Use of pou2f2 gene or protein in screening products for preventing or treating atherosclerosis

Abstract

The present application relates to the field of medicine, in particular to the use of POU2F2 gene or protein in screening products for preventing or treating atherosclerosis. The present application first discloses that POU2F2 can be used as a key pathogenic target of atherosclerosis, and on this basis, a candidate drug linagliptin is screened. The experimental results show that in the in vitro atherosclerosis model, linagliptin can significantly inhibit the expression of POU2F2 and down-regulate the levels of inflammatory factors and adhesion molecules. The findings clarify the mechanism of linagliptin in regulating POU2F2 related signal pathways, inhibiting vascular inflammatory response and reducing vascular endothelial damage, and fully show that it has a good clinical application prospect in preparing drugs for preventing or treating atherosclerosis and its related cardiovascular and cerebrovascular diseases.

Description

Technical Field

[0001] This invention relates to the pharmaceutical field, and in particular to the use of the POU2F2 gene or protein in screening products for the prevention or treatment of atherosclerosis. Background Technology

[0002] Atherosclerosis (AS) and the cardiovascular diseases it causes (such as coronary heart disease and cerebral infarction) are major causes of death worldwide. With an aging population and the prevalence of high-fat diets and sedentary lifestyles, the incidence of these diseases continues to rise, placing a heavy burden on global public health systems. Endothelial cell damage and dysfunction are considered the initiating steps in the development of atherosclerosis, leading to inflammatory responses and upregulation of adhesion molecules, which promote lipid deposition and plaque formation.

[0003] Currently, drug development for atherosclerosis largely focuses on regulating lipid metabolism disorders and intervening in classical signaling pathways in inflammatory responses. Common treatment strategies include lowering blood lipid levels with statins or targeting classical inflammatory pathways such as NF-κB, JAK-STAT, and TNFα. However, these signaling pathways have numerous branches and complex regulation within the body, making it difficult to achieve ideal therapeutic effects by directly targeting them.

[0004] With the development of systems biology, researchers have gradually recognized that transcription factors (TFs), as key upstream nodes regulating inflammatory gene networks, are more advantageous intervention targets. As key regulators of gene expression, transcription factors can simultaneously regulate multiple downstream target genes involved in inflammatory responses and endothelial dysfunction. Therefore, targeting key transcription factors holds promise for achieving synergistic regulation of multiple downstream inflammatory pathways from the source, providing new ideas and strategies for the treatment of atherosclerosis and related diseases.

[0005] However, targeting transcription factors directly for intervention faces significant scientific challenges in practice. First, transcription factors typically possess a broad gene regulatory spectrum, and their downstream target genes often involve antagonistic or even diametrically opposed biological processes, meaning that inhibiting a particular transcription factor may produce unintended biological effects. Second, intracellular inflammatory regulatory networks exhibit high robustness and compensatory mechanisms; when the function of a transcription factor is suppressed, the cell may activate functionally overlapping alternative transcription factors to maintain the transcriptional activity of downstream inflammatory genes.

[0006] Currently, there is a lack of research on ideal transcription factor targets for the prevention and treatment of atherosclerosis and related diseases. Summary of the Invention

[0007] In view of the shortcomings of the prior art described above, and in order to solve the problem that there is a lack of ideal transcription factor targets for the prevention and treatment of atherosclerosis in the prior art, the purpose of this application is to provide the use of the POU2F2 gene or protein in screening products for the prevention or treatment of atherosclerosis, so as to solve the problems in the prior art.

[0008] To achieve the above and other related objectives, this application first provides the use of the POU2F2 gene or protein in screening any of the following products:

[0009] 1) Products for the prevention or treatment of atherosclerosis;

[0010] 2) Products for the prevention or treatment of atherosclerotic cardiovascular diseases;

[0011] 3) Products for the prevention or treatment of vascular endothelial dysfunction and its related complications.

[0012] This application further provides the use of the POU2F2 inhibitor in the preparation of any of the following products:

[0013] 1) Products for the prevention or treatment of atherosclerosis;

[0014] 2) Products for the prevention or treatment of atherosclerotic cardiovascular diseases;

[0015] 3) Products for the prevention or treatment of vascular endothelial dysfunction and its related complications.

[0016] This application subsequently provides for the use of the POU2F2 inhibitor in the preparation of any of the following products:

[0017] I. Products that inhibit the transcription of inflammatory cytokine genes in arterial endothelial cells;

[0018] II. Products that inhibit the transcription of adhesion molecule genes in arterial endothelial cells;

[0019] III. Products that inhibit the expression of inflammatory factors in arterial endothelial cells;

[0020] IV. Products that inhibit the expression of adhesion molecules in arterial endothelial cells.

[0021] This application then provides a method for screening potential products for the prevention or treatment of atherosclerosis, atherosclerotic cardiovascular disease, or vascular endothelial dysfunction and their related complications, comprising the following steps:

[0022] 1) Obtain the molecular structure of the candidate product;

[0023] 2) Molecular docking simulation was performed between the candidate product and the POU2F2 protein to obtain the optimal binding energy and binding region information;

[0024] 3) Result determination: If the optimal binding energy is less than or equal to the preset threshold and the binding region is located in the POU structural domain, the candidate product is determined to be a potential product; otherwise, it is determined to be a product without potential.

[0025] This application then provides a system for screening potential products for the prevention or treatment of atherosclerosis, atherosclerotic cardiovascular disease, or vascular endothelial dysfunction and their related complications, the system comprising:

[0026] The structure acquisition module is used to acquire the molecular structure of candidate products;

[0027] The docking simulation module is used to simulate molecular docking between the candidate product and the POU2F2 protein, and output the optimal binding energy and binding region information.

[0028] The result determination module is used to make a determination based on the optimal binding energy and binding region information. If the optimal binding energy is less than or equal to a preset threshold and the binding region is located in the POU structural domain, the candidate product is determined to be a potential product; otherwise, it is determined to be a product without potential.

[0029] The present application further provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the above-described filtering method.

[0030] Finally, this application provides a computer processing device, including a processor and the aforementioned computer-readable storage medium, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the aforementioned screening method.

[0031] Compared with the prior art, the beneficial effects of this application are as follows:

[0032] 1) This application comprehensively utilizes transcriptome differential analysis (limma package), progeny signaling pathway activity analysis (decoupleR package), NetAct core transcription factor network construction, machine learning (LASSO + RF), and sRACIPE dynamic network simulation to identify POU2F2 as a key pathogenic node of atherosclerosis, and on this basis, constructs a platform that can be used to screen drugs for atherosclerosis and related diseases.

[0033] 2) This application continues to target the transcription factor POU2F2, and has obtained the candidate drug linagliptin. To verify its efficacy, this application uses ox-LDL to induce human aortic endothelial cells to construct an in vitro atherosclerosis model. Experiments have confirmed that linagliptin can significantly inhibit the expression of POU2F2 in this pathological model and downregulate the levels of inflammatory factors and adhesion molecules (IL-6, ICAM-1, and VCAM-1). This discovery reveals the effect of linagliptin in regulating POU2F2-related signaling pathways, inhibiting vascular inflammatory responses, and reducing vascular endothelial damage, clearly demonstrating its clinical application potential in the preparation of drugs for the prevention and / or treatment of atherosclerosis and related cardiovascular and cerebrovascular diseases (including but not limited to coronary heart disease and cerebral infarction). Attached Figure Description

[0034] Figure 1 This is a volcano plot of differentially expressed genes; the horizontal axis represents the logarithm of the fold change in differential expression (log2), and the vertical axis represents the negative logarithm of statistical significance (-log2). 10 (Corrected P-value); red dots represent significantly upregulated genes with a P-value ≥ 1.5 and a corrected P-value < 0.05, blue dots represent significantly downregulated genes, and gray dots represent genes with no significant difference.

[0035] Figure 2 This is a differential gene pathway analysis diagram; the horizontal axis represents the pathway name, and the vertical axis represents the difference score of pathway activity between groups (enrichment score). Red represents activated pathways, and blue represents inhibited pathways.

[0036] Figure 3 The core is the transcription factor network; nodes represent transcription factors, and lines represent the regulatory relationship between TF and target genes; the POU2F2 node (labeled at the center) satisfies centrality (DC) ≥4, median centrality (BC) >0.0073, and tight centrality (CC) ≥0.3956, and is the core regulatory hub.

[0037] Figure 4 This is a network homeostasis simulation diagram; the vertical axis represents the core transcription factor (TF) name, and the horizontal axis represents the homeostasis simulation count (Count).

[0038] Figure 5 This is a knockdown simulation plot; the horizontal axis represents the name of the core transcription factor (TF), and the vertical axis represents the proportion of phenotypes (0-1.0); green bars represent the proportion of normal phenotypes, and brown bars represent the proportion of disease phenotypes.

[0039] Figure 6 This is a graph showing the results of machine learning; where, Figure 6 The left side shows the ROC curve of LASSO regression; Figure 6 The right side shows the results of the random forest.

[0040] Figure 7 This diagram confirms the key pathogenic nodes of atherosclerosis.

[0041] Figure 8 This is a diagram annotating dimensionality reduction clustering and cell type; where Fibroblasts represent fibroblasts; Smoothmuscle cells represent smooth muscle cells; Endothelial cells represent endothelial cells; Mast cells represent mast cells; B cells represent B lymphocytes; T cells represent T lymphocytes; and Monocytes / Macrophages represent monocytes / macrophages.

[0042] Figure 9 This is a graph showing the transcriptional activity analysis of POU2F2.

[0043] Figure 10 This is a graph showing the functional correlation analysis specific to cell type.

[0044] Figure 11 Analysis graph for metabolic reprogramming analysis.

[0045] Figure 12 This is a diagram showing the molecular docking results.

[0046] Figure 13 This is a diagram showing the distribution of cell types targeting the drug (linagliptin).

[0047] Figure 14 The image shows the qPCR results of POU2F2 and inflammatory gene expression in the HAEC cell model.

[0048] Figure 15 The graph shows the efficiency of POU2F2 knockdown and the validation of inflammatory markers.

[0049] Figure 16 The image shows the Western blot results of POU2F2 protein expression in the HAEC cell model.

[0050] Figure 17 This is a block diagram of a system for screening potential products for the prevention or treatment of atherosclerosis, atherosclerotic cardiovascular disease, or vascular endothelial dysfunction and its related complications.

[0051] Figure 18 This is a flowchart of a method for screening potential products for the prevention or treatment of atherosclerosis, atherosclerotic cardiovascular disease, or vascular endothelial dysfunction and its related complications. Detailed Implementation

[0052] To make the inventive objectives, technical solutions, and beneficial effects of this application clearer, the following description, in conjunction with embodiments, further illustrates this application. It should be understood that the embodiments described are for illustrative purposes only and are not intended to limit the scope of the application. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this description.

[0053] The first aspect of this application provides for the use of the POU2F2 gene or protein in screening any of the following products:

[0054] 1) Products for the prevention or treatment of atherosclerosis;

[0055] 2) Products for the prevention or treatment of atherosclerotic cardiovascular diseases;

[0056] 3) Products for the prevention or treatment of vascular endothelial dysfunction and its related complications.

[0057] In some specific embodiments, the atherosclerotic cardiovascular disease is selected from coronary heart disease, ischemic stroke, and peripheral artery disease.

[0058] The term "vascular endothelial dysfunction" is a vascular disease characterized by abnormal function of vascular endothelial cells. Its core manifestations include decreased vasodilation capacity, weakened anticoagulation function, enhanced inflammatory response, and increased permeability. It is considered an early stage and independent risk factor for cardiovascular diseases such as atherosclerosis.

[0059] In some specific embodiments, the vascular endothelial dysfunction complication is selected from diabetic vascular complications, hypertension, and pulmonary hypertension.

[0060] In some specific implementations, the product is selected from pharmaceuticals, health products, or food.

[0061] In some specific embodiments, the product has the function of inhibiting the transcription of the POU2F2 gene in arterial endothelial cells.

[0062] In some specific embodiments, the product has the function of inhibiting the transcription of inflammatory factor genes in arterial endothelial cells.

[0063] In some specific embodiments, the product has the function of inhibiting the transcription of adhesion molecule genes in arterial endothelial cells.

[0064] In some specific embodiments, the product has the function of inhibiting the expression of POU2F2 protein in arterial endothelial cells.

[0065] In some specific embodiments, the product has the function of inhibiting the expression of inflammatory factors in arterial endothelial cells.

[0066] In some specific embodiments, the product has the function of inhibiting the expression of adhesion molecules in arterial endothelial cells.

[0067] Preferably, the inflammatory factor is selected from IL-6.

[0068] Preferably, the adhesive molecules are selected from ICAM-1 and / or VCAM-1.

[0069] The second aspect of this application provides for the use of the POU2F2 inhibitor in the preparation of any of the following products:

[0070] 1) Products for the prevention or treatment of atherosclerosis;

[0071] 2) Products for the prevention or treatment of atherosclerotic cardiovascular diseases;

[0072] 3) Products for the prevention or treatment of vascular endothelial dysfunction and its related complications.

[0073] The term "POU2F2 inhibitor" refers to a class of substances that can specifically bind to the POU2F2 protein or regulate its signaling pathway, thereby inhibiting the transcription or translation of the POU2F2 gene, reducing the stability of the POU2F2 protein or promoting its degradation, blocking the binding of the POU2F2 protein to specific DNA recognition elements in the promoter region of target genes, or interfering with the transcriptional activation function mediated by the POU2F2 protein after binding to DNA.

[0074] In some specific embodiments, the POU2F2 inhibitor is selected from small molecule compounds, nucleic acid molecules, or antibodies.

[0075] Preferably, the small molecule compound is selected from linagliptin.

[0076] Linagliptin is an oral hypoglycemic agent that belongs to the dipeptidyl peptidase-4 (DPP-4) inhibitor class and is used to treat type 2 diabetes.

[0077] Preferably, the target sequence of the nucleic acid molecule comprises the sequence shown in SEQ ID NO: 1.

[0078] The term "target sequence" refers to the segment in the POU2F2 gene corresponding to the identified and silenced mRNA fragment.

[0079] SEQ ID NO. 1: GCTACCGACACCAAATCTA.

[0080] Preferably, the nucleic acid molecule is siRNA, which comprises a first strand and a second strand, the first strand and the second strand being complementary to form an RNA dimer.

[0081] More preferably, the nucleotide sequence of the first chain comprises the sequence shown in SEQ ID NO: 2.

[0082] SEQ ID NO. 2: GCUACCGACACCAAAUCUA.

[0083] In some specific embodiments, the atherosclerotic cardiovascular disease is selected from coronary heart disease, ischemic stroke, and peripheral artery disease.

[0084] In some specific embodiments, the vascular endothelial dysfunction complication is selected from diabetic vascular complications, hypertension, and pulmonary hypertension.

[0085] In some specific implementations, the product is selected from pharmaceuticals, health products, or food.

[0086] In some specific embodiments, the product has the function of inhibiting the transcription of the POU2F2 gene in arterial endothelial cells.

[0087] In some specific embodiments, the product has the function of inhibiting the transcription of inflammatory factor genes in arterial endothelial cells.

[0088] In some specific embodiments, the product has the function of inhibiting the transcription of adhesion molecule genes in arterial endothelial cells.

[0089] In some specific embodiments, the product has the function of inhibiting the expression of POU2F2 protein in arterial endothelial cells.

[0090] In some specific embodiments, the product has the function of inhibiting the expression of inflammatory factors in arterial endothelial cells.

[0091] In some specific embodiments, the product has the function of inhibiting the expression of adhesion molecules in arterial endothelial cells.

[0092] Preferably, the inflammatory factor is selected from IL-6.

[0093] Preferably, the adhesive molecules are selected from ICAM-1 and / or VCAM-1.

[0094] The third aspect of this application provides the use of the POU2F2 inhibitor in the preparation of any of the following products:

[0095] I. Products that inhibit the transcription of inflammatory cytokine genes in arterial endothelial cells;

[0096] II. Products that inhibit the transcription of adhesion molecule genes in arterial endothelial cells;

[0097] III. Products that inhibit the expression of inflammatory factors in arterial endothelial cells;

[0098] IV. Products that inhibit the expression of adhesion molecules in arterial endothelial cells.

[0099] In some specific implementations, the inflammatory factor is selected from IL-6.

[0100] In some specific embodiments, the adhesive molecules are selected from ICAM-1 and / or VCAM-1.

[0101] In some specific embodiments, the product is selected from endothelial cell culture medium additives.

[0102] In the third aspect, the "endothelial cell culture medium additive" refers to a culture medium additive specifically designed for the in vitro culture of arterial endothelial cells. By inhibiting the expression of inflammatory factors and adhesion molecules, it maintains the resting state phenotype of endothelial cells and reduces spontaneous or stress-induced background inflammation during culture, thereby providing a more stable experimental model that is closer to the physiological state for vascular biology and anti-inflammatory research.

[0103] The fourth aspect of this application provides a method for screening potential products for the prevention or treatment of atherosclerosis, atherosclerotic cardiovascular disease, or vascular endothelial dysfunction and their related complications, comprising the following steps:

[0104] 1) Obtain the molecular structure of the candidate product;

[0105] 2) Molecular docking simulation was performed between the candidate product and the POU2F2 protein to obtain the optimal binding energy and binding region information;

[0106] 3) Result determination: If the optimal binding energy is less than or equal to the preset threshold and the binding region is located in the POU structural domain, the candidate product is determined to be a potential product; otherwise, it is determined to be a product without potential.

[0107] The flowchart of the method provided in the fourth aspect of this application is as follows: Figure 18 As shown.

[0108] In some specific implementations, the preset threshold is -7.0 kcal / mol.

[0109] In some specific implementations, in step 1), the molecular structure data of the candidate drug is obtained from the ChemSrc database, and its structure is in SDF format.

[0110] The term "SDF" (Structure Data File) is a standard file format that can store molecular structures. It contains not only the three-dimensional coordinates of atoms, but also the chemical bond relationships between atoms (single bonds, double bonds, etc.) and other properties of the molecule.

[0111] In some specific implementations, in step 2), the structural data of the POU2F2 protein is obtained from the RCSBPDB database, and its PDB ID is 9dzm.

[0112] In some specific implementations, in step 2), the molecular docking simulation is a blind docking.

[0113] In some specific implementations, in step 2), the docking platform used for the molecular docking simulation is CBDock2.

[0114] The term "POU domain" refers to the DNA-binding domain in the POU2F2 protein, which consists of a POU-specific domain (POUs) and a POU-homological domain (POUh). These two domains are linked by a flexible linker and work together to recognize and bind to the octamer motif (ATGCAAAT) in the promoter or enhancer region of target genes, thereby mediating the regulatory function of POU2F2 as a transcription factor in B cell development and immune response.

[0115] The fifth aspect of this application provides a system for screening potential products for the prevention or treatment of atherosclerosis, atherosclerotic cardiovascular disease, or vascular endothelial dysfunction and their related complications, the system comprising:

[0116] The structure acquisition module is used to acquire the molecular structure of candidate products;

[0117] The docking simulation module is used to simulate molecular docking between the candidate product and the POU2F2 protein, and output the optimal binding energy and binding region information.

[0118] The result determination module is used to make a determination based on the optimal binding energy and binding region information. If the optimal binding energy is less than or equal to a preset threshold and the binding region is located in the POU structural domain, the candidate product is determined to be a potential product; otherwise, it is determined to be a product without potential.

[0119] The module diagram of the system provided in the fifth aspect of this application is as follows: Figure 17 As shown.

[0120] In some specific implementations, the preset threshold is -7.0 kcal / mol.

[0121] Since the system described in this aspect and the method described in the fourth aspect are essentially the same in principle, the specific implementation examples can be used interchangeably and will not be repeated. It should be noted that the division of the various modules in the above system is merely a logical functional division; in actual implementation, they can be fully or partially integrated into a single physical entity, or physically separated. These modules can be implemented entirely in software via processor calls; they can be fully implemented in hardware; or some modules can be implemented via processor calls in software, while others are implemented in hardware. For example, the structure acquisition module can be a separate processor, or it can be integrated into a chip. Alternatively, it can be stored in memory as program code, and called and executed by a processor. The implementation of other modules is similar. Furthermore, these modules can be fully or partially integrated together, or implemented independently. The processor mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, the above modules can be completed through integrated logic circuits in the processor element or through software instructions. For example, these modules can be configured as one or more integrated circuits, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Field Programmable Gate Arrays (FPGAs) or Graphics Processing Units (GPUs). As another example, when a module is implemented using processor scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SoC).

[0122] The sixth aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the fourth aspect.

[0123] In this respect, "computer-readable storage medium" may include read-only memory, random access memory, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, flash memory, USB flash drive, portable hard drive, or any other medium capable of storing desired program code in the form of instructions or data structures and accessible by a computer. It should be understood that computer-readable storage medium excludes links, carrier waves, signals, or other transient media, and is intended for use with non-transient, tangible storage media.

[0124] A seventh aspect of this application provides a computer processing apparatus, including a processor and a computer-readable storage medium as described in the sixth aspect, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the method described in the fourth aspect.

[0125] The present invention will be further illustrated by the following examples, but these examples do not limit the scope of the invention.

[0126] When numerical ranges are given in the embodiments, it should be understood that, unless otherwise stated in the present invention, both endpoints of each numerical range and any value between the two endpoints may be selected. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. All reagents or instruments whose manufacturers are not specified are conventional products that can be purchased commercially. In addition to the specific methods, equipment, and materials used in the embodiments, based on the knowledge of the prior art possessed by one of ordinary skill in the art and the description of this invention, any prior art methods, equipment, and materials similar to or equivalent to those described, used, and materials in the embodiments of this invention may be used to implement this invention.

[0127] Example 1: POU2F2 is a key pathogenic node in atherosclerosis.

[0128] 1.1 Data Sources and Processing

[0129] Data download and preprocessing: The expression matrix (exp) and clinical information (pdata) were extracted using the getGEO() function of the GEOquery package, and the between-samples were normalized using the normalizeBetweenArrays() function of the limma package.

[0130] ID conversion: Load the hugeies10sttranscriptcluster.db database, obtain the correspondence between probe IDs (probe_id) and gene symbols (symbol) through the toTable() function, and perform ID matching using inner_join().

[0131] Deduplication and filtering: Remove duplicate values ​​in the order of "probe ID → gene symbol" (!duplicated()), delete missing values ​​using na.omit(), and finally retain the expression matrix of unique genes.

[0132] Differential analysis: The design matrix and contrast matrix were constructed using the limma package. Differentially expressed genes were screened using lmFit(), contrasts.fit(), and eBayes(), with the following screening criteria: |log2FC| ≥ 1.0, adj.P.Val < 0.05 (screening results are shown below). Figure 1 (As shown).

[0133] 1.2 Signal Path Analysis

[0134] The decoupleR package, combined with the Progeny human signal transduction model (containing 14 core pathways), was used to perform pathway activity analysis on differentially expressed genes. The specific steps are as follows:

[0135] Load 14 signaling pathway networks (including MAPK, TGFβ, NFκB, etc.) from the progeny database;

[0136] The pathway activity score at the sample level was calculated using the run_wmean() function (parameters: times = 100, minsize = 5).

[0137] Standardize the pathway activity matrix (scale()) and plot a pathway activity heatmap (pheatmap package).

[0138] Based on the t-values ​​of differentially expressed genes, the differences in pathway activity between groups were calculated using run_wmean(), and a pathway activity bar chart was plotted to screen for pathways that were significantly activated or inhibited.

[0139] The results show (e.g.) Figure 2 (as shown)

[0140] Inflammatory pathways such as JAKSTAT, NFκB, and TNFα were significantly activated (activity score > 1.5).

[0141] The TGFβ pathway was significantly inhibited (activity score < 1.5).

[0142] 1.3 Construction of Transcription Factor Network

[0143] The specific steps for building the TFTarget control network using the NetAct package are as follows:

[0144] 1) Load the human transcription factor target gene database (hDB, built into NetAct).

[0145] 2) Transcription factor screening: The TF_Selection() function (parameters: minSize = 5, method = "fast", qval = 0.01) is used to screen for significantly enriched transcription factors (TFs) based on differential gene results.

[0146] 3) Transcription factor activity calculation: The activity of the target TF in each sample is calculated using the TF_Activity() function in conjunction with the gene expression matrix;

[0147] 4) Control network filtering: Use the TF_Filter() function (parameters: miTh = 0.05, nbins = 8, corMethod = "spearman", DPI = TRUE) to construct a high-confidence TFTarget control network;

[0148] 5) Core TF Identification: Calculate the centrality (Degree, Betweenness centrality, Closeness centrality) of TFs in the network, and select 25 core TFs (selection thresholds: DC≥4, BC>0.0073, CC≥0.3956, determined based on the 95th quantile of the network centrality distribution), among which POU2F2 is located at the center of the network (e.g., Figure 3 (As shown).

[0149] 1.4 Dynamic Simulation and Knockdown Test

[0150] Steady-state simulation and knockdown prediction of the core TF control network were performed using the sRACIPE package. The specific parameters and steps are as follows:

[0151] 1.) Network steady-state simulation:

[0152] Input the TFTarget control network (tf_links) constructed from NetAct.

[0153] Call the sracipeSimulate() function (parameters: numModels = 50, integrateStepSize = 0.1, simulationTime = 500, plots = FALSE).

[0154] After the simulation results were normalized by sracipeNormalize(), a histogram of the network steady-state distribution was plotted (Figure 4) to show the network steady-state characteristics under disease conditions.

[0155] 2) Knock-down simulation:

[0156] Based on the steady-state simulation results, a systematic knockdown simulation was performed on all transcription factors in the network to screen candidate TTFs consistent with disease promotion / protection roles:

[0157] If a certain TF is highly expressed / highly active in the disease group (pathogenic TF), then its knockdown should lead to a decrease in the proportion of the disease phenotype;

[0158] If a certain TF is expressed at low levels / has low activity in the disease group (protective TF), then knocking it down should lead to an increase in the proportion of the disease phenotype;

[0159] TFs that conform to the above trends are considered to have a clear disease regulation direction in steady-state simulations.

[0160] The specific steps for the knockdown simulation are as follows:

[0161] Call the sracipeKnockDown() function (parameters: reduceProduction = 50, nClusters = 2, plotToFile = FALSE) to perform a knockdown simulation on all core TFs, reducing their expression by 50%.

[0162] The change in the ratio of normal to disease phenotypes after statistical knockdown was compared with the baseline simulation results.

[0163] Screen for TFs that meet the expected trend: that is, the proportion of disease decreases after knocking down the originally highly expressed TF, or the proportion of disease increases after knocking down the originally low expressed TF.

[0164] Screening results: A total of 9 key transcription factors (PML, POU2F2, JUN, CREB1, ATF1, TFAP2ASP1, PPARA) were screened, and their knockdown effect was consistent with the homeostatic phenotype. Figure 5 ).

[0165] 1.5 Machine Learning (LASSO and Random Forest)

[0166] 1) LASSO regression (Least Absolute Shrinkage and Selection Operator)

[0167] Package: R language "glmnet" package (v4.1-7).

[0168] Methodology: LASSO regression uses L1 regularization to achieve variable selection and coefficient shrinkage, compressing the coefficients of some unimportant variables to zero, thereby improving the model's predictive accuracy and interpretability.

[0169] Analysis steps:

[0170] Input the transcription factor activity matrix (sample × TF activity score) calculated by NetAct.

[0171] Cross-validation was performed using the glmnet() function (cv.glmnet), with family set to "binomial" (binary classification).

[0172] The optimal lambda value (lambda.min) was determined by 10-fold cross-validation, and transcription factors corresponding to non-zero coefficients were screened.

[0173] Plotting the ROC curve of LASSO regression to evaluate model performance (e.g.) Figure 6 (As shown on the left).

[0174] 2) Random Forest (RF)

[0175] Package: R language "randomForest" package (v4.7-1.1).

[0176] Method principle: Random forest is an ensemble learning algorithm based on Bootstrap aggregation (Bagging). By constructing multiple decision trees and summarizing the prediction results, it can provide a stable evaluation of feature importance without pre-setting the distribution of variables.

[0177] Analysis steps:

[0178] The transcription factor activity matrix was used as the input feature, and the sample grouping (lesion vs. normal) was used as the response variable.

[0179] Call the randomForest() function to build a classification model (parameters: ntree = 500, mtry = sqrt(p), where p is the number of features);

[0180] The mean decline Gini index of each transcription factor was calculated as an importance score;

[0181] Transcription factors were sorted in descending order of importance score and the top 10 were selected (e.g., ...). Figure 6 (As shown on the right).

[0182] 3) Identification of candidate transcription factors

[0183] The intersection of the screening results from the two methods is taken; transcription factors jointly identified by LASSO regression and random forest are considered as candidate targets with high diagnostic value.

[0184] The results are as follows:

[0185] LASSO regression: Five variables with non-zero coefficients were selected from all transcription factors and were significantly associated with atherosclerosis.

[0186] Random Forest: Sort transcription factors by importance score and select the top 10.

[0187] Intersection analysis: Both methods jointly identified POU2F2 and NOTCH3 as key candidate transcription factors.

[0188] 1.6 Identification of key pathogenic factors in atherosclerosis

[0189] 1) 25 core TTFs obtained from network analysis (see 1.3 Transcription Factor Network Construction).

[0190] 2) Nine key TFs obtained from dynamic simulation (see 1.4 Dynamic Simulation and Knockdown Experiment).

[0191] 3) Two TFs selected by machine learning (LASSO and Random Forest) (see 1.5 Machine Learning).

[0192] Taking the intersection of the above three types of information, only POU2F2 (such as...) is ultimately retained. Figure 7 (As shown).

[0193] Example 1 combines transcriptome differential analysis (limma package), progeny signaling pathway activity analysis (decoupleR package), NetAct core TF network construction, machine learning (LASSO + RF), and sRACIPE dynamic network simulation to clarify the role of POU2F2 as a key regulator of atherosclerosis. It confirms that POU2F2 is a key pathogenic node in atherosclerosis and can serve as a core target for screening anti-atherosclerotic drugs.

[0194] Example 2: Single-cell RNA sequencing to analyze cell type-specific functions of POU2F2

[0195] This embodiment utilizes the publicly available dataset GSE159677 (10x Genomics platform) to perform in-depth analysis on 51,981 single cells of carotid artery atherosclerosis, clarifying the cell type-specific regulatory mechanism of POU2F2 in atherosclerosis.

[0196] 2.1 Data Acquisition and Preprocessing

[0197] Data source: GSE159677 dataset, which contains paired tissue samples of the atherosclerotic core (AC) and proximal adjacent (PA) regions.

[0198] Quality control and filtration:

[0199] Use Scanpy (v1.9.3) for data processing.

[0200] Cell filtration standards: 500-6000 genes detected, mitochondrial gene ratio <20%, UMI number 1000-50000.

[0201] Gene expression matrix normalization: log1p transformation was performed using the normalize_total function (target sum 10,000).

[0202] 2.2 Dimensionality Reduction Clustering and Cell Type Annotation

[0203] Highly variable gene selection: Use the highly_variable_genes function (parameters: min_mean=0.0125, max_mean=3, min_disp=0.5).

[0204] Dimensionality reduction analysis: PCA was performed on the hypervariable genes, and the top 20 principal components were selected (based on elbow plot and variance explained rate).

[0205] Nonlinear dimensionality reduction and clustering:

[0206] Visualization using UMAP;

[0207] Clustering was performed using the Leiden algorithm (resolution=0.5).

[0208] Cell type annotation (based on classic marker genes):

[0209] T cells: CD3D, CD3E, CD4, CD8A.

[0210] B cells: CD79A, MS4A1, CD19.

[0211] Monocytes / macrophages: CD14, CD68, LYZ, FCGR3A.

[0212] Smooth muscle cells: ACTA2, MYH11, TAGLN, CNN1.

[0213] Endothelial cells: PECAM1, VWF, CDH5, CLDN5.

[0214] Fibroblasts: COL1A1, COL1A2, DCN, LUM.

[0215] Mast cells: TPSAB1, CPA3, KIT.

[0216] Tissue-specific cellular composition analysis: AC tissue showed a higher proportion of endothelial cells and monocytes / macrophages compared to PA tissue, while PA tissue contained more smooth muscle cells and fibroblasts, reflecting the characteristic inflammatory and endothelial activation state of late-stage lesions (e.g., Figure 8 (As shown).

[0217] 2.3 POU2F2 transcriptional activity analysis

[0218] Activity score calculation: Based on 44 validated POU2F2 target genes, the POU2F2 transcriptional activity score for each cell was calculated using Scanpy's score_genes function.

[0219] Differential analysis: Wilcoxon rank-sum test was used to compare the POU2F2 activity of each cell type in AC and PA.

[0220] Key findings: POU2F2 mRNA expression was significantly increased in AC monocytes / macrophages (log2FC=1.79, P<1.97e24); however, POU2F2 transcriptional activity was significantly higher in AC endothelial cells and fibroblasts than in PA tissues (FC=2.79 and 2.83, P<1e70), suggesting that POU2F2 functional activation is cell type specific and not entirely dependent on mRNA expression levels (e.g., Figure 9 (As shown).

[0221] 2.4 Cell type-specific functional correlation analysis (e.g.) Figure 10 (As shown)

[0222] Spearman correlation analysis: assessing the correlation between POU2F2 activity and the expression of inflammation / adhesion molecules.

[0223] Findings in AC endothelial cells:

[0224] POU2F2 activity was significantly positively correlated with adhesion molecules ICAM1, VCAM1, and SELE (P<1e07).

[0225] It was negatively correlated with the inflammatory chemokines CCL2 and CCL4 (P<0.05).

[0226] Findings in PA endothelial cells:

[0227] POU2F2 activity was significantly positively correlated with pro-inflammatory factors IL1B, TNF, and IFNG (P<1e04).

[0228] It was significantly negatively correlated with the activity of the oxidative phosphorylation pathway (P<3.38e09).

[0229] Functional Interpretation: POU2F2 mainly regulates general inflammatory responses in adjacent tissues (PA), while in the lesion core area (AC), it specifically controls endothelial adhesion and leukocyte recruitment, suggesting that its function has an environment-dependent switching characteristic.

[0230] 2.5 Metabolic reprogramming analysis (e.g.) Figure 11 (As shown)

[0231] Metabolic pathway activity score: Based on the KEGG pathway gene set, the activity of the following pathways was calculated using the score_genes function:

[0232] Oxidative phosphorylation.

[0233] Glycolysis / gluconeogenesis.

[0234] Pentose phosphate pathway.

[0235] Fatty acid degradation.

[0236] Fatty acid biosynthesis.

[0237] Citric acid cycle (TCA cycle).

[0238] Amino acid metabolism (including alanine, aspartic acid, and glutamic acid metabolism).

[0239] Pyrimidine metabolism.

[0240] Purine metabolism.

[0241] Tissue-specific metabolic characteristics:

[0242] AC endothelial cells: Oxidative phosphorylation was significantly inhibited, consistent with the Warburg effect.

[0243] AC monocytes / macrophages: Fatty acid degradation is significantly enhanced, adapting to the lipid-rich plaque microenvironment.

[0244] AC fibroblasts: Increased pyrimidine metabolic activity supports proliferation needs.

[0245] Amino acid metabolism (alanine, aspartic acid, glutamic acid): Cell type-specific differences were observed between AC and PA.

[0246] POU2F2 and its association with metabolism: POU2F2 activity was significantly correlated with changes in the above-mentioned metabolic pathways, suggesting that it not only regulates inflammatory gene programs, but also coordinates the metabolic reprogramming of diseased cells, forming an "inflammatory metabolism" regulatory network.

[0247] Conclusion of Example 2:

[0248] Single-cell analysis revealed a cell-type-specific regulatory pattern of POU2F2 in atherosclerosis: its transcriptional activity is mainly concentrated in endothelial cells and fibroblasts in the core of the lesion, and is closely associated with adhesion molecule expression, metabolic reprogramming, and inflammatory responses. This finding provides a precise cell-type targeting basis for subsequent drug screening, namely, an ideal POU2F2 regulator should be able to effectively penetrate these two cell types and inhibit their functional activity.

[0249] Example 3: Drug prediction and molecular docking based on POU2F2

[0250] 3.1 Candidate Drug Screening Process

[0251] Input POU2F2 into DGIdb (Drug–Gene Interaction Database) for drug prediction.

[0252] Drug candidate identified: Linagliptin.

[0253] 3.2 Molecular docking steps

[0254] Download the POU2F2 structure (PDB ID: 9dzm) from RCSBPDB.

[0255] Obtain the SDF structure of Linagliptin from ChemSrc.

[0256] Blind integration was performed using the CBDock2 platform.

[0257] 3.3 Docking Results (e.g.) Figure 12 (As shown)

[0258] Optimal binding energy: –7.3 kcal / mol.

[0259] The drug is predicted to be located near the DNA-binding region (POU domain), where it can generate hydrogen bonds and hydrophobic interactions. Binding to this region could potentially interfere with the binding of POU2F2 to DNA, thereby inhibiting its transcriptional activity.

[0260] 3.4 Analysis of cell type distribution of drug targets (e.g.) Figure 13 (As shown)

[0261] Analysis strategy: Evaluate the cell type-specific expression of known target genes of Linagliptin (ABCB1, DPP4, SLC22A1 / 2 / 3) in AC vs PA.

[0262] Key findings:

[0263] ABCB1: Significantly downregulated in AC endothelial cells (FC=0.54, P=0.000385).

[0264] DPP4 was significantly upregulated in AC endothelial cells and fibroblasts (FC=1.67 and 1.97), precisely overlapping with the highly active cell type of POU2F2.

[0265] SLC22A3: Downregulated in AC smooth muscle cells (FC=0.51, P=0.010362).

[0266] Clinical significance: The co-expression pattern of DPP4 and POU2F2 in AC endothelial cells and fibroblasts supports the hypothesis that Linagliptin exerts its anti-atherosclerotic effect by directly regulating POU2F2 in these key cell types.

[0267] 3.5 Conclusion of Example 3:

[0268] Linagliptin can bind stably to POU2F2 and is a potential POU2F2 modulator.

[0269] Example 4: In vitro validation of Linagliptin regulating POU2F2

[0270] 4.1 Experimental Samples and Reagents

[0271] 1) Experimental samples.

[0272] Cells: Human aortic endothelial cells (HAEC).

[0273] 2) Core reagents and their sources

[0274] The core reagents are all commonly used and validated products in the laboratory, specifically including:

[0275] siPOU2F2 (POU2F2 siRNA) is available in 5 nmol / tube and 2.5 nmol / sub-packet.

[0276] si-NC (negative control siRNA) is a target-free sequence control that matches si-POU2F2.

[0277] The transfection reagent was Lipofectamine 3000 (Invitrogen).

[0278] The specific culture medium for HAEC is EGM-2 (PromoCell).

[0279] Highly oxidized low-density lipoprotein (ox-LDL) with a final concentration of 50 μg / mL, catalog number YB-002-1.

[0280] Linagliptin is administered at a concentration of 10 μM. The brand name is MCE, and the catalog number is HY-10284.

[0281] RNA extraction was performed using Trizol reagent (Invitrogen).

[0282] To remove residual DNA from RNA, use DNase I (Thermo Fisher).

[0283] The reverse transcription kit used was the SuperScript First Strand Synthesis System (VazymeBiotech, #R223-01).

[0284] The qPCR kit was PowerUp SYBR Green master mix (Vazyme Biotech, #Q511-02).

[0285] Protein extraction was performed using RIPA buffer (Beyotime, #P0013C).

[0286] Protein concentration was detected using the BCA Protein Quantitative Kit (Beyotime, #P0012S).

[0287] Protein electrophoresis was performed using a 10% SDS-PAGE gel (Epizyme, #PG112).

[0288] PVDF membrane (Millipore, #IPVH00010) for protein transfer.

[0289] POU2F2 primary antibody (Abclonal A21297) was diluted 1:1000.

[0290] The primary antibody (CST 5174S) was diluted 1:5000.

[0291] The HRP-labeled secondary antibody was diluted at a ratio of 1:5000.

[0292] Chemiluminescence assay kit for protein development (Epizyme, #SQ201L).

[0293] 3) Key sequence information

[0294] POU2F2 siRNA sequence:

[0295] Forward primer GCUACCGACACCAAAUCUA(dT)(dT)(5'->3') (SEQ ID NO: 2).

[0296] Reverse primer UAGAUUUGGUCCGUAGC(dT)(dT)(5'->3') (SEQ ID NO: 3).

[0297] qPCR primer sequences:

[0298] POU2F2 (person):

[0299] Forward primer GAGGAGCCCAGTGATTCTGGA(5'->3') (SEQ ID NO: 4).

[0300] Reverse primer GAAGCGGGAAATGGTCGTC (5'->3') (SEQ ID NO: 5).

[0301] IL6 (person):

[0302] Forward primer GCAACACACAAGTTTGCTGATT(5'->3') (SEQ ID NO: 6).

[0303] Reverse primer CCTTCCCAAGGGCATTCTCTG (5'->3') (SEQ ID NO: 7).

[0304] ICAM1 (person):

[0305] Forward primer GTATGAACTGAGCAATGTGCAAG(5'->3') (SEQ ID NO: 8).

[0306] Reverse primer GTTCCACCCGTTCTGGAGTC(5'->3') (SEQ ID NO: 9).

[0307] VCAM1 (person):

[0308] Forward primer CAGTAAGGCAGGCTGTAAAAGA(5'->3') (SEQ ID NO: 10).

[0309] Reverse primer TGGAGCTGGTAGACCCTCG(5'->3') (SEQ ID NO: 11).

[0310] GAPDH (human):

[0311] Forward primer GGAGCGAGATCCCTCCAAAAT (5'->3') (SEQ ID NO: 12).

[0312] Reverse primer GGCTGTTGTCATACTTCTCATGG (5'->3') (SEQ ID NO: 13).

[0313] 4.2 Cell Culture and Grouping

[0314] 1) Cell inoculation

[0315] HAEC at 5×10 5 Inoculate each well with one seed, add EGM2 medium containing 10% FBS, and incubate at 37°C and 5% CO2 until the confluence reaches 60%–70%.

[0316] 2) Transfection

[0317] Transfection preparation: Replace with antibiotic-free EGM2 medium and equilibrate for 1 h.

[0318] siRNA transfection: Following the Lipofectamine 3000 instructions, mix siPOU2F2 or siNC (final concentration 50 nM) with the transfection reagent at a ratio of 1:2, incubate at room temperature for 20 min, and then add to the corresponding wells.

[0319] Six hours after transfection, all wells were replaced with EGM2 medium containing 10% FBS and cultured for another 18 hours.

[0320] 3) Grouping and Stimulus

[0321] According to the experimental design, the following 6 groups were set up, and corresponding treatments were added (the transfection process for groups 5 and 6 is shown in 2).

[0322] PBS control group (Group 1): PBS buffer was added.

[0323] ox-LDL model group (group 2): Add ox-LDL to a final concentration of 50 μg / mL and treat for 24 h.

[0324] PBS+Linagliptin group (group 3): 10 μM Linagliptin was added, without ox-LDL stimulation.

[0325] ox-LDL+Linagliptin group (group 4): 50 μg / mL ox-LDL + 10 μM Linagliptin were added and treated for 24 h.

[0326] ox-LDL+siNC group (group 5): After transfection with siNC, 50 μg / mL ox-LDL was added and the treatment was carried out for 24 h.

[0327] ox-LDL+siPOU2F2 group (group 6): After transfection with siPOU2F2, 50 μg / mL ox-LDL was added and the treatment was carried out for 24 h.

[0328] 4) Sample collection

[0329] After stimulation, the cells were washed twice with PBS, and the cell pellet was collected and divided into two parts for RNA extraction and protein extraction, respectively.

[0330] 4.3 qPCR detection of gene expression

[0331] 1) RNA extraction and purification

[0332] Add 1 mL of Trizol reagent to the cell pellet, vortex to mix, and let stand at room temperature for 5 min; add 200 μL of chloroform, shake for 15 s, and let stand at room temperature for 3 min; centrifuge at 12000 g for 15 min at 4℃, and take the upper aqueous phase.

[0333] Add 500 μL of isopropanol and let stand at room temperature for 10 min; centrifuge at 12000 g for 10 min at 4℃ and discard the supernatant; wash the precipitate twice with 75% ethanol, dry it and dissolve it in RNase-free water.

[0334] Add DNase I (1 U / μg RNA) and incubate at 37°C for 30 min to remove residual DNA; use NanoDrop ND1000 to detect RNA purity (A260 / A280 = 1.8-2.0) and concentration.

[0335] 2) Reverse transcription and qPCR

[0336] Take 1 μg of total RNA and perform reverse transcription according to the kit instructions (42℃ for 60 min, 70℃ for 15 min).

[0337] The cDNA was diluted 10-fold and used as a template for qPCR amplification. The reaction mixture (20 μL) consisted of 2 μL of cDNA template, 0.8 μL each of forward and reverse primers (10 μM), 10 μL of PowerUp SYBR Green master mix, and 6.4 μL of RNase-free water.

[0338] Reaction program: Pre-denaturation 95℃ 3 min; Cyclic phase 95℃ 10 s, 60℃ 30 s, for a total of 40 cycles; Melting curve analysis 95℃ 15 s, 60℃ 1 min, 95℃ 15 s.

[0339] Instrument: QuantStudio 6 Flex Real-Time PCR System.

[0340] 3) Experimental data

[0341] Relative expression levels were calculated using the 2^–ΔΔCt method, with GAPDH as the internal reference gene.

[0342] ox-LDL model group (group 2) vs PBS control group (group 1): The mRNA expression levels of POU2F2, IL-6, ICAM-1, and VCAM-1 were all significantly increased (P<0.001). Figure 14 ).

[0343] In the ox-LDL+Linagliptin group (group 4) vs. the ox-LDL model group (group 2), the mRNA expression levels of POU2F2, IL-6, ICAM-1, and VCAM-1 were all significantly reduced (P<0.01). Figure 14 ).

[0344] In the ox-LDL+siPOU2F2 group (group 6) vs. the ox-LDL+siNC group (group 5), the expression level of POU2F2 mRNA was significantly reduced (P<0.001), and the expression levels of IL-6, ICAM-1, and VCAM-1 mRNA were also significantly reduced (P<0.01). Figure 15 ).

[0345] 4) Experimental Conclusions

[0346] ox-LDL significantly upregulated the mRNA expression of POU2F2 and inflammatory genes (IL-6, ICAM-1, VCAM-1) in HAEC, and the endothelial injury model was successfully constructed.

[0347] Linagliptin can specifically inhibit ox-LDL-induced upregulation of POU2F2 while downregulating the expression of inflammatory genes.

[0348] Transfection with siPOU2F2 effectively knocked down POU2F2 mRNA, and knocking down POU2F2 replicated the anti-inflammatory effect of Linagliptin, confirming that POU2F2 is a key regulatory target of the inflammatory response.

[0349] 4.4 Western Blot detection of protein expression

[0350] 1) Protein extraction and quantification

[0351] Add RIPA buffer containing protease inhibitors (100 μL / well of a 6-well plate) to the cell pellet and lyse on ice for 30 min; centrifuge at 12000 g for 10 min at 4 °C and collect the supernatant (total protein).

[0352] Protein concentrations were detected using a BCA kit and adjusted to a uniform level (2 μg / μL).

[0353] 2) Electrophoresis and immunoblotting

[0354] Protein denaturation: Add 5× loading buffer, boil at 95℃ for 10 min, and cool to room temperature.

[0355] SDS-PAGE electrophoresis: Load 20 μg protein per well, electrophoresis at 80 V for 30 min (stacking gel), and at 120 V for 60 min (separating gel). The electrophoresis buffer was Tris-glycine buffer (pH 8.3).

[0356] Transfer: Wet transfer method, transfer at 300 mA constant current for 90 min to transfer protein to PVDF membrane. The transfer buffer contains 20% methanol in Tris-glycine buffer.

[0357] Sealing: Sealing with 5% skim milk at room temperature for 1 hour.

[0358] Primary antibody incubation: POU2F2 primary antibody (1:1000) and GAPDH primary antibody (1:5000) were incubated overnight at 4°C.

[0359] Secondary antibody incubation: Wash the membrane 3 times with TBST (10 min each time), add HRP-labeled secondary antibody (1:5000), and incubate at room temperature for 1 h.

[0360] Development: Wash the film 3 times with TBST (10 min each time), add chemiluminescent reagent, and expose to develop in a developer for 10–60 s.

[0361] 3) Experimental data ( Figure 16 )

[0362] Strip grayscale value analysis: ImageJ software calculates the POU2F2 / GAPDH ratio.

[0363] The ox-LDL model group (group 2) vs PBS control group showed a significantly increased POU2F2 / GAPDH ratio (P<0.001).

[0364] The ox-LDL+Linagliptin group (group 4) vs. the ox-LDL model group (group 2): the POU2F2 / GAPDH ratio was significantly reduced (P<0.01).

[0365] 4) Experimental Conclusions

[0366] Protein level studies confirmed the upregulation of POU2F2 by ox-LDL, consistent with qPCR results.

[0367] Linagliptin can effectively inhibit ox-LDL-induced expression of POU2F2 protein, further confirming its regulatory role.

[0368] 4.5 Conclusion of Example 4

[0369] The oxidized low-density lipoprotein (ox-LDL)-induced human aortic endothelial cell injury model is a classic in vitro cell model for studying the pathogenesis of atherosclerosis and the effects of drug intervention. This model is based on the "endothelial injury response hypothesis" of atherosclerosis, which ox-LDL, as a key pathogenic factor, can directly act on vascular endothelial cells, mimicking the pathological changes in the early stages of the disease: promoting monocyte adhesion to the endothelium by upregulating the expression of adhesion molecules such as ICAM-1 and VCAM-1; and simultaneously increasing the release of pro-inflammatory factors such as IL-6, activating the local inflammatory cascade. This model highly summarizes the core pathological processes in the initiation stage of atherosclerosis, including endothelial activation, inflammatory response, and monocyte recruitment, and is therefore widely used in screening candidate drugs with vascular protective effects.

[0370] Based on this model, this embodiment found that Linagliptin can significantly downregulate the expression of ICAM-1, VCAM-1, and IL-6 in ox-LDL-induced human aortic endothelial cells. Its mechanism of action is as follows: by inhibiting ox-LDL-triggered endothelial cell overactivation, it reduces IL-6-mediated local inflammatory response and decreases ICAM-1 / VCAM-1-mediated monocyte adhesion and subendothelial migration, thereby blocking foam cell formation and lipid streaks at their source. This intervention not only delays the onset of atherosclerosis in the early stages of the disease, but more importantly, in the already formed plaque microenvironment, persistent inflammation and endothelial adhesion molecule expression also exacerbate inflammatory cell aggregation and weaken fibrous cap stability within the plaque. The anti-inflammatory and anti-adhesion effects of this drug can effectively reduce plaque vulnerability, delay disease progression, and protect the integrity of vascular endothelial function, thereby preventing acute cardiovascular and cerebrovascular events (such as myocardial infarction and stroke). Therefore, this drug has important value in the prevention and treatment of atherosclerosis, atherosclerotic cardiovascular disease, vascular endothelial dysfunction and its related complications.

[0371] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this invention should still be covered by the claims of this application.

Claims

1. The use of the POU2F2 gene or protein in screening any of the following products: 1) Products for the prevention or treatment of atherosclerosis; 2) Products for the prevention or treatment of atherosclerotic cardiovascular diseases; 3) Products for the prevention or treatment of vascular endothelial dysfunction and its related complications.

2. The use as described in claim 1, characterized in that, The atherosclerotic cardiovascular diseases mentioned are selected from coronary heart disease, ischemic stroke, and peripheral artery disease; And / or, the vascular endothelial dysfunction complication is selected from diabetic vascular complications, hypertension, and pulmonary hypertension.

3. The use as described in claim 1, characterized in that, The product has at least one of the following functions: a) Inhibit transcription of the POU2F2 gene in arterial endothelial cells; b) Inhibit the transcription of inflammatory factor genes in arterial endothelial cells; c) Inhibit the transcription of adhesion molecule genes in arterial endothelial cells; d) Inhibit the expression of POU2F2 protein in arterial endothelial cells; e) Inhibit the expression of inflammatory factors in arterial endothelial cells; f) Inhibit the expression of adhesion molecules in arterial endothelial cells.

4. The use as described in claim 3, characterized in that, The inflammatory factor is selected from IL-6; and / or the adhesion molecule is selected from ICAM-1 and / or VCAM-1.

5. Use of POU2F2 inhibitors in the preparation of any of the following products: 1) Products for the prevention or treatment of atherosclerosis; 2) Products for the prevention or treatment of atherosclerotic cardiovascular diseases; 3) Products for the prevention or treatment of vascular endothelial dysfunction and its related complications.

6. The use as described in claim 5, characterized in that, The POU2F2 inhibitor is selected from small molecule compounds, nucleic acid molecules, or antibodies.

7. The use as described in claim 6, characterized in that, The small molecule compound is selected from linagliptin; and / or, the target sequence of the nucleic acid molecule comprises the sequence shown in SEQ ID NO:

1.

8. The use as described in claim 6, characterized in that, The nucleic acid molecule is siRNA, which comprises a first strand and a second strand, the first strand and the second strand being complementary to form an RNA dimer; preferably, the nucleotide sequence of the first strand comprises the sequence shown in SEQ ID NO:

2.

9. The use as described in claim 5, characterized in that, The atherosclerotic cardiovascular disease is selected from coronary heart disease, ischemic stroke, and peripheral artery disease; and / or, the endothelial dysfunction complication is selected from diabetic vascular complications, hypertension, and pulmonary hypertension.

10. Use of POU2F2 inhibitors in the preparation of any of the following products: I. Products that inhibit the transcription of inflammatory cytokine genes in arterial endothelial cells; II. Products that inhibit the transcription of adhesion molecule genes in arterial endothelial cells; III. Products that inhibit the expression of inflammatory factors in arterial endothelial cells; IV. Products that inhibit the expression of adhesion molecules in arterial endothelial cells; Preferably, the inflammatory factor is selected from IL-6; and / or, the adhesion molecule is selected from ICAM-1 and / or VCAM-1.

11. A method for screening potential products for the prevention or treatment of atherosclerosis, atherosclerotic cardiovascular disease, or vascular endothelial dysfunction and its related complications, comprising the following steps: 1) Obtain the molecular structure of the candidate product; 2) Molecular docking simulation was performed between the candidate product and the POU2F2 protein to obtain the optimal binding energy and binding region information; 3) Result determination: If the optimal binding energy is less than or equal to the preset threshold and the binding region is located in the POU structural domain, the candidate product is determined to be a potential product; otherwise, it is determined to be a product without potential. Preferably, the preset threshold is -7.0 kcal / mol.

12. A system for screening potential products for the prevention or treatment of atherosclerosis, atherosclerotic cardiovascular disease, or vascular endothelial dysfunction and their related complications, said system comprising: The structure acquisition module is used to acquire the molecular structure of candidate products; The docking simulation module is used to simulate molecular docking between the candidate product and the POU2F2 protein, and output the optimal binding energy and binding region information. The result determination module is used to make a determination based on the optimal binding energy and binding region information. If the optimal binding energy is less than or equal to a preset threshold and the binding region is located in the POU structural domain, the candidate product is determined to be a potential product; otherwise, it is determined to be a product without potential. Preferably, the preset threshold is -7.0 kcal / mol.

13. A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method of claim 11.

14. A computer processing apparatus comprising a processor and a computer-readable storage medium of claim 13, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the method of claim 11.

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