Pharmaceutical analysis method based on biomics and application

Abstract

The invention provides a pharmaceutical analysis method based on biomics and application, and belongs to the technical field of medical analysis. Comprising the following steps: S1, experiment grouping: screening participants according to a preset grouping standard, and randomly dividing the participants into a medicine group and a placebo group; s2, experimental process and sample collection: collecting detection samples of the drug group and the placebo group at three time nodes, namely before medication, during the experiment and after the experiment; s3, sample analysis; and S4, analyzing a metabolic pathway where the drug plays a role, and enriching metabolites and drug target genes. Through specific standard component grouping, sample collection in a preset stage and multi-omics comprehensive analysis, metabonomics and transcriptomics combined analysis is carried out on each experimental group, metabolism and transcription co-enrichment pathways are screened, and then metabolic pathways, enriched metabolites and drug target genes in which drugs play roles, and interaction relationships are analyzed. The potential mechanism of the medicine playing a role in relieving diseases is analyzed through a multi-omics combined technology, and reference is provided for follow-up medicine development and application.

Description

Technical Field

[0001] The present invention belongs to the field of medical analysis technology, and specifically relates to a bio-omics-based drug analysis method and an application of the bio-omics analysis method in menopausal syndrome drug analysis. Background Art

[0002] Bioomics mainly includes genomics, transcriptomics, proteomics and metabolomics. Genomics explains the structure of the entire genome, the relationship between structure and function, and the interaction between genes. Transcriptomics studies gene transcription and transcriptional regulation in cells at the RNA level. Proteomics mainly studies the composition and dynamic laws of all proteins in cells. Metabolomics is the determination of the composition of all small molecules (Mr≤1500) in a cell of an organism, and studies its dynamic changes, establishes a system metabolic map, and directly reflects the impact of the environment on the cell state. The research and development of bioomics allows us to have a more comprehensive understanding of the internal mechanisms of organisms and the regulation of life activities, which helps to reveal the mechanism of disease occurrence, study the mechanism of action of drugs, and thus find treatments for diseases and promote the development of personalized medicine.

[0003] The Chinese invention patent application with publication number CN115223661A and invention name “Biomics Data Analysis System” discloses bioomics research, especially a bioomics data analysis system for studying biological characteristics, which specifically includes a bioomics data acquisition subsystem, a bioomics data analysis subsystem, and a bioomics data output subsystem. The omics data is input into the bioomics data analysis subsystem, and then the input-output transformation neural network is trained. The network will automatically screen useful features under the action of the loss function, and map the features to a low-dimensional space. The data will be processed with a unified goal through an end-to-end method. This technical solution mainly focuses on the processing of bioomics data, which improves the credibility and accuracy of downstream tasks in data classification, clustering and visualization. It does not involve the use of bioomics data at the front end to explore the mechanism of disease onset and drug treatment.

[0004] The Chinese invention patent application with the publication number CN110286233A and the invention name "A biomarker metabolic pathway and analysis method and application" discloses metabolic biomarkers stearic acid, phytosphingosine, glycine, glutamine and phospholipids; the targets of the metabolic biomarkers are target proteins related to the nervous system, immune response and endocrine system of depression; the metabolic pathways are PI3K-Akt signaling pathway, mTOR signaling pathway, MAPK signaling pathway, erbB signaling pathway, neurotrophin signaling pathway, Rap1 signaling pathway and Ras signaling pathway. A functional network analysis method suitable for the characteristics of depression has been established. This technical solution only explores metabolic biomarkers and related pathways related to depression from the perspective of metabolomics, and has certain limitations on mechanism research. Summary of the invention

[0005] Therefore, the technical problem to be solved by the present invention is a drug analysis method and application based on bio-omics.

[0006] The present invention provides a technical solution: a drug analysis method based on bioomics, comprising the following steps: S1. Experimental grouping: participants were screened according to the preset inclusion criteria, and those who met the inclusion criteria were randomly divided into the drug group and the placebo group; S2. Experimental process and sample collection: samples were collected from the drug group and the placebo group at three time points: before medication, during the experiment, and at the end of the experiment; S3. Sample analysis, including: S3.1. Metabolomics analysis to screen out differential metabolites and differential metabolite enrichment pathways; S3.2, transcriptomic analysis, screening of differentially expressed genes and differentially expressed gene enrichment pathways; S3.3. Perform metabolomics and transcriptomics combined analysis on the drug group and the placebo group to screen for metabolic and transcriptional co-enriched pathways; S4. Analyze the metabolic pathways, enriched metabolites and drug target genes through which drugs exert their effects.

[0007] Preferably, the method further comprises: Step S3.4: Perform network pharmacology analysis to analyze the relationship between drug components and signal pathways and the relationship between drug components and differentially expressed genes, and screen out drug target genes of active drug components.

[0008] Preferably, the step 3.1 comprises: S3.1.1. After processing the samples, perform quantitative metabolite analysis; S3.1.2, through VIP value, FC value and PThe three parameters were used to identify the differential metabolites; S3.1.3. Perform KEGG enrichment analysis on differential metabolites.

[0009] Preferably, the step 3.2 comprises: S3.2.1, transcriptome library preparation and sequencing; S3.2.2. Data analysis, using FC values ​​and P The values ​​were screened to find differentially expressed genes between the drug group and the placebo group; S3.2.3. KEGG pathway enrichment analysis obtained differentially expressed genes.

[0010] Preferably, step 3.3 comprises conducting metabolomics and transcriptomics analysis on the drug group before and after medication, the placebo group before and after medication, the drug group and the placebo group before medication, and the drug group and the placebo group after medication.

[0011] The present invention also provides another technical solution: application of a bio-omics analysis method in drug analysis of menopausal syndrome, the bio-omics analysis method comprising: S1. Experimental grouping: participants were screened according to the preset inclusion criteria, and those who met the inclusion criteria were randomly divided into the drug group and the placebo group; S2. Experimental process and sample collection: samples were collected from the drug group and the placebo group at three time points: before medication, during the experiment, and at the end of the experiment; S3. Sample analysis, including: S3.1. Metabolomics analysis to screen out differential metabolites and differential metabolite enrichment pathways; S3.2, transcriptomic analysis, screening of differentially expressed genes and differentially expressed gene enrichment pathways; S3.3. Perform metabolomics and transcriptomics combined analysis on the drug group and the placebo group to screen for metabolic and transcriptional co-enriched pathways; S3.4. Conduct network pharmacology analysis to analyze the relationship between drug components and signal pathways and between drug components and differentially expressed genes, and screen out drug target genes of active drug components; S4. Analyze the metabolic pathways, enriched metabolites and drug target genes through which drugs exert their effects.

[0012] Preferably, the step S3.1 comprises: S3.1.1. After processing the samples, perform quantitative metabolite analysis; S3.1.2, through VIP value, FC value and P The three parameters were used to identify the differential metabolites; S3.1.3. Perform KEGG enrichment analysis on differential metabolites.

[0013] Preferably, the step S3.1 screens and obtains homovanillic acid, a metabolite enriched in the dopaminergic synaptic pathway, menaquinone, a metabolite enriched in the vitamin digestion and absorption pathway, and homovanillic acid, a metabolite enriched in the tyrosine metabolic pathway.

[0014] Preferably, the step S3.2 comprises: S3.2.1, transcriptome library preparation and sequencing; S3.2.2. Data analysis, using FC values ​​and P The values ​​were screened to find differentially expressed genes between the drug group and the placebo group; S3.2.3. KEGG pathway enrichment analysis obtained differentially expressed genes.

[0015] Preferably, in step S3.2, CREB1, ATF2 and FOS are screened as drug target genes.

[0016] Beneficial effects: The drug analysis method based on bio-omics provided by the present invention is carried out. The grouping is carried out according to specific criteria, samples are collected at preset stages, and multi-omics comprehensive analysis is performed. Metabolomics and transcriptomics are combined for each experimental group to screen metabolic and transcriptional co-enrichment pathways, and then the metabolic pathways, enriched metabolites and drug target genes, and interaction relationships in which the drugs play a role are analyzed. The potential mechanism of the drug in alleviating symptoms is analyzed by multi-omics combined technology, providing a reference for subsequent drug development and application.

[0017] The application of the bio-omics analysis method provided by the present invention in the analysis of drugs for menopausal syndrome, through metabolomics analysis, it is determined that the enrichment of differential metabolites in the dopaminergic synaptic pathway and the vitamin digestion and absorption pathway of the drug action has statistical differences. Through transcriptomics analysis, the importance of the dopaminergic synaptic pathway in the 21 pathways enriched with differentially expressed genes before and after drug treatment is determined. Combined with network pharmacology analysis, FOS, ATF2 and CREB1 genes are screened as target genes for drug action. The application of the present invention provides a research direction for the in-depth development of subsequent treatment plans for menopausal syndrome based on the mechanism of drug action. Not only does it enhance the validity of the research results, but it also helps to determine the regulatory pathways and drug target genes for future therapeutic interventions. In the clinical research part, a standardized scale is used to assess the anxiety level, rather than relying on subjective judgment methods, which enhances the accuracy of the research results. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings: Figure 1 This is a schematic diagram of the process of Example 1 of the present invention; Figure 2a-2b This is a schematic diagram of the analysis of the differential metabolite homovanillic acid in the two experimental groups before and after medication in Example 2 of the present invention; Figure 3a-Figure 3b This is a schematic diagram of the analysis of the differential metabolite menaquinone in the two experimental groups before and after medication in Example 2 of the present invention; Figure 4 A comprehensive network diagram of the interactions between differentially expressed genes and differential metabolites of the drug group of the present invention before and after medication; Figure 5 This is a schematic diagram of the relationship between the various drug components and signal pathways of the drug analyzed in the present invention. DETAILED DESCRIPTION

[0019] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. The principles and features of the present invention are described below in combination with the accompanying drawings. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other without conflict. The embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0020] Example 1 This example provides a method for drug analysis based on bio-omics, see Figure 1 , including the following steps: S1. Experimental grouping: participants were screened according to the preset inclusion criteria, and those who met the inclusion criteria were randomly divided into the drug group and the placebo group; S2. Experimental process and sample collection: samples were collected from the drug group and the placebo group at three time points: before medication, during the experiment, and at the end of the experiment; S3. Sample analysis, including: S3.1. Metabolomics analysis to screen out differential metabolites and differential metabolite enrichment pathways; S3.2, transcriptomic analysis, screening of differentially expressed genes and differentially expressed gene enrichment pathways; S3.3. Perform metabolomics and transcriptomics combined analysis on the drug group and the placebo group to screen for metabolic and transcriptional co-enriched pathways; S4. Analyze the metabolic pathways, enriched metabolites and drug target genes through which drugs exert their effects.

[0021] Furthermore, step S3 also includes: S3.4, performing network pharmacology analysis to analyze the relationship between drug components and signal pathways and the relationship between drug components and differentially expressed genes, and screening out drug target genes of active drug components.

[0022] Specifically, S1 includes: The screening criteria for participants to be included are preset according to the conditions of the analyzed drug treatments, including but not limited to factors such as the participants' age, health status, conditions of the analyzed drug treatments, mental conditions, and lifestyle habits.

[0023] A randomized design was used to divide participants into the drug group and the placebo group. The randomization schedule was generated using computer software. The randomization sequence was generated using SAS software (SAS Institute, Cary, North Carolina, USA). Participants and investigators were unaware of the group assignment throughout the study.

[0024] The drug used in the placebo group is a simulated agent of the real drug, which has the same visual and taste characteristics, as well as similar packaging and labels as the real drug used in the drug group, but does not have therapeutic functions and effects.

[0025] S2 includes: questionnaire scores and baseline scores for all participants, which are evaluated before and after medication, and clinical outcomes are determined by analyzing the changes in questionnaire scores and baseline scores before and after the treatment cycle. In the experiment, "in A" is the entry group of the drug group, and "out A" is the exit group of the drug group. Similarly, "in B" refers to the entry group of the placebo group, and "out B" refers to the exit group of the placebo group.

[0026] S3 includes: S3.1. Perform metabolic analysis using metabolite identification technology.

[0027] For example, serum samples were collected and diluted with LC-MS grade water to a final concentration of 53% methanol. The samples were then centrifuged at 15,000 g and 4°C for 20 min. Finally, the supernatant was injected into the LCMS / MS system for analysis. Metabolites were quantitatively analyzed using an ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) system. The raw data files generated by UPLC-MS were processed using Compound Discoverer3.1 (CD3.1, ThermoFisher), and peak alignment, peak picking, and quantification were performed for each metabolite. Afterwards, the peak intensity was normalized to the total spectral intensity. It was then compared with the mzCloud, mzVault, and Masslist databases to obtain accurate qualitative and relative quantitative results.

[0028] Metabolites were annotated using the KEGG database (https: / / www.genome.jp / kegg / pathway.html), HMDB database (https: / / hmdb.ca / metabolites), and LIPIDMaps database (http: / / www.lipidmaps.org / ). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed on metabolic data processing software, such as metax software. Univariate analysis (t-test) was used to calculate statistical significance ( P value). VIP>1 and P Metabolites with values ​​< 0.05 and fold change 22 or FCS 0.5 were considered differential metabolites. Volcano plots were generated using ggplot2 in R based on the log2 (fold change) and -log10 ( P The enriched metabolites were screened.

[0029] The comprehensive distribution of differential metabolites between the drug group and the placebo group before and after medication was obtained, including common and differential metabolites between the two groups, and the relationship between the differential metabolites; the comparative analysis results of differential metabolites between the drug group and the placebo group before medication, after medication, before and after medication, and before and after medication. KEGG pathway enrichment analysis was used to determine the main biological functions performed by differential metabolites. The enrichment results come from the KEGG pathway, and the hypergeometric test technology is used to identify the pathways that enrich the differential metabolites relative to the entire identified metabolite background. In this way, the differential metabolites involved in the main biochemical metabolism and signal transduction pathways are determined. Satisfy P KEGG pathways with a threshold value <0.05 were considered to be significantly enriched in the differential metabolites. Drug-related pathways and differential metabolites were obtained through analysis.

[0030] S3.2. Transcriptome sequencing and analysis.

[0031] First, library preparation and sequencing were performed. RNA integrity was assessed using the RNA kit (RNA Nano 6000 Assay Kit) of the Bioanalyzer2100 system (Agilent Technologies, CA, USA). Total RNA was used as input material for RNA sample preparation. Library preparation was performed using the TruSeg PE Cluster Kit v3 (Illumina). After evaluating the quality of the library, sequencing was performed on the Illumina Novaseg platform to generate 150 bp double-end sequences.

[0032] Analyze transcriptome data. The raw data were processed by fastp software, that is, sequences containing adapters, sequences containing ploy-N, and low-quality sequences were removed from the raw data to obtain high-quality data. Then, the cleaned double-end sequences were aligned with the reference genome GRCh38 / hg38 using the transcriptome alignment tool (Hisat2 v2.0.5). The number of sequences for each gene was counted using feature counting v1.5.0-p3. The FPKM (Reads Per Kilobase of exon model per Million mapped reads) of each gene was then calculated based on the length of each gene and the number of sequences of the gene. The DESeq2 R (1.20.0) software package was used to perform differential expression analysis on the data of the drug group and the placebo group. The Benjamini and Hochberg method was used to control the false discovery rate to adjust the obtained P Value. After adjustment, DESeq2 R (1.20.0) found P Genes with a value of ≤0.05 were classified as differentially expressed. Common gene enrichment analysis tools (such as the ClusterProfiler package) were used to perform enrichment analysis of differentially expressed genes, including Gene Ontology (GO) and KEGG enrichment analysis of differentially expressed genes, in which gene length bias was corrected. P GO or KEGG terms with values ​​< 0.05 were considered to be significantly enriched for differentially expressed genes.

[0033] Compare and analyze the differential gene enrichment in the drug action pathway between the drug group and the placebo group before and after medication. Differentially expressed genes were identified by FC (Fold Change) and value. The threshold was set to |log2FC]≥1, P The value was ≤0.05, and the number of differentially expressed genes was screened. The comprehensive distribution of differentially expressed genes in the two groups before and after medication was analyzed, including the genes that overlapped and were differentially expressed between the two groups, so as to understand the relationship between the differentially expressed genes in the two experimental groups. After further excluding the overlapping genes in the two groups, the differentially expressed genes in the drug group and the differentially expressed genes in the placebo group were analyzed. KEGG pathway enrichment analysis was then used to determine the main biological functions performed by the differentially expressed genes, including and using the hypergeometric test method to identify pathways that enriched the differentially expressed genes relative to all identified gene backgrounds, and then determine the main biochemical, metabolic and signal transduction pathways related to the differentially expressed genes. Finally, the enriched pathways corresponding to the specific biological process in the drug group were obtained.

[0034] S3.3. Perform a combined metabolomics and transcriptomics analysis on the drug group and the placebo group to analyze the signal transduction pathways in which the drug acts in both metabolomics and transcriptomics, as well as the changes in metabolites and the transformation of expressed genes.

[0035] S3.4. Network pharmacology analysis of drugs, including: (1) Screening out target genes related to the main components of the drugs from the network pharmacology database (Traditional Chinese Medicine Systems Pharmacology, TCMSP); (2) Intersecting the screened target genes related to the main components of the drugs with the differentially expressed genes obtained from the drug group analysis to screen out overlapping genes; (3) Using the KEGG database to perform pathway enrichment analysis on the overlapping genes to obtain significantly different pathways, and then screen out and analyze the main pathways of drug action; (4) Comprehensively analyzing the network relationship formed by overlapping genes, the main components of the drugs and the main pathways to obtain the genes that play a role in the main pathways, that is, the drug target genes.

[0036] Statistical analysis in the present invention includes: Statistical analysis of continuous variables with normal distribution is expressed as mean t standard deviation. Categorical variables are expressed as numbers and percentages. Comparison of continuous variables between groups was evaluated by analysis of variance. Depending on the sample size, Pearson chi-square test or Fisher's exact test was used to compare categorical data frequencies. For comparisons involving non-normally distributed data sets, Wilcoxon rank sum test was used. SAS software version 9.4 was used for statistical analysis. P <0.05 was considered statistically significant.

[0037] The present invention combines metabolomics and transcriptomics to analyze drug mechanisms. "Omics" encompasses a comprehensive approach to examine the complete molecular spectrum of cells and tissues, including various disciplines such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and epitranscriptomics. Transcriptomics is the study of the transcriptome, including gene structure, expression, and regulation, while metabolomics is the downstream of genomics, transcriptomics, and proteomics, and aims to characterize a large number of metabolites in biological systems that are related to pathophysiological interventions or genetic modifications. Through the method of the present invention, the potential mechanism of drug therapy is analyzed based on multi-omics, thereby providing a basis and ideas for future drug improvement or development of new drugs.

[0038] Example 2 This example provides an application of a bio-omics analysis method in the analysis of drugs for menopausal syndrome, see Figures 2a to 5 , the bio-omics analysis method comprises: S1. Experimental grouping: participants were screened according to the preset inclusion criteria, and those who met the inclusion criteria were randomly divided into the drug group and the placebo group; S2. Experimental process and sample collection: samples were collected from the drug group and the placebo group at three time points: before medication, during the experiment, and at the end of the experiment; S3. Sample analysis, including: S3.1. Metabolomics analysis to screen out differential metabolites and differential metabolite enrichment pathways; S3.2, transcriptomic analysis, screening of differentially expressed genes and differentially expressed gene enrichment pathways; S3.3. Perform metabolomics and transcriptomics combined analysis on the drug group and the placebo group to screen for metabolic and transcriptional co-enriched pathways; S3.4. Conduct network pharmacology analysis to analyze the relationship between drug components and signaling pathways. The relationship between drug ingredients and differentially expressed genes, screening out drug target genes of active drug ingredients; S4, analyzing the metabolic pathways through which drugs exert their effects, enriched metabolites and drug target genes.

[0039] The therapeutic drug used in this example is Xiangshao granules, which are produced by Sichuan Hairong Pharmaceutical Co., Ltd., China; other experimental reagents, unless otherwise specified, are commercially available products.

[0040] In this embodiment, a double-blind, randomized, placebo-controlled, multicenter design was used to study the effects of drugs on perimenopausal and postmenopausal Chinese women and their mechanisms of action based on transcriptomic analysis. 360 participants were randomly divided into two groups: one group took therapeutic drugs (n=180) and the other group took placebo (n=180) for 8 weeks. The main outcome measure was the Self-Rating Anxiety Scale (SAS) score, which was assessed at baseline, 4 weeks and 8 weeks. The main focus was on the changes in SAS scores observed after the 8-week intervention period. In addition, in a subset of 96 patients, blood samples were collected at enrollment and after 8 weeks of treatment for metabolomics and transcriptomics analysis, as well as network pharmacology analysis, to study the mechanism of influence on anxiety symptoms.

[0041] In the present embodiment, the therapeutic drug is taken as an example of Xiangshao Granules, which is a pure herbal medicine with Cyperus rotundus and White Peony Root as the main drugs, and Bupleurum Root, Chuanxiong Rhizoma and Toosendan Fructus as auxiliary drugs. In animal experiments and randomized controlled trials, it has been observed that it has a very strong safety and significant therapeutic effect in the treatment of perimenopausal symptoms, particularly the symptoms related to emotional symptoms. Existing studies believe that the regulation of drugs to women's perimenopausal depression symptoms, potential mechanisms may involve the increase of regulating serum neurotransmitter levels and hippocampal brain-derived neurotrophic factor (BDNF), which has been identified as an antidepressant factor. Bupleurum saponin A is considered to be the active ingredient related to these mechanisms. However, the mechanism of drug relief of perimenopausal anxiety symptoms needs further study.

[0042] Specific: Based on 360 participants, 180 of whom were assigned to the drug group and 180 to the placebo group. Participants in the two groups showed similar baseline characteristics, including age, menopausal status, and SAS scores. No statistically significant differences were shown in these variables. In the clinical analysis, the completion rate of the drug group was 165 / 180 (91.7%) and the completion rate of the placebo group was 162 / 180 (90.0%), of which 166 participants in the drug group and 165 participants in the placebo group completed at least 4 weeks of the study. A total of 96 patients were included in the omics study, providing comprehensive omics data for entry (in) and exit (out) samples. Each patient provided a transcriptome and a metabolome sample at two different time points before and after medication. A total of 384 omics samples were obtained, including 192 transcriptome samples and 192 metabolic samples. The entry group of the drug group was marked as "in A" and the exit group was marked as "out A". Similarly, the entry group of the placebo group was marked as "in B" and the exit group was called "outB". Through quality inspection, 4 transcriptome samples and 7 metabolome samples were found to be unqualified, and a total of 373 samples were included in the omics analysis.

[0043] After the two groups of participants were treated with medication according to the above experimental process, the changes in anxiety symptoms in both groups were significantly lower than before treatment ( P <0.0001), the drug group scored 47.7±8.78 at 4 weeks and 44.7±7.94 at 8 weeks; the placebo group scored 47.8±8.27 at 4 weeks and 47.2±8.79 at 8 weeks. After 8 weeks, the SAS score of the drug group was significantly lower than that of the placebo group ( P =0.0086). The proportion of participants in the drug group who were classified as "cured" increased from 58.6% at 4 weeks to 69.8% at 8 weeks. The cure rates in the placebo group were 54.0% and 56.9% at the 4-week and 8-week time points, respectively. The clinical improvement in both groups after 8 weeks was statistically significant ( P =0.0151).

[0044] The metabolites of the drug group and the placebo group before and after medication were compared and analyzed by VIP (Variable Importance in The Projection), FC (Fold Change) and P The three parameters were used to identify differential metabolites. VIP was calculated by PLS-DA model, referring to the contribution of metabolites to the group. FC was defined as the ratio of each metabolite to the mean of the quantitative values ​​of all biological replicates between groups. P The values ​​were calculated using t-test, indicating that the difference was significant. The threshold was set as VIP>1.0, FC>1.2 or |log2FC]≥0.26,P Value <0.05. The number of differential metabolites screened is shown in Table 1. Compared with the placebo group, the number of metabolite types in the drug group before and after medication increased significantly. In addition, 28 differential metabolites were screened in the drug group and 9 differential metabolites in the placebo group, of which 6 were common metabolites between the drug group and the placebo group.

[0045] Table 1 The number of metabolites with different concentrations between the drug group and the placebo group

[0046] KEGG pathway enrichment analysis was used to determine the main biological functions performed by differential metabolites. The enrichment results were derived from the KEGG pathway, and the hypergeometric test technique was used to identify the pathways that enriched the differential metabolites relative to the entire identified metabolite background. In this way, the differential metabolites involved in the main biochemical metabolism and signal transduction pathways were determined. P KEGG pathways with a value <0.05 threshold were considered to be significantly enriched in the differential metabolites. Referring to Table 2, through KEGG pathway enrichment analysis, this example identified two statistically significant pathways, namely: dopaminergic synapse ( P <0.05), vitamin digestion and absorption ( P <0.05, and tyrosine metabolism ( P =0.053) showed the same trend.

[0047] Table 2 KEGG pathway analysis results of differential metabolite enrichment

[0048] Among them, the dopaminergic synaptic pathway and the vitamin digestion and absorption pathway are more prominent. Figure 2a-2b and Figure 3a-Figure 3b , showing the differences in metabolites between the drug group and the placebo group before and after medication, Figure 2a-2b The metabolite homovanillic acid is shown, Figure 3a-Figure 3b The metabolite mephenone is shown. Through pathway analysis, the enriched metabolites of dopaminergic synaptic pathway, mephenone, vitamin digestion and absorption, and tyrosine pathway were obtained.

[0049] The differential gene enrichment in the dopaminergic synaptic pathway between the drug group and the placebo group before and after medication was compared and analyzed. Differentially expressed genes were identified by FC (Fold Change) and values. The threshold was set to |log2FC]≥1, PThe value was ≤0.05, and the number of differentially expressed genes screened was shown in Table 3. Compared with the placebo group, the number of differentially expressed genes in the drug group increased significantly. In addition, 101 overlapping genes were screened in the drug group and the placebo group. After excluding the overlapping genes, there were 1136 differentially expressed genes in the drug group and 602 differentially expressed genes in the placebo group.

[0050] Table 3 Number of differentially expressed genes between the drug group and the placebo group

[0051] KEGG pathway enrichment analysis was used to determine the main biological functions performed by differentially expressed genes. Enrichment results were derived from KEGG pathways, and the hypergeometric test method was used to identify pathways that were enriched for differentially expressed genes relative to all identified gene backgrounds. This was used to determine the main biochemical, metabolic, and signal transduction pathways associated with differentially expressed genes. A total of 11 enriched pathways corresponding to specific biological processes were shown in the drug group, while only 2 enriched pathways were shown in the placebo group.

[0052] Metabolomics and transcriptomics were combined for the drug group and the placebo group. Among them, there were two enriched pathways in the drug group, namely the dopaminergic synaptic pathway and the vitamin digestion and absorption pathway. Before and after medication, the dopaminergic synaptic pathway showed significant changes in both transcriptomics and metabolomics analysis ( P Value < 0.05), while vitamin digestion and absorption pathways showed significant differences only in metabolomics analysis ( P Value < 0.05), there was no statistical difference in transcriptomic analysis ( P Value < 0.75). No transcriptional metabolic co-enriched pathways were shown in the placebo group. Figure 4 , showing the comprehensive network diagram of the differentially expressed genes and differential metabolites in the drug group before and after medication. Among them, the genes are represented by ovals, namely LMBRD1, PLCB4, PPP2R5C, GNG2, CREB1, GNB4, PLCB1, ATF2, FOS, GSK3A, PPP2R2A, PPP2CA, PPP1CB, PRKACB, GNAI3, GNG3, and DRD4. The darker the background color of the ovals, the higher the difference in transcriptome analysis. P The smaller the value, the higher the significance; the signal pathways are represented by rectangles, which are the dopaminergic synaptic pathway, vitamin digestion and absorption pathway, and tyrosine pathway, and the metabolites are represented by hexagons, which are homovanillic acid and mephenone. The dark connecting lines represent the correlation between the signal pathway and the gene and metabolite, and the light connecting lines represent the correlation between the metabolite and the gene.

[0053] The network pharmacology analysis of the drug includes: (1) screening target genes related to the main ingredients of the drug from the network pharmacology database (Traditional Chinese Medicine Systems Pharmacology, TCMSP), and based on the drug group of this embodiment, screening and identifying 590 target genes corresponding to the active small molecules of the main ingredients of the drug; (2) after intersecting the 590 target genes with the 1237 differentially expressed genes in the drug group, screening out 24 overlapping genes, wherein the drug ingredients include white peony root, cyperus rotundus, ligusticum chuanxiong, fructus aurantii, pinellia tuber, cardamom, costus root, and liquorice; the 24 overlapping genes are TERF1, PTGS2, CHUK, PPARD, KCNH2, ATF2, GLYATL1, ME TAP2, DMPK, CDKN2AIP, RELA, PPARG, NOS3, PCNA, ACSL4, ALB, CXCL10, FASN, JAK2, HIF1A, CREB1, CXCL8, AHR, FOS; (3) Pathway enrichment analysis was performed on these 24 overlapping genes using the KEGG database, and 15 significantly different pathways were obtained. The top 15 enriched pathways of the overlapping genes were ranked by the difference value, and one of the important pathways was the dopaminergic synaptic pathway; (4) A comprehensive analysis of the network relationship formed by the 24 overlapping genes, the main components of the drug, and the dopaminergic synaptic pathway. See Figure 5 As shown in the figure, the drug components are represented by green diamonds, including white peony root, cyperus rotundus, Chuanlianzi, bupleurum, chuanxiong, fructus aurantii, pinellia tuber, cardamom, costusroot, and liquorice; the dopaminergic synaptic pathway is represented by a purple rectangle, and the genes are represented by ovals, including CXCL10, CXCL8, CHUK, FASN, NOS3, AHR, PPARD, HIF1A, PPARG, FOS, PTGS2, KCNH2, RELA, CDKN2AIP, METAP2, DMPK, ALB, ATF2, ACSL4, JAK2, TERF1, GLYATL1, PCNA, and CREB1; the size and font of the graphic represent the degree value, and the larger the size, the higher the degree value. As can be seen in the figure, the genes that play a role in the dopaminergic synaptic pathway, namely FOS, ATF2, and CREB1, are highlighted in yellow. See Table 4, which shows the correlation between the three target genes in the dopaminergic synaptic pathway and the drug components.

[0054] Table 4 Relationship between target genes and drug components involved in dopaminergic synaptic pathways

[0055] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A drug analysis method based on bioomics, characterized in that: The steps include: S1. Experimental grouping: participants were screened according to the preset inclusion criteria, and those who met the inclusion criteria were randomly divided into the drug group and the placebo group; S2. Experimental process and sample collection: samples were collected from the drug group and the placebo group at three time points: before medication, during the experiment, and at the end of the experiment; S3. Sample analysis, including: S3.

1. Metabolomics analysis to screen out differential metabolites and differential metabolite enrichment pathways; S3.2, transcriptomic analysis, screening of differentially expressed genes and differentially expressed gene enrichment pathways; S3.

3. Perform metabolomics and transcriptomics combined analysis on the drug group and the placebo group to screen for metabolic and transcriptional co-enriched pathways; S4. Analyze the metabolic pathways, enriched metabolites and drug target genes through which drugs exert their effects.

2. The drug analysis method according to claim 1, characterized in that: Also includes: Step S3.4: Perform network pharmacology analysis to analyze the relationship between drug components and signal pathways and the relationship between drug components and differentially expressed genes, and screen out drug target genes of active drug components.

3. The drug analysis method according to claim 1, characterized in that: The step S3.1 comprises: S3.1.

1. After processing the samples, perform quantitative metabolite analysis; S3.1.2, through VIP value, FC value and P The three parameters were used to identify the differential metabolites; S3.1.

3. Perform KEGG enrichment analysis on differential metabolites.

4. The drug analysis method according to claim 1, characterized in that: The step S3.2 comprises: S3.2.1, transcriptome library preparation and sequencing; S3.2.

2. Data analysis, using FC values ​​and P The values ​​were screened to find differentially expressed genes between the drug group and the placebo group; S3.2.

3. KEGG pathway enrichment analysis obtained differentially expressed genes.

5. The drug analysis method according to claim 1, characterized in that: The step S3.3 includes conducting metabolomics and transcriptomics analysis on the drug group before and after medication, the placebo group before and after medication, the drug group and the placebo group before medication, and the drug group and the placebo group after medication.

6. Application of bio-omics analysis method in drug analysis of menopausal syndrome, characterized in that: The biomics analysis method comprises: S1. Experimental grouping: participants were screened according to the preset inclusion criteria, and those who met the inclusion criteria were randomly divided into the drug group and the placebo group; S2. Experimental process and sample collection: samples were collected from the drug group and the placebo group at three time points: before medication, during the experiment, and at the end of the experiment; S3. Sample analysis, including: S3.

1. Metabolomics analysis to screen out differential metabolites and differential metabolite enrichment pathways; S3.2, transcriptomic analysis, screening of differentially expressed genes and differentially expressed gene enrichment pathways; S3.

3. Perform metabolomics and transcriptomics combined analysis on the drug group and the placebo group to screen for metabolic and transcriptional co-enriched pathways; S3.

4. Conduct network pharmacology analysis to analyze the relationship between drug components and signal pathways and between drug components and differentially expressed genes, and screen out drug target genes of active drug components; S4. Analyze the metabolic pathways, enriched metabolites and drug target genes through which drugs exert their effects.

7. The use according to claim 6, characterized in that: The step S3.1 comprises: S3.1.

1. After processing the samples, perform quantitative metabolite analysis; S3.1.2, through VIP value, FC value and P The three parameters were used to identify the differential metabolites; S3.1.

3. Perform KEGG enrichment analysis on differential metabolites.

8. The use according to claim 7, characterized in that: The step S3.1 screens and obtains the metabolite homovanillic acid enriched in the dopaminergic synaptic pathway, the metabolite menaquinone enriched in the vitamin digestion and absorption pathway, and the metabolite homovanillic acid enriched in the tyrosine metabolic pathway.

9. The use according to claim 6, characterized in that: The step S3.2 comprises: S3.2.1, transcriptome library preparation and sequencing; S3.2.

2. Data analysis, using FC values ​​and P The values ​​were screened to find differentially expressed genes between the drug group and the placebo group; S3.2.

3. KEGG pathway enrichment analysis obtained differentially expressed genes.

10. The use according to any one of claims 6 to 9, characterized in that: The drug action pathway screened out was the dopaminergic synaptic pathway, the differential metabolite was homovanillic acid holmenazone, and the drug target genes were CREB1, ATF2 and FOS.

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