Familial hypercholesterolemia exosome targeted delivery system based on multi-omics data

Abstract

The invention discloses a familial hypercholesterolemia exosome targeted delivery system based on multi-omics data, and relates to the technical field of biomedicine, the system comprises: a multi-omics data integration module for collecting and processing patient and public database data to construct a multi-omics data set; the targeting ligand screening module is used for extracting features and screening and verifying efficient targeting ligands; the exosome preparation module is used for culturing cells and separating and detecting exosomes; the exosome modification load module modifies the load; the system efficiency verification module constructs an animal model and evaluates and optimizes the system efficiency; according to the method, a multi-omics data integration module is constructed, patients and public database data are fused, a disease characteristic spectrum is accurately described, and it is ensured that targeted ligands are accurately screened; through exosome preparation and modification load technology innovation, efficient delivery of treatment substances is realized, the targeting ability of the system is enhanced, an efficient and stable delivery system is constructed, animal model verification shows that the exosome can reduce blood fat and improve pathological indexes, and a new way is developed for treatment.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to an exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data. Background Technology

[0002] Familial hypercholesterolemia (FHH), a hereditary metabolic disease, is characterized by significantly elevated levels of low-density lipoprotein cholesterol (LDL-C) in the blood, which can easily lead to early-onset coronary heart disease and other cardiovascular complications. With the rapid development of omics technologies such as genomics and proteomics, multi-omics data integration and analysis has become an important means to reveal disease mechanisms and discover potential therapeutic targets. Exosomes, as key mediators of intercellular communication, have shown great potential in the field of drug delivery due to their good biocompatibility, low immunogenicity, and ability to cross biological barriers. The exosome-targeted delivery system for familial hypercholesterolemia based on multi-omics data combines the depth of multi-omics research with the advantages of exosome delivery, aiming to achieve precise delivery of therapeutic drugs and improve treatment efficacy.

[0003] Traditional treatments for familial hypercholesterolemia mainly include statins and bile acid sequestrants. While these methods can lower blood lipid levels to some extent, they suffer from significant individual variability, noticeable side effects, and poor long-term adherence. More importantly, traditional drug delivery systems lack targeting, resulting in uneven drug distribution throughout the body and difficulty in achieving effective concentrations at the lesion site, thus limiting treatment efficacy. Furthermore, analyses based on single-aspect-data analysis often fail to fully reflect the complex mechanisms of the disease, leading to inaccurate target selection and further impacting treatment efficiency. Therefore, developing a new technology that can precisely target diseased cells and efficiently deliver therapeutic drugs is crucial for improving the treatment outcomes of familial hypercholesterolemia. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an exosome-based targeted delivery system for familial hypercholesterolemia based on multi-omics data. By integrating multi-omics data such as genomics and proteomics, the system accurately screens target ligands and uses exosomes as natural carriers to efficiently deliver therapeutic active substances to diseased cells. This system achieves precise targeted drug delivery, improves treatment efficacy, and reduces the distribution of drugs in non-target tissues and side effects.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a multi-omics data-based exosome targeted delivery system for familial hypercholesterolemia, comprising:

[0006] Multi-omics data integration module: Collects genomic and proteomic data from patients with familial hypercholesterolemia, integrates multi-omics data from public databases, standardizes and normalizes the collected data, and uses a multi-omics feature weighted fusion algorithm to fuse the data and construct a multi-omics dataset;

[0007] Targeted ligand screening module: Extracts features related to binding with lesion cells in familial hypercholesterolemia from multi-omics datasets, screens candidate targeted ligands through ligand-cell affinity prediction algorithm, and obtains efficient targeted ligands through experimental verification;

[0008] Exosome preparation module: Hepatocytes or mesenchymal stem cells are cultured, cell supernatant is collected, exosomes are separated by ultracentrifugation, particle size is analyzed by a nanoparticle tracking analyzer, and CD63, CD9, and CD81 markers are detected by Western blotting.

[0009] Exosome modification and loading module: The screened target ligands are modified onto the surface of exosomes by chemical coupling, and the active substances for treating familial hypercholesterolemia are loaded into exosomes by electroporation or passive loading.

[0010] System performance verification module: A familial hypercholesterolemia animal model was constructed. The model animals were grouped and given different preparations. After continuous administration, serum and liver tissue samples were collected to detect blood lipid levels and liver tissue pathological indicators. The delivery system performance index algorithm was used to evaluate the system performance and optimize it.

[0011] Furthermore, in the multi-omics data integration module, the genomic data of patients with familial hypercholesterolemia are obtained from peripheral blood or liver tissue samples of the patients through next-generation sequencing technology, including mutations and single nucleotide polymorphisms of the LDLR, APOB, and PCSK9 genes; the proteomic data are obtained from the above samples through liquid chromatography-tandem mass spectrometry technology, including the expression levels of lipid metabolism-related proteins and cell surface receptor proteins; public databases include TCGA, GEO, and ProteomeXchange, from which genomic and proteomic data related to familial hypercholesterolemia are obtained.

[0012] Furthermore, in the multi-omics data integration module, the expression for the multi-omics feature weighted fusion algorithm is: ,in, For the first The fusion feature value of each sample, As an omics type, For the first Omics for the first The weighting coefficients of the sample. For the first Omics The z-score-normalized feature values ​​of the sample For the first Omics Importance score of sample features The total number of omics types, No. The first in omics The original feature values ​​of each sample.

[0013] Furthermore, in the targeted ligand screening module, the expression for the ligand-cell affinity prediction algorithm is: ,in, For the first The first targeted ligand and the first The binding affinity score of the diseased cells. For the first The quantum mechanical characteristics of a targeted ligand No. One candidate targeting ligand, Prot For the first Structural characteristics of cell surface receptor proteins It is the first FH lesion cells, SVM is a support vector machine model, Topo For the first The ligand and the first Topological matching score of the cells.

[0014] Furthermore, in the targeted ligand screening module, the specific steps for experimentally verifying efficient targeted ligands are as follows:

[0015] Synthesize candidate target ligands selected by the ligand-cell affinity prediction algorithm;

[0016] The fluorescently labeled ligand was obtained by labeling the candidate targeting ligand with FITC using the N-hydroxysuccinimide ester method.

[0017] Hepatocytes and macrophages derived from patients with familial hypercholesterolemia were cultured in DMEM medium containing 10% fetal bovine serum at 37°C and 5% CO2 until the logarithmic growth phase, and the cell concentration was adjusted to 1×10⁻⁶. 6 cells / mL;

[0018] Take 1 mL of the cell suspension with the adjusted concentration, mix it with the fluorescently labeled ligand, and incubate it together at 37°C and 5% CO2 for 2 hours;

[0019] After incubation, the cells were washed three times with PBS buffer to remove unbound free ligands;

[0020] The washed cells were analyzed using flow cytometry, with the excitation wavelength set at 488 nm and the emission wavelength at 525 nm.

[0021] Using familial hypercholesterolemia lesion cells without added fluorescently labeled ligands as a negative control, the number of cells with fluorescence signal intensity higher than the threshold of this negative control was counted and recorded as the number of positive cells;

[0022] The binding rate is calculated using the formula: (Number of positive cells / Total number of cells detected) × 100%; where the total number of cells detected is the total number of cells actually detected by flow cytometry.

[0023] Ligands with a binding rate of ≥80% were selected and defined as highly efficient targeting ligands.

[0024] Furthermore, in the exosome preparation module, the specific steps of the ultracentrifugation method are as follows: the cell supernatant is centrifuged sequentially at 300×g for 10 min, 2000×g for 20 min, and 10000×g for 30 min at 4°C. The supernatant is then centrifuged at 100000×g for 70 min, and the precipitate is resuspended in PBS. When the nanoparticle tracking analyzer is used for detection, the temperature is set to 25°C, the detection time to 60 seconds, and the number of detections to 3. The exosome particle size distribution is required to be 30-150 nm with a peak value of 80-120 nm.

[0025] Furthermore, in the exosome modification and loading module, the chemical coupling adopts a click chemistry method, specifically: the targeting ligand is modified with an azide group, and the exosome is modified with an alkynyl group by 1,2-distearate-sn-glycerol-3-phosphoethanolamine-N-propynyloxysuccinimide. The two are mixed at a molar ratio of 1:50 and reacted at 37°C for 2 hours under 1 mmol / L CuSO4 catalysis. The active substances are LDLR mRNA and PCSK9 monoclonal antibody. LDLR mRNA is loaded using an Amaxa Nucleofector electroporation system with parameters of 1600V, 20ms, and 1 pulse. PCSK9 antibody is passively loaded by incubation at 4°C for 12 hours.

[0026] Furthermore, in the efficacy verification module of the targeted delivery system, the familial hypercholesterolemia animal model is a low-density lipoprotein receptor gene knockout mouse. During modeling, the mice are fed a high-fat diet containing 21% fat and 0.15% cholesterol for 8 weeks. The groups include a PBS control group, an unmodified exosome group, a non-targeted modified exosome group, and a targeted delivery system group, with 10 mice in each group. The mice are administered the drug once a week via tail vein injection at a dose of 200 μg of exosome protein per mouse for 8 weeks.

[0027] Furthermore, in the exosome carrier preparation module, the cultured cells are HepG2 cells or mesenchymal stem cells, cultured in serum-free DMEM / F12 medium for 48-72 hours. Before collecting the supernatant, it is necessary to confirm that the cell confluence reaches 80%-90% and there is no obvious cell apoptosis.

[0028] Furthermore, in the targeted delivery system efficacy verification module, the formula for the delivery system effectiveness index algorithm is as follows: ,in, This is the overall performance index of the delivery system, ranging from 0 to 100. A score of ≥80 indicates an optimal delivery system. For targeted scoring, For active substance loading efficiency, To score bioavailability, , , These are the weighting coefficients.

[0029] Compared with existing technologies, this exosome-targeted delivery system for familial hypercholesterolemia based on multi-omics data has the following advantages:

[0030] I. This invention constructs a multi-omics data integration module to systematically collect and fuse genomic and proteomic data from patients with familial hypercholesterolemia. Combining this with public database resources, it employs an advanced feature-weighted fusion algorithm to accurately characterize the disease's feature map. This strategy not only enhances the depth and breadth of data utilization but also ensures the accuracy of targeted ligand screening. By using a ligand-cell affinity prediction algorithm, it efficiently identifies targeted ligands that specifically bind to diseased cells, and its efficiency has been experimentally verified. This series of technologies provides a solid scientific foundation for exosome targeted delivery systems, significantly improves the precision and effectiveness of treatment, and promotes the development of personalized medicine.

[0031] II. This invention achieves highly efficient delivery of therapeutic active substances through innovative exosome preparation and modification loading technology. Ultracentrifugation combined with nanoparticle tracking analysis ensures the high purity and uniformity of exosomes. Simultaneously, chemical coupling is used to precisely modify the exosome surface with targeting ligands, enhancing the system's targeting capability. Furthermore, electroporation and passive loading methods are employed to effectively load active substances such as LDLR mRNA and PCSK9 monoclonal antibody into exosomes, constructing a highly efficient and stable targeted delivery system. Validation in animal models shows that this system can significantly reduce blood lipid levels and improve liver tissue pathological indicators, demonstrating good therapeutic potential and bioavailability, thus opening up new avenues for the treatment of familial hypercholesterolemia.

[0032] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0034] Figure 1 Workflow diagram of an exosome-targeted delivery system for familial hypercholesterolemia based on multi-omics data;

[0035] Figure 2 This is a diagram illustrating the interactive framework of an exosome-targeted delivery system for familial hypercholesterolemia based on multi-omics data. Detailed Implementation

[0036] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0037] Example 1:

[0038] Construction and application of an exosome-targeted delivery system for patients with familial hypercholesterolemia carrying LDLR gene mutations.

[0039] First, peripheral blood and liver tissue samples were collected from patients with familial hypercholesterolemia (FH) carrying LDLR gene mutations. Peripheral blood samples were used to extract genomic data, while liver tissue samples were used for both genomic and proteomic data extraction. These samples, directly derived from lesion-related tissues of FH patients, provide raw biological information closely related to the disease pathogenesis, laying the foundation for subsequent screening of targeted features and the construction of a precise delivery system. For genomic data, next-generation sequencing (NGS) was used, focusing on detecting LDLR gene mutations and single nucleotide polymorphisms. This technology can efficiently capture abnormal sites at the gene level and identify the core pathogenic gene variants in FH patients. For proteomic data, liquid chromatography-tandem mass spectrometry (LC-MS / MS) was used, focusing on detecting the expression levels of lipid metabolism-related proteins and cell surface receptor proteins. This technology can accurately quantify protein expression differences and locate key protein targets associated with binding to FH lesion cells.

[0040] Subsequently, genomic and proteomic data related to familial hypercholesterolemia were retrieved and downloaded from public databases and integrated with previously collected patient sample data to form an initial multi-omics dataset. The addition of public databases expands the data scale, reduces the limitations of individual samples, and improves the representativeness of the dataset. The data in the initial dataset were standardized and normalized to eliminate batch and magnitude differences between data from different sources, ensuring data comparability on the same dimension and avoiding the impact of inconsistent data formats on subsequent analysis results. After processing, a multi-omics feature weighted fusion algorithm was used to fuse the standardized and normalized multi-omics data. The expression for the multi-omics feature weighted fusion algorithm is: ,in, For the first The fusion feature value of each sample, As an omics type, For the first Omics for the first The weighting coefficients of the sample. For the first Omics The z-score-normalized feature values ​​of the sample For the first Omics Importance score of sample features The total number of omics types, No. The first in omics The algorithm uses the original feature values ​​of each sample to assign corresponding weights to the association degree of FH disease based on different omics data, highlighting key pathogenic features. Ultimately, it constructs a comprehensive and targeted multi-omics dataset for subsequent research, providing precise data support for targeted ligand screening. Figure 1 As stated above.

[0041] From the constructed multi-omics dataset, features related to binding to familial hypercholesterolemia (FH) lesion cells were extracted, including cell surface receptor protein structural features and lipid metabolism-related protein expression features. These features directly correspond to specific molecular markers of FH lesion cells. Focusing on these features ensures that the ligands selected subsequently have clear targets, reducing non-specific binding. Based on the extracted features, a large number of candidate target ligands were screened using a ligand-cell affinity prediction algorithm. The expression for the ligand-cell affinity prediction algorithm is as follows: ,in, For the first The first targeted ligand and the first The binding affinity score of the diseased cells. For the first The quantum mechanical characteristics of a targeted ligand No. One candidate targeting ligand, Prot For the first Structural characteristics of cell surface receptor proteins It is the first FH lesion cells, SVM is a support vector machine model, Topo For the first The ligand and the first The algorithm uses topological matching scores for cells to simulate the interaction process between ligands and cell surface targets, quantifies the affinity between the two, and initially screens out candidate target ligands with high potential to bind to diseased cells, greatly reducing the workload of subsequent experimental verification and improving screening efficiency.

[0042] Experimental validation was performed on the candidate targeting ligands obtained from the initial screening: First, these candidate targeting ligands were synthesized, and FITC-labeled with fluorescein at the amino site of the ligands using the N-hydroxysuccinimide method to complete the fluorescent labeling. The introduction of fluorescein allows for direct tracking of the ligand-cell binding process, facilitating subsequent detection and quantification. Simultaneously, hepatocytes and macrophages from FH patients carrying the LDLR gene mutation were cultured in DMEM medium containing 10% fetal bovine serum at 37°C and 5% CO2 until the logarithmic growth phase. Cell growth status was observed during this period, and the cell concentration was adjusted to 1×10⁻⁶ cells / year after the cell density reached the target. 6 Cells in the logarithmic growth phase have high activity and vigorous metabolism, which can more realistically simulate the state of diseased cells in vivo and ensure the reliability of ligand binding experiment results.

[0043] One mL of a pre-adjusted cell suspension was mixed with the fluorescently labeled candidate ligand and incubated at 37°C and 5% CO2 for 2 hours. This temperature and CO2 concentration are consistent with the human physiological environment, ensuring cell and ligand activity and maximizing their binding. After incubation, the cells were washed three times with PBS buffer to thoroughly remove unbound free ligands and prevent interference with the detection results. Flow cytometry was used to detect the washed cells, with an excitation wavelength of 488 nm and an emission wavelength of 525 nm. Unlabeled FH disease cells were used as a negative control. Flow cytometry accurately identifies and counts cells with fluorescent signals, and comparison with the negative control eliminates non-specific fluorescence interference.

[0044] The number of cells with fluorescence signal intensity higher than the negative control threshold in the statistical detection results is the number of positive cells, and the binding rate is calculated using the formula: Binding rate = (Number of positive cells / Total number of cells detected) × 100%, where the total number of cells detected is the total number of FH lesion cells actually detected by flow cytometry. Ligands with a binding rate ≥ 80% are selected as highly efficient targeting ligands for subsequent use. This binding rate standard ensures that the ligand can bind to FH lesion cells efficiently and specifically, providing a core guarantee for the targeting of the subsequent delivery system.

[0045] HepG2 cells were selected as the source cells for exosomes. These cells belong to the hepatocyte line and are highly compatible with the cell characteristics of the main sites of FH lesions. The exosomes secreted by HepG2 cells are more easily recognized and taken up by liver disease cells, making them suitable as a carrier for FH treatment. Serum-free DMEM / F12 medium was used for culture for 48-72 hours. The serum-free medium avoids interference from impurities and proteins in serum and exosomes in subsequent separation and purification, ensuring the purity of the final exosomes. Before collecting the cell supernatant, the cell state was observed to confirm that the cell confluence reached 80%-90% and that there was no obvious apoptosis. This degree of confluence ensures that the cells secrete sufficient exosomes, and the absence of apoptosis avoids contamination of the supernatant by the contents released from cell rupture, ensuring that the cell state meets the requirements for exosome extraction.

[0046] Collect the cell supernatant that meets the requirements and separate exosomes using ultracentrifugation: Place the cell supernatant in a 4°C environment and centrifuge at 300×g for 10 minutes to remove cell precipitate. This step can initially remove large-volume impurities. Take the supernatant after centrifugation and centrifuge at 2000×g for 20 minutes to remove larger cell debris and further purify the supernatant. Take the supernatant again and centrifuge at 10000×g for 30 minutes to remove smaller impurity particles and reduce interference in subsequent exosome purification. Finally, take the supernatant and centrifuge at 100000×g for 70 minutes. This speed can specifically precipitate exosomes. After centrifugation, collect the precipitate and resuspend it in PBS buffer to obtain a preliminary exosome suspension. PBS buffer can maintain the osmotic pressure and activity of exosomes and avoid their structural damage.

[0047] The resuspended exosomes were analyzed for particle size using a nanoparticle tracking analyzer. The detection temperature was set at 25℃, the detection time at 60 seconds, and the number of detections was 3 times to ensure that the exosome particle size distribution was within the range of 30-150 nm, with the peak particle size within the range of 80-120 nm. Exosomes in this size range have good biomembrane penetration and cellular uptake efficiency, making them ideal drug delivery carriers. At the same time, Western blotting was used to detect markers on the surface of exosomes. These markers are exosome-specific proteins. The detection results can verify the purity and integrity of exosomes, exclude contamination from other vesicles or impurities, and finally obtain qualified exosomes, providing a high-quality carrier for subsequent modification and loading steps.

[0048] Click chemistry was used to modify qualified exosomes with targeted ligands. First, the highly efficient targeted ligands obtained in the previous screening were modified with azide groups. At the same time, the exosomes were modified with 1,2-distearyl-sn-glycerol-3-phosphoethanolamine-N-propynyloxysuccinimide for alkyne modification. The azide group and the alkyne group can react specifically. After modification, the modified targeted ligands and exosomes were mixed at a molar ratio of 1:50, and 1 mmol / L CuSO4 was added as a catalyst. The reaction was carried out at 37°C for 2 hours. The click chemistry method has high reaction efficiency and strong specificity, which can ensure that the targeted ligands are stably and uniformly bound to the surface of exosomes, enabling the exosomes to recognize FH diseased cells. Subsequently, it can accurately locate the lesion site and reduce non-specific effects on normal cells.

[0049] LDLR mRNA and PCSK9 monoclonal antibody were selected as active substances for the treatment of familial hypercholesterolemia. After entering FH diseased cells, LDLR mRNA can guide the cells to synthesize LDLR protein, replenish LDLR that is missing or dysfunctional due to gene mutation, and enhance the cells' ability to clear low-density lipoprotein cholesterol. PCSK9 monoclonal antibody can bind to intracellular PCSK9 protein, inhibit its degradation of LDLR, and prolong the action time of LDLR on the cell surface. The synergistic effect of the two can improve the dyslipidemia of FH from different mechanisms. Two active substances were loaded into modified exosomes using different methods: For LDLR mRNA, the Amaxa Nucleofector electroporation system was used for loading, with electroporation parameters set to 1600V, 20ms, and 1 pulse. These parameters can efficiently open the exosome membrane channels without damaging the exosome structure, allowing LDLR mRNA to enter the exosome. For PCSK9 monoclonal antibody, a passive loading method was used, incubating the antibody and exosome at 4°C for 12 hours. The low temperature environment can maintain antibody activity, and the antibody passively diffuses into the exosome through concentration gradient, ultimately resulting in an exosome-targeted delivery system for familial hypercholesterolemia that can carry dual active substances and has strong targeting.

[0050] A familial animal model of hypercholesterolemia was constructed: Low-density lipoprotein receptor (LDL-R) gene knockout mice were selected as the model animals. These mice congenitally lack the Ldlr gene, which is highly similar to the pathological mechanism of human FH patients carrying the LDLR gene mutation. The mice were fed a high-fat diet containing 21% fat and 0.15% cholesterol for 8 weeks. The high-fat diet accelerated the increase of blood lipids and liver lesions in the mice, simulating the disease progression of human FH. The successful construction of the model was confirmed by detecting the serum blood lipid levels of the mice, providing an animal model that conforms to clinical pathological characteristics for subsequent validation of the efficacy of the delivery system.

[0051] The successfully constructed animal models were randomly divided into four groups of 10 mice each: a PBS control group, an unmodified exosome group, a non-targeted modified exosome group, and a targeted delivery system group. This multi-group control setup eliminated the interference of PBS, empty exosomes, and non-targeted exosomes on the experimental results, clearly demonstrating the contribution of targeted modification and active substances to the therapeutic effect. Mice in each group were administered the drug via tail vein injection. This route allows the delivery system to rapidly enter the bloodstream and directly reach major diseased organs such as the liver, meeting the drug delivery requirements for FH treatment. The administration frequency was once a week, with a dose of 200 μg / mouse based on exosome protein content, for 8 weeks. This fixed administration regimen ensured the repeatability and comparability of the experiment, ensuring that the only difference between groups was the delivery system. The PBS control group received an equal volume of PBS buffer, the unmodified exosome group received exosomes without targeted ligand modification and without loaded with active substances, and the non-targeted modified exosome group received exosomes with non-targeted ligand modification and loaded with the same active substances.

[0052] After drug administration, serum and liver tissue samples were collected from each group of mice. Serum samples were used to detect lipid levels, including total cholesterol and low-density lipoprotein cholesterol. These indicators are core biochemical markers for evaluating the efficacy of FH treatment and directly reflect the degree of improvement in lipid abnormalities by the delivery system. Liver tissue samples were prepared into pathological sections, and pathological staining was used to observe liver lesions and detect pathological indicators. This histological approach validated the repair effect of the delivery system on liver damage and comprehensively evaluated the treatment efficacy.

[0053] Finally, the performance of the targeted delivery system constructed in this embodiment is evaluated using the delivery system performance index algorithm. The formula for the delivery system performance index algorithm is as follows: ,in, As a comprehensive performance index of the delivery system, For targeted scoring, For active substance loading efficiency, To score bioavailability, , , The algorithm comprehensively considers the targeting score, active substance loading efficiency, bioavailability score, and corresponding weighting coefficients, and can comprehensively measure the performance of the delivery system from three key dimensions: targeting accuracy, drug carrying capacity, and in vivo absorption effect. The comprehensive efficiency index is calculated. If the comprehensive efficiency index is ≥80, the delivery system is determined to be the optimal delivery system, which can ensure its good effectiveness and reliability in subsequent clinical applications and can stably play a role in the treatment of familial hypercholesterolemia.

[0054] In summary, this embodiment targets patients with familial hypercholesterolemia carrying LDLR gene mutations. A multi-omics data integration module was used to combine patient samples with public database data. Data was constructed through standardization and a multi-omics feature weighted fusion algorithm. A ligand-cell affinity prediction algorithm was then used to screen for highly efficient targeting ligands, and flow cytometry was used to verify their effectiveness. HepG2 cells were selected, and qualified exosomes were prepared using ultracentrifugation. Click chemistry was used to modify the exosomes, loading them with LDLR mRNA and PCSK9 monoclonal antibody. Finally, the system was validated in a low-density lipoprotein receptor gene knockout mouse model. The delivery system efficacy index algorithm was used for evaluation, resulting in a precisely targeted exosome delivery system that synergistically improves blood lipids. Once the target is met, it can be used to treat this type of patient.

[0055] Example 2:

[0056] Construction and application of an exosome-targeted delivery system for patients with familial hypercholesterolemia carrying APOB gene mutations.

[0057] Peripheral blood and liver tissue samples were collected from patients with familial hypercholesterolemia (FH) carrying the APOB gene mutation. Peripheral blood samples were used to extract genomic data, while liver tissue samples were used to extract genomic and proteomic data. Next-generation sequencing (NGS) was used for genomic analysis, focusing on APOB gene mutations and single nucleotide polymorphisms. Liquid chromatography-tandem mass spectrometry (LC-MS / MS) was used for proteomic analysis, focusing on the expression levels of lipid metabolism-related proteins and cell surface receptor proteins.

[0058] Genomic and proteomic data related to familial hypercholesterolemia were downloaded from the TCGA, GEO, and ProteomeXchange databases and integrated with patient sample data to form an initial multi-omics dataset. The initial data underwent standardization and normalization to eliminate data differences. Then, a multi-omics feature-weighted fusion algorithm was used to fuse the multi-omics data. The expression for the multi-omics feature-weighted fusion algorithm is as follows: To construct a multi-omics dataset suitable for FH patients carrying the APOB gene mutation, such as... Figure 2 As stated above.

[0059] From the aforementioned multi-omics datasets, features associated with binding to FH lesion cells carrying APOB gene mutations were extracted, including cell surface receptor features related to APOB gene expression and protein features related to abnormal lipid metabolism. Based on these features, candidate target ligands were screened using a ligand-cell affinity prediction algorithm. The expression for the ligand-cell affinity prediction algorithm is as follows: Preliminary candidate ligands were obtained.

[0060] Experimental validation of candidate ligands: After synthesizing the candidate ligands, FITC was labeled at the amino site of the ligands using the N-hydroxysuccinimide method; hepatocytes and macrophages derived from FH patients carrying APOB gene mutations were cultured in DMEM medium containing 10% fetal bovine serum at 37°C in a 5% CO2 incubator until the logarithmic growth phase, and the cell concentration was adjusted to 1×10⁻⁶. 6 per mL.

[0061] Mix 1 mL of cell suspension with the fluorescently labeled candidate ligand and incubate at 37°C and 5% CO2 for 2 hours. After incubation, wash the cells three times with PBS to remove free ligands. Detect the binding rate using flow cytometry, with diseased cells without fluorescent ligands as a negative control. The formula is: Binding rate = (Number of positive cells / Total number of cells detected) × 100%. Ligands with a binding rate ≥ 80% are selected as highly effective targeting ligands.

[0062] Mesenchymal stem cells were selected as the exosome source cells and cultured in serum-free DMEM / F12 medium for 48-72 hours. Before collecting the cell supernatant, the cell confluence was confirmed to be 80%-90% and there was no obvious apoptosis.

[0063] Cell supernatant was separated by ultracentrifugation: at 4°C, the cells were first centrifuged at 300×g for 10 minutes and the supernatant was collected; then centrifuged at 2000×g for 20 minutes and the supernatant was collected; then centrifuged at 10000×g for 30 minutes and the supernatant was collected; finally, the cells were centrifuged at 100000×g for 70 minutes and the precipitate was collected and resuspended in PBS.

[0064] The particle size of exosomes was detected using a nanoparticle tracking analyzer. The temperature was set to 25℃, the detection time to 60 seconds, and the number of detections to 3 times, ensuring that the particle size distribution was 30-150nm and the peak value was 80-120nm. The exosomes were qualified by detecting CD63, CD9, and CD81 markers using Western blotting, and pure exosomes were obtained.

[0065] Exosome modification was performed using click chemistry: the highly efficient targeting ligand was modified with an azide group, and the exosomes were modified with an alkynyl group using 1,2-distearate-sn-glycerol-3-phosphoethanolamine-N-propynyloxysuccinimide; the ligand and exosomes were mixed at a molar ratio of 1:50, 1 mmol / L CuSO4 was added, and the reaction was carried out at 37 °C for 2 hours to complete the chemical coupling of the targeting ligand.

[0066] LDLRmRNA and PCSK9 monoclonal antibody were selected as active substances: LDLRmRNA was loaded using the Amaxa Nucleofector electroporation system with parameters set to 1600V, 20ms, and 1 pulse; PCSK9 monoclonal antibody was passively loaded by incubation at 4°C for 12 hours, resulting in an exosome-targeted delivery system for patients with APOB gene mutation FH.

[0067] FH animal model construction: Low-density lipoprotein receptor gene knockout mice were fed a high-fat diet containing 21% fat and 0.15% cholesterol for 8 weeks to confirm the success of the model.

[0068] The model mice were divided into four groups (n=10 per group): PBS control group, unmodified exosome group, non-targeted modified exosome group, and targeted delivery system group. Exosome protein was injected once weekly via tail vein at a dose of 200 μg / mouse for 8 weeks. The PBS control group received an equal volume of PBS, the unmodified group received unmodified empty exosomes, and the non-targeted group received non-targeted modified exosomes loaded with active substances.

[0069] Serum and liver tissue samples were collected after drug administration: serum lipids were measured, and pathological changes in liver tissue were observed. The delivery system effectiveness was evaluated using a delivery system effectiveness index algorithm, and the overall effectiveness index was calculated. The formula for the delivery system effectiveness index algorithm is as follows: If the value is ≥80, it is considered the optimal delivery system and can be used to treat patients with FH carrying the APOB gene mutation.

[0070] In summary, this embodiment targets patients with familial hypercholesterolemia carrying APOB gene mutations. First, it integrates patient samples with APOB-related multi-omics data from public databases, and constructs a library using a multi-omics feature weighted fusion algorithm. Then, it screens and validates highly efficient targeting ligands suitable for the mutated diseased cells. Exosomes are prepared from mesenchymal stem cells and tested for quality through ultracentrifugation. After modification, dual-active substances are loaded, synergistically improving blood lipids. The system is validated in a low-density lipoprotein receptor gene knockout mouse model, and evaluated using a delivery system efficacy index algorithm. Finally, a biocompatible, highly targeted, and effective exosome delivery system is constructed, suitable for the treatment of this type of patient.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-omics data-based exosome targeted delivery system for familial hypercholesterolemia, characterized in that, The system includes: Multi-omics data integration module: Collects genomic and proteomic data from patients with familial hypercholesterolemia, integrates multi-omics data from public databases, standardizes and normalizes the collected data, and uses a multi-omics feature weighted fusion algorithm to fuse the data and construct a multi-omics dataset; Targeted ligand screening module: Extracts features related to binding with lesion cells in familial hypercholesterolemia from multi-omics datasets, screens candidate targeted ligands through ligand-cell affinity prediction algorithm, and obtains efficient targeted ligands through experimental verification; Exosome preparation module: Hepatocytes or mesenchymal stem cells are cultured, cell supernatant is collected, exosomes are separated by ultracentrifugation, particle size is analyzed by a nanoparticle tracking analyzer, and CD63, CD9, and CD81 markers are detected by Western blotting. Exosome modification and loading module: The screened target ligands are modified onto the surface of exosomes by chemical coupling, and the active substances for treating familial hypercholesterolemia are loaded into exosomes by electroporation or passive loading. System performance verification module: A familial hypercholesterolemia animal model was constructed. The model animals were grouped and given different preparations. After continuous administration, serum and liver tissue samples were collected to detect blood lipid levels and liver tissue pathological indicators. The delivery system performance index algorithm was used to evaluate the system performance and optimize it.

2. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the multi-omics data integration module, the genomic data of patients with familial hypercholesterolemia are obtained from peripheral blood or liver tissue samples through next-generation sequencing technology, including mutations and single nucleotide polymorphisms of the LDLR, APOB, and PCSK9 genes; the proteomic data are obtained from the above samples through liquid chromatography-tandem mass spectrometry technology, including the expression levels of lipid metabolism-related proteins and cell surface receptor proteins; public databases include TCGA, GEO, and ProteomeXchange, from which genomic and proteomic data related to familial hypercholesterolemia are obtained.

3. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the multi-omics data integration module, the expression for the multi-omics feature weighted fusion algorithm is: ,in, For the first The fusion feature value of each sample, As an omics type, For the first Omics for the first The weighting coefficients of the sample. For the first Omics The z-score-normalized feature values ​​of the sample For the first Omics Importance score of sample features The total number of omics types, No. The first in omics The original feature values ​​of each sample.

4. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the targeted ligand screening module, the expression for the ligand-cell affinity prediction algorithm is: ,in, For the first The first targeted ligand and the first The binding affinity score of the diseased cells. For the first The quantum mechanical characteristics of a targeted ligand No. One candidate targeting ligand, Prot For the first Structural characteristics of cell surface receptor proteins No. FH lesion cells, SVM is a support vector machine model, Topo For the first The ligand and the first Topological matching score of the cells.

5. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the targeted ligand screening module, the specific steps for experimentally verifying efficient targeted ligands are as follows: Synthesize candidate target ligands selected by the ligand-cell affinity prediction algorithm; The fluorescently labeled ligand was obtained by labeling the candidate targeting ligand with FITC using the N-hydroxysuccinimide ester method. Hepatocytes and macrophages derived from patients with familial hypercholesterolemia were cultured in DMEM medium containing 10% fetal bovine serum at 37°C and 5% CO2 until the logarithmic growth phase, and the cell concentration was adjusted to 1×10⁻⁶. 6 cells / mL; Take 1 mL of the cell suspension with the adjusted concentration, mix it with the fluorescently labeled ligand, and incubate it together at 37°C and 5% CO2 for 2 hours; After incubation, the cells were washed three times with PBS buffer to remove unbound free ligands; The washed cells were analyzed using flow cytometry, with the excitation wavelength set at 488 nm and the emission wavelength at 525 nm. Using familial hypercholesterolemia lesion cells without added fluorescently labeled ligands as a negative control, the number of cells with fluorescence signal intensity higher than the threshold of this negative control was counted and recorded as the number of positive cells; The binding rate is calculated using the formula: (Number of positive cells / Total number of cells detected) × 100%; where the total number of cells detected is the total number of cells actually detected by flow cytometry. Ligands with a binding rate of ≥80% were selected and defined as highly efficient targeting ligands.

6. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the exosome preparation module, the specific steps of the ultracentrifugation method are as follows: the cell supernatant is centrifuged sequentially at 4°C at 300×g for 10 min, 2000×g for 20 min, and 10000×g for 30 min. The supernatant is then centrifuged at 100000×g for 70 min, and the precipitate is resuspended in PBS. When the nanoparticle tracking analyzer is used for detection, the temperature is set to 25°C, the detection time to 60 seconds, and the number of detections to 3. The exosome particle size distribution is required to be 30-150 nm with a peak value of 80-120 nm.

7. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the exosome modification and loading module, chemical coupling is performed using a click chemistry method. Specifically, the targeting ligand is modified with an azide group, and the exosomes are modified with an alkynyl group using 1,2-distearate-sn-glycerol-3-phosphoethanolamine-N-propynyloxysuccinimide. The two are mixed at a molar ratio of 1:50 and reacted at 37°C for 2 hours under 1 mmol / L CuSO4 catalysis. The active substances are LDLR mRNA and PCSK9 monoclonal antibody. LDLR mRNA is loaded using an Amaxa Nucleofector electroporation system with parameters of 1600V, 20ms, and 1 pulse. PCSK9 antibody is passively loaded by incubation at 4°C for 12 hours.

8. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the efficacy verification module of the targeted delivery system, the familial hypercholesterolemia animal model is a low-density lipoprotein receptor gene knockout mouse. During modeling, the mice were fed a high-fat diet containing 21% fat and 0.15% cholesterol for 8 weeks. The groups included a PBS control group, an unmodified exosome group, a non-targeted modified exosome group, and a targeted delivery system group, with 10 mice in each group. The mice were administered the drug once a week via tail vein injection at a dose of 200 μg of exosome protein per mouse for 8 weeks.

9. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the exosome carrier preparation module, the cultured cells are HepG2 cells or mesenchymal stem cells, cultured in serum-free DMEM / F12 medium for 48-72 hours. Before collecting the supernatant, it is necessary to confirm that the cell confluence reaches 80%-90% and there is no obvious cell apoptosis.

10. The exosome targeted delivery system for familial hypercholesterolemia based on multi-omics data according to claim 1, characterized in that, In the targeted delivery system efficacy verification module, the formula for the delivery system effectiveness index algorithm is as follows: ,in, This is the overall performance index of the delivery system, ranging from 0 to 100. A score of ≥80 indicates an optimal delivery system. For targeted scoring, For active substance loading efficiency, To score bioavailability, , , These are the weighting coefficients.

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