemicals contributing to the resistant responses of soybean lines with the Rpp6 resistant gene. In our study, PI 230970 (Rpp2) exhibited the highest level of genistin. However, no genistin was detected in another reported soybean rust resistant genotype SBR 45 (PI 417125) possessing Rpp2 resistant gene. Pathogenicity experiments are needed to perform to determine if SBR 45 possess the Rpp2 resistant reaction after inoculating with P. pachyrhizi. In addition, SBR 29 (PI 200456), which carries the soybean rust Rpp5 resistance gene, contained 0.09 (mg/g) of glycitein, a compound that was absent in all other 51 tested genotypes including five other genotypes with Rpp 5 gene (Table 1). The role of glycitein in the resistant response of the soybean PI 200456, however, is not known.
UHPLC/DAD (260 nm) chromatograms of (A) reference standards; (B) soybean seed sample (PI230970, SBR5). Peak identification: 1. daidzin; 2. glycitin; 3. genistin; 4. malonyldaidzin; 5. malonylglycitin; 6. daidzein; 7. glycitein; 8. genistein.
Isoflavones are a group of phenolic compounds commonly found in the legume (Fabaceae) family. Soybean isoflavone is an important secondary metabolite accumulated in soybean. It has been reported that isoflavones contribute to overall human health, including chronic diseases [25, 26]. Isoflavones have also been reported to mediate important interactions with plant-associated microbes, including defense from pathogens and in nodulation [27].
Soybean rust (SBR) is one of the most important soybean foliar diseases occurring in many major soybean-producing countries. In a reported study on the importance of phenolic metabolism to limit the growth of P. pachyrhizi that causes soybean rust, it was found that inoculation of soybean plants with the pathogen resulted in increased accumulation of isoflavonoids and flavonoids in leaves of all soybean genotypes tested [28]. Although the soybean phytoalexin glyceollin was not detected in leaves of uninfected plants, accumulation of this compound at marked levels occurred in rust-infected leaves [28]. In another study testing a susceptible genotype PI 636463, significant production of defense secondary metabolites including phenylpropanoids, terpenoids and flavonoids were found when P. pachyrhizi infected soybean [29]. Exploration of the relationship between isoflavone content and soybean genotypes with known resistance genes would help reveal the biochemical basis of soybean resistance to disease. Experiments are underway to analyze isoflavone content of soybean resistant and susceptible genotypes after pathogen (P. pachyrhizi and D. longicolla) inoculation and non-inoculation treatments.
In this study, a conclusion could not be drawn about the direct correlations between the studied compounds and phenotypic traits illustrating the resistance/susceptibility to the pathogens. To address this issue, phytopathogenic experiments will be conducted to test selected representative genotypes, such as PI 518671 (Williams 82), which is a well-known susceptible genotype to soybean rust [18], Phomopsis seed decay [19, 20], and purple seed stain [21], as well as the resistant line PI 567102B that contains the soybean rust Rpp6 gene [18]. Soybean samples will be collected from replicated tests directly for the phytochemical analyses.
Together, information obtained from comprehensive phytochemical profiling of soybean genotypes with their reactions to pathogens will facilitate selection of soybean lines for breeding for resistance to diseases.
Conclusion
In this study, a comprehensive phytochemical profiling of soybean genotypes was conducted to explore various reactions to soybean diseases including soybean rust, Phomopsis seed decay, and purple seed stain using GC-MS and UHPLC/DAD-MS. The results revealed significant diversity in the isoflavone profiles among the genotypes studied. Notably, certain genotypes containing specific soybean rust resistance genes exhibited higher levels of particular isoflavone components compared to others. For example, genotypes carrying the soybean rust Rpp6 gene consistently contained elevated levels of daidzin, glycitin, genistin, and malonyldaidzin. Furthermore, genotypes harboring the Rpp2 (SBR5) gene had a distinct accumulation of genistin and malonyldaidzin, while one genotype possessing the Rpp5 gene (SBR29) exclusively contained the compound glycitein. These results highlight the potential correlation between soybean rust resistance genes and the biosynthesis of key phytochemicals in soybean, underscoring the importance of genetic factors in shaping the phytochemical composition of soybean varieties. Such insights could contribute to the development of disease-resistant soybean cultivars with enhanced nutritional and functional attributes. Overall, this study sheds light on the intricate interplay between genetics, phytochemistry, and disease resistance in soybeans, offering valuable implications for soybean breeding and agricultural practices.
Supporting information
S1 Table. List of important phytocompounds tentatively identified in the methanolic seed extract of soybean genotypes by GC-MS.
https://doi.org/10.1371/journal.pone.0308489.s001
(DOCX)
S2 Table. Compositional data expressed as different groups of compounds used for PCA.
https://doi.org/10.1371/journal.pone.0308489.s002
(DOCX)
Acknowledgments
This research was supported by the USDA-ARS Projects 6066-21220-014-000D, 6066-21220-016-00D (Crop Genetics Research Unit, ARS-USDA) and 6060-41000-015-000D (Natural Products Utilization Research Unit, ARS-USDA). We thank Adam L. Mahan, Curator at the USDA Soybean Germplasm Collection for providing soybean seeds. In Memory of Dr. Pengyin Chen, who made incredible marks in this research and contributed to increase soybean seeds for the experiments. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy. USDA is an equal opportunity provider and employer.
References
- 1.
Hymowitz T. Speciation and cytogenetics. In: Boerma HR., Specht JE, Editors. Soybeans: Improvement, Production, and Uses. 3rd ed. Agron. Monogr. 16. ASA, CSSA, and SSSA. Madison. WI, USA; 2004. pp. 97–136.
- 2. Lin F, Chhapekar SS., Vieira CC, Silva MPD, Rojas A, Lee D, et al. Breeding for disease resistance in soybean: a global perspective. Theoretical and Applied Genetics (2022) 135:3773–3872 pmid:35790543
- 3. Hartman GL, West ED, Herman HK. Crops that feed the World 2. Soybean—worldwide production, use, and constraints caused by pathogens and pests. Food Sec. 2011; 3:5–17.
- 4.
Wilson RF. Seed composition. In: Boerma HR, Specht JE, Editors. Soybeans: Improvement, Production, and Uses. 3rd ed. Agron. Monogr. 16. ASA, CSSA, and SSSA. Madison. WI, USA; 2004. pp. 621–677.
- 5. Qin P, Wang T, Luo Y. A review on plant-based proteins from soybean: Health benefits and soy product development. J. Agriculture and Food Res. 2022; 7.
- 6. Wu H, Zhang Z, Huang H, Li Z. Health benefits of soy and soy phytochemicals. AME Med J 2017; 2:162.
- 7. Seraçm MR, Aydın S, Sayın C. Comprehensive evaluation of performance, combustion, and emissions of soybean biodiesel blends and diesel fuel in a power generator diesel engine, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020; 42:18, 2316–2331,
- 8.
Hartman GL, Rupe JC, Sikora EJ et al Compendium of soybean diseases, 5th edn. American Phytopathological Society, St. Paul, Minnesota, USA. 2015; p 201.
- 9. Li S., Hartman LG, Boykin D. Aggressiveness of Phomopsis longicolla and other Phomopsis spp. on soybean. Plant Dis. 2010; 94(8):1035–1040.
- 10. Li S, Smith R. Phenotypic evaluation of soybean genotypes for their reaction to a Mississippi isolate of Phakopsora pachyrhizi causing soybean rust. Plants. 2023; 12–1797: 1–14.
- 11. Luthria DL, Biswas R, Natarajan S. Comparison of extraction solvents and techniques used for the assay of isoflavones from soybean. Food Chem. 2007; 105:325–333.
- 12. Lee SJ, Kim JJ, Moon HI, Ahn JK, Chun SC, Jung WS, et al. Analysis of isoflavones and phenolic compounds in Korean soybean Glycine max (L.) seeds of different seed weights. J. Agric. Food Chem. 2008; 56:2751–2758.
- 13. Gutierrez E., Wang T, Fehr WR. Quantification of sphingolipids in soybeans. J. Am. Oil Chem. Soc. 2004; 81:737–742.
- 14. Sakai T, Kogiso M. Soy isoflavones and immunity. J. Med. Invest. 2008; 55:167–173. pmid:18797128
- 15. Sun J-M, Sun BL Han FX, Yan SR, Yang H, Kikuchi A. Rapid HPLC method for determination of 12 isoflavone components in soybean seeds. Agricultural Sci. in China 2011; 10(1):70–77.
- 16. Pereira PAP, Ferreira ICFR, Marcelino F, Valentão P, Paula B, Andrade PB, et al. Phenolic compounds and antimicrobial activity of olive (Olea europaea L. Cv. Cobrançosa) leaves. Molecules 2007; 12:1153–1162.
- 17. Zheng W, Wang SY. Antioxidant activity and phenolic compounds in selected herbs. J. Agric. Food Chem. 2001; 49:5165–5170. pmid:11714298
- 18. Li S, Smith JR, Ray JD, Frederick RD. Identification of a new soybean rust resistance gene in PI 567102B. Theor. Appl. Genet. 2012; 125:133–142. pmid:22374138
- 19. Li S. Development of a seedling inoculation technique for rapid evaluation of soybean for resistance to Phomopsis longicolla under controlled conditions. Plant Methods. 2018; 14:81. Available: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0348-x.
- 20. Li S, Sciumbato G, Rupe J, Shannon G, Chen P, Boykin D. Evaluation of commercial soybean cultivars for reaction to Phomopsis seed decay. Plant Dis. 2017; 101(12):1990–1997. pmid:30677383
- 21. Li S, Sciumbato G, Boykin D, Shannon G, Chen P. Evaluation of soybean genotypes for reaction to natural field infection by Cercospora species causing purple seed stain. PloS ONE. 2019; 14 (10): e0222673.
- 22. Aparna V, Dileep KV, Mandal PK, Karthe P, Sadasivan C, Haridas M. Anti‐inflammatory property of n‐hexadecanoic acid: structural evidence and kinetic assessment. Chemical Biol. Drug design. 2012; 80(3):434–9. pmid:22642495
- 23. Carrillo PC, Cavia Camarero MD, Alonso de la TS. Role of oleic acid in immune system; mechanism of action; a review. Nutrición Hospitalaria, 2012; 27:978–990. pmid:23165533
- 24. Wijendran V, Hayes KC. Dietary n-6 and n-3 fatty acid balance and cardiovascular health. Annu. Rev. Nutr. 2004; 24:597–615. pmid:15189133
- 25. Laurenz R, Tumbalam P, Naeve S, Thelena KD. Determination of isoflavone (genistein and daidzein) concentration of soybean seed as affected by environment and management inputs. Sci Food Agric 2017; 97:3342–3347. pmid:27981593
- 26. Barnes S, Kim H and Xu J, Soy in the prevention and treatment of chronic diseases. Congresso Brasileiro de Soja. 1999; 1:295–308.
- 27. Polturak G, Misra RC, El-Demerdash A, Owen C, Steed A, McDonald HP, et al. Discovery of isoflavone phytoalexins in wheat reveals an alternative route to isoflavonoid biosynthesis. Nature Communications 2023; 14:6977. pmid:37914713
- 28. Lygin AV., Li S, Vittal R, Widholm JM, Hartman GL, Lozovaya VV. The importance of phenolic metabolism to limit the growth of Phakopsora pachyrhizi. Phytopathology. 2009; 99 (12):1412–1420.
- 29. Silva E., Graça JPED, Porto C, Prado RMDP, Hoffmann-Campo CB, Mauricio Conrado Meyer MCO, et al. Unraveling Asian Soybean Rust metabolomics using mass spectrometry and molecular networking approach. Sci. Reports. 2020; 10:138. pmid:31924833
- 30. Ray JD, Morel W, Smith JR, Frederick RD, Miles MR. Genetics and mapping of adult plant rust resistance in soybean PI 587886 and PI 587880A. Theor. Appl. Genet. 2009; 119, 271–280. pmid:19396573
- 31. McLean RJ, Byth DE. Inheritance of resistance to rust (Phakopsora pachyrhizi) in soybeans. Aust. J. Agric. Res. 1980; 31:951–956.
- 32. Chakraborty N, Curley J, Frederick RD, Hyten DL, Nelson RL, Hartman GL, et al. Mapping and confirmation of a new allele at Rpp1from soybean PI 594538A conferring RB lesion type resistance to soybean rust. Crop Sci. 2009; 49:783–790.
- 33. Paul C, Motter HZ, Walker DR. Reactions of soybean germplasm accessions to six Phakopsora pachyrhizi. isolates from the United States. Plant Dis. 2020; 104:1087–1095.
- 34. Hartwig EE, Bromfield KR. Relationships among three genes conferring resistance to rust in soybeans. Crop Sci. 1983; 23:237–239.
- 35. Walker DR, Samuel C, McDonald SC, Harris DK, Boerma HR, Buck JW, et al. Genomic regions associated with resistance to soybean rust (Phakopsora pachyrhizi) under field conditions in soybean germplasm accessions from Japan, Indonesia and Vietnam. Theor. Appl. Genet. 2022; 135(9):3073–3086. pmid:35902398
- 36. Walker DR, Harris DK, King ZR, Li Z, Boerma HR, Buckley JB, et al. Evaluation of soybean germplasm accessions for resistance to Phakopsora pachyrhizi populations in the southeastern United States, 2009–2012. Crop Sci. 2014; 54:1673–1689.
- 37. Walker DR, Boerma HR, Phillips DV, Schneider RW, Buckley JB, Shipe ER, et al. Evaluation of USDA soybean germplasm accessions for resistance to soybean rust in the southern United States. Crop Sci. 2011; 51:678–693.
- 38. Li S. Reaction of soybean rust-resistant lines identified in Paraguay to Mississippi isolates of Phakopsora pachyrhizi. Crop Sci. 2009; 49:887–894.
- 39. Vuong TD, Walker DR, Nguyen BT, Nguyen TT, Dinh HX, Hyten DL, et al. Molecular characterization of resistance to soybean rust (Phakopsora pachyrhizi Syd. & Syd.) in soybean cultivar DT 2000 (PI 635999). PLoS ONE 2016; 11(12):e0164493.
- 40. Kendrick MD, Harris DK, Ha B-K, Hyten DL, Cregan PB, Frederick RD, et al. Identification of a second Asian soybean rust resistance gene in Hyuuga soybean. Phytopathology. 2011; 101:535–543. pmid:21244223
- 41. Hartwig EE. Identification of a fourth major gene conferring resistance to soybean rust. Crop Sci. 1986; 26:1135–1136.
- 42. Garcia A, Calvo ES, de Souza Kiihl RA, Harada A, Hiromoto DM, Vieira LG. Molecular mapping of soybean rust (Phakopsora pachyrhizi) resistance genes: discovery of a novel locus and alleles. Theor. Appl. Genet. 2008; 117:545–553.
- 43. Liu M, Li S, Swaminathan S, Sahu BB, Leandro LF, Cardinal AJ, et al. Identification of a soybean rust resistance gene in PI 567104B. Theor. Appl. Genet. 2016; 129:863–877. pmid:26952252
- 44. Childs SP, King ZR, Walker DR, Harris DK, Pedley KF, Buck JW, et al. Discovery of a seventh Rpp soybean rust resistance locus in soybean accession PI 605823. Theor. Appl. Genet. 2018; 131:27–41. pmid:28980046
- 45. Laperuta LDC, Aria CAA, Aliny Simony Ribeiro ASR, Breno Francovig Rachid BF, Pierozzi PHB, Toledo JFF, et al. New genes conferring resistance to Asian soybean rust: allelic testing for the Rpp2 and Rpp4 loci. Pesq. agropec. bras., Brasília. 2008; 43:1741–1747.
- 46. Alloatti J, Chen P, Zeng A, Li S, Rupe J, Florez-Palacios L, et al., Inheritance of and molecular markers for purple seed stain resistance in soybean. Euphytica 2015; 206:701–709.
- 47. Li S, Smith J, Nelson R. Resistance to Phomopsis seed decay identified in maturity group V soybean plant introductions. Crop Sci. 2011; 51(6):2681–2688.
- 48. Jijakli H, Nadi AB, Cook L, Best L, Sener A, Malaisse WJ. Insulinotropic action of methyl pyruvate: enzymatic and metabolic aspects. Archives of biochemistry and biophysics. 1996; 335(2):245–57. pmid:8914921
- 49. Ciriminna R, Fidalgo A, Ilharco LM, Pagliaro M. Dihydroxyacetone: an updated insight into an important bioproduct. ChemistryOpen 2018; 7:233–236. pmid:29531886
- 50. Ravindran DR, Bharathithasan M, Ramaiah P, Rasat MS, Rajendran D, Srikumar S, et al. Chemical composition and larvicidal activity of flower extracts from Clitoria ternatea against Aedes (Diptera: Culicidae). J. Chemistry. 2020; 2020:1–9.
- 51. Bingham AF, Birch GG, De Graaf C, Behan JM, Perring KD. Sensory studies with sucrose-maltol mixtures. Chemical Senses. 1990; 15(4):447–56.
- 52. Men YJ, Hu HY, Li FM. Effects of the novel allelochemical ethyl 2-methylacetoacetate from the reed (Phragmitis australis Trin) on the growth of several common species of green algae. J. Applied Phycology. 2007; 19:521–7.
- 53. Zhao L, Chen J, Su J, Li L, Hu S, Li B, et al. In vitro antioxidant and antiproliferative activities of 5-hydroxymethylfurfural. J. Agricultural and Food Chemistry. 2013;61(44):10604–11. pmid:24107143
- 54. Alghamdi SS, Khan MA, El-Harty EH, Ammar MH, Farooq M, Migdadi HM. Comparative phytochemical profiling of different soybean (Glycine max (L.) Merr) genotypes using GC–MS. Saudi J. Biol. Sci. 2018; 25(1):15–21.
- 55. Kranjčec B, Papeš D, Altarac S. D-mannose powder for prophylaxis of recurrent urinary tract infections in women: a randomized clinical trial. World J. Urology. 2014; 32:79–84. pmid:23633128
- 56. Zhao G, Etherton TD, Martin KR, Gillies PJ, West SG, Kris-Etherton PM. Dietary α-linolenic acid inhibits proinflammatory cytokine production by peripheral blood mononuclear cells in hypercholesterolemic subjects. The American J. Clinical Nutrition. 2007; 85(2):385–91.
- 57. Petrosino S, Di Marzo V. The pharmacology of palmitoylethanolamide and first data on the therapeutic efficacy of some of its new formulations. British J. Pharmacology. 2017; 174(11):1349–65.
- 58. Reddy LH, Couvreur P. Squalene: A natural triterpene for use in disease management and therapy. Advanced drug delivery reviews. 2009; 61(15):1412–26. pmid:19804806
- 59. Ganem-Quintanar A, Quintanar-Guerrero D, Buri P. Monoolein: a review of the pharmaceutical applications. Drug development and industrial pharmacy. 2000; 26(8):809–20. pmid:10900537
- 60. Reiter E, Jiang Q, Christen S. Anti-inflammatory properties of α-and γ-tocopherol. Molecular Aspects of Medicine. 2007; 28(5–6):668–91.
- 61. Yoshida Y, Niki E. Antioxidant effects of phytosterol and its components. J. Nutritional Science and Vitaminology. 2003; 49(4):277–80. pmid:14598915
- 62. Panda S, Jafri M, Kar A, Meheta BK. Thyroid inhibitory, antiperoxidative and hypoglycemic effects of stigmasterol isolated from Butea monosperma. Fitoterapia. 2009; 80(2):123–6. pmid:19105977
- 63. Balamurugan R, Duraipandiyan V, Ignacimuthu S. Antidiabetic activity of γ-sitosterol isolated from Lippia nodiflora L. in streptozotocin induced diabetic rats. European J. Pharmacology. 2011; 667(1–3):410–8.