Method for preferably selecting nervous system disease drugs based on BDNF / TrkB signaling pathway expression characteristics

Abstract

The invention provides a method for optimizing nervous system disease drugs based on BDNF / TrkB signal pathway expression characteristics, and belongs to the field of bioinformatics and drug optimization. According to the method, by analyzing transcriptome data of nervous system disease patients and normal people, differential expression genes related to BDNF / TrkB signal channels are recognized, and drugs capable of reversing gene expression changes are mined in a drug perturbation transcriptome database. The method comprises the following specific steps: acquiring and analyzing transcriptome data of nervous system disease patients and normal people in a database, and identifying differential genes related to a BDNF / TrkB pathway; collecting and integrating drug perturbation transcriptome data; and according to the identified differential genes, screening drugs capable of restoring gene expression to a normal level from drug perturbation transcriptome data. The invention can realize drug optimization for specific nervous system diseases (such as depression), provides a basis for drug action mechanism research, and has important significance for clinical drug optimization.

Description

Technical Field

[0001] The present invention relates to the fields of bioinformatics and drug optimization, and in particular to a technical method for drug optimization for nervous system diseases and its application. Background Art

[0002] The BDNF / TrkB signaling pathway plays an important role in the occurrence and development of a variety of neurological diseases. Studies have shown that this signaling pathway is associated with diseases such as depression, central nervous system injury, epilepsy, neurodegenerative diseases, neuropsychiatric diseases, Rett syndrome, and diabetes-induced apoptosis of cerebral cortex cells. In patients with depression, the levels of BDNF and its receptor TrkB are reduced, and antidepressant treatment can restore these levels. The BDNF / TrkB signaling pathway is essential for neuronal survival, structural changes, and plasticity, and therefore has potential therapeutic value in the treatment and recovery of neurodegenerative and neurotraumatic diseases. In addition, activation of the BDNF / TrkB signaling pathway is also considered to have new therapeutic strategies in the treatment of neuropsychiatric diseases. In the treatment of Rett syndrome, 5-HT1A receptor agonists improve symptoms by rescuing neuronal transmission damage and partially improving the CREB / BDNF signaling pathway. Finally, exercise training helps inhibit diabetes-induced apoptosis of cerebral cortex cells by enhancing the BDNF / TrkB signaling pathway. These findings highlight the versatility and importance of the BDNF / TrkB signaling pathway in neurological health and disease.

[0003] Transcriptome refers to the collection of all mRNAs in a cell or tissue under specific conditions. By measuring the transcriptome of cells in diseased individuals and performing differential expression analysis with cells under normal conditions, genes and expression pathways related to the disease can be discovered. It is an important tool for studying cell function and regulatory mechanisms, and has broad application prospects in the biomedical field. Drug perturbation transcriptomics refers to the scientific field of changing the gene expression pattern of cells or organisms through drug intervention, and then studying the effects of drugs on gene expression and their mechanisms of action. Specifically, when a drug acts on a cell, it may affect the expression of specific genes, leading to changes in protein content and activity levels, which can be measured by omics methods. By analyzing the changes in gene expression after drug perturbation, researchers can understand the effects of drugs on cell signaling pathways, metabolic pathways and other biological processes, provide patients with accurate guidance on the most appropriate drug type and optimal dose, and improve the efficacy and safety of drugs. The application of combined transcriptomics and metabolomics analysis in the study of drug action mechanisms is helpful for the development of new drugs, improving drug efficacy and evaluating drug toxicity.

[0004] Transcriptome analysis of diseased samples can reveal changes in gene expression in cells under disease conditions, which may involve key disease-related genes and signaling pathways. Drug-perturbed transcriptomes reflect the effects of drugs on cellular gene expression, including drug-induced upregulation or downregulation of gene expression. Combining the information provided by both can achieve drug development and screening, drug optimization, personalized medication, and other purposes. However, there is currently no direct and comprehensive method for using disease-related genes in drug-perturbation databases to perform drug optimization for the disease. Summary of the invention

[0005] The present invention proposes a method for mining drugs with opposite perturbation effects on transcriptomes in a drug perturbation transcriptome database for specific neurological diseases, especially depression, based on their transcriptome information. The present invention aims to optimize or reposition drugs so as to restore the gene expression of patients to normal levels.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway, comprising:

[0008] Obtain transcriptome data of patients with neurological diseases and normal subjects;

[0009] Obtain genes related to the BDNF / TrkB signaling pathway, and divide the up- and down-regulation of genes related to the BDNF / TrkB signaling pathway according to the logFC value based on the transcriptome data; genes related to the BDNF / TrkB signaling pathway with stable up- and down-regulation trends are regarded as genes related to nervous system diseases;

[0010] Collect transcriptome data perturbed by the drug to be optimized;

[0011] Based on the genes related to nervous system diseases, drugs that can up-regulate or down-regulate the expression of the genes in the opposite direction of the disease trend are mined in the drug perturbation data, which are the preferred drugs for nervous system diseases.

[0012] Furthermore, the nervous system disease is a cerebral cortical cell apoptosis disorder caused by depression, central nervous system damage, epilepsy, neurodegenerative disease, neuropsychiatric disease, Rett syndrome or diabetes.

[0013] Furthermore, the neurological disease is depression, wherein the transcriptome data of patients with depression are from multiple data sets in the GEO database, and the transcriptome data of 21 brain regions in which BDNF expression is normal are selected.

[0014] Furthermore, the genes related to the BDNF / TrkB signaling pathway are obtained as follows:

[0015] Through gene interaction network analysis, taking BDNF / TrkB as the network center, genes related to this pathway were obtained, that is, genes related to the BDNF / TrkB signaling pathway were obtained.

[0016] Furthermore, the genes related to the BDNF / TrkB signaling pathway with stable up- and down-regulation trends are taken as genes related to nervous system diseases, specifically:

[0017] Genes with the same up- and down-regulation trends in more than 70% of the samples were selected as BDNF / TrkB-related genes for depression.

[0018] Furthermore, the transcriptome data perturbed by the preferred drug comes from the CMAP_LINCS2020 dataset of the clue database.

[0019] Furthermore, drugs that can up-regulate the gene expression and are opposite to the disease trend are mined in the drug perturbation data, specifically:

[0020] The drug whose disturbance intensity log2FC on the gene is greater than 2 and whose disturbance trend on the gene is opposite to the change trend in the patient is selected as the preferred drug for nervous system diseases.

[0021] Furthermore, the nervous system disease is depression, wherein the preferred drugs include bortezomib, mitoxantrone, phenyl bromide, fidarest, androsterone, tizanidine, trametinib, pifenpropizone, S-rolipram and mycophenolate mofetil.

[0022] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway is implemented.

[0023] A storage medium comprising computer executable instructions, wherein when the computer executable instructions are executed by a computer processor, a method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway is implemented.

[0024] The technical solution of the present invention is developed by measuring the therapeutic effect of drugs on specific diseases from the perspective of expression omics, which helps to deeply understand the mechanism of action of drugs and is of great significance for clinical drug optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1This is a flow chart of a method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway of the present invention;

[0026] Figure 2 This is a flowchart of differentially expressed genes analysis for depression in Example 1;

[0027] Figure 3 This is a flow chart of the drug optimization process according to differentially expressed genes of depression in Example 1. DETAILED DESCRIPTION

[0028] The present invention provides a method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway, and mining drugs with opposite perturbation effects on the transcriptome in a drug perturbation transcriptome database according to their transcriptome information. Specifically, Figure 1 As shown, the following steps are included:

[0029] Step 1: Obtain transcriptome data of patients with neurological diseases and normal subjects;

[0030] In this step, for which type of nervous system disease, the transcriptome data of the corresponding nervous system disease patient is selected. In the present invention, the nervous system disease can be depression, central nervous system damage, epilepsy, neurodegenerative diseases, neuropsychiatric diseases, Rett syndrome or cerebral cortex apoptosis caused by diabetes. In a specific implementation case, taking depression as an example, several data sets (such as GSE102556, GSE185855, GSE45468, GSE45642, GSE53987, GSE87610, etc.) can be obtained from the GEO database, and a total of 23 transcriptome data sets of different brain regions are included. Further, the BDNF expression levels in the two brain regions of aINS and Nac are low, and the remaining 21 different brain region data sets can be selected as the final transcriptome data of the nervous system disease patient obtained for further analysis.

[0031] Step 2: Obtain genes related to the BDNF / TrkB signaling pathway, and divide the up- and down-regulation of genes related to the BDNF / TrkB signaling pathway according to the logFC value based on the transcriptome data; genes related to the BDNF / TrkB signaling pathway with stable up- and down-regulation trends are regarded as genes related to nervous system diseases;

[0032] In this step, the genes related to nervous system diseases are mainly obtained by screening genes related to the BDNF / TrkB signaling pathway. In a specific implementation case, genes related to the BDNF / TrkB signaling pathway are first extracted by performing gene interaction network analysis, and the up-regulation (logFC>0) and down-regulation (logFC<0) of genes are distinguished according to the logFC values ​​(log2-fold change) of patients with nervous system diseases and normal people, i.e., logFC=log2 (average expression of the experimental group / average expression of the control group). Further, the average expression in the above formula is the average expression of a single gene in two groups of transcriptomes. If the logFC of the gene is calculated to be greater than 2, it can be considered that the gene is expressed more in the patient, and vice versa, it is considered to be down-regulated if it is less than -2. It is set to be greater than 2 as up-regulation. Less than -2 is down-regulated in order to screen out genes with significant differential expression. The expression changes of genes related to the BDNF / TrkB pathway in the transcriptome data set are analyzed, and genes with consistent change trends in more than 70% of the data sets (i.e., 15 or more brain regions) are determined as corresponding nervous system disease-related genes.

[0033] Step 3: Collect transcriptome data perturbed by the drug to be optimized;

[0034] Generally, the perturbation transcriptome data of small molecule drugs are collected from the Clue database (CMAP_LINCS2020). These data include a large number of annotated small molecule compounds that induce gene expression profiles in different cell lines. The collected data are organized and formatted into a drug perturbation database.

[0035] Step 4: Based on the genes related to nervous system diseases, drugs that can up-regulate or down-regulate the gene expression in the drug perturbation data and are opposite to the disease trend are selected as the preferred nervous system disease drugs.

[0036] In this step, based on the neurological disease-related genes determined in the first step, drugs that can perturb the expression trend of these genes to make them opposite to the changing trend in the disease state are screened in the drug perturbation database. Specifically, the absolute value of log2FC in the drug perturbation transcriptome data must be greater than 2 to ensure that the perturbation intensity of the drug on gene expression is strong enough. At the same time, the perturbation direction of the drug on the target gene needs to be opposite to the changing trend of gene expression in the patient, that is, if the gene is upregulated in the patient, the drug should downregulate it; if the gene is downregulated in the patient, the drug should upregulate it. In this way, qualified small molecule drugs are screened out and sorted according to the sum of their logFC absolute values ​​for the target gene. The larger the value obtained, the stronger the perturbation of the drug on the target gene, and the more likely it is to reverse the symptoms of the disease, that is, the preferred neurological disease drug is obtained.

[0037] The present invention measures the therapeutic effect of a drug on a specific disease from the perspective of expression omics, deeply understands the mechanism of action of the drug, and then uses disease-related genes to screen clinical drugs in a drug perturbation database, which has a good screening effect. In order to better illustrate the purpose, technical solution and advantages of the present invention, the present invention will be further described in conjunction with specific embodiments.

[0038] Embodiment 1:

[0039] A method for selecting depression treatment drugs based on the expression characteristics of the BDNF / TrkB signaling pathway, such as Figure 2 As shown in the process, we first obtained the transcriptome data of 23 brain regions of patients with depression (patient group) and normal people (control group) from six datasets in the GEO database (GSE102556, GSE185855, GSE45468, GSE45642, GSE53987, GSE87610). The expression of BDNF in two brain regions, aINS and Nac, was low, so the other 21 brain region datasets were used for further analysis.

[0040] Through gene interaction network analysis, taking BDNF / TrkB as the network center, genes related to this pathway were obtained. The up- and down-regulation of genes were divided according to the positive and negative logFC calculated for each gene in the patient group and the control group (normal people), that is, when logFC>0, the gene was considered to be up-regulated, and when logFC<0, the gene was considered to be down-regulated in patients.

[0041] The expression changes of genes related to the BDNF / TrkB pathway in 21 transcriptome data sets were analyzed, and genes with the same change trend related to depression in more than 70% of the data sets, that is, more than 15 brain region data sets, were used as BDNF / TrkB related genes for depression. According to the logFC value analysis of each gene, it was found that 14 of the 72 genes had different expressions between patients and normal individuals, and 70% or more of the data were consistent with the change trend. That is, the 14 genes were determined to be the preferred depression-related target genes for subsequent drugs. The 14 depression-related genes identified are shown in Table 1. The up- and down-regulation of the gene was determined by the positive and negative logFC.

[0042] Table 1 14 depression-related genes

[0043]

[0044] The perturbed transcriptome information of small molecule drugs is collected in the clue database. This embodiment collects the CMAP_LINCS2020 data set, which includes information such as gene expression data, perturber information, and metadata. The information of all small molecule drugs is integrated and duplicates are removed. From the siginfo file, compoundinfo file, trt_cp and other files provided by the CMAP_LINCS2020 data set, the drug perturbation transcriptome data, drug targets, and drug CID numbers are read respectively. The unified data format consists of drug information, target sites, and perturbation conditions (log2FC) on target expression, thereby forming a drug perturbation database, and this information is written into the dataframe and saved in the data file. It contains 33,248 small molecule drugs and their respective gene expression perturbation spectrum data.

[0045] Based on the 14 important depression-related genes identified above, drugs that perturb these 14 genes were searched in the drug perturbation database. Compounds whose targets include the 14 genes in Table 1 have the possibility of directly affecting depression-related expressions.

[0046] Among the drugs that have been screened to disturb genes related to depression, continue to follow Figure 3 The process and conditions shown in the figure are used for screening. First, the absolute value of log2FC in the transcriptome data of drug perturbation needs to be greater than 2 to ensure that the intensity of drug perturbation on the gene is large enough. Furthermore, the perturbation trend of the drug on the target gene should be opposite to the change trend in the patient's body, that is, the up-regulated genes among the 14 depression-related genes in patients with depression need to be up-regulated by the drug, and the down-regulated genes in patients with depression need to be down-regulated by the drug. The drug determined in this way reverses the trend of gene expression changes in depression and has a high enough intensity.

[0047] Specifically, we should first screen out drugs whose perturbation trends meet the requirements, that is, the perturbation trend of the target gene should be opposite to the change trend in the patient's body. Then add up the absolute logFC values ​​of the depression BDNF / TrkB related genes affected by these drugs. The larger the value obtained, the stronger the perturbation of the drug on the target gene, and the more likely it is to reverse the depressive symptoms.

[0048] Finally, 552 qualified small molecule drugs were screened out from the perturbation database, and these compounds were sorted from high to low according to their sum.logFC values, thus completing the drug optimization for depression. Among the 552 small molecule drugs selected, 10 drugs for depression treatment that have been marketed are included, which illustrates the effectiveness of the method of the present invention. The results of partial drug optimization are shown in Table 2. The 10 marketed drugs in the optimization results are shown in Table 3.

[0049] Table 2 Results of the selection of some drugs for depression

[0050]

[0051]

[0052] Table 3 10 marketed drugs in the selected results

[0053]

[0054] The present invention also provides an electronic device, comprising one or more processors, for implementing a method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway in the above embodiment.

[0055] The electronic device of the present invention can be applied to any device with data processing capability, and the device with data processing capability can be a device or apparatus such as a computer.

[0056] The device embodiment can be implemented by software, hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capability in which it is located to read the corresponding computer program instructions in the non-volatile memory into the memory for execution. From the hardware level, in addition to the processor, memory, network interface, and non-volatile memory, any device with data processing capability in which the device in the embodiment is located can also include other hardware according to the actual function of the device with data processing capability, which will not be described in detail.

[0057] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0058] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0059] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway according to the above embodiment is implemented.

[0060] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capability and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

Claims

1. A method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway, characterized in that: include: Obtain transcriptome data of patients with neurological diseases and normal subjects; Obtain genes related to the BDNF / TrkB signaling pathway, and divide the up- and down-regulation of genes related to the BDNF / TrkB signaling pathway according to the logFC value based on the transcriptome data; genes related to the BDNF / TrkB signaling pathway with stable up- and down-regulation trends are regarded as genes related to nervous system diseases; Collect transcriptome data perturbed by the drug to be optimized; Based on the genes related to nervous system diseases, drugs that can up-regulate or down-regulate the expression of the genes in the opposite direction of the disease trend are mined in the drug perturbation data, which are the preferred drugs for nervous system diseases.

2. The method according to claim 1, characterized in that The nervous system disease is depression, central nervous system damage, epilepsy, neurodegenerative diseases, neuropsychiatric diseases, Rett syndrome or cerebral cortical cell apoptosis caused by diabetes.

3. The method according to claim 1, characterized in that The nervous system disease is depression, wherein the transcriptome data of patients with depression are from multiple data sets in the GEO database, and the transcriptome data of 21 brain regions in which BDNF expression is normal are selected.

4. The method according to claim 1, characterized in that: The genes related to the BDNF / TrkB signaling pathway are obtained as follows: Through gene interaction network analysis, taking BDNF / TrkB as the network center, genes related to this pathway were obtained, that is, genes related to the BDNF / TrkB signaling pathway were obtained.

5. The method according to claim 1, characterized in that The genes related to the BDNF / TrkB signaling pathway with stable up- and down-regulation trends are taken as the genes related to nervous system diseases, specifically: Genes with the same up- and down-regulation trends in more than 70% of the samples were selected as BDNF / TrkB-related genes for depression.

6. The method according to claim 1, characterized in that The transcriptome data perturbed by the preferred drug comes from the CMAP_LINCS2020 data set of the clue database.

7. The method according to claim 1, characterized in that In the drug perturbation data, drugs that can up-regulate the gene expression and reverse the disease trend are mined, specifically: The drug whose disturbance intensity log2FC on the gene is greater than 2 and whose disturbance trend on the gene is opposite to the change trend in the patient is selected as the preferred drug for nervous system diseases.

8. The method according to claim 1, characterized in that The nervous system disease is depression, and the preferred drugs include bortezomib, mitoxantrone, phenyl bromide, fidarest, androsterone, tizanidine, trametinib, pifenac, S-rolipram and mycophenolate mofetil.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway as described in any one of claims 1 to 8 is implemented.

10. A storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, implement a method for selecting drugs for nervous system diseases based on the expression characteristics of the BDNF / TrkB signaling pathway as described in any one of claims 1 to 8.

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