CN110556158B - Screening method of anti-myocardial fibrosis drug - Google Patents

Screening method of anti-myocardial fibrosis drug Download PDF

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CN110556158B
CN110556158B CN201910818494.7A CN201910818494A CN110556158B CN 110556158 B CN110556158 B CN 110556158B CN 201910818494 A CN201910818494 A CN 201910818494A CN 110556158 B CN110556158 B CN 110556158B
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李鹏
赵晋忠
张武霞
孟锦鑫
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Abstract

The invention discloses a screening method of an anti-myocardial fibrosis drug, which comprises the following steps: obtaining known marker genes of myocardial fibrosis; utilizing known marker genes to extract other genes or proteins which have biological relation with the known marker genes from biological big data, and limiting the genes which can be expressed in heart tissues to construct a specific myocardial fibrosis pathological gene module; constructing a drug data set with transcriptomics data to obtain drug transcription spectrum data; predicting the myocardial fibrosis resistance of the corresponding medicine according to the transcription spectrum data of the medicine by adopting a gene expression deduction method, and screening the required medicine according to the myocardial fibrosis resistance of the medicine. The invention constructs a tissue-specific myocardial fibrosis module based on the different interaction of myocardial fibrosis marker genes and other genes, screens anti-myocardial fibrosis potential medicaments through a computer, screens in batches, improves the accuracy, and has high efficiency and low cost.

Description

Screening method of anti-myocardial fibrosis drug
Technical Field
The invention belongs to the technical field of biological medicines, and relates to a screening method of an anti-myocardial fibrosis drug.
Background
Myocardial fibrosis refers to excessive accumulation of collagen fibers in the normal tissue structure of the myocardium, a significant increase in collagen concentration in heart tissue, or a change in collagen composition. These changes often severely affect cardiac function, leading to the development of a variety of cardiovascular diseases and ultimately to heart failure. Anti-myocardial fibrosis is one of the key treatment strategies for improving various cardiovascular diseases, but the drug development is mainly based on scattered research aiming at related targets and lacks of systematicness.
To date, many marker genes for myocardial fibrosis have been discovered, such as Periostin (POSTN), platelet-derived growth factor receptor alpha (PDGFRA), discoidin domain receptor 2(Ddr2), and the like. These marker genes are not or rarely expressed in normal heart tissue, but are highly expressed when cardiac myocardial fibrosis occurs, and have a high correlation with the severity of myocardial fibrosis.
The traditional method mainly targets at the significant difference genes, and the method only targets at the difference genes actually loses much useful information, for example, the standard selection of the significance of the difference genes is different, such as P <0.01 and P <0.05, and the number of the selected difference genes is greatly different. For another example, many genes may not be significantly different in expression, but have significant effects and are screened out by a fold difference standard (e.g., fold difference >2, etc.) set in advance; difficult to screen in batches, inefficiency, accuracy are poor, increase the cost of later stage test verification to a great extent.
Disclosure of Invention
In order to solve the problems, the invention provides a screening method of an anti-myocardial fibrosis drug, which constructs a tissue-specific myocardial fibrosis module based on different interactions of a myocardial fibrosis marker gene and other genes, screens the anti-myocardial fibrosis potential drug by a computer, improves the accuracy, has high efficiency and low cost, and solves the problems in the prior art.
The technical scheme adopted by the invention is that the screening method of the myocardial fibrosis resisting medicine is specifically carried out according to the following steps:
s1, obtaining known marker genes of myocardial fibrosis;
s2, fishing other genes or proteins which have biological relation with the known marker genes from the big biological data by using the known marker genes, and constructing a specific myocardial fibrosis pathological gene module by limiting the genes which can be expressed in heart tissues;
s3, constructing a drug data set with transcriptomics data, and acquiring drug transcription spectrum data;
s4, predicting the anti-myocardial fibrosis capability of the corresponding drug according to the transcription spectrum data of the drug by adopting a gene expression deduction method, and screening the needed drug according to the anti-myocardial fibrosis capability of the drug.
Further, in S2, the pathological gene modules of specific myocardial fibrosis include a co-expressed pathological gene module, a function-related pathological gene module, and a protein-interacting pathological gene module; the co-expressed pathological gene module extracts a gene which is obviously co-expressed with the known marker gene of the myocardial fibrosis by using a seek database and taking the known marker gene of the myocardial fibrosis as a bait, and retains the gene which can be expressed in the heart tissue by using the gene tissue specific expression information of the HPRD database; the pathological gene module related to functions extracts a heart tissue specific expression gene which is obviously related to the functions of a known marker gene of myocardial fibrosis by using a humanbase database; the protein interaction pathological gene module collects other genes having protein interaction with known marker genes of myocardial fibrosis by using the data of protein-protein interaction, and retains genes capable of being expressed in heart tissues by using the gene tissue specific expression information of the HPRD database; the pathological gene module of the specific myocardial fibrosis is the union, intersection or single pathological gene module of the three pathological gene modules.
Further, in S1, the known marker genes for myocardial fibrosis include periostin, platelet-derived growth factor receptor α, discoidin domain receptor 2.
Further, the step S3 specifically includes the following steps:
s31, organic small molecule compounds: acquiring the names and structural information of all compounds from an FTP system of a PubChem molecular database; removing inorganic compounds in the MESH medical subject word system; finding out GDS data containing compound name information, manually comparing and confirming that the corresponding experiment is a sequencing experiment performed aiming at the drug, and downloading corresponding GDS transcription spectrum data;
s32, preparing a compound Chinese medicine and a single Chinese medicine: firstly, manually searching and collecting Chinese medicine transcription profile data in a GEO database; secondly, extracting the drug names of the typical Chinese herbal compound and the single Chinese herbal medicine from the Chinese herbal medicine database, and manually searching pinyin, English and Latin of the drug names in the GEO database to obtain omics data of the typical Chinese herbal compound and the single Chinese herbal medicine;
s33, acquiring drug-induced gene expression profile data of the organic small molecule compound, the Chinese herbal compound and the single Chinese herbal medicine through a ConnectivityMap database.
Further, the step S4 specifically includes the following steps:
s41, calculating the difference of transcription profile data under the action of a drug and in a normal state, wherein a positive value indicates that the gene expression level is up-regulated under the induction of the drug, a negative value indicates that the gene expression level is down-regulated under the induction of the drug, and the differential gene profiles induced by the drug are sorted from big to small according to the difference to form a gene expression sequence of the drug;
s42, calculating the enrichment degree of the myocardial fibrosis pathological gene module in the drug transcription profile data by using a Kolmogorov-Smirnov test statistical method:
Figure BDA0002186918920000031
Figure BDA0002186918920000032
ES(S)＝Phit(S，i)-Pmiss(S，i) (3)
wherein, gjRepresenting the Gene expression sequence { g1，…，gNJ, N represents the total number of the entire drug transcript profile genes, i represents the gene expression order of the drugColumn, i ═ g1，…，gNS represents a specific myocardial fibrosis pathological gene module, NRRepresenting the number of pathological module genes of myocardial fibrosis; phit(S, i) the ratio of the occurrence of the pathological gene module of specific myocardial fibrosis in the gene expression sequence of the drug, Pmiss(S, i) represents the proportion of the specific myocardial fibrosis pathological gene module which does not appear in the gene expression sequence of the medicine, and ES (S) represents the enrichment degree of the specific myocardial fibrosis pathological gene module in the gene expression sequence of the medicine; predicting the myocardial fibrosis resistance of the medicine according to the ES (S) value.
The invention has the advantages that the invention constructs a tissue-specific myocardial fibrosis module based on the different interaction of myocardial fibrosis marker genes and other genes, is applied to the screening of anti-myocardial fibrosis drugs, screens the anti-myocardial fibrosis potential drugs in batches by a computer, ranks the anti-myocardial fibrosis capacity of the drugs, and does not screen a large amount of compounds blindly in subsequent experiments, thereby having high efficiency and low cost.
The gene expression deduction method is adopted to predict the myocardial fibrosis resistance of the corresponding drug according to the transcription spectrum data of the drug, so that the information loss caused by directly comparing the significantly different genes is avoided, and the problem of poor screening accuracy of the traditional method is solved; in documents and databases, a large amount of drug transcriptome data exists, the screening method of the anti-myocardial fibrosis drug of the invention fully utilizes the existing drug transcriptome data to predict the anti-myocardial fibrosis capability of the corresponding drug, and screens the needed drug according to the anti-myocardial fibrosis effect of the drug; for the existing medicines, relocation of the medicines in disease treatment is facilitated; for other compounds, prodrugs are provided for the development of therapeutic drugs for myocardial fibrosis; myocardial fibrosis is a common pathological symptom of various heart diseases, and anti-myocardial fibrosis drugs are potential drugs for treating the heart diseases, such as myocardial infarction, heart failure and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the screening method of the present invention.
Fig. 2 is an external view of the heart of a four-group animal test of the present invention.
Figure 3 is a cardiac weight index comparison of four animal trials of the present invention.
FIG. 4a is a graph showing the results of hematoxylin-eosin staining in the blank control animal test of the present invention.
FIG. 4b is a graph showing the results of hematoxylin-eosin staining in the animal test of the model group of the present invention.
FIG. 4c is a graph showing the results of hematoxylin-eosin staining in the 1 β -hydroxy alantolactone-administered group of animals in the present invention.
FIG. 4d is a graph showing the results of sirius staining in animals treated with fosinopril according to the present invention.
FIG. 5a is a graph of sirius staining results of a blank control animal test of the present invention.
FIG. 5b is a graph of sirius staining results of the model group animal experiment of the present invention.
FIG. 5c is a graph showing the results of sirius staining in the animal test of the 1 β -hydroxy alantolactone-administered group of the present invention.
FIG. 5d is a graph showing the results of sirius staining in animals treated with fosinopril according to the present invention.
FIG. 6 shows the results of the determination of the percentage of Collagen content in Collagen from Collagen-I in the results of the sirius staining using Image-Pro Plus software.
FIG. 7 shows the results of the Collagen content percentage measurement of Collagen-III in the results of sirius staining using Image-Pro Plus software.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Under the background of the great development of modern biology omics, gene expression data, functional data and mutual relation data among genes are extremely rich, a myocardial fibrosis marker gene is taken as a core, functional related genes of myocardial fibrosis are fished from biological big data, a pathological module of the myocardial fibrosis is finally formed, a medicine capable of obviously reducing the module gene is screened by a gene expression deduction method on the basis of the pathological module, a potential anti-myocardial fibrosis medicine is obtained, and finally, experiments prove that the medicine can be used for treating myocardial fibrosis.
The screening method of the myocardial fibrosis resisting medicine is specifically carried out according to the following steps as shown in figure 1:
s1, obtaining known marker genes of myocardial fibrosis; known marker genes for myocardial fibrosis, including Periostin (POSTN), Platelet Derived Growth Factor Receptor Alpha (PDGFRA), discoidin domain receptor 2(Ddr2), and the like, are obtained from the medical literature.
S2, fishing other genes or proteins which have biological relation with the known marker genes from the big biological data by using the known marker genes, and constructing a specific myocardial fibrosis pathological gene module by limiting the genes which can be expressed in heart tissues; aiming at the diseases (myocardial fibrosis) to be treated, the known marker genes of the myocardial fibrosis are used as baits, and the existing pathological research literature and omics research big data are fully utilized.
The pathological gene module of specific myocardial fibrosis is formed by linking different genes or proteins together in a certain biological relationship; the pathological gene module of the specific myocardial fibrosis comprises a coexpression pathological gene module, a function-related pathological gene module and a protein interaction pathological gene module; the pathological gene module of the specific myocardial fibrosis is the union, intersection or single pathological gene module of the three pathological gene modules; intersection shows that the genes exist in the three modules, so that the relationship between the genes and myocardial fibrosis is necessarily more close, finally, the finally screened drugs mainly affect the intersection genes, and the types of the screened drugs are relatively single. The union includes genes in all modules, and the screened drugs may affect more types of genes and the screened drug types are more numerous and miscellaneous.
Co-expression means that the genes in the module are expressed in a consistent trend during the pathological process; the co-expressed pathological gene module, such as a gene which is obviously co-expressed with the known marker gene of myocardial fibrosis by using a seek database (http:// seek. princeton. edu /), and a gene which is obviously co-expressed with the known marker gene of myocardial fibrosis by using a known marker gene of myocardial fibrosis as a bait, and a gene which can be expressed in heart tissues by using HPRD database (http:// www.hprd.org /) gene tissue specific expression information.
Functionally related means that there is a functional interconnection between genes in the modules; a pathology gene module related to functions, such as a heart tissue specific expression gene which is extracted by utilizing a humanbase database (https:// hb. flatronentitite. org /) and is obviously related to the functions of known marker genes of myocardial fibrosis.
Protein interaction refers to the pathological gene modules where different proteins can interact with each other, such as using protein-protein interaction (PPI) data, collecting other genes that have protein interactions with known marker genes for myocardial fibrosis, using HPRD database (http:// www.hprd.org /) gene tissue-specific expression information, and retaining genes that can be expressed in cardiac tissue.
Different gene modules can be formed by different mutual connection modes of different genes, the three genes are representative and are a means for finding out the pathological gene module of the myocardial fibrosis, and the known marker genes of the myocardial fibrosis can be used for fishing the genes related to the pathological process of the myocardial fibrosis by virtue of the connection.
S3, constructing a drug data set with transcriptomics data, and acquiring drug transcription spectrum data; aims to collect the transcription profile data of the drugs as much as possible and provide basic screening data for the subsequent drug screening.
S31, organic small molecule compounds: the method comprises the following steps of firstly obtaining the names and the structural information of all compounds from an FTP system of a PubPhem molecular database (https:// PubPhem. ncbi. nlm. nih. gov /) with the latest version, wherein the current PubPhem database contains more than 9000 ten thousand compounds with the structural information; since the database of PubChem contains a lot of information of Inorganic compounds, further using MESH medical subject matter word system, selecting all the bottom subject words in "organic Chemicals [ D01 ]" subcategories under "Chemicals and Drugs [ D ]" main classification in tree category table as names of Inorganic compounds, and comparing with the compounds collected in PubChem to remove the Inorganic compounds; downloading all annotation files (Series Matrix files) of GDS type data containing PubMed document information in a GEO database by using an R language 'GEOquery packet', comparing and analyzing the GDS annotation files and a MESH subject term system, finding GDS data containing compound name information, further manually comparing and confirming that a corresponding experiment is a sequencing experiment performed aiming at a medicine, and downloading corresponding GDS transcription spectrum data by using the 'GEOquery packet'.
The information of whether the compound is a traditional Chinese medicine component is confirmed by comparing with traditional Chinese medicine-component associated information recorded in an existing traditional Chinese medicine Database, and the traditional Chinese medicine Database mainly used by the invention comprises TCMID, SymMap, YaTCM, TCMSP, a traditional Chinese medicine Database, TCM Database @ Taiwan and the like. The information of whether the compound is a western medicine is confirmed by comparing with the medicine information in a drug bank database.
S32, preparing a compound Chinese medicine and a single Chinese medicine: firstly, TCM (TCM short for Chinese medicine), traditional Chinese medicine, TCM formula, herbal formula and Chinese medicine are used as Chinese medicine keywords to manually search and collect Chinese medicine transcription spectrum data in a GEO database. Secondly, omics data of typical Chinese herbal compound and single Chinese herbal medicine are obtained by manually searching pinyin, English and Latin names in a GEO database, and the medicine names are extracted from a Chinese medicine database, such as a Chinese people's republic of China dictionary and a Chinese medicine dictionary.
S33, database: the ConnectivityMap (CMap) database (https:// close. io /) is a drug-induced gene expression profiling database, the old version containing 1309 drug molecules. Each drug is used for processing different human cell strains at different concentrations and for different durations, and the final cell sample is subjected to transcription spectrum sequencing by using gene chip HG-U133A of Affymetrix company; the new version is greatly increased in the data of medicines and cell strains, the database comprises cell lines processed by 20000 small molecules, and the sequencing technology is changed into the L1000 technology with lower cost.
Wherein the GDS transcription profile data, the Chinese medicine transcription profile data, the omics data and the drug-induced gene expression profile data are the transcription profile data of corresponding drugs.
S4, predicting the anti-myocardial fibrosis capability of the corresponding drug according to the transcription spectrum data of the drug by adopting a gene expression deduction method, and screening the needed drug according to the anti-myocardial fibrosis capability of the drug.
The principle of the gene expression deduction method is that after each drug acts on a cell or animal tissue model, a series of gene expression profiles can be changed, if the drug can obviously reduce the expression level of a myocardial fibrosis pathological gene module, the drug is meant to improve myocardial fibrosis; based on the principle, the enrichment degree of the myocardial fibrosis pathological gene module in the drug transcription profile data is calculated.
S41, calculating the difference of transcription profile data under the action of a drug and a normal state, wherein a positive value represents that the expression level of a gene under the action of the drug is higher than that under a normal condition, namely the expression level of the gene under the action of the drug is up-regulated, a negative value represents that the expression level of the gene under the action of the drug is lower than that under the normal condition, namely the expression level of the gene under the action of the drug is down-regulated, and the differential gene profiles induced by the drug are sorted from big to small according to the difference to form a gene expression sequence of the drug, and the closer to the upstream of the sequence, the more genes of which the expression level is up-regulated under the action of the drug is obvious, and the closer to the downstream, the more genes of which the expression level is down-regulated under the action of the drug is obtained;
s42, calculating the enrichment degree of the specific myocardial fibrosis pathological gene module in the drug transcription profile data by using a Kolmogorov-Smirnov test statistical method:
Figure BDA0002186918920000071
Figure BDA0002186918920000072
ES(S)＝Phit(S，i)-Pmiss(S，i) (3)
wherein N represents the total number of whole drug transcript genes, i represents the gene expression sequence of the drug, and i ═ g1，…，gN}，gjRepresenting the Gene expression sequence { g1，…，gNJ gene of (1), S represents a specific myocardial fibrosis pathological gene module, NRRepresenting the number of pathological module genes of myocardial fibrosis; phit(S, i) the ratio of the occurrence of the pathological gene module of specific myocardial fibrosis in the gene expression sequence of the drug, Pmiss(S, i) represents the proportion of the specific myocardial fibrosis pathological gene module which does not appear in the gene expression sequence of the medicine, and ES (S) represents the enrichment degree of the specific myocardial fibrosis pathological gene module in the gene expression sequence of the medicine; the construction of the gene expression sequence is prepared for subsequent enrichment analysis, the gene expression sequence is arranged from large to small according to gene difference, the more the myocardial fibrosis pathological module is positioned at the upstream of the expression sequence in the enrichment analysis result, the medicament inclination is towards the upper myocardial fibrosis pathological module related gene, the more the myocardial fibrosis pathological module is positioned at the downstream of the expression sequence in the enrichment analysis result, the medicament inclination is downwards regulated to the myocardial fibrosis pathological module related gene. The smaller es(s), the more genes that indicate the presence of genes in the myocardial fibrosis pathological gene module downstream of the gene expression sequence of the drug, the greater the propensity of the myocardial fibrosis pathological gene module to this drug, since genes in the myocardial fibrosis pathological gene module are generally up-regulated in myocardial fibrosis; therefore, the smaller the ES (S), the stronger the anti-myocardial fibrosis ability of the medicine and the better the effect, and according to the ES (S) value, the anti-myocardial fibrosis of the selected medicineThe effect is sorted, and screening is carried out according to the anti-myocardial fibrosis effect sorting of the medicines.
The method screens potential anti-myocardial fibrosis medicines in batches through the computer, ranks the anti-myocardial fibrosis capacity of the screened medicines finally, and a large amount of compounds are not required to be screened blindly in subsequent experiments, so that the efficiency is high, and the cost is low; for example, after the same 100 candidate drugs are screened by the screening method, the rank is top, the myocardial fibrosis resistance is strong, and the experiment can prove that the rank is top; however, the traditional method directly made through experiments can only blindly search for the 100 components, and has long period and high cost.
And (3) screening effect verification:
1. the animal model verifies that the potential anti-myocardial fibrosis medicament screened by the invention;
the top ranked 1 β -hydroxy alantolactone in the screening results is taken as an example for illustration:
40 clean grade male C57BL/6 mice, aged between 4-5 weeks, weighed 18-20 g. The medicine is divided into a blank control group, an isoproterenol administration model group, an isoproterenol +1 beta-hydroxy alantolactone administration group and an isoproterenol + fosinopril positive medicine group at random. Model groups for isoproterenol administration: the injection is constructed by continuously injecting isoproterenol for a plurality of days, wherein the injection days are 14 days, the first day is injected with isoproterenol of 20mg/kg, the second day is injected with isoproterenol of 10mg/kg, and the 3 rd to 14 th days are injected with isoproterenol of 5mg/kg every day. Isoproterenol +1 beta-hydroxy alantolactone administration group: on the same basis as the operation of the model group for administration of isoproterenol, the 1 beta-hydroxy alantolactone is perfused for 3g/kg per day for 14 days. The isoproterenol + fosinopril positive medicine group is infused with fosinopril at 0.165mg/kg per day for 14 days on the same basis as the operation of the isoproterenol administration model group. And (5) irrigating distilled water for the blank control group and the model group.
After 1.2 weeks, the left ventricular function of the animals was first observed using a vivo small animal ultrasound system, and the animals were anesthetized with isoflurane, the systolic Left Ventricular Inside Diameter (LVIDs), the diastolic left ventricular inside diameter (LVIDd), the left ventricular Ejection Fraction (EF), and the Fractional Shortening (FS). And after the measured data are collected, performing statistical analysis.
1.3 animal hearts were collected rapidly and photographed. The heart weight was weighed and the heart weight index (HWI ═ heart weight/length of tibia) was calculated. Paraffin sections of cardiac tissue were constructed by conventional procedures. The hematoxylin-eosin staining method is used for observing the overall pathological changes of the heart tissue. The method comprises the steps of dyeing Collagen I and Collagen III by a sirius dyeing method, quantifying different fiber contents by a polarized light microscope, and detecting the content percentages of the Collagen I and Collagen III by using Image-Pro Plus software.
1.4 statistical processing data were statistically analyzed using SSPS10.0 software, with experimental results expressed as X ± SE, using ANOVA one-way analysis of variance, with P ═ 0.05 as the significance test level.
2. The experimental results are as follows:
2.1 the ultrasonic test result of the mice shows that the cardiac function of the mice with isoproterenol +1 beta-hydroxy alantolactone groups is obviously enhanced. After the administration of the mouse for 2 weeks, before material drawing, the vivo ultrasonic detection is carried out, the heart ejection condition of the mouse is related to the myocardial state, the myocardial fibrosis can affect the left ventricular ejection of the mouse, so the systolic period and the diastolic period capacity of the left ventricle are detected, and the fraction of the left ventricular ejection is measured, which is shown in the table 1:
TABLE 1 mouse left ventricular function test results
Figure BDA0002186918920000091
The "###" in table 1 represents that p is less than 0.01 compared to the blank control group; "+" indicates p <0.05 compared to model group; "x" represents p <0.01 compared to model group; as can be seen from the cardiac function indices in table 1, the model groups administered with isoproterenol had significantly lower EF and FS (P <0.01) compared to the blank control group. LVIDs and LVIDd were significantly increased (P < 0.01). These data suggest that the left ventricular structure of the animal model is pathologically altered and cardiac function is deteriorated. After the isoproterenol +1 beta-hydroxy alantolactone administration group is treated in the animal model for 2 weeks, EF and FS are obviously increased (P <0.01) and LVIDs are obviously reduced (P <0.01) compared with the model group. These data indicate that 1 β -hydroxy alantolactone can inhibit ventricular dilatation and delay cardiac remodeling, thereby improving cardiac function.
2.2 visual observation showed that the heart was significantly enlarged in the model group compared to the blank control group. After treatment with isoproterenol +1 β -hydroxy alantolactone, the change in the morphological characteristics of the heart was improved compared to the model group (fig. 2). Meanwhile, as shown in fig. 3, the HWI in the model group was significantly increased (P <0.01) compared to the blank control group, and the increase of HWI was inhibited by the treatment with 1 β -hydroxy alantolactone (P < 0.01); in FIG. 3, "###" represents p <0.01 as compared to the blank control group; "x" represents p <0.01 compared to model group.
2.3 hematoxylin-eosin staining experiments show that the myocardium of the blank control group is normal and the arrangement of the myocardial cells is regular. The model group shows pathological phenotypes such as disorder, hypertrophy, necrosis, normal structure loss, myofibrinolysis and the like of the myocardial cells. After treatment with isoproterenol +1 β -hydroxy alantolactone, most of these pathological phenotypes were replaced by well-arranged cardiomyocytes, see fig. 4a-4 d. In the sirius dyeing experiment, a red polarized light method is utilized, as shown in fig. 5a-5d, the Collagen fibers of Collagen-I are observed to be lighter in arrangement, stronger in bilateral refraction, and yellow, orange and red thick layers. Red indicates fiber thickness. The Collagen fibers of Collagen-III are arranged in a dispersed net shape, have weak bilateral refraction and are green thin fibers. After two weeks, the model group collagen was more, thicker, brighter, and more disorganized than the placebo group collagen. The ratio of Collagen type I and Collagen type III was significantly higher than that of the placebo group (P < 0.01). Compared with the model group, the isoproterenol +1 beta-hydroxy alantolactone administration group has less, thinner, darker and more ordered collagen. The ratio of Collagen type I and Collagen type III in the 1 beta-hydroxy alantolactone administration group is obviously lower than that in the model group (P <0.01), and the results are shown in FIGS. 6-7.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (4)

1. A screening method of an anti-myocardial fibrosis drug is characterized by comprising the following steps:
s1, obtaining known marker genes of myocardial fibrosis;
s2, fishing other genes or proteins which have biological relation with the known marker genes from the big biological data by using the known marker genes, and constructing a specific myocardial fibrosis pathological gene module by limiting the genes which can be expressed in heart tissues;
s3, constructing a drug data set with transcriptomics data, and acquiring drug transcription spectrum data;
s4, predicting the myocardial fibrosis resistance of the corresponding drug according to the transcription spectrum data of the drug by adopting a gene expression deduction method, and screening the needed drug according to the myocardial fibrosis resistance of the drug;
in the S2, the specific myocardial fibrosis pathological gene modules comprise a co-expressed pathological gene module, a function-related pathological gene module and a protein interaction pathological gene module;
the co-expressed pathological gene module extracts a gene which is obviously co-expressed with the known marker gene of the myocardial fibrosis by using a seek database and taking the known marker gene of the myocardial fibrosis as a bait, and retains the gene which can be expressed in the heart tissue by using the gene tissue specific expression information of the HPRD database;
the pathological gene module related to functions extracts a heart tissue specific expression gene which is obviously related to the functions of a known marker gene of myocardial fibrosis by using a humanbase database;
the protein interaction pathological gene module collects other genes having protein interaction with known marker genes of myocardial fibrosis by using the data of protein-protein interaction, and retains genes capable of being expressed in heart tissues by using the gene tissue specific expression information of the HPRD database;
the pathological gene module of the specific myocardial fibrosis is the union set, intersection set or single pathological gene module of the three pathological gene modules.
2. The method for screening an anti-myocardial fibrosis drug according to claim 1, wherein in S1, the known marker genes for myocardial fibrosis include periostin, platelet-derived growth factor receptor α, and discoidin domain receptor 2.
3. The method for screening an anti-myocardial fibrosis drug according to claim 1, wherein the step S3 is specifically performed according to the following steps:
s31, organic small molecule compounds: acquiring the names and structural information of all compounds from an FTP system of a PubChem molecular database; removing inorganic compounds in the MESH medical subject word system; finding out GDS data containing compound name information, manually comparing and confirming that the corresponding experiment is a sequencing experiment performed aiming at the drug, and downloading corresponding GDS transcription spectrum data;
s32, preparing a compound Chinese medicine and a single Chinese medicine: firstly, manually searching and collecting Chinese medicine transcription profile data in a GEO database; secondly, extracting the drug names of the typical Chinese herbal compound and the single Chinese herbal medicine from the Chinese herbal medicine database, and manually searching pinyin, English and Latin of the drug names in the GEO database to obtain omics data of the typical Chinese herbal compound and the single Chinese herbal medicine;
s33, acquiring drug-induced gene expression profile data of the organic small molecule compound, the Chinese herbal compound and the single Chinese herbal medicine through a ConnectivityMap database.
4. The method for screening an anti-myocardial fibrosis drug according to claim 3, wherein the step S4 is specifically performed according to the following steps:
s41, calculating the difference of transcription profile data under the action of a drug and in a normal state, wherein a positive value indicates that the gene expression level is up-regulated under the induction of the drug, a negative value indicates that the gene expression level is down-regulated under the induction of the drug, and the differential gene profiles induced by the drug are sorted from big to small according to the difference to form a gene expression sequence of the drug;
s42, calculating the enrichment degree of the myocardial fibrosis pathological gene module in the drug transcription profile data by using a Kolmogorov-Smirnov test statistical method:
Figure FDA0003245213340000021
Figure FDA0003245213340000022
ES(S)＝Phit(S，i)-Pmiss(S，i) (3)
wherein, gjRepresenting the Gene expression sequence { g1，…，gNJ, N represents the total number of entire drug transcript profile genes, i represents the gene expression sequence of the drug, i ═ g1，…，gNS represents a specific myocardial fibrosis pathological gene module, NRRepresenting the number of pathological module genes of myocardial fibrosis; phit(S, i) the ratio of the occurrence of the pathological gene module of specific myocardial fibrosis in the gene expression sequence of the drug, Pmiss(S, i) represents the proportion of the specific myocardial fibrosis pathological gene module which does not appear in the gene expression sequence of the medicine, and ES (S) represents the enrichment degree of the specific myocardial fibrosis pathological gene module in the gene expression sequence of the medicine; predicting the myocardial fibrosis resistance of the medicine according to the ES (S) value.
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Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1759182A (en) * 2002-11-22 2006-04-12 克雷顿研究院 Compositions and systems for the regulation of genes
CN102166346A (en) * 2011-04-25 2011-08-31 湖南师范大学 Lrrc10 micromolecules and application thereof to preparation of medicaments for treating myocardial hypertrophy
CN102507883A (en) * 2011-11-16 2012-06-20 上海生物信息技术研究中心 Method for predicating novel curative effects of medicament based on biological process and application of method
CN103146801A (en) * 2011-12-07 2013-06-12 上海市普陀区中心医院 Method for screening anti-intestinal cancer drugs
CN104031987A (en) * 2014-05-12 2014-09-10 贵州省人民医院 Application of miRNA to treat myocardial fibrosis diseases
WO2014149972A1 (en) * 2013-03-15 2014-09-25 The Scripps Research Institute Systems and methods for genomic annotation and distributed variant interpretation
CN105349642A (en) * 2015-10-30 2016-02-24 北京泱深生物信息技术有限公司 Acute myocardial infarction marker and application thereof
CN105497896A (en) * 2015-03-31 2016-04-20 苏州大学 Tumor drug-resistant target and application thereof
CN106709275A (en) * 2017-02-04 2017-05-24 上海喆之信息科技有限公司 Restricted type cardiomyopathy gene data processing device
WO2017201497A1 (en) * 2016-05-19 2017-11-23 Oncoscar Llc Methods of diagnosis and therapeutic targeting of clinically intractable malignant tumors
CN108875297A (en) * 2018-07-16 2018-11-23 王亚帝 Utilize the method for miRNA-gene coexpression neural network forecast anthracene nucleus medicament cardiac muscle cell gap junction communication exception
CN109192252A (en) * 2018-08-23 2019-01-11 南开大学 Co-express purposes of the transcription group of period circadian rhythm in mechanism of drug action discovery
CN109872776A (en) * 2019-02-14 2019-06-11 辽宁省肿瘤医院 A kind of screening technique and its application based on weighted gene coexpression network analysis to gastric cancer potential source biomolecule marker

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1759182A (en) * 2002-11-22 2006-04-12 克雷顿研究院 Compositions and systems for the regulation of genes
CN102166346A (en) * 2011-04-25 2011-08-31 湖南师范大学 Lrrc10 micromolecules and application thereof to preparation of medicaments for treating myocardial hypertrophy
CN102507883A (en) * 2011-11-16 2012-06-20 上海生物信息技术研究中心 Method for predicating novel curative effects of medicament based on biological process and application of method
CN103146801A (en) * 2011-12-07 2013-06-12 上海市普陀区中心医院 Method for screening anti-intestinal cancer drugs
WO2014149972A1 (en) * 2013-03-15 2014-09-25 The Scripps Research Institute Systems and methods for genomic annotation and distributed variant interpretation
CN104031987A (en) * 2014-05-12 2014-09-10 贵州省人民医院 Application of miRNA to treat myocardial fibrosis diseases
CN105497896A (en) * 2015-03-31 2016-04-20 苏州大学 Tumor drug-resistant target and application thereof
CN105349642A (en) * 2015-10-30 2016-02-24 北京泱深生物信息技术有限公司 Acute myocardial infarction marker and application thereof
WO2017201497A1 (en) * 2016-05-19 2017-11-23 Oncoscar Llc Methods of diagnosis and therapeutic targeting of clinically intractable malignant tumors
CN106709275A (en) * 2017-02-04 2017-05-24 上海喆之信息科技有限公司 Restricted type cardiomyopathy gene data processing device
CN108875297A (en) * 2018-07-16 2018-11-23 王亚帝 Utilize the method for miRNA-gene coexpression neural network forecast anthracene nucleus medicament cardiac muscle cell gap junction communication exception
CN109192252A (en) * 2018-08-23 2019-01-11 南开大学 Co-express purposes of the transcription group of period circadian rhythm in mechanism of drug action discovery
CN109872776A (en) * 2019-02-14 2019-06-11 辽宁省肿瘤医院 A kind of screening technique and its application based on weighted gene coexpression network analysis to gastric cancer potential source biomolecule marker

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Peng Li 等.Network pharmacology based investigation of the effects of herbal ingredients on the immune dysfunction in heart disease.《Pharmacological Research》.2019,第141卷 *
Prediction of Drug–Gene Interaction by Using Metapath2vec;Siyi Zhu 等;《Frontiers in Genetics》;20180731;第9卷;1-10 *
Transcriptome inference and systems approaches to polypharmacology and drug discovery in herbal medicine;Peng Li 等;《Journal of Ethnopharmacology》;20170131;第195卷;127–136 *
基于 LINCS 转录组大数据的药物诱导基因共表达网络构建;宋欣雨 等;《军事医学》;20180630;第42卷(第6期);425-426 *
基于基因表达谱筛选乳腺癌他莫昔芬耐药的生物标志物与治疗药物;张晶晶 等;《临床与病理杂志》;20180731;第38卷(第7期);1392-1398 *

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