Method for predicting multi-element combined toxicity interaction between emerging fusarium toxin and traditional fusarium toxin

Abstract

The invention discloses a method for predicting multi-element combined toxicity interaction of emerging fusarium toxin and traditional fusarium toxin, and belongs to the technical field of combined toxicity research. The method disclosed by the invention comprises the following steps: designing a multi-element toxin mixture consisting of four kinds of ennisin and deoxynivalenol by applying a uniform design ray method, and carrying out nonlinear least square fitting on concentration-effect data by adopting a two-parameter nonlinear function to obtain an experimental observation curve; calculating the mixed combined action effect of the multi-element toxins by using three classic combined toxicity evaluation models, and drawing a model prediction curve; the interaction type between the substances is judged by comparing the deviation between the experimental observation curve and the model prediction curve; the method can realize effective simulation of mycotoxin complex environment concentration composition, and is simple, convenient and rapid to operate, low in evaluation cost and accurate in evaluation result.

Description

Technical Field

[0001] The invention relates to a method for predicting the multi-component combined toxicity interaction between emerging Fusarium toxins and traditional Fusarium toxins, belonging to the technical field of combined toxicity research. Background Art

[0002] Mycotoxins are typical microbial toxins and are secondary metabolites produced during fungal growth. Deoxynivalenol (DON), a traditional Fusarium toxin, is widely found and commonly found as a common contaminant in food and feed around the world. In recent years, emerging mycotoxins represented by enniomycin (ENNs) have been reported more and more frequently. Not only are their types increasing rapidly, but they also co-contaminate with traditional mycotoxins. Due to the lack of toxicological data, the risks to plants and animals are still unclear.

[0003] Both deoxynivalenol (DON) and ennisones (ENNs) are toxins of Fusarium, and the risk of exposure to both toxins is extremely high. Therefore, the study of the combined toxicity of deoxynivalenol (DON) and ennisones (ENNs) plays an important guiding role in ensuring food safety and promoting the formulation of hazard limit standards.

[0004] At present, most of the research on joint toxicity focuses on binary mixtures, and there are few studies on mixtures of three or more components, and even fewer on the joint toxicity of emerging toxins. When the number of components in a mixture increases, it is easy to exceed the toxic effect threshold of each toxin, so more and more toxin research is devoted to its comprehensive toxicity, especially to explore the types of toxicological interactions.

[0005] The commonly used mixture toxicity research methods in the existing technology, such as the equal toxicity method and the fixed concentration method, can only be used for one or several specific mixtures. When the number of components in the mixture increases, the concentration and proportion of each component will increase exponentially. It is not only difficult to simulate the diverse concentration changes of the mixed system of harmful substances in the actual environment, but also greatly increase the experimental cost.

[0006] Traditional model organism toxicity evaluation methods have some limitations, such as significant metabolic and genetic differences, ethical controversies in animal experiments, long testing cycles, and high costs. Summary of the invention

[0007] In view of the defects and shortcomings of the prior art, the purpose of the present invention is to provide a method for the combined toxic effects of an emerging Fusarium toxin ennivalenol (ENNs) and a traditional Fusarium toxin deoxynivalenol (DON) on hepatocytes HepG2, which can more objectively and comprehensively predict the potential threat to human health under complex exposure to fungal toxins.

[0008] In order to achieve the above purpose, the technical solutions provided are as follows:

[0009] The present invention provides a method for predicting the combined toxicity interaction between emerging Fusarium toxins and traditional Fusarium toxins, comprising the following steps:

[0010] (1) Design a multi-component mixed system of emerging Fusarium toxins and traditional Fusarium toxins using the uniform design ray method (UD-Ray); and uniformly mix the toxins according to the designed ratio and dilute them into a series of toxin working solutions with a concentration gradient;

[0011] (2) obtaining HepG2 cells in the logarithmic growth phase and inoculating and culturing them; incubating the HepG2 cells with the toxin working solution of step (1) and continuing to culture them in an incubator; and then using the CCK-8 reagent method to determine the activity inhibition rate of the toxin working solution on the HepG2 cells;

[0012] (3) A two-parameter nonlinear function was used to perform nonlinear least squares fitting on the concentration-effect data to obtain the experimental observation curve; three classic joint toxicity evaluation models were used to calculate the combined effects of multiple toxin mixtures, and the model prediction curve was drawn; the type of interaction between substances was determined by comparing the deviation between the experimental observation curve and the model prediction curve. When the model prediction curve was above, within, or below the 95% confidence interval (OCI) of the experimental observation curve, it was considered that antagonism, additive, or synergistic effects occurred between the components of the mixture.

[0013] In one embodiment, the emerging Fusarium toxins are enantiomycin A (ENA), enantiomycin A1 (ENA1), enantiomycin B (ENB) and enantiomycin B1 (ENB1).

[0014] In one embodiment, the traditional Fusarium toxin is deoxynivalenol (DON).

[0015] In one embodiment, the uniformly designed ray method (UD-Ray) described in step (1) is to design a ray composed of a multi-component mixed system of four ennivalenols (ENNs) and deoxynivalenol (DON), and the UD used represents U6 (6 5 ), where 6 is the number of concentration effect values, subscript 6 is the number of experiments, that is, the number of rays, including UD-1, UD-2, UD-3, UD-4, UD-5 and UD-6; superscript 5 is the number of toxins.

[0016] In one embodiment, the concentration effect value refers to the inhibition rate of toxin on cell activity, which is set to the concentration effect value at 5%, 10%, 15%, 20%, 30% and 50% respectively.

[0017] In one embodiment, the HepG2 cell seeding density in step (2) is 10,000-60,000 cells / well.

[0018] In one embodiment, the toxin working solution in step (2) is used to incubate HepG2 cells for 24-72 hours.

[0019] In one embodiment, the CCK-8 reagent method in step (2) is specifically as follows: after the HepG2 cells are incubated with the toxin working solution, CCK-8 reagent (10%, v / v) is added, and the cells are again placed in a constant temperature incubator and cultured in the dark for 1-2 hours, and then placed in a multifunctional microplate reader to detect the absorbance value of each well at 450nm.

[0020] CCK-8 reagent contains -8 [its chemical name is: 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfonic acid benzene)-2H-tetrazole monosodium salt], which is reduced to a highly water-soluble yellow formazan dye by dehydrogenase in cells under the action of electron carrier 1-methoxy-5-methylphenazine dimethyl sulfate. The amount of formazan generated is proportional to the number of living cells, so this property can be used to directly perform cell proliferation and toxicity analysis.

[0021] In one embodiment, the two-parameter nonlinear function in step (3) refers to the Logit function and the Weibull function.

[0022] In one embodiment, the concentration-effect data in step (3) refers to the data of the gradient toxin working solution concentration and its corresponding cell activity inhibition rate.

[0023] In one embodiment, the nonlinear least squares fitting in step (3) is based on the function fitting of the Origin software, and the function with the best fitting effect is screened by comparing the relationship coefficient (R) and the root mean square error (RMSE), that is, the higher the R value or the lower the RMSE value, the better the fitting effect.

[0024] In one embodiment, the three classical combined toxicity evaluation models described in step (3) refer to the concentration addition (CA) model, the independent action (IA) model and the two-stage prediction (TSP) model.

[0025] Compared with the prior art, the advantages of the present invention are:

[0026] (1) The method of the present invention is efficient and accurate. It detects toxic effects under different exposure modes through single toxin experiments and combined toxicity experiments. It uses the uniform design ray (UD-Ray) method to design a multivariate mixture composed of mycotoxins, which can fully simulate complex environmental exposure conditions with as few experiments as possible. It selects three different models to evaluate the toxic effects of the mixture, providing reliable predictions for the determination of the combined effects of mycotoxins.

[0027] (2) The method of the present invention is highly practical and applicable to the detection of toxic effects of various cell models. It has strong repeatability and can simultaneously detect the toxicity of mixtures of different substances in different proportions, thus providing a reliable technical means for risk assessment of mycotoxin mixtures in the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The figures are comparison diagrams of the experimental observation values ​​and the model prediction values ​​of the quaternary mycotoxin mixture of Example 1; (A) is UD-1; (B) is UD-2; (C) is UD-3; (D) is UD-4; (E) is UD-5; (F) is UD-6;

[0029] Figure 2 The figures are comparison diagrams of the experimental observation values ​​and model prediction values ​​of the five-component mycotoxin mixture of Example 2; (A) is UD-1; (B) is UD-2; (C) is UD-3; (D) is UD-4; (E) is UD-5; and (F) is UD-6. DETAILED DESCRIPTION

[0030] In order to better understand the present invention, the present invention is further explained below in conjunction with the embodiments, but the present invention is not limited to the following embodiments.

[0031] Sources of materials involved in the present invention:

[0032] The CCK-8 kit was purchased from Shanghai Biotech Biotechnology Co., Ltd.

[0033] Example 1

[0034] A method for predicting the multi-combination toxicity interaction between emerging Fusarium toxins and traditional Fusarium toxins, the method comprising the following steps:

[0035] (1) Obtain HepG2 cells in the logarithmic growth phase, seed them in a 96-well plate at a density of 10,000 cells / well, and culture them in a constant temperature incubator for 24 h;

[0036] (2) The uniform design ray method (UD-Ray) was used to design a quaternary toxin mixture consisting of enfamil A (ENA), enfamil A1 (ENA1), enfamil B (ENB) and enfamil B1 (ENB1). The UD table used was U6 (6 4 ), where 6 is the number of concentration effect values, subscript 6 is the number of experiments, i.e. the number of rays, and superscript 4 is the number of toxins. The UD table used is shown in Table 1:

[0037] Table 1U6(6 4 ) Uniform design of concentration groups of quaternary mycotoxin mixtures

[0038]

[0039] Note: IC5, IC 10 、IC 15 、IC 20 、IC 30 and IC 50 They refer to the concentrations at which the toxin inhibits cell activity by 5%, 10%, 15%, 20%, 30% and 50% respectively.

[0040] (3) The toxins were uniformly mixed according to the designed ratio, and diluted to 9 toxin working solutions with a 1.5-fold increase in concentration. HepG2 cells were incubated with the toxin mixture and cultured in a biological incubator for 48 h;

[0041] (4) After the HepG2 cells were incubated with toxins, the cell proliferation activity was detected using a cell proliferation-toxicity detection CCK-8 kit. The specific operation was to add 10 μL of CCK-8 reagent (10%, v / v) to each well, incubate in a constant temperature incubator in the dark for 1 hour, and then place the cells in a multifunctional microplate reader to detect the absorbance value of each well at 450 nm, and calculate the cell activity inhibition rate;

[0042] (5) The concentration-effect relationship of each group of mixed toxins was fitted using the Logit function and Weibull function in the Origin software, and the concentration-effect data were fitted by nonlinear least squares using a two-parameter nonlinear function. The function with the best fitting effect was screened by comparing the relationship coefficient (R) and the root mean square error (RMSE). That is, the higher the R value or the lower the RMSE value, the better the fitting effect. The Logit function and Weibull function can be expressed by equations (1) and (2), respectively:

[0043] E=1 / (1+exp(-α-βlog 10 (c))) (1)

[0044] E=1-exp(-exp(α+βlog 10 (c))) (2)

[0045] Among them, E is the effect of toxin production (0≤E≤1); α and β are model parameters; c is the concentration of fungal toxin.

[0046] After fitting, the parameters and corresponding IC of the toxicity of the quaternary toxin mixture to HepG2 cells were obtained. 50 See Table 2 for the values:

[0047] Table 2 Fitting values ​​and IC of toxicity of quaternary toxin mixture to HepG2 cells 50 value

[0048]

[0049]

[0050] The results show that the Logit function has better fitting effect for UD-1, UD-3 and UD-6, while the Weibull function has better fitting effect for UD-2, UD-4 and UD-5. After obtaining the best fitting function, the experimental observation curve is drawn using this function.

[0051] (6) The concentration addition (CA) model and the independent action (IA) model were used to calculate the combined effect of the quaternary toxin mixture and draw the model prediction curve; the type of interaction between the substances was determined by comparing the deviation between the experimental observation curve and the model prediction curve. When the model prediction curve was above, within or below the 95% confidence interval (OCI) of the experimental observation curve, it was considered that antagonism, additive or synergistic effects occurred between the components of the mixture.

[0052] The two prediction models can be expressed by equation (3) and equation (4) respectively.

[0053]

[0054] Among them, EC xmix is the total concentration of toxins when the mixture produces effect x; p i is the proportion of toxin i in the mixture; n is the number of toxins in the mixture; EC xi is the concentration of toxin i when it produces effect x.

[0055]

[0056] Among them, E(c mix ) is the total effect of the mixture; E(c i ) is the effect produced by toxin i.

[0057] Comparison of experimentally observed values ​​and model predictions for a quaternary mycotoxin mixture is shown in Figure 1 .

[0058] Depend on Figure 1 It can be seen that the independent action (IA) model and the concentration addition (CA) model generally underestimate the toxicity of the quaternary mixture of ennes (ENNs), especially at medium and high concentrations, which means that ENNs coexist in these concentration ranges and exhibit synergistic effects; by comparing the degree of curve shift and r value, the closer the r value is to 1, the better; the concentration addition (CA) model seems to have more accurate prediction ability than the independent action (IA) model, especially in the mixture of UD-1, UD-3, UD-4 and UD-5. Therefore, as an emerging toxin, ennes (ENNs) need to pay special attention to the strong synergistic effect of its mixture at a specific concentration.

[0059] Example 2

[0060] A method for predicting the multi-combination toxicity interaction between emerging Fusarium toxins and traditional Fusarium toxins, the method comprising the following steps:

[0061] (1) Obtain HepG2 cells in the logarithmic growth phase, seed them in a 96-well plate at a density of 20,000 cells / well, and culture them in a constant temperature incubator for 24 h;

[0062] (2) The uniform design ray method (UD-Ray) was used to design a five-component toxin mixture consisting of enfamil A (ENA), enfamil A1 (ENA1), enfamil B (ENB), enfamil B1 (ENB1) and deoxynivalenol (DON). The UD table used was U6 (6 5 ), where 6 is the number of concentration effect values, subscript 6 is the number of experiments, i.e. the number of rays, and superscript 5 is the number of toxins. The UD table used is shown in Table 3:

[0063] Table 3U6(6 5 ) Uniform design of concentration groups of five-component mycotoxin mixture

[0064]

[0065] Note: IC5, IC 10 、IC 15 、IC 20 、IC 30 and IC 50 They refer to the concentrations at which the toxin inhibits cell activity by 5%, 10%, 15%, 20%, 30% and 50% respectively.

[0066] (3) The toxins were uniformly mixed according to the designed ratio, and diluted to 9 toxin working solutions with a 1.5-fold increase in concentration. HepG2 cells were incubated with the toxin mixture and cultured in a biological incubator for 48 h;

[0067] (4) After the HepG2 cells were incubated with toxins, the cell proliferation activity was detected using a cell proliferation-toxicity detection (CCK-8) kit; the specific operation was to add 10 μL of CCK-8 reagent (10%, v / v) to each well, incubate in a constant temperature incubator in the dark for 1.5 h, place the cells in a multifunctional microplate reader to detect the absorbance value of each well at 450 nm, and calculate the cell activity inhibition rate;

[0068] (5) The concentration-effect relationship of each group of mixed toxins was fitted using the Logit function and Weibull function in the Origin software, respectively. A two-parameter nonlinear function was used to perform nonlinear least squares fitting on the concentration-effect data. The function with the best fitting effect was screened by comparing the relationship coefficient (R) and the root mean square error (RMSE). That is, the higher the R value or the lower the RMSE value, the better the fitting effect.

[0069] After fitting, the parameters and corresponding IC of the toxicity of the five-component toxin mixture to HepG2 cells were obtained. 50 See Table 4 for the values:

[0070] Table 4 Fitting values ​​and IC of toxicity of five-component toxin mixture to HepG2 cells 50 value

[0071]

[0072] The results show that the Logit function has better fitting effect for UD-1, UD-3 and UD-5, while the Weibull function has better fitting effect for UD-2, UD-4 and UD-6. After obtaining the best fitting function, the experimental observation curve is drawn using this function.

[0073] (6) The concentration addition (CA) model, the independent action (IA) model, and the two-stage prediction (TSP) model developed based on the two models are used to calculate the combined effect of the five-component toxin mixture, and the model prediction curve is plotted. The type of interaction between substances is determined by comparing the deviation between the experimental observation curve and the model prediction curve. When the model prediction curve is above, within, or below the 95% confidence interval (OCI) of the experimental observation curve, it is considered that antagonism, addition, or synergism occurs between the components of the mixture. The two-stage prediction (TSP) model can be expressed by formula (5):

[0074]

[0075] Among them, E(c mix,i ) is the combined effect of toxins with similar MOAs; n is the number of components distinguished by MOAs in the mixed system.

[0076] Comparison of experimental observations and model predictions for a five-component mycotoxin mixture is shown in Figure 2 .

[0077] Depend on Figure 2 It can be seen that compared with the quaternary mixture, the model prediction curve of the quinary mixture is closer to the experimental observation value, indicating that the strong synergistic effect between the mixtures is weakened; the prediction ability of the independent action (IA) model and the concentration addition (CA) model has been improved, and the prediction ability of the two-stage prediction (TSP) model is also relatively reliable. This may be due to the fact that the structures of deoxynivalenol (DON) and enninosporins (ENNs) are very different, breaking the similar action system in the original quaternary mixture.

Claims

1. A method for predicting the combined toxicity interaction between emerging Fusarium toxins and traditional Fusarium toxins, characterized in that: The steps include: (1) Designing a multi-component mixed system of emerging Fusarium toxins and traditional Fusarium toxins using the uniform design ray method; and uniformly mixing and diluting the toxins into a series of toxin working solutions with a concentration gradient according to the designed ratio; (2) obtaining HepG2 cells in the logarithmic growth phase and inoculating and culturing them; incubating the HepG2 cells with the toxin working solution of step (1) and continuing to culture them in an incubator; and then using the CCK-8 reagent method to determine the activity inhibition rate of the toxin working solution on the HepG2 cells; (3) A two-parameter nonlinear function was used to perform nonlinear least squares fitting on the concentration-effect data to obtain the experimental observation curve; three classic joint toxicity evaluation models were used to calculate the combined effects of multiple toxin mixtures, and the model prediction curve was drawn; the type of interaction between substances was determined by comparing the deviation between the experimental observation curve and the model prediction curve. When the model prediction curve was above, within, or below the 95% confidence interval of the experimental observation curve, it was considered that antagonism, additive, or synergistic effects occurred between the components of the mixture.

2. The method according to claim 1, characterized in that The emerging Fusarium toxins are fusarium A, enfusarium A1, enfusarium B and enfusarium B1; the traditional Fusarium toxin is deoxynivalenol.

3. The method according to claim 1, characterized in that The uniform design ray method described in step (1) is to design a ray composed of a five-component mixed system of four enfusin and deoxynivalenol, and the UD used represents U6 (6 5 ), where 6 is the number of concentration effect values, subscript 6 is the number of experiments, that is, the number of rays, including UD-1, UD-2, UD-3, UD-4, UD-5 and UD-6; superscript 5 is the number of toxins.

4. The method according to claim 1, characterized in that: The concentration effect value refers to the inhibition rate of toxin on cell activity, which is set to the concentration effect value when it is 5%, 10%, 15%, 20%, 30% and 50% respectively.

5. The method according to claim 1, characterized in that The HepG2 cell seeding density in step (2) is 10,000-60,000 cells / well.

6. The method according to claim 1, characterized in that The CCK-8 reagent method in step (2) is specifically as follows: After the HepG2 cells are incubated with the toxin working solution, the CCK-8 reagent is added, and the cells are again placed in a constant temperature incubator and cultured in the dark for 1-2 hours, and then placed in a multifunctional microplate reader to detect the absorbance value of each well at 450nm.

7. The method according to claim 1, characterized in that The two-parameter nonlinear function in step (3) refers to the Logit function and the Weibull function.

8. The method according to claim 1, characterized in that The concentration-effect data in step (3) refers to the data of the gradient toxin working solution concentration and its corresponding cell activity inhibition rate.

9. The method according to claim 1, characterized in that: The nonlinear least squares fitting in step (3) is based on the function fitting of the Origin software. The function with the best fitting effect is screened by comparing the relationship coefficient R and the root mean square error RMSE. That is, the higher the R value or the lower the RMSE value, the better the fitting effect.

10. The method according to claim 1, characterized in that The three classical combined toxicity evaluation models described in step (3) refer to the concentration addition model, the independent action model and the two-stage prediction model.

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