CN110299189B - Method for optimizing anti-fibrosis soybean isoflavone composition - Google Patents
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Abstract
The invention discloses an anti-fibrosis soybean isoflavone composition optimization method, belonging to the technical field of biological compositions. According to the invention, response surface test data are used as training samples of the 4-8-1 structure neural network, a prediction model of the soybean isoflavone composition with different concentrations and the HSC-T6 cell proliferation inhibition rate is established, and a simulated annealing algorithm is used for optimizing the network. The trained neural network has a better prediction function, and the average relative error between the predicted value and the measured value is better than that of the response surface model. The optimized soybean isoflavone composition comprises the following components: 4 mu M of glycitein, 1.993 mu M of genistein, 2.996 mu M of daidzein and 15.776 mu M of glycitin, the cell proliferation inhibition rate and relative error are 66.93 percent and 1.57 percent respectively, which are superior to 60.85 percent and 2.26 percent of the optimal composition optimized by the design of a response surface; the apoptosis rate of the optimized composition is also larger than that of the optimal composition optimized by a response surface, and a prediction model utilizing a neural network-simulated annealing algorithm is an effective optimization method and provides reference for high-throughput screening.
Description
Technical Field
The invention relates to the technical field of biological compositions, in particular to an anti-fibrosis soybean isoflavone composition optimization method.
Background
The incidence and mortality of hepatic fibrosis are high, and no medicine with satisfactory curative effect exists so far. Hepatic fibrosis is caused by massive deposition of collagen-containing ECM (ECM) in liver tissue, and is characterized in that fibrosis function of Hepatic Stellate Cells (HSCs) is over-expressed, liver-related cells secrete transforming growth factor beta 1 (TGF-beta 1) and platelet-derived growth factor (PDGF) under pathological injury stimulation, TGF-beta 1 activates HSCs, PDGF promotes massive proliferation of activated HSCs, and activated HSCs secrete TGF-beta 1 and PDGF automatically, so that HSC activation and proliferation effects are maintained and amplified. Therefore, inhibition of HSC proliferation and induction of apoptosis are important means to combat liver fibrosis.
SoybeanIsoflavone is one of the main active ingredients in soybean meal, soybean dregs and soybean milk. The soybean isoflavone has effects of preventing and improving hepatic fibrosis. Soy isoflavones are effective in preventing liver fibrosis in a rat model induced by Thioacetamide (TAA), and may be associated with inhibition of ECM synthesis and HSCs proliferation. Zhaoyuang et al found that soybean isoflavone group significantly reduced CCl 4 And hepatic fibrosis rats modeled by olive oil, and simultaneously reduces the expression of alpha-SMA and TGF-beta 1 proteins. Although soybean isoflavone has anti-fibrosis ability, 9 kinds of soybean isoflavone are known, and the interaction between different monomer isoflavone is less studied. The soybean isoflavone monomer composition is used as a base to develop the health-care food for preventing hepatic fibrosis, so that the health-care food not only has positive promotion effect on solving the problem of preventing hepatic fibrosis, but also provides a new way for recycling soybean isoflavone. The neural network and the simulated annealing algorithm are important nonlinear modeling optimization methods and are widely applied to extraction and separation processes of bioactive substances and activity screening.
Disclosure of Invention
The invention provides an optimization method of an anti-fibrosis soybean isoflavone composition.
In order to achieve the purpose, the following technical scheme is adopted:
a method for optimizing an anti-fibrosis soybean isoflavone composition includes the steps of firstly establishing a response surface model and a neural network model, taking response surface test data as training samples of the neural network, establishing a prediction model of different concentrations of soybean isoflavone compositions and HSC-T6 cell proliferation inhibition rates, then optimizing the network by using a simulated annealing algorithm to obtain an optimal composition, and finally detecting the effect of the optimal composition on HSC-T6 cell apoptosis.
The method for establishing the response surface comprises the following steps: determining variable range by adopting Box-Behnken, selecting four isoflavones of glycitein, genistein, daidzein and glycitin with different concentrations as independent variables, and taking HSC-T6 cell proliferation inhibition rate as response value; the method for establishing the neural network model comprises the following steps: response surface experimental data are utilized, neural network modeling is adopted, 4 factors are used as input of the network, the HSC-T6 cell proliferation inhibition rate is output of the network, the number of nodes is 8, and the 4-8-1 neural network is constructed.
The detection method of the inhibition rate of HSC-T6 cell proliferation comprises the following steps:
1) HSC-T6 cells were cultured at 37 ℃ and 5% CO 2 The culture solution DMEM contains 10% fetal calf serum, 100U/mL penicillin and 100 mu g/mL streptomycin;
2) At 6X 10 4 Inoculating cells into a 96-well culture plate at the density of each well, wherein the inoculation amount of each well is 100 mu L, continuously culturing for 6h after the cells are attached to the wall, only adding a culture medium into a blank group, adding an isovolumetric culture medium and cells into a control group, adding isovolumetric soybean isoflavone compositions, culture media and cells with different concentrations into an experimental group, changing the blank group into a serum-free culture solution after culturing for 48h, adding 20 mu L MTT solution with the concentration of 5mg/mL into each well, continuously culturing for 4h, removing supernatant, adding 150 mu L DMSO into each well, shaking for 10min, and measuring the OD value of each well at 570nm by using a microplate reader;
3) Calculating the inhibition rate of HSC-T6 cell proliferation by adopting the following formula:
inhibition rate/% =
The response surface optimization analysis adopts Design expert8.06 (Demo) software, and the establishment of the neural network model and the simulation annealing algorithm optimization adopt MATLAB2016a (Trial) software.
After determining the optimal composition, the effect of the optimal composition on the apoptosis of HSC-T6 cells is detected by the following detection method: cells from the different treatments for 24 hours were collected, washed twice with phosphate buffered saline, gently resuspended in binding buffer, and incubated with Annexin V-FITC and PI for 10 minutes in the dark using flow cytometer Accuri C6 detection and data analysis.
The optimized soybean isoflavone composition obtained by the method for optimizing the anti-fibrosis soybean isoflavone composition comprises 4 mu M of glycitein, 1.993 mu M of genistein, 2.996 mu M of daidzein and 15.776 mu M of glycitin.
Compared with the prior art, the invention has the following beneficial effects: the trained neural network has a better prediction function, and the average relative error between a predicted value and an actual measurement value is 0.62 percent and is better than 1.27 percent of that of a response surface model. The inhibition rate and relative error of the optimized soybean isoflavone composition on cell proliferation are 66.93 percent and 1.57 percent respectively, which are better than 60.85 percent and 2.26 percent of the optimized composition designed and optimized by a response surface; the apoptosis rate of the optimized composition is also larger than that of the optimal composition responding to surface optimization, and the results show that a prediction model utilizing a neural network-simulated annealing algorithm is an effective optimization method and has certain referential significance on an anti-fibrosis high-throughput screening model.
Drawings
Fig. 1 is a diagram of a neural network structure.
FIG. 2 is a graph of neural network regression.
FIG. 3 is a graph of neural network error performance.
FIG. 4 is a graph of the predicted results of the simulated annealing algorithm.
FIG. 5 is a graph of the detection of apoptotic Annexin V/PI double staining of soy isoflavones and compositions; wherein A: blank control; b: a response surface optimizing composition; c: a neural network optimizing composition; d: and (4) analyzing the number of the apoptotic cells.
Detailed Description
The present invention will be described in detail below with reference to the drawings and examples, but the practice of the invention is not limited thereto.
1. The materials and equipment used in the following examples are as follows:
glycitein, daidzein, and genistein: purity 98%, shanghai-sourced leaf Biotechnology, inc.;
tetramethylazodicarbonyl blue (MTT) and dimethyl sulfoxide (DMSO): sigma company;
DMEM and fetal bovine serum: gibco corporation;
annexin V-FITC/PI apoptosis kit: invitrogen corporation;
antibiotics (penicillin and streptomycin): hyclone company;
HSC-T6 cells murine hepatic stellate cells: nanjing Kaiky Biotech Co;
CO 2 incubator and Mutiskan Go microplate reader: american HeatElectric company;
accuri C6 flow cytometer: BD corporation, usa;
a vacuum drier: eppendorf corporation;
TDL-166RH high speed refrigerated centrifuge, shanghai' an pavilion scientific instruments and plants.
The detection method of the proliferation inhibition rate of the HSC-T6 cells comprises the following steps:
1) HSC-T6 cell culture at 37 ℃ and 5% CO 2 The culture medium DMEM contains 10% fetal calf serum, 100U/mL penicillin and 100 mug/mL streptomycin;
2) At 6X 10 4 Inoculating cells into a 96-well culture plate at the density of each well, wherein the inoculation amount of each well is 100 mu L, continuously culturing for 6h after the cells are attached to the wall, only adding a culture medium into a blank group, adding an isovolumetric culture medium and cells into a control group, adding isovolumetric soybean isoflavone compositions, culture media and cells with different concentrations into an experimental group, changing the blank group into a serum-free culture solution after culturing for 48h, adding 20 mu L MTT solution with the concentration of 5mg/mL into each well, continuously culturing for 4h, removing supernatant, adding 150 mu L DMSO into each well, shaking for 10min, and measuring the OD value of each well at 570nm by using a microplate reader;
3) Calculating the inhibition rate of HSC-T6 cell proliferation by adopting the following formula:
inhibition rate/% =
Example 1 response surface experimental design
The variable range is determined by adopting Box-Behnken in the experimental design of the response surface, and glycitein (X) is selected 1 ) Genistein (X) 2 ) Daidzein (X) 3 ) And glycitin (X) 4 ) Four different concentrations of isoflavones were used as independent variables, and the HSC-T6 cell proliferation inhibition (Y) was used as response values, and the test factors and levels are encoded in table 1.
TABLE 1 factor level coding scheme
| Level of | X 1 Concentration/. Mu.M | X 2 Concentration/. Mu.M | X 3 Concentration/. Mu.M | X 4 Concentration/. Mu.M |
| -1 | 1 | 2 | 3 | 4 |
| 0 | 2 | 4 | 6 | 8 |
| 1 | 4 | 8 | 12 | 16 |
The results of the response surface experiment and the ANOVA table are shown in tables 2-3 according to the factor level coding table in Table 1. R2 adj =0.96081, R in table 3 2 =0.92162, regression model P<0.0001, and the distortion term P = 0.8284>And 0.05, the parameters show that the established response surface quadratic polynomial actual value and the predicted value are well fitted and can be used for prediction.
TABLE 2 response surface test design and results
The regression equation is:
Y=55.00+1.54*X 1 +2.16*X 2 -1.67*X 3 -1.95*X 4 -1.21*X 1 *X 2 +1.20*X 1 * X 3 +1.28*X 1 *X 4 +0.27*X 2 *X 3 -3.98*X 2 *X 4 +0.16* X 3 * X 4 +2.47* X 1 2-3.74* X 2 2-1.04 * X 3 2-2.38*X 4 2. the optimal conditions obtained using the regression model were: glycitein (4. Mu.M), genistein (0.88. Mu.M), daidzein (3. Mu.M) and glycitin (16. Mu.M), under which the cytostatic rate was 62.26%.
TABLE 3 regression model analysis of variance
| Source | Sum of squares | Degree of freedom | Mean square | F value | P value | Significance of |
| Model (model) | 439.1322 | 14 | 31.36659 | 24.51674 | < 0.0001 | *** |
| X 1 | 28.61341 | 1 | 28.61341 | 22.3648 | 0.0003 | *** |
| X 2 | 55.77141 | 1 | 55.77141 | 43.59202 | < 0.0001 | *** |
| X 3 | 33.63401 | 1 | 33.63401 | 26.28899 | 0.0002 | *** |
| X 4 | 45.59101 | 1 | 45.59101 | 35.63482 | < 0.0001 | *** |
| X 1 X 2 | 5.880625 | 1 | 5.880625 | 4.596411 | 0.0501 | |
| X 1 X 3 | 5.76 | 1 | 5.76 | 4.502128 | 0.0522 | |
| X 1 X 4 | 6.5536 | 1 | 6.5536 | 5.122421 | 0.0400 | * |
| X 2 X 3 | 0.297025 | 1 | 0.297025 | 0.232161 | 0.6374 | |
| X 2 X 4 | 63.28203 | 1 | 63.28203 | 49.46246 | < 0.0001 | *** |
| X 3 X 4 | 0.1089 | 1 | 0.1089 | 0.085118 | 0.7748 | |
| X 1 2 | 39.63217 | 1 | 39.63217 | 30.97728 | < 0.0001 | *** |
| X 2 2 | 90.88409 | 1 | 90.88409 | 71.03677 | < 0.0001 | *** |
| X 3 2 | 7.024781 | 1 | 7.024781 | 5.490706 | 0.0344 | * |
| X 4 2 | 36.68547 | 1 | 36.68547 | 28.67408 | 0.0001 | *** |
| Residual error | 17.91153 | 14 | 1.279395 | |||
| Missimilitude term | 9.96525 | 10 | 0.996525 | 0.501631 | 0.8284 | |
| Absolute error | 7.94628 | 4 | 1.98657 | |||
| Sum of | 457.0438 | 28 |
* Indicates that the difference is very significant (P)<0.001 Denotes that the difference is highly significant (P)<0.01 Denotes that the difference is significant (P)< 0.05）；R2 adj=0.96081，R 2 =0.92162。
Example 2 neural network modeling
Response surface experimental data are utilized, neural network modeling is adopted, 4 factors are used as input of the network, the HSC-T6 cell proliferation inhibition rate is output of the network, the number of nodes is 8, and a 4-8-1 neural network is constructed, and the structure of the neural network is shown in figure 1.
After the neural network model is established, training data required by artificial neural network optimization are determined by adopting response surface experimental data, 29 samples are randomly divided into 23 (80%) Training groups (Training), 3 (10%) verification groups (Validation) and 3 (10%) detection groups (Test), and a Levenberg-Marquardt algorithm is selected for Training. As can be seen from fig. 2, the predicted value and the measured value of the trained neural network have better correlation, the correlation coefficients of the regression lines of the three types of sample data are 0.98924,1,0.80998, and the correlation coefficients of the regression lines of all the groups of training samples are 0.98551. After 7 times of iterative training, the validation error of the network is 0.406, and the training requirement is met, which indicates that the neural network has good convergence and predictability, as shown in fig. 3.
Example 3 response surface and neural network prediction and validation test
Training a mature neural network to establish a nonlinear mapping relation between the 4 isoflavone compositions with different concentrations and the cell proliferation inhibition rate, and taking a simulation function as an optimization function of a simulated annealing algorithm. Due to the initial value (X) of the simulated annealing algorithm 0 ) Different search paths are different and may produce different results. To reduce the error caused by the initial value selection, 4 different initial values were selected, and the result was the average value. Table 4 and fig. 4 show the search results and the optimal parameters for 4 different initial values.
TABLE 4 simulated annealing algorithm prediction results
| X 0 /μM | Number of iterations | X 1 /μM | X 2 /μM | X 3 /μM | X 4 /μM | Predicted value/%) | |
| A | [2,4,6,8 ] | 2832 | 4.000 | 1.990 | 3.000 | 16.000 | 68.0796 |
| B | [4,8,12,16] | 2296 | 4.000 | 1.982 | 2.985 | 15.984 | 68.0443 |
| C | [1,2,3,4 ] | 2533 | 4.000 | 2.000 | 2.997 | 15.120 | 67.8693 |
| D | [2, 3, 12, 4] | 3127 | 4.000 | 1.998 | 3.000 | 16.000 | 68.0074 |
| Average | 4.000 | 1.993 | 2.996 | 15.776 | 68.0002 |
As can be seen from the two model verification tests (see Table 5), the relative error between the predicted value and the measured value of the response surface regression analysis is 2.26%, while the relative error between the predicted value and the measured value of the artificial neural network is only 1.57%, which indicates that the prediction capability of the artificial neural network is better than that of the response surface.
TABLE 5 comparison of the optimization results of the two methods
Example 4 apoptosis detection
Cells from the different treatments for 24 hours were collected, washed twice with phosphate buffered saline, gently resuspended in binding buffer, and incubated with Annexin V-FITC and PI for 10 minutes in the dark using flow cytometer Accuri C6 detection and data analysis.
And (3) treating the cells by using a blank non-treatment (A), a response surface optimization composition (B) and a neural network optimization composition (C), and detecting the apoptosis condition by an Annexin V/PI double staining method. The results in fig. 5 show that the placebo early apoptosis rate was 1.0% and late apoptosis rate was 2.2%, response surface optimized composition (B) was 5.8% and 16.0%, respectively, and neural network optimized composition (C) was 10.0% and 29.8%. These results indicate that neural network optimization of soy isoflavone compositions is more capable of inducing apoptosis. These results are consistent with cell proliferation inhibition experiments.
The above disclosure is only for the specific embodiment of the present invention, but the present invention is not limited thereto, and any variations that can be considered by those skilled in the art should fall within the scope of the present invention.
Claims (5)
1. An anti-fibrosis soybean isoflavone composition optimization method is characterized by firstly establishing a response surface and a neural network model, taking response surface test data as a training sample of a neural network, establishing a prediction model of different concentrations of soybean isoflavone compositions and HSC-T6 cell proliferation inhibition rate, then optimizing the network by using a simulated annealing algorithm to obtain an optimal composition, and finally detecting the effect of the optimal composition on HSC-T6 cell apoptosis;
the method for establishing the response surface comprises the following steps: determining variable range by adopting Box-Behnken, selecting four isoflavones of glycitein, genistein, daidzein and glycitin with different concentrations as independent variables, and taking HSC-T6 cell proliferation inhibition rate as response value;
the method for establishing the neural network model comprises the following steps: response surface experimental data are utilized, neural network modeling is adopted, 4 factors are used as input of the network, the HSC-T6 cell proliferation inhibition rate is output of the network, the number of nodes is 8, and the 4-8-1 neural network is constructed.
2. The method for optimizing the soybean isoflavone composition for anti-fibrosis according to claim 1, wherein the method for detecting the inhibition rate of HSC-T6 cell proliferation comprises the following steps:
1) HSC-T6 cell culture at 37 ℃ and 5% 2 The culture medium DMEM contains 10% fetal calf serum, 100U/mL penicillin and 100 mug/mL streptomycin;
2) At 6X 10 4 Inoculating cells into 96-well culture plate at a density of one well per well, wherein the inoculation amount of each well is 100 μ L, continuously culturing for 6h after the cells adhere to the wall, adding culture medium into blank group, adding equal volume into control group, and culturingCulturing medium and cells, adding soybean isoflavone composition, culture medium and cells with different concentrations and the same volume into an experimental group, culturing for 48h, then replacing with serum-free culture solution, adding 20 mu L MTT solution with the concentration of 5mg/mL into each hole, continuously culturing for 4h, discarding supernatant, adding 150 mu L DMSO into each hole, shaking for 10min, and measuring the OD value of each hole at 570nm by using an enzyme labeling instrument;
3) Calculating the inhibition rate of HSC-T6 cell proliferation by adopting the following formula:
3. the method for optimizing an anti-fibrotic soy isoflavone composition according to claim 1, wherein the response surface optimization analysis uses Design expert8.06 (Demo) software, and the neural network modeling and simulated annealing algorithm optimization uses MATLAB2016a (Trial) software.
4. The method for optimizing the soybean isoflavone composition for anti-fibrosis according to claim 1, wherein the detection method of HSC-T6 apoptosis is as follows: cells from the different treatments for 24 hours were collected, washed twice with phosphate buffered saline, gently resuspended in binding buffer, and incubated with annexin V-FITC and PI for 10 minutes in the dark using a flow cytometer Accuri C6 detection and data analysis.
5. The optimized soy isoflavone composition for fibrosis prevention obtained by the method for optimizing an anti-fibrotic soy isoflavone composition according to claim 1, wherein the soy isoflavone composition comprises 4 μ M of glycitein, 1.993 μ M of genistein, 2.996 μ M of daidzein, and 15.776 μ M of glycitin.
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| CA2362268A1 (en) * | 1999-12-15 | 2001-06-21 | Amino Up Chemical Co., Ltd. | Novel substance derived from basidiomycetes culture, method for producing it and its use |
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