Method for evaluating specific toxicity risk of liquid crystal monomer based on combination of network toxicology and concentration

Abstract

The invention discloses a method for evaluating the specific toxicity risk of a liquid crystal monomer based on network toxicology and concentration combination, and belongs to the field of environmental science. The specific toxicity risk of the liquid crystal monomer is evaluated based on the actual occurrence concentration of the liquid crystal monomer in the environment in combination with network toxicology. Comprising the steps of detecting liquid crystal monomer concentration occurrence in an environment sample; obtaining a liquid crystal monomer action target gene; acquiring a target gene corresponding to specific toxicity, and performing protein network interaction and degree calculation on the target gene; and carrying out content weight assignment on the target spot, and screening the key pollutants by taking the content weight as a toxicity score. Compared with the prior art, when the network toxicology is used for researching the toxicity risk of the liquid crystal monomer, the influence of the concentration on a potential target spot is considered, the content of toxic components and the frequency of acting on the target spot by the components are combined with a target spot degree value, and a traditional qualitative network is converted into a quantitative network, so that the reliability of a research result is improved; a new thought is provided for evaluating the toxicity risk of the liquid crystal monomer.

Description

Technical Field

[0001] The present invention belongs to the field of environmental science and technology, and specifically relates to a method for evaluating the specific toxicity risk of liquid crystal monomers based on a combination of network toxicology and the environmental occurrence concentration of liquid crystal monomers. Background Art

[0002] Liquid crystal monomers (LCMs) are a class of organic compounds with diphenyl or dicyclohexane as the backbone structure. They are widely used in liquid crystal displays of various electronic products, and their production and use have continued to grow year by year. It is estimated that the demand for LCMs used in liquid crystal displays has increased to about thousands of tons. They show characteristics such as persistence, long-range migration, bioaccumulation and biotoxicity in the environment. They are a new type of persistent organic pollutants, posing potential risks to the ecological environment and human health. At present, the presence of liquid crystal monomers has been detected in media such as sediments, landfill leachate, indoor and outdoor dust, air and biological samples such as human blood, among which the highest concentration is found in the atmosphere and dust. There are also reports that liquid crystal monomers in the environment may enter the human body through the food chain, respiratory inhalation, dietary intake, etc., thereby causing adverse effects on the reproductive system. Network toxicology refers to the construction of a specific network model, describing the toxicological properties of the research object, and using network analysis to predict the toxicity of compounds, so as to understand the toxic and side effects of compounds on the body and predict the main compounds that exert toxicity.

[0003] Current approaches to studying the specific toxicity risks of liquid crystal monomers have the following problems:

[0004] (1) Traditional methods for studying the toxicity risk of liquid crystal monomers involve establishing animal models or cell models, which consume a lot of manpower, material resources and financial resources. Therefore, it is urgent to develop new methods to evaluate the toxicity risk of liquid crystal monomers.

[0005] (2) There are relevant literatures on the use of network toxicology to study the toxic risks of liquid crystal monomers. However, most studies only incorporate qualitative analysis results when constructing component-target-effect pathway networks using network toxicology, ignoring the role of the effector biological network of each pollutant concentration in the environment, which affects the accuracy of the analysis results. Therefore, there is an urgent need for in-depth exploration and application of quantitative network toxicology technology. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a more comprehensive assessment method for the specific toxicity risk of liquid crystal monomers in the environment, thereby promoting the improvement of liquid crystal monomer toxicity assessment. The method includes detecting the concentration of liquid crystal monomers in actual environmental samples, collecting liquid crystal monomer structural information and its target, and toxic gene targets, and the intersection of the target with the liquid crystal monomer is regarded as a potential target for the toxicity of the liquid crystal monomer. The obtained potential targets are imported into the STRING database for protein interaction analysis, and the generated results are imported into the Cytoscape software, and the degree centrality (Degree) is calculated using the CytoNCA plug-in. At the same time, the peak area weight of each liquid crystal monomer is calculated according to the content of the liquid crystal monomer in the actual environment, and the weight is used to assign weights to the targets, and the number of times each target is acted upon by different components is counted.

[0007] The CytoNCA plug-in was used to calculate the degree centrality, betweenness centrality, and closeness centrality, and the targets with values ​​greater than the median were selected as core targets, and the targets were sorted by degree value to obtain the key targets for the toxicity of liquid crystal monomers. A network of liquid crystal monomers and targets with specific toxicity was constructed, and the product of node connectivity and content weight was used as the toxicity score to screen the key hazards of liquid crystal monomers. The Metascape database was used to perform gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the core targets. The potential mechanism of toxicity risk of liquid crystal monomers was obtained through GO analysis and KEGG enrichment analysis.

[0008] The specific toxicity described in the above scheme is at least one of neurotoxicity and reproductive toxicity.

[0009] In order to achieve the above object, the present invention provides a method for evaluating the specific toxicity risk of liquid crystal monomers based on combining network toxicology with the concentration of liquid crystal monomers in the actual environment. The specific steps are as follows:

[0010] 1. Obtaining the structure of liquid crystal monomers: Look up the Chemical Abstracts Service (CAS) code of the liquid crystal monomer, enter the CAS number in the organic small molecule biological activity data (Pubchem), and obtain the two-dimensional simplified molecular input line system (SMILES) structure of the liquid crystal monomer.

[0011] 2. Liquid crystal monomer target acquisition: The SMILES structure of the liquid crystal monomer was input into the SwissTargetPrediction database, the species was set to "Homo sapiens" to predict the targets of environmental pollutants, and the target statistics were performed using the common name.

[0012] 3. Acquisition of specific toxic gene targets: Enter the keyword "Reproductive toxicity" in the GeneCards database, predict the targets related to specific toxicity, sort them in descending order according to the size of the "score", and select genes with a "score" value higher than the median to establish a specific toxic target library.

[0013] 4. Acquisition of common targets of liquid crystal monomer toxicity: A Venn diagram is established using the liquid crystal monomer targets and specific toxicity targets obtained above. The intersection of the two is the common target of liquid crystal monomer targets and specific toxicity. The common target is considered to be a potential target of liquid crystal monomers causing specific toxicity.

[0014] 5. Construction of protein interaction (PPI) network and acquisition of core targets: The common targets of the intersection of the obtained liquid crystal monomer targets and specific toxicity were imported into the STRING database to establish a PPI network, and the protein type was set to human for retrieval. The minimum required interaction confidence (Minimum required interaction score) was set to "HighConfidence>0.7", the network type was "full STRING network", the meaning of the network node was "evidence", and the free targets were deleted to establish the PPI network. Then the results were imported into Cytoscape3.10.1, and the degree centrality (Degree, DC), node betweenness (Betweenness, BC) and closeness centrality (Closeness, CC) values ​​were calculated using the CytoNCA plug-in. Targets with Degree, Betweenness and Closeness values ​​greater than the median were selected as the core targets of liquid crystal monomer toxicity, and the core target PPI network was established to show the interaction relationship between key targets. The larger the node, the higher the degree value, indicating that the protein is more strongly related to toxicity.

[0015] 6. Detection and weight calculation of liquid crystal monomers in dust: The concentration of 70 liquid crystal monomers in dust was detected using a comprehensive two-dimensional gas chromatography time-of-flight mass spectrometer (GC×GC-TOF-MS), and the weight of each substance was calculated based on its content.

[0016] 7. Toxic component-target acquisition: Using the previously acquired targets, assign weights to the Degree, Betweenness and Closeness values ​​of the targets according to the calculated weights, and count the number of times each target is acted upon by different components. Then, sort the shared targets according to their weight values, and select targets with Degree, Betweenness and Closeness weight values ​​≥ the median as potential key targets for the next step of analysis.

[0017] 8. Reconstruct the protein interaction (PPI) network: import the potential targets obtained in step 7 into the STRING database, set the protein type to "Homosapiens", set the "Minimum required interaction score" to "High Confidence>0.7", the network type to "full STRING network", the meaning of the network node to "evidence", and delete the free targets for PPI analysis. Import the PPI network into Cytoscape3.10.1 in "TSV" format for visualization and drawing the PPI network diagram to show the interaction relationship between key targets. The higher the degree value and the larger the node, the stronger the correlation between the protein and reproductive toxicity.

[0018] 9. GO functional enrichment analysis and KEGG pathway enrichment analysis to explore its potential reproductive toxicity mechanism: Metascape database was used to conduct GO functional enrichment analysis and KEGG pathway enrichment analysis on the core targets of liquid crystal monomers that cause reproductive toxicity, further study the biological characteristics of liquid crystal monomers that cause reproductive toxicity, and determine their potential mechanisms of action. At the same time, the weighted degree value was used as the liquid crystal monomer toxicity score and sorted, and the top four compounds were used as the key toxicants in the liquid crystal monomers.

[0019] The present invention provides a method for evaluating the specific toxicity risk of liquid crystal monomers based on the combination of network toxicology and concentration, which has the following beneficial effects and advantages:

[0020] 1. The method for assessing the specific toxicity risk of liquid crystal monomers based on network toxicology combined with concentration described in the present invention performs data mining and analysis through a database, relies on the STRING platform to perform protein-protein interactions, screens key targets, constructs a key target interaction network and GO functional analysis and KEGG enrichment analysis of key targets, uses the weighted degree value as the liquid crystal monomer toxicity score and sorts it, thereby obtaining the key hazards in the liquid crystal monomer, providing a new idea for assessing the adverse health risks of liquid crystal monomers.

[0021] 2. Compared with traditional network toxicology, this study combined the occurrence concentration of liquid crystal monomers in the actual environment, and converted the traditional qualitative network into a quantitative network by assigning target weights based on the content of toxic components and the frequency of the components' action on the targets, thereby improving the reliability of the research results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Flowchart of the study on the method of assessing the specific toxicity risk of liquid crystal monomers based on network toxicology combined with concentration.

[0023] Figure 2This is a Venn diagram of liquid crystal monomer targets and reproductive toxicity targets.

[0024] Figure 3 PPI network diagram of core targets.

[0025] Figure 4 This is a GO function enrichment analysis diagram.

[0026] Figure 5 This is the KEGG signaling pathway enrichment analysis diagram.

[0027] Figure 6 This is a diagram of the liquid crystal monomer-target-pathway. DETAILED DESCRIPTION

[0028] In order to make the purpose / technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. The following embodiments are only further explanations and descriptions of the present invention. For those of ordinary skill in the art, all other embodiments obtained without creative work are within the scope of protection of the present invention.

[0029] Example 1: Method for evaluating the reproductive toxicity risk of liquid crystal monomers based on network toxicology combined with concentration

[0030] 1. Liquid crystal monomer structure and target acquisition

[0031] The SMILES structures of 70 liquid crystal monomers were obtained from the PubChem database. The SMILES structures of the liquid crystal monomers were input into the SwissTargetPrediction database, the species was set to Homo sapiens, and the targets (Commonname) of the liquid crystal monomers were predicted, and a total of 780 targets were obtained.

[0032] 2. Acquisition of reproductive toxicity targets

[0033] The keyword "Reproductivetoxicity" was entered into the Gene Cards database to predict targets related to reproductive toxicity. The category was set as protein coding, and 7309 toxicity targets were obtained.

[0034] 3. Acquisition of common targets of liquid crystal monomers and reproductive toxicity

[0035] The obtained targets of liquid crystal monomers and reproductive toxicity targets were input into the Venn diagram platform to construct a Venn diagram to determine the common targets of reproductive toxicity and liquid crystal monomers. A total of 600 potential targets were obtained, and these 600 potential targets were considered to be potential reproductive toxicity targets of liquid crystal monomers ( Figure 2 ).

[0036] 4. Protein-protein interaction (PPI) network construction

[0037] The common targets of liquid crystal monomers and reproductive toxicity were imported into the STRING database, the protein type was set to Homosapiens, the minimum required interaction score was set to "High Confidence>0.7", the network type was "full STRING network", the meaning of the network node was "evidence", and the free targets were deleted to establish the PPI network. The PPI network was imported into the software Cytoscape3.10.1 in "TSV" format, and the CytoNCA plug-in was used for topological analysis through "tools-network analyzer-network analysis" to calculate the degree centrality (Degree), betweenness centrality (Betweenness) and closeness centrality (Closeness) values, respectively, and the calculation formula is as follows:

[0038] The degree centrality calculation formula is as follows:

[0039]

[0040] Where: k i Indicates the number of edges connected to the existing node i;

[0041] N-1 represents the number of edges connecting node i to other nodes.

[0042] The calculation formula of the betweenness centrality of a node is as follows:

[0043]

[0044] in: represents the number of shortest paths passing through node i;

[0045] g st Represents the number of shortest paths connecting s and t.

[0046] The calculation formula of the node's closeness centrality is as follows:

[0047]

[0048] Where: d ij Represents the distance between two nodes, d i Represents the average distance from the node to the rest of the points;

[0049] The inverse of the average distance is the closeness centrality.

[0050] 5. Detection and weight calculation of liquid crystal monomers in dust

[0051] The concentration of 70 liquid crystal monomers in dust was detected by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF-MS), and the weight of each substance was calculated according to its content. The weight calculation results are shown in Table 1.

[0052] 6. Toxic component-target acquisition

[0053] Using the targets obtained above, the Degree, Betweenness and Closeness values ​​of the targets were weighted according to the weights calculated in Table 2, and the number of times each target was acted upon by different components was counted. The shared targets were then sorted according to their weight values, and targets with Degree, Betweenness and Closeness weight values ​​≥ the median were selected as potential key targets for the next step of analysis.

[0054] 7. Reconstruct the protein-protein interaction (PPI) network

[0055] The potential targets obtained in step 6 were imported into the STRING database, the protein type was set to "Homosapiens", the "Minimum required interaction score" was set to "High Confidence>0.7", and the other parameters were the same as in step 4. The free targets were deleted and the PPI analysis was performed again. The PPI network was imported into Cytoscape 3.10.1 in "TSV" format for visualization and drawing of the PPI network diagram ( Figure 3 ), showing the interaction relationship between key targets. The node color reflects the degree of the target. The higher the degree value, the darker the node color, indicating that the protein is more strongly related to reproductive toxicity.

[0056] 8.GO functional enrichment analysis and KEGG pathway enrichment analysis to explore its potential reproductive toxicity mechanism

[0057] Metascape database was used to perform GO functional enrichment analysis and KEGG pathway enrichment analysis on the core targets of liquid crystal monomers causing reproductive toxicity, to further study the biological characteristics of liquid crystal monomers causing reproductive toxicity and determine their potential mechanisms of action. GO functional enrichment analysis obtained a total of 263 statistically significant biological functions, and the GO terms were sorted according to P-value, and the top 10 terms with the lowest P-value in biological processes, cellular components, and molecular functions were selected for visualization ( Figure 4). A total of 236 biological processes that the screened targets may be involved in were counted, among which the top ones include response to reactive oxygen species, influence on mammary epithelial development, positive regulation of cell migration, regulation of miRNA transcription, etc.; 6 cellular components mainly involve cell fronts, cell cilia, transcriptional regulatory complexes and cell axons; 21 molecular functions mainly include protein kinase binding, nitric oxide synthase regulation activity, DNA transcription factor binding, and phosphatase binding. KEGG pathway enrichment analysis obtained a total of 46 statistically significant signal pathways, and the KEGG pathways were also sorted according to P-value, and the top 20 items with the lowest P-value were selected for visualization ( Figure 5 ). The KEGG pathway enrichment results were mainly closely related to the five signaling pathways: estrogen signaling pathway, cancer pathway, endocrine disruption signaling pathway, and lipid and atherosclerosis pathway.

[0058] Among them, the six core targets of AKT1, BCL2, EGFR, ESR1, HSP90AA1, and SRC are all involved in the estrogen signaling pathway, further confirming that the estrogen signaling pathway is of great significance in the potential mechanism of liquid crystal monomer-induced reproductive toxicity. Other pathways are also closely related to reproductive toxicity, so the "liquid crystal monomer-target-pathway" map was constructed ( Figure 6 ), which more intuitively reflects the connection between liquid crystal monomers and core targets, as well as the interaction between core targets and potential pathways of reproductive toxicity. GO functional enrichment analysis and KEGG signaling pathway enrichment analysis showed that liquid crystal monomers can participate in affecting mammary epithelial development, nitric oxide biosynthesis and phosphatase binding biological processes and induce reproductive toxicity by regulating estrogen signaling pathways.

[0059] At the same time, the weighted degree values ​​were used as the toxicity scores of liquid crystal monomers and ranked, and the top four compounds were used as the key hazards of reproductive toxicity in liquid crystal monomers (Table 2). The results showed that the top four liquid crystal monomers that played a major role were 3cHCaA2OP, 3cHBzoFCP, 4PMeOPa, and 5bcHCaAFP.

[0060] Table 1 Liquid crystal monomer concentration in dust and weight of each pollutant

[0061]

[0062]

[0063]

[0064] Table 2 Liquid crystal monomer value toxicity score

[0065]

[0066]

Claims

1. A method for evaluating the specific toxicity risk of liquid crystal monomers based on network toxicology combined with concentration, characterized in that: The following steps are involved: 1). Obtaining the structure of liquid crystal monomers: Look up the CAS code of the liquid crystal monomer, enter the CAS number in the organic small molecule biological activity data Pubchem, and obtain the two-dimensional simplified molecular input line system structure SMILES of the liquid crystal monomer; 2). Input the SMILES structure of the liquid crystal monomer into the SwissTargetPrediction database to predict the target of the liquid crystal monomer and perform target statistics by common name; 3). Enter the keyword "Reproductive toxicity" in the GeneCards database to predict the targets associated with specific toxicity; 4). Establish a Venn diagram for the targets obtained in step 2) and step 3), and the intersection of the two is the potential target of the liquid crystal monomer causing specific toxicity; 5). Import the obtained potential targets into the STRING database to establish a PPI network, and then import the results into Cytoscape3.10.1 to calculate the degree centrality and the node betweenness and closeness value; 6). Detection and weight calculation of liquid crystal monomers in dust: Use comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to detect the concentration of 70 liquid crystal monomers in dust, and calculate the weight of each substance based on its content; 7) Based on the acquired potential targets, weight the Degree, Betweenness and Closeness values ​​of the targets according to the weights calculated in step 6), and count the number of times each target is acted upon by different components; Then, the shared targets were sorted according to their weight values, and the targets with Degree, Betweenness, and Closeness weight values ​​≥ the median were selected as potential key targets.

2. The method according to claim 1, characterized in that: 8). Import the key targets obtained in step 7) into the STRING database for PPI analysis; import the PPI network into Cytoscape3.10.1 to visualize the PPI network diagram and display the interaction relationship between the key targets.

3. The method according to claim 1, characterized in that: 9). Use the Metascape database to perform GO functional enrichment analysis and KEGG pathway enrichment analysis on the targets obtained in step 7) to study the biological characteristics of the toxicity risk of liquid crystal monomers and determine their potential mechanisms of action; use the weighted degree values ​​as the toxicity score of the liquid crystal monomers and rank them, and the top four compounds are used as the key hazards causing toxicity risks in liquid crystal monomers.

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