Agaricus heterocystis (AGH) mushroom an indigenous edible mushroom collected from south eastern part of India, belongs to the order Agaricales are widely known for its edibility and medicinal values comprising about 9387 species in 347 genera [1]. Generally, mushrooms are traditionally used in many Asian countries as food and medicine and are becoming more important in the diet as described [2-4]. In view of their medicinal properties, mushrooms contain abundance of secondary metabolites that possess multitude biological activities with lower toxicity and fewer side effects than chemical drugs and hence they were used as a valuable chemical resource for drug discovery [5-7]. Therefore, mushrooms represent a potential valuable resource for natural drugs [8]. The polysaccharide and polysaccharide related compounds from mushrooms play a vital role in developing or mediating the human immune system against tumor and tumor associated diseases. The polysaccharide exists as a structural component of cell wall of mushrooms that is composed of two major types: One is a rigid fibrillar of chitin (or cellulose) and the other one is matrix-like β-glucan, α-glucan and glycoproteins [9-11]. In addition, these polysaccharide macromolecules are structurally diverse in class and relatively widespread in occurrence has the possibility to carry highest capacity of biological information used for food and medicinal values [12,13]. The discovery and characterization of different types of polysaccharide is imperative hotspot research to confirm the compound is safe for functional foods or medicine. Growth optimization conditions for extracellular polysaccharide production from mushroom by following traditional method more materials, space and it is time consuming. Alternative and efficient methods required for the maximum production of polysaccharide having biological activity [14,15].

Hence, the objective of the present study is to determine the most suitable conditions required for the maximum production of extracellular polysaccharide from the mushroom AGH collected indigenously using Response Surface Methodology (RSM), employing three-variables in Box-Behnken statistical design. The optimum fermentation conditions obtained from the designed experiment were verified using validation experiments.

Mushroom

The pure culture of AGH (Figure 1a) was obtained from the Culture Collection Centre, Center for Advance Studies in Botany, University of Madras, the gene bank accession number FJ222602 (NCBI EU028347) in Potato-Dextrose-Agar (PDA) plates (Figure 1b). The medium used for submerged culture condition was Potato Dextrose Broth (PDB).

Experimental Design for Response Surface Methodology

The levels of the significant parameters and their interactions between variables, which influence the Extra-Cellular Polysaccharide (ECP) concentration, were analyzed and optimized by Box-Behnken Methodology [16].

The study and the experimental plan consisted of 15 trials with each independent variable at three different levels, Low (-1), Medium (0) and High (+1). The variables and their coded levels used for the study are shown in Table 1. All the experiments were done in triplicates and the average of ECP concentration obtained was taken as the dependent variable or response (Y_EPC).

For predicting the optimum point, a second order polynomial function was fitted to correlate relationship between independent variables and responses (ECP production). For the three factors, the corresponding equation is:

Y = Co+a₁A+a₂B+a₃C+a₁₁A²+a₂₂B²+a₃₃C²+a₁₂AB+a₁₃AC+a₂₃BC

(1) 

Where, Y represents the response variable, Co is model constant, a_1, a₂ and a₃ are linear coefficients, a₁₁A², a₂₂B² and a₃₃C² are interactions effect coefficients, a₁₁, a₂₂ and a₃₃ are quadratic coefficients where A, B and C denote the coded levels of variables.

The software Minitab for windows, version 12, was used for multiple regression analysis of the experimental data obtained. The multiple coefficients of correlation R and the determinations were calculated to evaluate the performance of the regression equation. The response surface plots were also given by using the above-mentioned software. The optimum condition for enhanced yield of polysaccharide production was therefore obtained by solving the regression equation.

Extraction of ECP 

After submerged cultivation, 1 ml of the fermented filtrate was filtered through Whatman No.1 filter paper. The filtrate was T with 85% ethanol, centrifuged at 3000 rpm for 5 minutes and the supernatant was removed. The sediments were dissolved in distilled water to a certain volume in which the ECP concentrations was estimated [17].

Estimation of ECP Concentration 

To 1.0 ml of the sample, 1.0 ml of 5% phenol and 5.0 ml of concentrated sulphuric acid was added and the content was mixed thoroughly by shaking. Then the solution could stand for 15 minutes in a boiling water bath. After cooling, the Optical Density (OD) of the solution was read at 490 nm using spectrophotometer. The amount of total carbohydrate was calculated using a standard graph prepared from D-glucose following the method by Dubois et al. [17]. The values are expressed as µg/ml for ECP.

NMR Spectrum

Thirty mg of the substance obtained through precipitation using ethanol was dissolved in distilled water and subjected to 1H and 13C NMR spectral analysis using BRUKER 300 MHz instrument.

Figure 1(a-b): Agaricus heterocystis (VKJ17), (a) Fruiting Body in Laboratory Condition and (b) Mycelial Growth in PDA Plate

Antiangiogenic Effect 

Ex vivo anti-angiogenic activity of ECP from AGH mushroom was measured by Chorioallantoic membrane (CAM) assay as described by Ribatti [18], for which a group of ten 7-day-old fertilized eggs were incubated at 37.5°C with 55% humidity. On day 8, a 1-cm² window was carefully created on the broadside of the egg, in which a known volume of test sample with 10 mg/ml stock solution was loaded on to the filter paper disk in varying concentrations of 50, 100 and 200 µg, respectively and then placed into CAM, after which a permeable sticky tape was immediately appended to the window. After incubation for 3 days (until day 11), the eggshell was pushed aside around the window and the blood vessels were photographed.

RSM Model Fitting

Response surface optimization by Box-Behnken model, a statistical analysis called multiple regression or regression co-efficient. A total of 15 runs were used to optimize the range and levels of chosen variables. The Box-Behnken design was carried out using the software Minitab for windows, version 15. The amount of extra cellular polysaccharide production was optimized against three factors and their design of experiment is given in Table 1. Each run was performed in triplicate and the values are given in Table 2. Maximum yield of ECP (2932 µg/ml) was recorded under the experimental conditions of pH 6.5, temperature 29.5°C and dextrose 1.125 g/100 ml.

A second order polynomial function was fitted, for predicting the optimum point within experimental constraints, to the experimental results of polysaccharide production:

Y_EPC = 2881-156A+115B+425C+47AB-109AC+208BC-1444A²-744B²-832C²

(2)

The statistical significance was checked by F-test and the analysis of variance (ANOVA) for the fitted quadratic polynomial mode is summarized in Table 3. It is evident from the mode F-value or a very low probability value (p<0.01). The advantage of the model can be checked by the determination coefficient R² and the multiple correlation coefficient R _­­­. The closer the value of R is to 100%, the better is the correlation between the measured and the predicted values. In this experiment, the value of R² and adjacent R² measured was 95.1 and 86.3% for polysaccharide production. These values indicated a high degree of correlation between the experimental and the predicted values. Here, the p-value of the model was smaller than 0.0001, which indicated that the model was suitable for use in this experiment.

The regression coefficient and the corresponding p-values are presented in Table 3. From the p-values of each model term, it could be concluded that three linear coefficients (A, B, C), three quadratic coefficient (A², B², C²) and three cross product coefficients (AB, AC, BC) are significant. The designed experimental data (Table 2) shows that the polynomial model for the EPC was regressed as shown below (in term of code factors).

The response surface plots representing ECP production from AGH changes in independent variables A₁, B₁ and C₁ is shown clearly in Figure 2 and the optimum conditions for obtaining maximum responses laid in the current experimental region. Figure 2a showed the effect of temperature, pH and their interaction in the yield of maximum extracellular polysaccharide production at pH 6.5±0.2 and at temperature 29.5°C. Figure 2b shows the effect of pH, carbohydrate (dextrose) source and their interaction in the maximum yield of extracellular polysaccharide at pH 6.5±0.2. The required carbohydrate (dextrose) source is 1.125 g/100 ml. Figure 2c showed the effect of temperature, carbohydrate (dextrose) source and their interaction in the maximum yield of extracellular polysaccharide at 29.5°C. The required carbohydrate source is 1.125 g/100 ml.

Table 1: The Variables and Their Coded Levels used in Optimization Study

Table 2: Box-Behnken Design Matrix with the Experimental Values

Figure 2(a-c): Response Surface Plots Represents the Effect of Temperature, pH and Carbohydrate Source in the Yield of Extra Cellular Polysaccharide (ECP) (a) RS Plot of ECP vs. Temperature and pH, (b) RS Plot of ECP vs. Carbohydrates and pH and (c) RS Plot of ECP vs. Carbohydrates and Temperature

Table 3: Analysis of Variance for the Fitted Quadratic Polynomial Mode Polysaccharide Production in AGH Mushroom

R²-0.991; Adj R²-0.986

The optimal values of the selected variables were obtained by solving the regression equation 2. The optimum value of the test variables in un-coded (actual value) units were culture temperature 30.9°C, optimum pH 4.9 and the required carbohydrate source for maximum extracellular polysaccharide production at 1.99 g/100 ml. Under these conditions, the maximum predicted yield of ECP was obtained at 2438.6 µg/ml. The excellent correlation between predicted and measured values of each model justifies the validity of both the response models. The amount of carbon source required for AGH for the maximum ECP production was similar with the previous reported [15,19,20] that 2.84 g/100 ml glucose was required for the maximum ECP production from Agaricus blazei. The required pH and temperature for polysaccharide production from AGH was in contrast with previous report by Cui et al. [21] and Osińska-Jaroszuk et al. [22], this difference may be because, the choice of organism taken for the study belongs to a different species.

NMR Profile of AGH Polysaccharide

NMR technique was used to elucidate the primary structure of ECP extracted from AGH cultivated under
submerged condition. The ¹H-NMR spectrums contained for five anomeric/protons of extracellular polysaccharide 5.13, 5.12, 4.79, 4.54 and 4.53 are shown in Figure 3a. The sugar residues are designated as A to E according to their decreasing anomeric chemical shifts. Residues A has anomeric signal at 5.13 indicates that it is a 2, 4 (-α-D-Glyc)-p→, the anomeric signal from residue B is 5.12 shows that it is an α-linked residue. Residue C has anomeric signal at 4.79 indicates that it is a β-linked residue. Residue ‘D’ has an anomeric signal atet 4.54 indicating that it is a β-linked residue. Residue ‘E’ has an anomeric signal at 4.53 indicates that it is a β-linked residue.

The ¹³C-NMR spectroscopy analysis of ECP reviewed the sample is simple in structure indicates a homopolysacccharide type may be produce by AGH (Figure 3b) shows that the absence of signals at 180-120 ppm means the polysaccharide was not contaminated by phenolic compounds. The presence of glucose was observed through signal at 95.90 ppm. Other important signals in the present spectra are found close in the areas of 60-80 ppm, where they are related to C_2, C₃, C₄, C₅ and C₆ of that carbohydrate.

The chemical structure of polysaccharides analyzed using NMR shows that extracts have high polysaccharide content composting of β-Glucose units (β-Glucan). The ¹³C spectrum by Gonzaga et al. [23] and Yalin et al. [24] and stated that the signal of 103.5 ppm and ¹H NMR signal at 0.5-2.7 ppm indicates the presence of β-glucose [6,25-27] reported that the presence of Glucan spectra would be of at-least 6 signals in single magnitude (110-60). Similar results were observed by the NMR spectrum.

Figure 3(a-b): NMR data of polysaccharide from AGH (a) ¹H NMR spectrum of Extracellular polysaccharide (ECP) from AGH grown under submerged culture condition and (b) ¹³C NMR spectrum of Extracellular polysaccharide from AGH grown under submerged culture condition

The spectrum was determined in D₂O at 50°C

Figure 4(a-d): In vivo CAM Assay. Photomicrographs of CAM in the Control and AGH ECP Treated Samples, (a) Control and experimental samples treated with AGH ECP at a concentration of (b)50, (c) 100 and (d) 200 µg/ml

Antiangiogenic Effect of AGH

 The anti-angiogenic effect of the ECP from AGH mushroom was determined using the chorioallantoic membrane assay with hen’s embryo. The CAM angiogenesis model is particularly a convenient technique for anatomical characterization [18] and it has several advantages over mammalian models because vascularization of CAM is subject to regulations through fluxes, pressure, shear stress and growth factors. The AGH ECP treatment inhibited angiogenesis in dose dependent manner, observed that lesser branching of blood vessels on the chorioallantoic membrane when compared with the normal hen’s embryo without polysaccharide treatment (Figure 4). Similar results were observed for polysaccharide isolated from the mushrooms A.xantha, R. ulmarius, A. murill (Brazilian mushroom) and A. cinnamomea. The antiangeogenic effect of AGH polysaccharide observed might inhibit tumor growth and angiogenesis through down-regulation of VEGF considered the important criterion for cancer cell progression [28]. Further study is needed to clarify whether polysaccharide extract may induce cytotoxicity and antiangiogenetic activity by down-regulating VEGF expression in other type of tumors.

The present study evident that the use of statistical method in determining the optimum condition and the factors required for the growth of microorganisms under laboratory condition proved to be useful, cost-effective and time saving. The experimental model described here can provide effective guidelines for polysaccharide production in large scale by industries form the mushroom AGH. However, further studies required to identify the medicinal properties of this mushroom polysaccharide.

1. Hibbett, D.S. et al. “A Higher-Level Phylogenetic Classification of the Fungi.” Mycological Research, vol. 111, no. 5, 2007, pp. 509-547. https://doi.org/10.1016/j. mycres.2007.03.004.

2. Jayachandran, M. et al. “A Critical Review on Health Promoting Benefits of Edible Mushrooms through Gut Microbiota.” International Journal of Molecular Sciences, vol. 18, no. 9, 2017. https://doi.org/10.3390/ijms180 91934.

3. Manzi, P. et al. “Nutrients in Edible Mushrooms: An Inter-Species Comparative Study.” Food Chemistry, vol. 65, no. 4, 1999, pp. 477-482. https://doi.org/10.1016/S0308-8146 (98)00212-X.

4. Sanmee, R. et al. “Nutritive Value of Popular Wild Edible Mushrooms from Northern Thailand.” Food Chemistry, vol. 82, no. 4, 2003, pp. 527-532. https://doi.org/10.1016/S03 08-8146(02)00595-2.

5. Bills, G.F. and J.B. Gloer. “Biologically Active Secondary Metabolites from the Fungi.” Microbiology Spectrum, vol. 4, no. 6, 2016. https://doi.org/10.1128/microbiolspec. FUNK-0009-2016.

6. Carbonero, E.R. et al. “Polysaccharides of Lichenized Fungi of Three Cladina spp.: Significance as Chemotypes.” Phytochemistry, vol. 61, no. 6, 2002, pp. 681-686. https:// doi.org/10.1016/S0031-9422(02)00363-1.

7. Gebreyohannes, G. et al. “Investigation of Antioxidant and Antimicrobial Activities of Different Extracts of Auricularia and Termitomyces Species of Mushrooms.” The Scientific World Journal, 2019, Article 7357048. https://doi.org/ 10.1155/2019/7357048.

8. Hyde, K.D. et al. “The Amazing Potential of Fungi: 50 Ways We Can Exploit Fungi Industrially.” Fungal Diversity, vol. 97, 2019, pp. 1-136. https://doi.org/10.1007/s13225-01 9-00430-9.

9. Baeva, E. et al. “Polysaccharides from Basidiocarps of Cultivating Mushroom Pleurotus ostreatus: Isolation and Structural Characterization.” Molecules, vol. 24, no. 15, 2019. https://doi.org/10.3390/molecules24152740.

10. Camilli, G. et al. “The Complexity of Fungal β-Glucan in Health and Disease: Effects on the Mononuclear Phagocyte System.” Frontiers in Immunology, vol. 9, 2018, pp. 673-673. https://doi.org/10.3389/fimmu.2018.00673.

11. Ruiz-Herrera, J. and S. Bartnicki-Garcia. “Proteolytic Activation and Inactivation of Chitin Synthetase from Mucor rouxii.” Journal of General Microbiology, vol. 97, no. 2, 1976, pp. 241-249. https://doi.org/10.1099/00221287-97-2-241.

12. Varki, A. and S. Kornfeld. “Historical Background and Overview.” Essentials of Glycobiology, edited by A. Varki et al., Cold Spring Harbor Laboratory Press, 2015, pp. 1-18.

13. Wasser, S.P. et al. “Dietary Supplements from Medicinal Mushrooms: Diversity of Types and Variety of Regulations.” International Journal of Medicinal Mushrooms, vol. 2, no. 1, 2000, p. 19. https://doi.org/10. 1615/IntJMedMushr.v2.i1.10.

14. Hereher, F. et al. “Cultural Optimization of a New Exopolysaccharide Producer ‘Micrococcus roseus.’” Beni-Suef University Journal of Basic and Applied Sciences, vol. 7, no. 4, 2018, pp. 632-639. https://doi.org/10.1016/j. bjbas.2018.07.007.

15. Hsu, K.D. et al. “Enhanced Active Extracellular Polysaccharide Production from Ganoderma formosanum Using Computational Modeling.” Journal of Food and Drug Analysis, vol. 25, no. 4, 2017, pp. 804-811. https://doi.org/ 10.1016/j.jfda.2016.12.006.

16. Box, G.E.P. and D.W. Behnken. “Simplex-Sum Designs: A Class of Second-Order Rotatable Designs Derivable from Those of First Order.” Annals of Mathematical Statistics, vol. 31, no. 4, 1960, pp. 838-864. https://doi.org/10.1214/ aoms/1177705661.

17. Dubois, M. et al. “Colorimetric Method for Determination of Sugars and Related Substances.” Analytical Chemistry, vol. 28, 1956, pp. 350-356.

18. Ribatti, D. “Chick Embryo Chorioallantoic Membrane as a Useful Tool to Study Angiogenesis.” International Review of Cell and Molecular Biology, vol. 270, 2008, pp. 181-224.

19. Biswas, J. and A.K. Paul. “Optimization of Factors Influencing Exopolysaccharide Production by Halomonas xianhensis SUR308 under Batch Culture.” AIMS Microbiology, vol. 3, no. 3, 2017, pp. 564-579. https://doi. org/10.3934/microbiol.2017.3.564.

20. Liu, G.Q. and X.L. Wang. “Optimization of Critical Medium Components Using Response Surface Methodology for Biomass and Extracellular Polysaccharide Production by Agaricus blazei.” Applied Microbiology and Biotechnology, vol. 74, no. 1, 2007, pp. 78-83. https://doi.org/10.1007/ s00253-006-0661-6.

21. Cui, F.J. et al. “Optimization of the Medium Composition for Production of Mycelial Biomass and Exo-Polymer by Grifola frondosa GF9801 Using Response Surface Methodology.” Bioresource Technology, vol. 97, no. 10, 2006, pp. 1209-1216. https://doi.org/10.1016/j.biortech. 2005.05.005.

22. Osińska-Jaroszuk, M. et al. “Extracellular Polysaccharides from Ascomycota and Basidiomycota: Production Conditions, Biochemical Characteristics and Biological Properties.” World Journal of Microbiology and Biotechnology, vol. 31, no. 12, 2015, pp. 1823-1848. https://doi.org/10.1007/s11274-015-1937-8.

23. Gonzaga, M.L.C. et al. “Isolation and Characterization of Polysaccharides from Agaricus blazei Murill.” Carbohydrate Polymers, vol. 60, no. 1, 2005, pp. 43-49. https://doi.org/10.1016/j.carbpol.2004.11.022.

24. Yalin, W. et al. “Isolation, Purification and Structural Investigation of a Water-Soluble Polysaccharide from Solanum lyratum Thunb.” International Journal of Biological Macromolecules, vol. 36, no. 4, 2005, pp. 241-245. https://doi.org/10.1016/j.ijbiomac.2005.06.007.

25. Fairweather, J.K. et al. “Structural Characterization by 13C-NMR Spectroscopy of Products Synthesized In Vitro by Polysaccharide Synthases Using 13C-Enriched Glycosyl Donors.” Glycobiology, vol. 14, no. 9, 2004, pp. 775-781. https://doi.org/10.1093/glycob/cwh097.

26. Richardson, P.M. et al. “Rapid 13C NMR Hyperpolarization Delivered from Para-Hydrogen Enables Low-Concentration Detection and Quantification of Sugars.” Chemical Science, 2019. https://doi.org/10.1039/c9sc034 50a.

27. Synytsya, A. and M. Novak. “Structural Analysis of Glucans.” Annals of Translational Medicine, vol. 2, no. 2, 2014, p. 17. https://doi.org/10.3978/j.issn.2305-5839.2014.02.07.

28. Abhinand, C.S. et al. “VEGF-A/VEGFR2 Signaling Network in Endothelial Cells Relevant to Angiogenesis.” Journal of Cell Communication and Signaling, vol. 10, no. 4, 2016, pp. 347-354. https://doi.org/10.1007/s12079-016-0352-8.