CN110299189A - A kind of isoflavones composition optimization method of anti-fibrosis - Google Patents
A kind of isoflavones composition optimization method of anti-fibrosis Download PDFInfo
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Abstract
The invention discloses a kind of isoflavones composition optimization methods for the anti-fibrosis for belonging to biological composition technical field.The present invention tests data using response surface experiments and establishes the prediction model of isoflavones various concentration composition Yu HSC-T6 cell proliferation inhibition rate as the training sample of 4-8-1 artificial neural, and is optimized using simulated annealing to network.Trained neural network has preferable forecast function, and the average relative error between predicted value and measured value is better than response surface model average relative error.Optimize isoflavones composition are as follows: 4 μM of Glycitein, 1.993 μM of genistein, 2.996 μM of daidzein, 15.776 μM of glycitin, cell proliferation inhibition rate and relative error are 66.93% and 1.57% respectively, better than the 60.85% and 2.26% of the optimal combination object that response surface design optimization goes out；The apoptosis rate of preferred composition is effective optimization method using neural network-simulated annealing prediction model, provides reference for high flux screening also greater than the optimal combination object that response surface design optimizes.
Description
Technical field
The present invention relates to biological composition technical fields, and in particular to a kind of isoflavones composition of anti-fibrosis is excellent
Change method.
Background technique
Liver fibrosis morbidity and mortality are all very high, there is no the satisfactory drug of curative effect so far.Liver fibrosis is liver
ECM(extracellular matrix, ECM in tissue containing collagen) it is a large amount of deposition caused by, core is hepatic stellate cells
The overexpression of (hepatic stellate cells, HSC) fibrosis function, under pathology damage stimulation, liver relevant cell
Secrete transforminggrowthfactor-β1 (transforming growth factor β 1, TGF-β 1) and platelet derived growth factor
(platelet-derived growth factor, PDGF), TGF-β 1 activate HSC, PDGF that the HSC of activation is promoted largely to increase
The HSC autocrine TGF-β 1 and PDGF grown, while activated, maintain and amplification HSC is activated and proliferation effect.Therefore, HSC is inhibited to increase
Growing and inducing its apoptosis is the important means of anti-hepatic fibrosis.
Isoflavones is one of dregs of beans, bean dregs, the main active in yellow serofluid.Isoflavones is to liver fibrosis
With prevention and improvement result.Isoflavones effectively prevents liver fiber in the rat model induced thioacetamide (TAA)
Change, it may be related to ECM synthesis and HSCs proliferation is inhibited.Zhao Yufang etc. has found the substantially reduced CCl of isoflavones group4And olive
The hepatic fibrosis rats of olive oil modeling, while reducing α-SMA, 1 protein expression of TGF-β.Although isoflavones has anti-fibrosis
Ability, but isoflavones, it has been known that there is 9 kinds of monomers, different monomers isoflavones repercussion study is less.With isoflavones list
Based on body composition, exploitation prevention liver fibrosis health food, not only to solves the problems, such as liver fibrosis prevention and treatment have actively
Progradation, and for isoflavones recycling new way is provided.Neural network and simulated annealing are important non-
Linear modelling optimization method has more application in bioactive substance extraction and separation process and screening active ingredients.
Summary of the invention
The present invention provides a kind of isoflavones composition optimization methods of anti-fibrosis.
In order to achieve the above objectives, it adopts the following technical scheme that
A kind of isoflavones composition optimization method of anti-fibrosis, initially sets up response surface and neural network model, with
Training sample of the response surface experiments data as neural network, establishes isoflavones various concentration composition and HSC-T6 cell
Then the prediction model of proliferation inhibition rate optimizes network using simulated annealing and obtains optimum combination object, finally examines
Survey effect of the optimum combination object to HSC-T6 Apoptosis.
Establish Response surface meth od are as follows: range of variables is determined using Box-Behnken, selects Glycitein, genistein, big
Four kinds of various concentration isoflavones of beans aglycon and glycitin are independent variable, using HSC-T6 cell proliferation inhibition rate as response；It builds
The method of vertical neural network model are as follows: response surface experimental data is utilized, using neural net model establishing, using 4 factors as network
Input, HSC-T6 cell proliferation inhibition rate is the output of network, and number of nodes 8 constructs the neural network of 4-8-1.
The detection method of HSC-T6 cell proliferation inhibition rate includes the following steps:
1) HSC-T6 cell culture is in 37 DEG C and 5% CO2Incubator in, culture solution DMEM contains 10% fetal calf serum, 100 U/
ML penicillin and 100 μ g/mL streptomysins；
2) with 6 × 104For the density inoculating cell in a/hole in 96 well culture plates, every hole inoculum concentration is 100 μ L, and cell is adherent
Afterwards, continue after cultivating 6 h, blank group only adds culture medium, and isometric culture medium and cell, the bodies such as experimental group addition are added in control group
Product various concentration isoflavones composition, culture medium and cell, are changed to serum-free medium, and every hole after cultivating 48 h
It is 5 mg/mL MTT solution that 20 μ L concentration, which are added, continues to cultivate 4 h, discards supernatant liquid, and 150 μ L DMSO concussion is added in every hole
After 10 min, the OD value in each hole at 570 nm is measured with microplate reader；
3) HSC-T6 cell proliferation inhibition rate is calculated, using following formula:
Inhibiting rate/%=
Response surface optimization analysis of the present invention uses Design Expert8.06(Demo) software, it is described to establish neural network model
MATLAB2016a(Trial is used with Simulated Anneal Algorithm Optimize) software.
After determining optimal combination object, effect of the optimal combination object to HSC-T6 Apoptosis, detection method are detected are as follows: collect
24 hours cells of different disposal, are washed twice with phosphate buffer salt, are lightly resuspended in combination buffer, and with
Annexin V-FITC and PI are incubated 10 minutes in the dark, are analyzed using flow cytometer Accuri C6 detection and data.
The optimization isoflavones composition that the isoflavones composition optimization method of above-mentioned anti-fibrosis obtains is Huang
4 μM of beans flavine, 1.993 μM of genistein, 2.996 μM of daidzein, 15.776 μM of glycitin.
Compared with prior art, the invention has the following beneficial effects: the trained neural networks of the present invention to have preferably
Forecast function, the average relative error 0.62% between predicted value and measured value, better than the 1.27% of response surface model.Optimization is big
Beans isoflavone compositions cell proliferation inhibiting rate and relative error are 66.93% and 1.57% respectively, are designed better than response surface
The 60.85% and 2.26% of the optimal combination object of optimization；The apoptosis rate of preferred composition optimizes most also greater than response surface design
Good composition, these results illustrate that using neural network-simulated annealing prediction model be effective optimization method, anti-
There is certain reference significance on fibrosis high flux screening model.
Detailed description of the invention
Fig. 1 is neural network structure figure.
Fig. 2 is neural net regression coordinate diagram.
Fig. 3 is neural network error performance figure.
Fig. 4 is simulated annealing prediction result figure.
Fig. 5 is the bis- dye method detection figures of apoptosis Annexin V/PI of isoflavones and composition；Wherein A: blank pair
According to；B: response surface optimization composition；C: Neural Network Optimization composition；D: apoptosis cell analysis.
Specific embodiment
The present invention is described in detail with reference to the accompanying drawings and examples, but implementation of the invention is not limited only to this.
1. material used in following embodiments and instrument are as follows:
Glycitein, Daidzein, daidzein and genistein: purity 98%, Shanghai Yuan Ye Bioisystech Co., Ltd；
Methyl thiazoly tetrazolium assay (MTT) and dimethyl sulfoxide (DMSO): Sigma company；
DMEM and fetal calf serum: Gibco company；
Annexin V-FITC/PI apoptosis kit: Invitrogen company；
Antibiotic (penicillin and streptomysin): Hyclone company；
HSC-T6 cell mouse hepatic stellate cells: scientific & technical corporation, Nanjing Keygen Biotech；
CO2Incubator and Mutiskan Go microplate reader: power & light company, the U.S.；
Accuri C6 flow cytometer: U.S. company BD；
Vacuum desiccator: Eppendorf company；
TDL-166RH high speed freezing centrifuge, Anting Scientific Instrument Factory, Shanghai.
The detection method of 2.HSC-T6 cell proliferation inhibition rate are as follows:
1) HSC-T6 cell culture is in 37 DEG C and 5% CO2Incubator in, culture solution DMEM contains 10% fetal calf serum, 100 U/
ML penicillin and 100 μ g/mL streptomysins；
2) with 6 × 104For the density inoculating cell in a/hole in 96 well culture plates, every hole inoculum concentration is 100 μ L, and cell is adherent
Afterwards, continue after cultivating 6 h, blank group only adds culture medium, and isometric culture medium and cell, the bodies such as experimental group addition are added in control group
Product various concentration isoflavones composition, culture medium and cell, are changed to serum-free medium, and every hole after cultivating 48 h
It is 5 mg/mL MTT solution that 20 μ L concentration, which are added, continues to cultivate 4 h, discards supernatant liquid, and 150 μ L DMSO concussion is added in every hole
After 10 min, the OD value in each hole at 570 nm is measured with microplate reader；
3) HSC-T6 cell proliferation inhibition rate is calculated, using following formula:
Inhibiting rate/%=
1 response surface experimental design of embodiment
Response surface experimental design determines range of variables using Box-Behnken, selects Glycitein (X1), genistein (X2),
Daidzein (X3) and glycitin (X4) four kinds of various concentration isoflavones are independent variable, with HSC-T6 cell proliferation inhibition rate (Y)
For response, experimental factor and level code are shown in Table 1.
1 factor level coding schedule of table
| It is horizontal | X 1 Concentration/μM | X 2 Concentration/μM | X 3 Concentration/μM | X 4 Concentration/μM |
| -1 | 1 | 2 | 3 | 4 |
| 0 | 2 | 4 | 6 | 8 |
| 1 | 4 | 8 | 12 | 16 |
According to 1 factor level coding schedule of table, response surface experimental result and analysis of variance table are shown in Table 2-3.R2 adj in table 3=
0.96081、R2=0.92162, regression model P<0.0001, loses quasi- item P=0.8284>0.05, these parameter declarations are built
Vertical response surface quadratic polynomial actual value and predicted value fitting preferably, can be used for predicting.
The design of 2 response surface experiments of table and result
The regression equation are as follows:
Y=55.00+1.54*X1+2.16*X2-1.67*X3-1.95*X4-1.21*X1*X2+1.20*X1* X3+1.28*X1*X4+
0.27*X2*X3-3.98*X2*X4+0.16* X3* X4+2.47* X12-3.74* X22-1.04 * X32-2.38*X42 utilize
The optimum condition that regression model obtains are as follows: Glycitein (4 μM), genistein (0.88 μM), daidzein (3 μM) and soya bean are yellow
Glycosides (16 μM), cell inhibitory rate is 62.26% with this condition.
3 regression model variance analysis of table
| Source | Quadratic sum | Freedom degree | It is square | F value | P value | Conspicuousness |
| Model | 439.1322 | 14 | 31.36659 | 24.51674 | < 0.0001 | *** |
| X1 | 28.61341 | 1 | 28.61341 | 22.3648 | 0.0003 | *** |
| X2 | 55.77141 | 1 | 55.77141 | 43.59202 | < 0.0001 | *** |
| X3 | 33.63401 | 1 | 33.63401 | 26.28899 | 0.0002 | *** |
| X4 | 45.59101 | 1 | 45.59101 | 35.63482 | < 0.0001 | *** |
| X1X2 | 5.880625 | 1 | 5.880625 | 4.596411 | 0.0501 | |
| X1X3 | 5.76 | 1 | 5.76 | 4.502128 | 0.0522 | |
| X1X4 | 6.5536 | 1 | 6.5536 | 5.122421 | 0.0400 | * |
| X2X3 | 0.297025 | 1 | 0.297025 | 0.232161 | 0.6374 | |
| X2X4 | 63.28203 | 1 | 63.28203 | 49.46246 | < 0.0001 | *** |
| X3X4 | 0.1089 | 1 | 0.1089 | 0.085118 | 0.7748 | |
| X1 2 | 39.63217 | 1 | 39.63217 | 30.97728 | < 0.0001 | *** |
| X2 2 | 90.88409 | 1 | 90.88409 | 71.03677 | < 0.0001 | *** |
| X3 2 | 7.024781 | 1 | 7.024781 | 5.490706 | 0.0344 | * |
| X4 2 | 36.68547 | 1 | 36.68547 | 28.67408 | 0.0001 | *** |
| Residual error | 17.91153 | 14 | 1.279395 | |||
| Lose quasi- item | 9.96525 | 10 | 0.996525 | 0.501631 | 0.8284 | |
| Absolute error | 7.94628 | 4 | 1.98657 | |||
| Summation | 457.0438 | 28 |
* * indicates that difference is extremely significant (P < 0.001), and * * indicates difference highly significant (P < 0.01), * expression significant difference (P <
0.05)；R2 adj=0.96081, R2=0.92162。
2 neural net model establishing of embodiment
Using response surface experimental data, using neural net model establishing, using 4 factors as the input of network, HSC-T6 cell increases
The output that inhibiting rate is network is grown, number of nodes 8 constructs the neural network of 4-8-1, and structure is shown in Fig. 1.
After Establishment of Neural Model, the training that artificial neural network optimization needs is determined using response surface experimental data
Data, are randomly divided into 23 (80%) training groups (Training) in 29 samples, 3 (10%) validation groups (Validation), and 3
A (10%) detection group (Test) selects the training of Levenberg-Marquardt algorithm.As shown in Figure 2, the nerve net after training
The predicted value of network has preferable correlation with measured value, and the related coefficient of three classes sample data regression straight line is respectively
0.98924,1,0.80998, the related coefficient of all groups of training sample regression straight lines is 0.98551.Passing through 7 repetitive exercises
The validation error of network is 0.406 afterwards, has reached training requirement, shows that the neural network has good convergence and prediction
Property, as shown in Figure 3.
3 response surface of embodiment and neural network prediction and verification test
4 kinds of isoflavones various concentration compositions of the mature neural network of training and cell proliferation inhibition rate it is nonlinear
Mapping relations, using simulated function as the optimizing function of simulated annealing.Due to the initial value (X of simulated annealing0) difference searches
Rope path is also different, it is possible to create different results.For error brought by reduction Initial value choice, 4 different initial values are selected,
It as a result is average value.Table 4 and Fig. 4 show the search result and optimized parameter of 4 different initial values.
4 simulated annealing prediction result of table
| X 0 /μM | The 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 |
| It is average | 4.000 | 1.993 | 2.996 | 15.776 | 68.0002 |
By two kinds of model verification test (see Table 5)s it is found that the predicted value of response surface regression analysis and the relative error of measured value are
2.26%, and the predicted value of artificial neural network and measured value relative error are only 1.57%, illustrate neural network prediction energy
Power is better than response surface.
5 two methods optimum results of table compare
The detection of 4 apoptosis of embodiment
24 hours cells of different disposal are collected, is washed twice with phosphate buffer salt, is lightly resuspended in combination buffer,
And incubated in the dark 10 minutes with Annexin V-FITC and PI, it is analyzed using flow cytometer Accuri C6 detection and data.
Cell is handled using blank non-treated (A), response surface optimization composition (B) and Neural Network Optimization composition (C),
The bis- dye methods of Annexin V/PI detect Apoptosis situation.Fig. 5 is the results show that blank control early apoptosis rate is 1.0% and evening
Phase apoptosis rate is 2.2%, and response surface optimization composition (B) is respectively 5.8% and 16.0%, and Neural Network Optimization composition
It (C) is 10.0% and 29.8%.These results show that being induced cell apoptosis in Neural Network Optimization isoflavones composition
Ability it is stronger.These results are unanimously to cell growth inhibition assay.
Disclosed above is only specific embodiments of the present invention, and still, the present invention is not limited to this, any this field
What technical staff can think variation should all fall into protection scope of the present invention.
Claims (6)
1. a kind of isoflavones composition optimization method of anti-fibrosis, which is characterized in that initially set up response surface and mind
Through network model, using response surface experiments data as the training sample of neural network, the combination of isoflavones various concentration is established
Then it is optimal to optimize acquisition to network using simulated annealing for the prediction model of object and HSC-T6 cell proliferation inhibition rate
Composition finally detects effect of the optimum combination object to HSC-T6 Apoptosis.
2. the isoflavones composition optimization method of a kind of anti-fibrosis according to claim 1, which is characterized in that described
Establish Response surface meth od are as follows: range of variables is determined using Box-Behnken, select Glycitein, genistein, daidzein and
Four kinds of various concentration isoflavones of glycitin are independent variable, using HSC-T6 cell proliferation inhibition rate as response；
The method for establishing neural network model are as follows: utilize response surface experimental data, using neural net model establishing, by 4 because
Input of the element as network, HSC-T6 cell proliferation inhibition rate are the output of network, and number of nodes 8 constructs the nerve net of 4-8-1
Network.
3. the isoflavones composition optimization method of a kind of anti-fibrosis according to claim 1, which is characterized in that described
The detection method of HSC-T6 cell proliferation inhibition rate includes the following steps:
1) HSC-T6 cell culture is in 37 DEG C and 5% CO2Incubator in, culture solution DMEM contains 10% fetal calf serum, 100 U/
ML penicillin and 100 μ g/mL streptomysins；
2) with 6 × 104For the density inoculating cell in a/hole in 96 well culture plates, every hole inoculum concentration is 100 μ L, after cell is adherent,
Continue after cultivating 6 h, blank group only adds culture medium, and isometric culture medium and cell is added in control group, and experimental group is added isometric
Various concentration isoflavones composition, culture medium and cell are changed to serum-free medium after cultivating 48 h, and every hole adds
Entering 20 μ L concentration is 5 mg/mL MTT solution, continues to cultivate 4 h, discards supernatant liquid, and 150 μ L DMSO concussion 10 is added in every hole
After min, the OD value in each hole at 570 nm is measured with microplate reader；
3) HSC-T6 cell proliferation inhibition rate is calculated, using following formula:
Inhibiting rate/%=。
4. the isoflavones composition optimization method of a kind of anti-fibrosis according to claim 1, which is characterized in that described
Response surface optimization analysis uses Design Expert8.06(Demo) software, it is described to establish neural network model and simulated annealing
Algorithm optimization uses MATLAB2016a(Trial) software.
5. the isoflavones composition optimization method of a kind of anti-fibrosis according to claim 1, which is characterized in that described
The detection method of HSC-T6 Apoptosis are as follows: collect 24 hours cells of different disposal, washed twice with phosphate buffer salt, gently
It is lightly resuspended in combination buffer, and is incubated in the dark 10 minutes with Annexin V-FITC and PI, using flow cytometer
Accuri C6 detection and data analysis.
6. the optimization isoflavones combination that the isoflavones composition optimization method of anti-fibrosis described in claim 1 obtains
Object, which is characterized in that the isoflavones composition is 4 μM of Glycitein, 1.993 μM of genistein, daidzein
2.996 μM, 15.776 μM 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 |
| CN109142606A (en) * | 2018-08-23 | 2019-01-04 | 广西壮族自治区中医药研究院 | Thick leaf kadsura longepedunculata reverses the in-vitro screening and its chemical composition analysis of liver fibrosis active site |
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