Differentiation of Heterodera glycines races by metabolomics

doi:10.21203/rs.3.rs-2061880/v1

Download PDF

Article

Differentiation of Heterodera glycines races by metabolomics

https://doi.org/10.21203/rs.3.rs-2061880/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

The nematode Heterodera glycines is one of the main phytosanitary problems of soybean and the main management strategy is the use of pathogen resistant cultivars. For this, it is necessary to characterize the populations of H. glycines. Race determination schemes present flaws and there is a demand for more practical methods. This study objective was to identify possible target metabolites for the characterization of populations of H. glycines. For this, the metabolic profile of the H. glycines races present in the Brazilian territory was analyzed by High-Performance Gas Chromatography linked to Mass Spectrometry and contrasted with a database. The results showed different metabolites profiles with the potential to be used as indicators of the races determined by the differentiating cultivars method. The concentrations patterns of metabolites found were different among the races analyzed and allow the individualization of each race within a single profile. These findings indicate the possibility of race discrimination based on metabolomics tests. Further investigations may provide fundamental information in the development of new rapid tests for the determination of H. glycines races.

The nematode species Heterodera glycines Ichinohe (1952) is one of the main phytosanitary problems of soybean (Glycine max [L.] Merr.) in the world [1, 2]. The symptoms of H. glycines in soybean crops are initially manifested by patches of yellow and stunted plants and low productivity. However, if the pathogen reaches high population rates, it can lead to an increase in premature plant mortality. In situations where there are very high populations of the nematode in the soil, productivity losses related to H. glycines can reach 100%.

The management strategies consists on crop rotation, soil management and the use of resistant cultivars, which is the most used H. glycynes control measure due to its practicality and economic viability [3, 4]. However, the use of resistant cultivars is difficult due to the high genetic variability of H. glycines [5–7]. The reproduction of H. glycines is amphimitic and the female can copulate with several males, promoting a great genetic variability within the species [8]. Because of those features, the management strategies of H. glycines by the continuous use of a resistant cultivar can promote a selective pressure where the individuals capable of parasitizing the root system of these cultivars will be selected and increase its population [9, 10]. This characteristic makes it necessary to constantly identify new sources of resistance [3, 11].

The selection of resistant cultivars was usually made through the identification of H. glycines races following the methodology of differentials soybean lines [12, 13]. Studies carried out in Brazil detected the presence of 11 races (1, 2, 3, 4, 4+, 5, 6, 9, 10, 14 and 14+) distributed across the producing states [14]. Races 4 + and 14 + differ from races 4 and 14 for their capacity to parasitize the Hartwig cultivar [10].

The determination of H.glycines races using the differential soybean lines demand time and involves soybean cultivars that are not adapted to the soil and climatic conditions of the brazilian territory [13]. Furthermore, the identification scheme is criticized for not having been updated even with the discovery of new resistant cultivars that could lead to an increase in the number of known races. Such modifications could lead to the conclusion that populations of a certain race should be separated into distinct races. Despite criticism by several field researchers, the procedure continues to be adopted by companies and soybean farmers to characterization of H. glycines populations in Brazil. An alternative scheme was proposed in which 7 cultivars with resistance are used for the identification of ‘HG types’ of H. glycynes for the characterization of populations in a specific region and not for the determination of races [4]. However, the method still presents some of the flaws of the race determination scheme and still generates some confusion amongst researchers and agribusiness professionals [11, 15].

Thus, there is currently a demand for the development of new techniques for H. glycines identification with a greater degree of practicality and applicability in management decision-making process [11]. Therefore, the objective of this project was to identify, through metabolomics techniques, different metabolites profiles that can increase the practicality in the characterization of H. glycines populations and generate knowledge for the elaboration of management techniques that can contribute to the increase in crop productivity.

Metabolites profiling.

The analysis of data acquired from mass spectrometry of material extracted from females of H. glycines indicated the presence of 245 metabolites. Considering the 75% frequency filter within each race and applying the Benjamini-Hochberg multiple testing correction, the statistical significance analysis (ANOVA) test suggested the presence of 8 metabolites that present a significant difference (p < 0.05 and fold change > 2) among the different races of H. glycines (Table 1).

Table 1

Statistical analysis: ANOVA, Tukey, p < 0.05, / Multiple Testing Correction: Benjamini-Hochberg, *Fold change* > 2.00. Blue: metabolites that showed a significant difference. Pink: metabolites that showed no significant difference. Gray: All metabolites analyzed
Races	R2	R9	R3	R5	R6	R4	R14	R4+	R10	R1	R14+
R2	8	2	2	2	2	1	2	1	2	2	2
R9	6	8	1	0	0	1	0	1	0	4	0
R3	6	7	8	1	1	0	1	0	1	3	1
R5	6	8	7	8	0	1	0	1	0	4	0
R6	6	8	7	8	8	1	0	1	0	4	0
R4	7	7	8	7	7	8	1	0	1	3	1
R14	6	8	7	8	8	7	8	1	0	4	0
R4+	7	7	8	7	7	8	7	8	1	3	0
R10	6	8	7	8	8	7	8	7	8	4	0
R1	6	4	5	4	4	5	4	5	4	8	4
R14+	6	8	7	8	8	7	8	8	8	4	8

Hierarchical clustering.

The H. glycines races were organized into hierarchical clusters according to metabolite similarity and the normalized intensity of the 8 metabolites that showed a significant difference between races was used (Fig. 1).

The 8 metabolites found were compared with the database and searched on the literature and organized in Table 2.

Table 2

The 8 metabolites with significant differences between races of *H glycines*.
Metabolite	Mass (Da)	PubChem CID
2,4-Dimethylbenzaldehyde	134.17	61814
1-Nonene, 4,6,8-trimethyl-	168.32	41077
Dodecyl acrylate	240.38	75084
2,5-Dimethylbenzaldehyde	134.17	22015
Stearic Acid	284.47	5281
Propanoic acid, 3-mercapto-, dodecyl ester	274.46	80796
Oxime-, methoxy-phenyl-_	151.16	9602988
Palmitic Acid	256.42	985

Brazil is currently one of the largest soybean producers in the world with an estimated production of 140.5 million tons in the 2021/22 crop and an export forecast of 89.31 million tons [16]. Estimates indicate that 49% of grain production in Brazil is correlated with soybeans [17]. The nematode H. glycines is one of the majors phytosanitary problems for the soybean crop. Productivity losses with the nematode in the largest producing regions are estimated at 5.24% in South America and 9.31% in North America [18]. The main management strategy for H. glycines is the development of pathogen resistant cultivars, which can result in higher productivity and lower reproduction rates [15]. For this purpose, a race identification scheme was used for many years to avoid population increases in the selected resistant cultivars [8–10].

The race determination scheme has been criticized by field researchers, but it continues to be adopted by several companies and soybean breeders to characterize the populations of H. glycines in Brazil. Alternatives presented for the characterization of H. glycines populations continue to present some of the flaws of the previous scheme and have created confusion among researchers and agribusiness professionals. Considering this, the demand for new techniques for characterizing H. glycines populations that facilitate management decisions remains high [11, 15].

In several studies, mass spectrometry has been used as a tool to identify pathogenic fungi, bacteria and parasites [19–24]. In addition, proteomic and metabolomic analyzes of nematodes and other parasites were also performed with various mass spectrometry techniques in several studies [25, 26]. The potential of the methodology has already been described in the identification of several parasites species, presenting the advantage of usually being an easy and fast method of identification. However, it is worth noting that the cost of acquiring the equipment is one of the biggest disadvantages of the methodology [27].

Our results showed significantly different metabolites with the potential to be used as indicators of the races determined by the differentiating cultivars method. The concentrations patterns of these metabolites found were different among the 11 races analyzed and allow the individualization of each one of the races within a single profile. Additional investigations may allow the corroboration of the results and the elucidation of the profile of all races present in the Brazilian territory. Complementary analyzes with the proper identification of these metabolites can generate knowledge of possible new compounds or targets for the development of tests for specific detection of the race.

Among races found on Brazilian territory, races 4 + and 14 + differ in the determination scheme of races 4 and 14 only by their ability to parasitize the Hartwig cultivar [10]. However, according to our findings races 4 and 4+, as well as races 14 and 14+, did not show similarities in the presence of metabolites that would allow the clustering of these groups. These findings may indicate that despite the similarity in the race determination scheme, these races do not have a metabolic profile that allows segregation from other nematode races. This result is in line with criticism that the increase in differential cultivars in the race determination test could lead to segregation of new races [4].

New tests for the detection and characterization of H. glycines are needed to increase the practicality of field management. Techniques such as multiplex sensors have been widely used for detection and identification of diseases in the field [28]. However, such techniques use phenotypic aspects of the plants and much of the productivity loss caused by H. glycines can occur without the manifestation of apparent symptoms in the crop [1, 29]. Despite not been a tool capable of on-site detection, the presence of functional biomarkers makes identification in the laboratory faster and simpler [26]. Therefore, laboratories with structure for mass spectrometry could perform the characterization of H. glycines populations based on the metabolic profile with a high level of reliability and eliminate the long process of race determination. Beyond that, new analyzes and the proper identification of these metabolites may provide sufficient information to search for ligands and create biosensors for faster identifications [30].

Our study suggests that the H. glycines races present in the Brazilian territory have different metabolic profiles. These findings indicate the possibility of race discrimination based on metabolomics tests. Further investigations may provide fundamental information in the development of new rapid tests for the determination of H. glycines races.

Experimental strategy.

The 11 races of H. glycines present in the brazilian territory were inoculated in the susceptible commercial cultivar Brasmax Potência RR (GDM Seeds) for the multiplication and collection maintenance. The metabolic profile of females of each race was analyzed 60 days after inoculation. The data obtained from the analysis of females in the susceptible cultivar were interpreted for the identification of functional biomarkers for the differentiation of H. glycines races and metabolic characterization of the infection process.

Metabolites extraction.

The H. glycines whole females were collected from the roots in triplicates of 5 individuals for each race in a 1.5 mL microtube. The extraction of metabolites was performed from 5 females of each race of Heterodera glycines, adding 1000 µL of spectroscopic grade methanol in a 2 mL microtube and incubating in an ultrasonic bath for 15 minutes. The material was then incubated for 4 hours in na ultrafreezer (-80ºC). O material foi posteriormente incubado por 4 horas em ultrafreezer (-80ºC). In the sequence, centrifuged for 15 min at 13000 g and the supernatant transferred to another 2 mL microtube, which was concentrated in a vacuum concentrator for 30 min. The material was stored in an ultrafreezer (-80ºC) until the time of analysis.

In the mass spectrometry analyses, the samples were resuspended in 700 µL of spectroscopic grade methanol, and filtered through a syringe tip filter with a pore size of 0.22 micrometer.

GC-MS.

Samples were analyzed in triplicate using a gas chromatography/mass spectrometry (GC/MS) system (Agilent Technologies model 7890B GC System/5977B GC/MSD). Capillary chromatography column DB-5HT (30 m x 250 µm x 0.25 µm). The chromatographic parameters were: injector temperature at 290°C, high purity helium as a carrier gas at a constant flow rate of 1,2 mL/min with 6 µL injections. The MS Scan was set on 50–550 m/z, with a 150 count threshold and 4 min solvent delay. The temperature was set to 150° C for the quadrupole MS and 280° C for the ion source.

Statistical analysis.

The acquired mass spectra were extracted using the software MassHunter Qualitative v. 10.0 and converted to “.CEF” extension using the “Molecular feature extraction tool. The software Agilent Mass Profiler Professional v. B.13.1.1 was used to filter and align the extracted metabolites. The filters used were: minimum absolute abundance = 5000 counts; minimum number of allowed ions = 2; all charges were permitted. Alignment parameters were: retention time tolerance of 0.15 min; mass window 15 ppm + 2mDa. Metabolites present in 75% of at least one group with a coefficient of variation below 25% were selected for further data analysis. Statistical analyzes were performed with log2 transformed values. Principal component analysis (PCA) was used to verify the analytical performance. Metabolites were identified using the METLIN database (included in the MPP).

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

We are thankful for UFU, CNPq, FAPEMIG, CAPES e INCT Teranano for the support. We are grateful for the support and guidance of Professor Dr. Luiz Ricardo Goulart Filho, from the Biotechnology Institute of the Federal University of Uberlândia, in memoriam.

Author contributions

G.N.M.C. Conception, conduction, analysis and interpretation of data, writing and revision of the manuscript. H.O.A.S.; L.P.M.; M.M.M.; L.M.B.; Performed investigation and statistical analysis. L.R.G.; C.U.V. supervised the project. All authors reviewed the manuscript.

Additional Information

Statement on research involving plants

All the plant involving procedures were in compliance with institutional, national, and international guidelines and legislation. No endangered species were used.

Funding

This research was funded by the INCT em Teranóstica e Nanobiotecnologia, CNPq: 403193/20022, FAPEMIG: CBB-APQ-03613-17 and CAPES. Federal University of Uberlândia provided physical conditions for experimental procedures.

Competing interests

The authors declare no competing interests.

Allen, T. et al. Soybean Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2010 to 2014. Plant Health Progress. 18, 19–27 (2017).
Koenning, S. & Wrather, J. Supression of Soybean Yield Potential in the Continetal United States by Plant Diseases from 2006 to 2009. Plant Health Progress; https://doi.org/10.1094/PHP-RS-16-0066 (2010).
Empresa Brasileira de Pesquisa Agropecuária https://www.embrapa.br/ (2015).
Niblack, T.L. et al. A revised classification scheme for genetically diverse populations of Heterodera glycines. Journal of Nematology, 34, 279–288 (2002).
Young, L. D. Yield loss in soybeans caused by Heterodera glycines. Journal of Nematology, 28, 604–607 (1996).
Li, Y. et al. Effect of the rgh1 gene on penetration, development and reproduction of Heterodera glycines race 3. Nematology, 6, 729–736 (2004).
Brucker, E. et al. Rhg1 alleles from soybean PI 437654 and PI 88788 respond differentially to isolates of Heterodera glycines in the greenhouse. Theoretical and Applied Genetics, 111, 40–44 (2005).
Triantaphyllou, A.C. & Esbenshade, P.R. Demonstration of multiple mating in Heterodera glycines with biochemical markers. Journal of Nematology, 22, 452–456 (1990).
Dias, W.P. et al. Quebra da resistência da cv. Hartwig por população de campo do nematoide de cisto da soja (Heterodera glycines). Pesquisa Agropecuária Brasileira, 33, 97 (1998).
Dias, W.P. et al. Nematoide de cisto da soja: biologia e manejo pelo uso da resistência genética. Nematologia Brasileira, 33, 1–16 (2009).
Niblack, T.L. et al. A Model Plant Pathogen from the Kingdom Animalia: Heterodera glycines, the Soybean Cyst Nematode. Annual Review of Phytopathology, 44, 283–303 (2006).
Golden, A.M. et al. Terminology an identity of infraspecific forms of the soybean cyst nematode (Heterodera glycines). Plant Disease Reporter, 54, 544–546 (1970).
Riggs, R. D. & Schmitt, D. P. Complete characterization of the race scheme for Heterodera glycines. Journal of Nematology, 20, 392–395 (1988).
Empresa Brasileira de Pesquisa Agropecuária https://www.embrapa.br/ (2013).
Tylka, G. L. Understanding Soybean Cyst Nematode HG Types and Races. Plant Health Progress, 17(2), 149–151 (2016).
Companhia Nacional de Abastecimento https://www.conab.gov.br/ (2022).
Ministério da Agricultura, Pecuária e Abastecimento https://www.gov.br/agricultura/pt-br/ (2017).
Savary, S. et al. The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution, 3, 430–439 (2019).
Fenselau, C. & Demirev, P. A. Characterization of intactcmicroorganisms by MALDI mass spectrometry. Mass Spectrometry Reviews, 20, 157–171 (2001).
Emonet, S. et al. Application and use of various mass spectrometry methods in clinical microbiology. Clinical Microbiology and Infection, 16, 1604–1613 (2010).
Croxatto, A. et al. Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiology Reviews, 36, 380–387 (2012).
Downard, K. M. Proteotyping for the rapid identification of influenza virus and other biopathogens. Chemical Society Reviews, 42, 8584–8595 (2013).
Nenoff, P. et al. MALDI-TOF mass spectrometry – a rapid method for the identification of dermatophyte species. Medical Mycology, 51, 17–24 (2013).
Singhal, N. et al. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis. Frontiers in Microbiology, 6, 791 (2015).
Watson, D. G. The potential of mass spectrometry for the global profiling of parasite metabolomes. Parasitology, 137, 1409–1423 (2009).
Wang, T. et al. High throughput LC-MS/MS-based proteomic analysis of excretory-secretory products from short-term in vitro culture of Haemonchus contortus. Journal of Proteomics, 204; doi: 10.1016/j.jprot.2019.05.003 (2019).
Singhal, N. et al. MALDI-TOF MS in clinical parasitology: applications, constraints and prospects. Parasitology, 143, 1491–1500 (2016).
Sankaran, S. & Ehsani, R. Detection of Huanglongbing-Infected Citrus Leaves Using Statistical Models with a Fluorescence Sensor. Applied Spectroscopy, 67, 463–469 (2013).
Mueller, D. S. et al. A Farmer’s Guide to Soybean Diseases. American Phytopathological Society, St. Paul, MN (2016).
Naresh, V. & Lee, N. A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors. Sensors, 21, 1109–1143 (2021).

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Differentiation of Heterodera glycines races by metabolomics

Status:

Version 1

Abstract

Figures

Introduction

Results

Discussion

Methods

Statistical analysis.

Declarations

References

Additional Declarations

Status:

Version 1