Combining Targeted Metabolite Analyses and Transcriptomics to Reveal Specific Chemical Composition and Associated Genes in Resistant Soybean Infected with Soybean Cyst Nematode

Abstract

Background: Soybean cyst nematode Heterodera glycines is one of the most devastating pathogens on soybean and causes severe annual yield loss worldwide. Different soybean varieties exhibit different responses to soybean cyst nematode infection at various levels, such as genomic, transcriptional, proteomic, and metabolomic levels. However, there were not yet any reports on the differential responses of resistant and susceptible soybeans infected with soybean cyst nematode by combining the metabolomic analyses and transcriptomics.

Results: In this study, a highly-resistant variety PI 437654 and three susceptible varieties Williams 82, Zhonghuang 13 and Hefeng 47 were used as the test materials to clarify the differences in metabolites and transcriptomics between resistant and susceptible soybeans before and after SCN infection. A local metabolite-calibrated database was used to identify potential differential metabolites, and the differences of metabolites and metabolic pathways were compared between the resistant and susceptible soybean varieties after inoculation with SCN. Totally, 37 differential metabolites and 20 KEGG metabolic pathways were identified, which were divided into three categories including the metabolites/pathways overlapped among resistant and susceptible soybeans, specific in susceptible or resistant soybeans, respectively. Twelve differential metabolites were found to be involved in predicted KEGG metabolite pathways. Moreover, 14 specifically differential metabolites such as significantly up-regulated nicotine and down-regulated D-aspartic acid, and their involved KEGG pathways such as tropane, piperidine and pyridine alkaloid biosynthesis, alanine, aspartate and glutamate metabolisms, sphingolipid metabolism and arginine biosynthesis were significantly changed and abundantly enriched in the resistant soybean, and likely played pivotal roles in defensing against SCN infection. Three key metabolites including N-acetyltranexamic acid, nicotine, and D, L-typotophan, which were found to be significantly up-regulated in the resistant soybean PI437654 infected by SCN, classified into two types and used for combination analyses with the transcriptomic expression profiling. Associated genes were predicted, and suggested their likely biological processes, cellular component, molecular function and involved pathways.

Conclusions: Our results not only found out the potential novel metabolites and associated genes in the resistant response of soybean to soybean cyst nematode, but also provided new insights into the interactions between soybean and soybean cyst nematode.

Background

Soybean cyst nematode (SCN), Heterodera glycines, is one of the most devastating pathogens on soybean and causes a large quantity of annual yield loss worldwide [17, 40, 49]. SCN is a typical obligate endoparasitic nematode, its life cycle has three stages: egg, juvenile and adult. Juvenile stage is a no-feeding stage of infection. The juveniles pierce the soybean root with a spear-like feeding structure called stylet to penetrate the soybean root [21, 49]. They invade plant roots and then migrate to vascular bundles, where they establish a complex feeding site called syncytium [30, 44]. The formation and maintenance of the syncytium lead to dramatic changes in internal root structure, affect plant root system, and cause plant damages. Meanwhile, soybean roots produce many defense-related compounds such as reactive oxygen species and activate hormone signaling pathways in response to nematode infection [27, 31], and the metabolites undergo tremendous changes in the soybean roots upon infection of SCN.

Recently, the use of "omics" technology to obtain biological data has been surged, the most commonly used omics are genomics, transcriptomics, proteomics, and metabolomics [53]. There are numerous research reports about soybean-SCN interactions by genomic and transcriptomic analyses. The genomes of SCN and many soybean varieties were uncovered as the solid foundations to further investigate their biology, characteristics and their potential interactions [38, 43, 61]. Many soybean resistant candidate loci and/or genes were identified based on the genome-wide association study (GWAS) [24, 59, 64]. Moreover, many studies on the expression profiling of different plant tissues, such as the whole root samples and the syncytium structure were carried out in susceptible and resistant soybean varieties infected with SCN, and a lot of differential expression genes and related KEGG pathways were predicted [34, 35, 45]. However, there are few studies on soybean-SCN interactions by proteomic and metabolomic analyses.

Metabolomics is used to characterize the metabolic patterns and further study the functions and significance of the key differential metabolites, which play important roles in biological and medical fields[42]. Plant metabolomics has gained cumulative attentions, and became a mature method for studying plant responses to both biotic and abiotic stresses. Therefore, plant metabolomics is considered as an indispensable tool in the study of plant-pathogen interaction [12]. Plants respond to pathogen infection by producing the defense metabolites, especially, the plant secondary metabolites play important roles in plant host’s resistance [33]. Metabolomics will provide technical support for the identification of plant metabolites which are potential sources of new compounds for nematode control [9]. Research on plant nematodes has become more and more abundant, especially on root-knot nematodes. Eloh et al. (2016) performed metabolomic analysis of root-knot nematodes treated with maleimide, and proved that maleimide could be used as a new potential nematicide [22]. In addition, they studied the levels of metabolites in tomato plants after root-knot nematode infection, and found that tomato roots altered the biochemical pathways in response to root-knot nematode infection [22]. Recently, Borges et al. (2018) investigated the metabolic profiles of both resistant and susceptible watermelon varieties against root-knot nematode infection by using nuclear magnetic resonance (NMR) and principal component analysis (PCA) [9]. However, there was few reports on SCN-soybean interactions. Differential proteins and metabolites between the resistant and susceptible soybean roots were identified with the traditional approaches more than ten years ago [1]. Recently, Kang et al. (2018) studied the effect of Bacillus simplex strain Sneb545 on soybean secondary metabolites under SCN infection by combining the advanced and high-throughput approaches of transcriptomic and metabolomic analyses, and found that Sneb545-treated soybeans showed a higher concentration of various nematicidal metabolites [32].

To date, there were no reports on the difference of metabolic pathways between resistant and susceptible soybeans with infection of SCN. In this study, a SCN-resistant soybean variety PI 437654 [3, 4], and three SCN-susceptible soybean varieties Williams 82 (WM82), Zhonghuang 13 (ZH13) and Hefeng 47 (HF47) were selected as the research materials, and LC/MS full-scan detection technology was used to investigate the differences of metabolites and metabolic pathways between the resistant and susceptible soybean varieties after inoculation with SCN. The metabolites overlapped among resistant and susceptible soybeans, specifically differential metabolites in resistant- or susceptible-soybeans and their potential KEGG metabolic pathways were identified. Moreover, the genes associated with the significantly up-regulated metabolites in the resistant soybean, which mainly included the resistant soybean PI437654–specific up-regulated and the overlapping metabolite with significantly up-regulated but simultaneously down-regulated metabolites, were predicted. The results not only found out potential novel metabolites and associated genes in the resistant response of soybean to soybean cyst nematode, but also provided new insights into the interaction between soybean and soybean cyst nematode.

Results

Discussion

A resistant soybean PI 437654 and three susceptible soybeans WM82, ZH13 and HF47 were used as the test materials to explore the changes of metabolites in soybean roots with and without SCN inoculation by LC/MS analyses in this study. Totally, 37 differential metabolites and 20 KEGG metabolic pathways were identified, which were divided into three categories including the metabolites/metabolic pathways overlapped among all the tested resistant and susceptible soybeans, specific in the susceptible soybeans and specific in the resistant soybeans (Table 1; Table 2). It was found that the same differential metabolites such as linolenic acid and L-arginine were probably involved in more than one KEGG metabolite pathways, while the differently differential metabolites such as nicotine and pipecolinic acid were likely involved in the identical tropane, piperidine and pyridine alkaloid biosynthesis pathway (Table 2). However, only 12 differential metabolites (32%) were found to be involved in the predicted KEGG metabolite pathways.

In this study, some basal defense-related potential metabolites, such as linolenic acid and 16-hydroxyhexadecanoic acid, were found in the resistant soybean (PI 437654) and the three susceptible soybeans (WM82, ZH13 and HF47), and our metabolomic analyses results were consistent with some previous reports. Linolenic acid is involved in the metabolic pathways including alpha-linolenic acid metabolism and biosynthesis of unsaturated fatty acids, and it was thought to be related to plant tolerance to low temperature, and the content of linolenic acid was proportional to the cold tolerance of wheat [29, 56, 62]. In addition, linolenic acid plays an important role in the defense response to pathogen infection and traumatic stress in Arabidopsis thaliana [7]. Unsaturated fatty acids are considered as a new generation of plant disease inducers. Amruthesh et al. (2005) investigated the role of unsaturated fatty acids in the resistance of pearl millet to downy mildew, and found that its stimulation induced strong plant resistance [2]. Clay et al. (2006) found that the host resistance was related to the higher content of unsaturated fatty acids in the nuclear membrane of resistant species on Gossypium barbadense [16].

The metabolites and metabolic pathways specific in the resistant soybean PI 437654 infected with SCN, mainly including L-arginine, phytosphingosine, D-aspartic acid and nicotine, probably played pivotal roles on plant stress, tolerance or resistance based on some related research literature. L-arginine is the precursor of nitric oxide (NO), while NO is synthesized by L-arginine through NO synthase in the L-arginine-NO pathway, which is widely considered as an important regulator of cell function and communication in physiological and pathophysiological states [23, 46, 47]. NO is a second messenger in the stress response, and is involved in plant responses to biotic and abiotic stresses [18, 66]. L-arginine is linked and involved in many KEGG metabolic pathways such as arginine and proline metabolism, aminoacyl-tRNA biosynthesis and ABC transporters. Many studies indicated that there was a positive correlation between proline accumulation and plant stress tolerance [8]. Aminoacyl-tRNA synthase is an important component of protein synthesis and catalyzes the connection of amino acids with their homologous tRNA. They form a different set of enzymes, ensuring the fidelity of the transfer of genetic information from DNA to proteins [50, 55]. ABC transporter is a member of the protein superfamily, it uses the energy from the hydrolysis of ATP to transport material over membranes [60]. Recently, ABC transporters attracted more attentions because the ABC gene was associated with pesticide resistance [25]. Phytosphingosine (PHS), a member of the sphingosine family, shows fungicidal activity [57]. Castro et al. (2008) studied the effect of PHS on cells of the Neurospora crassa and found that PHS reduced the viability of fungal cells and induced fungal apoptosis [13]. D-aspartic acid participated in alanine, aspartate and glutamate metabolism, while glutamate dehydrogenase (GDH) is a central enzyme in glutamate metabolism [5]. The enhancement of drought resistance in wheat was related to ammonia assimilation and enhanced NADH-GDH activity under low osmotic stress and high osmotic stress [65]. Nicotine was widely used in killing many kinds of insects [28]. Vänninen at el. (2011) discovered that nicotine has a strong killing effect on the adults of miridae [58]. Imidacloprid is a new generation of nicotine pesticides with low toxicity, and nicotine and imidacloprid reduced aphid fecundity by inhibiting the reproduction of nymphs with certain concentrations [19]. Nicotine is involved in the biosynthesis of tropane, piperidine and pyridine alkaloid in metabolic pathways, and alkaloid was found to have obvious inhibitory effect on sunflower downy mildew fungus Plasmopara halstedii [51]. Thus, L-arginine, phytosphingosine, D-aspartic acid and nicotine may be involved in the resistance of soybean against SCN.

Differently, the differential metabolite 2-oxo-4-methylthiobutanoic acid, which was overlapped in all the three susceptible soybeans but absent in resistant soybean, was predicted to participate into three KEGG metabolic pathways, including cysteine and methionine metabolism, glucosinolate biosynthesis and 2-oxocarboxylic acid metabolism. Moreover, 2-oxo-4-methylthiobutanoic acid was simultaneously down-regulated in the three susceptible soybeans (ZH13, WM82 and HF47) infected with SCN, which likely resulted in decreased biosynthesis of glucosinolate. It was reported that glucosinolate was a plant secondary metabolite and had a preventive effect against plant pathogens and soil-borne plant pests [10, 37]. Additionally, the metabolic pathways of flavonoid biosynthesis, diterpenoid biosynthesis, which were reported to be involved in plant resistance to pathogens, were found to be enriched in one or two susceptible soybean varieties [6, 48]. This type of differential metabolites and their related KEGG metabolite pathways might play potential roles in the manipulation of host innate immunity to establish and maintain the feeding sites for SCN development [26, 54].

A local metabolite-calibrated database was used to identify potential differential metabolites in this study, which safeguarded the accuracy and precision [38, 39], but also likely had some disadvantages to miss many important compounds due to its limited capacities. Nevertheless, in this study, the different metabolomic responses of the resistant and susceptible soybean varieties to SCN infection were thoroughly characterized, and some important and pivotal different metabolites and related KEGG metabolite pathways were identified, and their likely functions were predicted based on previous studies.

In this study, three key metabolites including N-acetyltranexamic acid, nicotine, and D, L-typotophan, which were significantly up-regulated in the resistant soybean PI437654 infected by SCN, were identified and classified into two types. Combination of the metabolomic analyses with the transcriptomic expression profiling aided to predict potential associated genes involved in the most significantly up-regulated metabolites of the resistant soybean variety PI437654, and suggest their likely biological processes, cellular component, molecular function and involved pathways.

Conclusion

In this study, the main metabolomic differences of the resistant and susceptible soybeans infected with SCN were characterized by using a broad-spectrum SCN-high resistant soybean PI 437654 versus three SCN-susceptible soybean varieties. Totally, 37 differential metabolites and 20 KEGG metabolic pathways were identified, which were divided into three categories including the metabolites/metabolic pathways overlapped among all the tested resistant and susceptible soybean, specific in susceptible or resistant soybeans. Twelve differential metabolites were found to be involved in the predicted KEGG metabolite pathways. Moreover, 14 differential metabolites such as significantly up-regulated nicotine and down-regulated D-aspartic acid, and their involved KEGG pathways such as tropane, piperidine and pyridine alkaloid biosynthesis, alanine, aspartate and glutamate metabolism, sphingolipid metabolism and arginine biosynthesis were significantly changed and abundantly enriched specifically in the resistant soybean PI 437654, and likely played pivotal roles in defensing SCN. Moreover, combination of the metabolomic analyses with the transcriptomic expression profiling aided to predict potential associated genes involved in the most significantly up-regulated metabolites of the resistant soybean variety PI437654, and suggest their likely biological processes, cellular component, molecular function and involved pathways. To our best knowledge, our study was the first to investigate the different responses of resistant and susceptible soybeans infected with SCN by combining metabolomic analyses and transcriptomics. Our results not only found out the potential novel metabolites and associated genes in the resistant response of soybean to soybean cyst nematode, but also provided new insights into the interactions between soybean and soybean cyst nematode.

Methods

Juveniles Staining And Cysts Counting

Soybean root samples were collected at 8 dpi, and were stained by acid magenta staining as previously described [11], and the morphology were observed and the numbers of SCN juveniles within the root tissues were counted under a microscope (OLYMPUS-SZ2-ILST). The cysts were harvested from both the root tissues and root soil at 60 dpi, and their numbers were counted with a stereoscope (OLYMPUS-BX53F).

Lc/ms Metabolic Sequencing

At 8 dpi, the root samples of the resistant soybean PI437654 and the three susceptible soybean WM82, ZH13 and HF47, as well as their corresponding controls, were harvested, which were named PI437654_SCN, PI437654_0, WM82_SCN, WM82_0, ZH13_SCN, ZH13_0, HF47_SCN and HF47_0, respectively. Each soybean root sample was ground into powder in liquid nitrogen. Equivalent 80 mg root powder was dropped into a 1.5 mL centrifuge tube. Add 20 µL internal standard (0.3 mg/mL L-2- chlorophenylalanine ) and 1 mL 70% methanol (methanol :water = 7:3 (volume ratio)); Place at -20 ℃ for 2 min to pre-cool, and grind with a grinding machine for 2 min (60 Hz); Then, ultrasonic extraction was placed at 4 °C for 30 min ; after standing at -20 °C for 20 min, the samples were centrifuged for 15 min (13,000 rpm, 4℃), and 200 µL supernatant was drawn with a syringe, each sample was repeated 3 times, and the same source supernatants were combined together. After filtering with a 0.22 µm organic phase pinhole filter, it was transferred to an LC injection vial and stored at -80 °C for LC-MS analysis [63]. Each treatment had six repetitions.

Metabolomic Analyses

The metabolites were identified using a local metabolite-calibrated database [38, 39]. Multivariate statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), was performed using SIMCA-P⁺14.0 version software (Umetrics, Umeå, Sweden) to distinguish differences in metabolic profiles between pairwise groups. The Hotelling’s T2 region, shown as an ellipse in score plots of the models, defines the 95% confidence interval of the modeled variation. In the t-test, a variable with p-value < 0.05 was considered as a difference variable. Metabolites with the VIP (variable importance in the projection) > 1.0 and p-value < 0.05 were selected as differential metabolites. KEGG pathways were predicted by the differential metabolites with the p-value < 0.05 [63].

Transcriptomics Analyses

For RNA extraction, the same six root samples were sampled and randomly divided into two groups for each soybean variety, representing two biological repeats. The mixed samples were named K45 and K31 (PI437654_SCN, PI437654_0), K46 and K33 (WM82_SCN, WM82_0), K47 and K35 (ZH13_SCN, ZH13_0), K48 and K37 (HF47_SCN, HF47_0), respectively. The total RNA isolation, RNA quality examination, library construction and RNA-Seq, sequence analyses, GO (Gene Ontology) and KEGG analyses were preformed as previously described [14, 36]. Genes with an absolute value of log2 (fold change) >0.5 were defined as the differentially expressed genes (DEGs).

Correlation Analysis Between Metabolome And Transcriptome Data

Correlation Analyses were preformed as previously described [14]. Pearson correlation coefficients were calculated for metabolome and transcriptome data integration. The mean content of metabolites of different soybean varieties (the resistant soybean PI437654 and the three susceptible soybean WM82, ZH13 and HF47) and the mean value of transcript abundance of DEGs together were calculated. The coefficients were calculated from log2 (fold change) of each metabolite and log2 (fold change) of each transcript. Correlations corresponding to a coefficient with R2 > 0.9 were selected. The relationships between metabolome and transcriptome data were demonstrated by using Cytoscape (The Cytoscape Consortium, San Diego, CA, USA, version 2.8.2). Top 150 associated genes were focused on in this study.

Declarations

Acknowledgements

Thanks to Dr. Peng Huan from Institute of Plant Protection, Chinese Academy of Agricultural Sciences for the methodological suggestions for the revision of this article. Thanks to Dr.Vijai Bhadauria from China Agricultural University for critical read proof.

Authors’ contributions

LAK and DLP conceived the study; XS and LAK wrote the manuscript. XS and QSC performed the experiments; XS and QSC conducted statistical analyses; SML revised the manuscript. All of the authors discussed the results and commented on the manuscript, and approved its submission for potential publication.

Funding

This project was supported by the Natural Science Foundation of China (31872924), the National Key R&D Program of China (2018YFD0201000), and National Technology System of Soybean Industry (CARS-04-PS05). The funders had no role in study design, data collection and analysis, data interpretation, or in writing of the manuscript.

Availability of data and materials

All data generated during this study are included in this published article and its supplementary information files, and the raw data used or analysed were submitted to China National GeneBank (No.CNP0001271).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

¹ Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China

² Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China

³ Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, 150086 Harbin, China

References

1. Afzal AJ, Natarajan A, Saini N, Iqbal MJ, Geisler M, Eishemy HA, Mungur R, Willmitzer L, Lightfoot DA. The nematode resistance allele at the Rhg1 locus alters the proteome and primary metabolism of soybean roots. Plant Physiol. 2009;151:1264–80.
2. Amruthesh KN, Geetha NP, Jørgensen HJL, Neergaard ED, Shetty HS. Unsaturated fatty acids from zoospores of Sclerospora graminicola induce resistance in pearl millet. Eur J Plant Pathol. 2005;111:125–37.
3. Anand SC, Gallo KM, Baker IA, Hartwig EE. Soybean plant introductions with resistance to races 4 or 5 of soybean cyst nematode. Crop. Sci. 1988;28.
4. Anand SC, Wrather JA, Shumway CR. Soybean genotypes with resistance to race of soybean cyst nematode. Crop. Sci. 1985;25.
5. Andreas P. Glutamate dehydrogenase deficiency in cerebellar degener-ations: clinical, biochemical and molecular genetics aspects. Can J Neurol Sci. 1993;20:109-S16.
6. Arfaoui A. Effect of rhizobium isolates on isoflavonoid levels in chickpea plants infected with Fusarium oxysporum f.sp. ciceris. Phytopathol Mediterr. 2006;45:24–34.
7. Asanori Y, Takashi Y, Morifumi H, Hideharu S, Jeanluc M, Kensuke K, Shigemi S, Koh I. Disease resistance against Magnaporthe grisea is enhanced in transgenic rice with suppression of ω-3 fatty acid desaturases. Plant Cell Physiol. 2007;48:1263–74.
8. Ashraf M, Foolad MR. Roles of glycine betaine and proline in improving plant abiotic stress resistance. Environ Exp Bot. 2007;59:206–16.
9. Borges RM, Rahil T, Santana DSJ, Andrade BT. Dereplication of plant phenolics using a mass-spectrometry database independent method. Phytochem Analysis. 2018;29:601–12.
10. Brown PD, Morra MJ. Control of soil-borne plant pests using glucosinolate-containing plants. Adv Agron. 1997;61:161–231.
11. Bybd DWJ, Kirkpatrick T, Barker KR. An improved technique for clearing and staining plant tissues for detection of nematodes. J Nematol. 1983;15:142–3.
12. Castro-Moretti FR, Gentzel I, Mackey A. Metabolomics as an emerging tool for the study of plant–pathogen interactions. Metabolites. 2020;10.
13. Castro A, Lemos C, Falcao A, Glass NL, Videira A. Increased resistance of complex I mutants to phytosphingosine-induced programmed cell death. J Biol Chem. 2008;283:19314–21.
14. Chen Q, Li M, Wang C, Li Z, Xu J, Zheng Q, Liu P, Zhou H. Combining targeted metabolites analysis and transcriptomics to reveal chemical composition difference and underlying transcriptional regulation in Maca (Lepidium Meyenii Walp.) ecotypes. Genes (Basel). 2018;9.
15. Chen X, Li S, Zhao X, Zhu X, Duan Y. Modulation of (homo) glutathione metabolism and H₂O₂ accumulation during soybean cyst nematode infections in susceptible and resistant soybean cultivars. Int J Mol Sci. 2020;21:388.
16. Clay WF, Bartkowski EJ, Katterman FRH. Nuclear deoxyribonucleic acid metabolism and membrane fatty acid content related to chilling resistance in germinating cotton (Gossypium barbadense). Physiol Plantarum. 2006;38:171–5.
17. Cook DE, Lee TG, Guo X, Melito S, Wang K, Bayless AM, Wang J, Hughes TJ, Willis D. K. Copy number variation of multiple genes at Rhg1 mediates nematode resistance in soybean. Science. 2012;338:1206–9.
18. Delledonne M, Xia Y, Dixon RA. Nitric oxide functions as a signal in plant disease resistance. Nature. 1998;394:585–8.
19. Devine GJ, Harling ZK, Scarr AW, Devonshire AL. Lethal and sublethal effects of imidacloprid on nicotine-tolerant Myzus nicotianae and Myzus persicae. Pest Manag Sci. 1996;48:57–62.
20. Douillet P. Disinfection of rotifer cysts leading to bacteria-free populations. J Exp Mar Biol Ecol. 1998;224:183–92.
21. Duan Y, Zheng Y, Chen L, Zhou X, Wang Y, Sun J. Effects of abiotic environmental factors on soybean cyst nematode. Agr Sci China. 2009;8:63–71.
22. Eloh K, Sasanelli N, Maxia A, Caboni P. Untargeted metabolomics of tomato plants after root-knot nematode infestation. J Agr Food Chem. 2016;64:5963–8.
23. Epstein FH. The L-arginine-nitric oxide pathway. New Engl J Med. 1993;329:2002.
24. Han Y, Zhao X, Cao G, Wang Y, Li Y, Liu D, Teng W, Zhang Z, Li D, Qiu L. Genetic characteristics of soybean resistance to HG type 0 and HG type 1.2.3.5.7 of the cyst nematode analyzed by genome-wide association mapping. BMC Genomics. 2015;16:598.
25. Hans M. ABC transporters and their role in protecting insects from pesticides and their metabolites. Adv Insect Physiol. 2014;46:1–72.
26. Hewezi T, Baum TJ. Manipulation of plant cells by cyst and root-knot nematode effectors. Mol Plant Microbe in. 2013;26:9–16.
27. Hu Y, You J, Li C, Williamson VM, Wang C. Ethylene response pathway modulates attractiveness of plant roots to soybean cyst nematode Heterodera glycines. Sci Rep. 2017;7:41282.
28. Huang C, Ren J, Liang Y. Studies on isolation of nicotine from tobacco and its effects on killing insects. Acta. Agric. Univ. Jiangxiensis. 2001.
29. John JB, Christiansen MN. Inhibition of linolenic acid synthesis and modification of chilling resistance in cotton seedlings. Plant Physiol. 1976;57:257–9.
30. Jones MGK. The development and function of plant cells modified by endoparasitic nematodes. Elsevier Inc. 1981.
31. Kandoth PK, Ithal N, Recknor J, Maier T, Nettleton D, Baum TJ, Mitchum MG. The soybean Rhg1 locus for resistance to the soybean cyst nematode Heterodera glycines regulates the expression of a large number of stress and defense-related genes in degenerating feeding cells. Plant Physiol. 2011;155:1960–75.
32. Kang W, Zhu X, Wang Y, Chen L, Duan Y. Transcriptomic and metabolomic analyses reveal that bacteria promote plant defense during infection of soybean cyst nematode in soybean. BMC Plant Biol. 2018;18:86.
33. Kantor M, Levi A, Thies J, Guner N, Parnham S. NMR analysis reveals a wealth of metabolites in root-knot nematode resistant roots of citrullus amarus watermelon plants. J Nematol. 2018;50:303–16.
34. Klink VP, Overall CC, Alkharouf NW, Macdonald MH, Matthews BF. Laser capture microdissection (LCM) and comparative microarray expression analysis of syncytial cells isolated from incompatible and compatible soybean (Glycine max) roots infected by the soybean cyst nematode (Heterodera glycines). Planta. 2007;226:1389–409.
35. Klink VP, Overall CC, Alkharouf NW, Macdonald MH, Matthews BF. A time-course comparative microarray analysis of an incompatible and compatible response by Glycine max (soybean) to Heterodera glycines (soybean cyst nematode) infection. Planta. 2007;226:1423–47.
36. Kong L, Wu D, Huang W, Peng H, Wang G, Cui J, Liu S, Li Z, Yang J, Peng D. Large-scale identification of wheat genes resistant to cereal cyst nematode Heterodera avenae using comparative transcriptomic analysis. BMC genomics. 2015;16:1–18.
37. Koroleva OA, Gibson TM, Cramer R, Stain C. Glucosinolate-accumulating S-cells in Arabidopsis leaves and flower stalks undergo programmed cell death at early stages of differentiation. Plant J. 2010;64:456–69.
38. Liu P, Luo J, Zheng Q, Chen Q, Zhai N, Xu S, Xu Y, Jin L, Xu G, Lu X. Integrating transcriptome and metabolome reveals molecular networks involved in genetic and environmental variation in tobacco. DNA. Res. 2020;27.
39. Liu P, Zhou H, Zheng Q, Lu P, Yu Y, Cao P, Chen W, Chen Q. An automatic UPLC-HRMS data analysis platform for plant metabolomics. Plant Biotechnol J. 2019;17:2038–40.
40. Liu S, Kandoth PK, Warren SD, Yeckel G, Heinz R, Yang AJ, Jamai C, Eimellouki A, Juvale T. PS. A soybean cyst nematode resistance gene points to a new mechanism of plant resistance to pathogens. Nature. 2012;492:256–60.
41. Lopeznicora HD, Craig JP, Gao X, Lambert KN, Niblack TL. Evaluation of cultivar resistance to soybean cyst nematode with a quantitative polymerase chain reaction assay. Plant Dis. 2012;96:1556–63.
42. Manchester M, Anand A. Metabolomics:strategies to define the role of metabolism in virus infection and pathogenesis. Adv Virus Res. 2017;98:57–81.
43. Masonbrink R, Maier TR, Muppirala U, Seetharam AS, Lord E, Juvale PS, Schmutz J, Johnson NT, Korkin D, Mitchum MG. The genome of the soybean cyst nematode (Heterodera glycines) reveals complex patterns of duplications involved in the evolution of parasitism genes. BMC Genomics. 2019;20:119.
44. Matthews KBF, Klink VP. Emerging approaches to broaden resistance of soybean to soybean cyst nematode as supported by gene expression studies. Plant Physiol. 2009;151:1017–22.
45. Mazarei M, Liu W, Alahmad H, Arelli PR, Pantalone VR Jr. CNS. Gene expression profiling of resistant and susceptible soybean lines infected with soybean cyst nematode. Theor Appl Genet. 2011;123:1193–206.
46. Moncada S, Higgs A. The L-arginine-nitric oxide pathway. New Engl J Med. 1993;329:2002–12.
47. Moncada S, Palmer RMJ, Higgs EA. Biosynthesis of nitric oxide from L-arginine: a pathway for the regulation of cell function and communication. Biochem Pharmacol. 1989;38:1709–15.
48. Moustafa EM. Leaf curl virus and the flavonoid content of resistant and susceptible strains of cotton. Nature. 1961;191:415.
49. Niblack TL, Lambert KN, Tylka GL. A model plant pathogen from the kingdom Animalia: Heterodera glycines, the soybean cyst nematode. Annu Rev Phytopathol. 2006;44:283–303.
50. Odonoghue P, Lutheyschulten Z. On the evolution of structure in aminoacyl-tRNA synthetases. Microbiol Mol Biol Rev. 2003;67:550–73.
51. Oros G. Ujváry, István. Botanical fungicides: natural and semisynthetic ceveratrum alkaloids. Pest Manag Sci. 1999;55:253–64.
52. Peng H, Cui J, Long H, Huang W, Kong L, Liu S, He W, Hu X, Peng D, John J. Novel pectate lyase genes of Heterodera glycines play key roles in the early stage of parasitism. PLoS One. 2016;11:e0149959.
53. Rachel C, Danyel J, Jos K, Jan BJJ. Transcriptomic and metabolomic data integration. Brief Bioinform. 2015;5:891–901.
54. Siddique S, Grundler FM. Parasitic nematodes manipulate plant development to establish feeding sites. Curr Opin Microbiol. 2018;46:102–8.
55. Szymański M, Deniziak M, Barciszewski J. The new aspects of aminoacyl-tRNA synthetases. Acta Biochim Pol. 2000;47:821–34.
56. Torresfranklin ML, Repellin A, Huynh VB, Arcylameta A, Zuilyfodil Y, Phamthi AT. Omega-3 fatty acid desaturase (FAD3, FAD7, FAD8) gene expression and linolenic acid content in cowpea leaves submitted to drought and after rehydration. Environ Exp Bot. 2009;65:162–9.
57. Valentijnbenz M. Phytosphingosine kills Candida albicans by disrupting its cell membrane. Biol Chem. 2010;391:65–71.
58. Vänninen I, Rönnqvist M, Dahlqvist M, Forsström J. As an invasive pest in Finnish tomato crops: attempt to eradicate the bugs with nicotine-based programmes. Iobc/wprs Bulletin. 2011.
59. Vuong TD, Sonah H, Meinhardt CG, Deshmukh R, Kadam S, Nelson RL, Shannon JG, Nguyen HT. Genetic architecture of cyst nematode resistance revealed by genome-wide association study in soybean. BMC Genom. 2015;16:593.
60. Waard MAD. Significance of ABC transporters in fungicide sensitivity and resistance. J Pestic Sci. 1997;51:271–5.
61. Wang Z, Tian Z. Genomics progress will facilitate molecular breeding in soybean. Sci China Life Sci. 2015;58:813–5.
62. Willemot C, Pelletier L. Influence of light and temperature on the linolenic acid content and frost resistance of winter wheat. Can. J. Plant. Sci. 1980.
63. Zeng C, Lin H, Liu Z, Liu Z. Metabolomics analysis of Camellia sinensis with respect to harvesting time. Food Res Int. 2020;128:108814.
64. Zhang H, Li C, Davis EL, Wang J, Griffin JD, Kofsky J, Song B. Genome-wide association study of resistance to soybean cyst nematode (Heterodera glycines) HG Type 2.5.7 in wild soybean (Glycine soja). Front Plant Sci. 2016;7:1214.
65. Zhang HN, Wang ZQ, Cui GJ, Lin TB. Difference in seedlings ammonium assimilation of wheat cultivars with different drought resistance under osmotic stress. China J Appl Ecol. 2009;20:2406–10.
66. Zhou B, Guo Z, Xing J, Huang B. Nitric oxide is involved in abscisic acid-induced antioxidant activities in Stylosanthes guianensis. J Exp Bot. 2005;56:3223–8.