Analysis and intervention method for muscle fiber pathological heterogeneity based on pdgfra signal regulation of myogenic progenitor cells

Abstract

The application discloses a kind of based on myogenic progenitor cell Pdgfra signal regulation Muscle fibrosis pathological heterogeneity analysis and intervention method, belong to biological medicine technical field.The present application is by constructing myogenic progenitor cell specific Pdgfra conditional knockout mouse MPKO and continuously activated mouse MPK, obtains craniomaxillofacial muscle and limb skeletal muscle and carries out histopathology, ultrastructure and oxidative stress detection, determines site-specific difference phenotype;Difference gene, signal pathway and cell subgroup change are analyzed by combining transcriptome sequencing and single-cell transcriptome sequencing, and the molecular mechanism of Pdgfra regulated muscle pathological heterogeneity is analyzed;With Pdgfra and difference gene coding protein as target point carries out artificial intelligence drug virtual screening, obtains candidate compound and verifies in vivo efficacy in MPK mouse.The present application provides a standardized technical means for mechanism analysis of muscle fibrosis pathological heterogeneity and rapid screening of anti-fibrosis drugs, and can be used for target mining and precise intervention research of myogenic degenerative diseases.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and specifically relates to a method for analyzing and intervening in the pathological heterogeneity of muscle fibrosis based on the regulation of Pdgfra signaling in myogenic progenitor cells. Background Technology

[0002] Myogenic degenerative diseases (such as Duchenne muscular dystrophy and age-related sarcopenia) are often accompanied by skeletal muscle fibrosis and fatty degeneration during their course. Both clinical observation and basic research have found that the degree of damage and fibrosis in craniofacial muscles (such as the masseter) is significantly more severe than in limb skeletal muscles (such as the quadriceps), suggesting significant pathological heterogeneity in different skeletal muscle sites to the same pathogenic stimuli. However, the underlying mechanisms leading to this site-specific difference remain unclear.

[0003] Pdgfra (platelet-derived growth factor receptor α) is a key signaling molecule regulating the process of tissue fibrosis. Previous studies have shown that Pdgfra can accumulate ectopically and promote fibrosis under pathological conditions, but its autonomous regulatory function in myogenic progenitor cells and its differentiated effects on craniofacial muscles and limb skeletal muscles still lack systematic research. Current techniques largely focus on the role of mesenchymal cells (such as fibroblasts and adipocytes) in muscle fibrosis, neglecting the contribution of Pdgfra signaling imbalance within myogenic progenitor cells to the pathological heterogeneity of muscles. This research gap leads to: unclear molecular basis of muscle-specific damage, a lack of therapeutic targets for precise intervention, and limited clinical treatment efficacy.

[0004] Therefore, there is an urgent need to establish a technical system that can systematically analyze the pathological heterogeneity of craniofacial muscles and limb skeletal muscles under the regulation of Pdgfra signaling in myogenic progenitor cells, and on this basis, realize the discovery of key targets and rapid screening of intervention drugs, so as to provide technical support for basic research and clinical translation of myogenic degenerative diseases. Summary of the Invention

[0005] In view of this, the present invention proposes a method for analyzing and intervening in the pathological heterogeneity of muscle fibrosis based on the regulation of Pdgfra signaling in myogenic progenitor cells. This method aims to provide precise therapeutic targets and intervention strategies for myogenic degenerative diseases by constructing genetically engineered mouse models, systematically comparing the pathological phenotypes of muscles in different locations, analyzing molecular mechanisms, and establishing an artificial intelligence drug screening system.

[0006] This invention provides a method for analyzing and intervening in the pathological heterogeneity of muscle fibrosis based on Pdgfra signal regulation of myogenic progenitor cells, comprising the following steps:

[0007] Step 1: Construct a mouse model regulated by myogenic progenitor cell-specific Pdgfra signaling, including a Pdgfra conditional knockout model and a Pdgfra sustained activation model.

[0008] Step 2: Take individual mice from the mouse model constructed in Step 1, and take samples from their craniofacial muscles and skeletal muscles of the limbs for histopathological examination, ultrastructural observation and oxidative stress level detection to determine site-specific differential phenotypes.

[0009] Step 3: Transcriptome sequencing and single-cell transcriptome sequencing were performed on the craniofacial muscles and limb skeletal muscles to analyze differentially expressed genes and their encoded proteins, signaling pathways and changes in cell subsets, and to elucidate the molecular mechanism by which Pdgfra signaling regulates muscle pathological heterogeneity.

[0010] Step 4: Using Pdgfra and differentially encoded proteins obtained through transcriptome sequencing and single-cell transcriptome sequencing in Step 3 as targets, perform artificial intelligence-based virtual drug screening to obtain candidate compounds;

[0011] Step 5: Administer the candidate compound to the Pdgfra sustained activation model mice to verify its in vivo efficacy.

[0012] Preferably, in step 1: the Pdgfra conditional knockout model is the Myf5-cre-mediated Pdgfra conditional knockout mouse model MPKO, which is formed by Pdgfrafl / fl Obtained by crossing mice with Myf5-cre tool mice;

[0013] The Pdgfra sustained activation model is the Myf5-cre mediated Pdgfra sustained activation mouse model MPK, which is created by using Pdgfra mice carrying the D842V activation mutation. K / + The mice were obtained by crossing Myf5-cre mice.

[0014] Preferably, the histopathological examination in step 2 includes: using HE staining to assess myofibril morphology, necrosis, inflammatory infiltration and tissue structure, using Masson staining to assess the degree of collagen deposition and fibrosis, and using Oil Red O staining to assess the level of fat infiltration.

[0015] The oxidative stress level detection includes: using a DHE probe to detect the in-situ reactive oxygen species level in tissues;

[0016] The ultrastructural observations include: using transmission electron microscopy to observe mitochondrial morphology, sarcomere structure, basement membrane integrity, and autophagosome formation.

[0017] Preferably, step 2 further includes immunofluorescence detection, wherein the immunofluorescence markers include one or more of Laminin, Ki67, Pax7 and Col1a1, used to assess basement membrane integrity, cell proliferation activity, number of muscle stem cells and degree of fibrosis.

[0018] Preferably, the analysis method for transcriptome sequencing in step 3 includes: aligning to the mm10 reference genome using STAR, screening for differentially expressed genes using Deseq2, and performing GO and KEGG pathway enrichment analysis;

[0019] The analysis methods for the single-cell transcriptome sequencing include: data normalization, dimensionality reduction clustering, and cell subpopulation annotation using the Seurat framework.

[0020] Preferably, the artificial intelligence-based virtual drug screening in step 4 includes:

[0021] (a) Dataset construction: Obtain target structure information of Pdgfra and differentially expressed gene-encoded proteins, collect active compounds as positive samples and inactive compounds as negative samples from compound databases, and obtain candidate compounds from natural product libraries;

[0022] (b) Construct a bimodal model based on graph convolutional networks and a large language model, train the model with the dataset, and predict the binding activity of compounds and targets;

[0023] (c) Screening candidate compounds from natural product libraries and compound databases, and performing ADMET property predictions;

[0024] (d) The candidate compounds were virtually docked using the molecular docking method, and the compounds with a binding affinity greater than 9 kcal / mol were retained as high-potential candidate compounds.

[0025] Preferably, the molecular docking is performed using AutoDock Vina software.

[0026] Preferably, the in vivo efficacy verification in step 5 includes: intraperitoneal injection of the candidate compound into MPK model mice, taking muscle tissue after administration, assessing the fibrosis area by Masson staining, and detecting the expression level of fibrosis-related proteins by Col1a1 and / or α-SMA immunofluorescence.

[0027] This invention also provides a target selection method for precise intervention in muscle fibrosis, comprising the following steps:

[0028] (a) Using steps (1) to (4) of the method described in any one of claims 1-8, the molecular mechanism by which Pdgfra regulates muscle pathological heterogeneity is analyzed and candidate compounds are screened.

[0029] (b) To verify the in vivo efficacy of the candidate compounds and identify effective intervention targets.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This invention provides a method for analyzing and intervening in the pathological heterogeneity of muscle fibrosis based on Pdgfra signaling regulation from myogenic progenitor cells. By constructing a mouse model of conditional knockout and sustained activation of Pdgfra specific to myogenic progenitor cells, and combining histopathological, ultrastructural, and oxidative stress detection, it is possible to directly compare site-specific differences in fibrosis, adipose tissue, mitochondrial morphology, and reactive oxygen species levels between craniofacial muscles and limb skeletal muscles, providing a standardized technical means for studying muscle pathological heterogeneity. Furthermore, by integrating transcriptome sequencing and single-cell transcriptome sequencing analysis, the molecular mechanisms by which Pdgfra signaling regulates muscle pathological heterogeneity can be systematically analyzed from the perspectives of differentially expressed genes and their encoded proteins, signaling pathways, and changes in cell subsets, and the proteins encoded by these differentially expressed genes can be directly identified as potential intervention targets. Furthermore, by establishing a dual-modal AI-based virtual drug screening system based on graph convolutional networks and large language models, and combining ADMET property prediction and molecular docking, high-potential anti-fibrotic candidate compounds can be rapidly screened from a large-scale compound library, and in vivo efficacy verification can be completed in MPK model mice, thus forming a complete technical closed loop from target discovery to efficacy verification. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating the technical route of the present invention.

[0034] Figure 2 HE staining (C-F') and Oil Red O staining (GJ) images of the masseter and quadriceps femoris muscles of 3-month-old WT and MPKO mice, scale bar 400 μm.

[0035] Figure 3This image shows the morphology and cell proliferation assessment of the basement membrane of myofibrils in the masseter and quadriceps muscles of 3-month-old WT, MPK, and MPKO mice. (A–F') are Laminin immunofluorescence staining images, and A'–F' are magnified views of the regions in A–F images, with white arrows indicating thickened basement membranes and white asterisks indicating discontinuities or breaks in the basement membrane. (G–L) are Ki67 and Laminin dual immunofluorescence staining images, with white arrows indicating Ki67-positive cells located in the myofibril stroma. (M–O) are quantitative analyses of muscle tissue morphology, where (M) represents the myofibril diameter (left) and cross-sectional area (right), (N) represents the number of cell nuclei within the myofibril basement membrane, and (O) represents the number of Ki67-positive cells outside the myofibril basement membrane. Scale bar: 400 μm.

[0036] Figure 4 Images show the ultrastructural observation of mitochondria in the masseter and quadriceps femoris muscles of 3-month-old WT, MPK, and MPKO mice, and the assessment of oxidative stress levels. (A–F) are transmission electron microscopy images; blue arrows indicate the Z-line structure of the sarcomere, and red boxes circle representative mitochondrial regions. Each image is accompanied by a high-magnification image of the area within the red box below. In MPK mice (B, E), mitochondria show swelling and vacuolation; in MPKO mice (C, F), mitochondria show an electron-dense concentric layered structure (scale bar 1 μm). (G–L) shows in-situ ROS level detection (scale bar 400 μm).

[0037] Figure 5 This is a transcriptomic differential analysis of the masseter and quadriceps femoris muscles in MPK mice. (A) shows a heatmap and protein-protein interaction analysis of masseter-preferred response genes, demonstrating the expression differences of genes related to pathways such as inflammation, immunity, mitochondrial and oxygen metabolism, and neural inhibition in the masseter and quadriceps femoris muscles of MPK mice. (B) shows a heatmap analysis of shared response genes in the masseter and quadriceps femoris muscles, covering gene sets related to muscle morphogenesis, protein stability, calcium homeostasis, apoptosis, muscle function, fast-twitch fibers, angiogenesis, and anti-apoptosis. (C) shows a heatmap and protein-protein interaction network analysis of quadriceps femoris-preferred response genes, demonstrating the expression differences of genes related to pathways such as extracellular matrix remodeling, muscle connective tissue fibrosis, cell cycle, and stem cells. Each row in the heatmap represents one gene, and each column represents one type of muscle tissue (QF: quadriceps femoris; MASS: masseter).

[0038] Figure 6This is a transcriptomic differential analysis of the masseter and quadriceps femoris muscles in MPKO mice. (A) shows a heatmap and protein-protein interaction analysis of masseter-preferred response genes, demonstrating the expression differences of genes related to pathways such as necrotizing inflammation, immunity, mitochondrial and oxygen metabolism in the masseter and quadriceps femoris muscles of MPKO mice. (B) shows a heatmap analysis of shared response genes in the masseter and quadriceps femoris muscles, displaying gene sets related to muscle atrophy, iron metabolism, apoptosis, muscle connective tissue fibrosis, lipid metabolism, extracellular matrix maintenance, anti-apoptosis, and autophagy. (C) shows a heatmap and protein-protein interaction network analysis of quadriceps femoris-preferred response genes, demonstrating the expression differences of genes related to pathways such as neural maintenance, vascular integrity maintenance, and cell cycle. Each row in the heatmap represents one gene, and each column represents one type of muscle tissue (QF: quadriceps femoris; MASS: masseter). Detailed Implementation

[0039] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto. Experimental methods not specified with specific conditions in the embodiments are generally carried out under conventional conditions or under conditions recommended by the manufacturer.

[0040] Figure 1 The entire technical route of this invention is fully presented: starting from the discovery of driver genes for muscle degenerative diseases, Pdgfra is identified as a key regulatory gene through single-cell transcriptome analysis, then myogenic progenitor cell-specific Pdgfra activation (MPK) and knockout (MPKO) mouse models are constructed, muscle pathological characteristics are analyzed through histological and cytological experiments, and then a Pdgfra regulatory network is constructed using multi-omics. Finally, the entire process of virtual drug screening using artificial intelligence is presented, clearly demonstrating the logical connection and technical integration of each research link.

[0041] Example 1: Construction of a genetically engineered mouse model

[0042] 1.1 Myogenic progenitor cell-specific Pdgfra conditional knockout model (Myf5-cre; Pdgfrafl / fl, MPKO)

[0043] A conditional knockout model was constructed using the Cre-LoxP system: a loxP site was inserted upstream of exon 1 of the Pdgfra gene, and a loxP site with PGK-neo was inserted downstream of exon 4, so that the key region (approximately exons 1-4) was sandwiched between the two loxPs. When Cre was not expressed, the gene was essentially normal; when Cre was present, the sequence between the two loxPs was recombinated and excised, and Pdgfra was inactivated, achieving tissue / cell-specific knockout, successfully constructing Pdgfrafl / fl mice. Mice aged 7-8 weeks and weighing over 30g were selected and housed at a female-to-male ratio of 2:1. Pregnancy emboli were checked the following day; the day a emboli was found was recorded as day 0.5 of gestation (E0.5). Offspring mice were bred according to standard breeding procedures. The Pdgfrafl / fl mice were then... Mice were crossed with Myf5-cre tool mice, and Myf5-cre was selected in the F1 generation; Pdgfra fl / + Mice were then compared with reproductive-age Pdgfrafl / fl. Mice were backcrossed to obtain F3 generation Myf5-cre; Pdgfrafl / fl homozygous knockout mice; wild-type (WT) mice from the same littermate were used as controls, and genotyping was completed by PCR amplification and agarose gel electrophoresis.

[0044] 1.2 Myogenic progenitor cell-specific Pdgfra sustained activation model (Myf5-cre; Pdgfra) K / + MPK)

[0045] Construct Pdgfra carrying the D842V activation mutation K / + Alleles, inserted SA-loxP-STOP-loxP regulatory element.

[0046] Mice aged 7-8 weeks and weighing over 30g were selected and housed together at a female-to-male ratio of 2:1. Pregnancy thrombi were checked the next day, and the day in which a thrombus was found was recorded as day 0.5 of pregnancy (E0.5). Offspring mice were bred according to the conventional breeding procedure.

[0047] Pdgfra K / + Mice were crossed with Myf5-cre mice, and the STOP sequence was removed using Cre recombinase to achieve sustained activation of myogenic progenitor cells-specific Pdgfra, resulting in MPK mice. Genotypes were confirmed by PCR using littermate WT mice as controls.

[0048] 1.3 Genotyping

[0049] 1.3.1 Extraction of genomic DNA from rat tail

[0050] Cut a 0.2 cm piece of rat tail tissue and place it in a 1.5 ml EP tube. Add 99 μl of lysis buffer and 1 μl of proteinase K. Incubate overnight at 56 °C until the tissue is completely lysed. Boil at 100 °C for 10 min to inactivate the proteinase. Centrifuge at 12,000 rpm for 4 min at room temperature. Use the supernatant as a template for PCR amplification.

[0051] 1.3.2 PCR amplification and electrophoretic identification

[0052] Using mouse tail genomic DNA as a template, Myf5-cre and Pdgfra were used to detect the virus. fl ,Pdgfra K / + PCR amplification was performed using specific primers. The reaction mixture consisted of 1 μL template DNA, 1 μL each of forward and reverse primers, 22 μL 2×PCR Mix, and 25 μL sterile water, for a total volume of 50 μL. Reaction conditions were as follows: 95℃ pre-denaturation for 5 min, 95℃ denaturation for 30 s, annealing for 45 s, and 72℃ extension for 1 min, for 35 cycles, followed by a final extension at 72℃ for 7 min. The amplified products were analyzed by 2% agarose gel electrophoresis and visualized using a gel imaging system.

[0053] 1.4 Acute muscle injury model (optional, for phenotypic amplification)

[0054] Three-month-old MPK, MPKO, and WT mice were used. Acute injury was induced by injecting 0.5% BaCl2 saline solution into the masseter and quadriceps muscles on one side, while an equal volume of PBS was injected into the contralateral side as a blank control. Seven days after injury, the mice were sacrificed and their muscle regeneration and repair capabilities were assessed.

[0055] Example 2: Sample Collection and Preprocessing

[0056] After euthanasia of mice by cervical dislocation, the masseter muscle (representing the craniofacial muscles) and quadriceps femoris muscle (representing the skeletal muscles of the limbs) were rapidly separated. The tissue was divided into three parts: one part of muscle tissue was fixed overnight in 4% paraformaldehyde (PFA) at 4 °C, dehydrated by a gradient of alcohols (25%, 50%, 65%, 80%, 95%, 100% I, 100% II) (4-5 h per gradient), cleared with xylene, embedded in paraffin, and after solidification, serially sectioned at a thickness of 10 μm, baked at 37 °C overnight, and stored at 4 °C for later use in paraffin section preparation;

[0057] One sample of mouse masseter muscle and quadriceps femoris muscle was fixed overnight in 15% sucrose PFA at 4 ℃. After the tissue settled, it was dehydrated in 30% sucrose PBS overnight. After settling again, it was embedded by OCT and stored at -20 ℃. The embedded tissue was fixed on a -20 ℃ cryostat and sectioned continuously at a thickness of 10 μm for frozen section preparation.

[0058] One sample was flash-frozen in liquid nitrogen and stored at −80°C for total RNA extraction.

[0059] Example 3: Histopathology and Phenotypic Detection

[0060] 3.1 HE staining

[0061] Paraffin sections were dewaxed and rehydrated, nuclei were stained with hematoxylin for 5-10 minutes, differentiated with hydrochloric acid-ethanol, and then blued back to blue with running water; cytoplasm was stained with eosin, dehydrated and cleared, and then mounted for observation of muscle fiber morphology, necrosis, inflammatory infiltration, and tissue structure. Results are as follows: Figure 2 As shown in (C-F'): compared with WT mice, MPKO mice have more regular muscle fiber structure in the masseter muscle, less necrotic area, and significantly reduced degree of inflammatory infiltration; while there is little difference between the quadriceps femoris muscles of the two genotypes.

[0062] 3.2 Masson staining

[0063] Dewax and rehydrate paraffin sections, stain with acid fuchsin-pouncin for 6–7 min, and rinse quickly with tap water 3 times;

[0064] After mordanting with phosphomolybdic acid for 5 min, collagen fibers were stained with aniline blue for 30 s–1 min, differentiated with 0.2% glacial acetic acid for about 3 times, and then mounted. The collagen deposition area and degree of fibrosis were observed and quantitatively analyzed under a microscope.

[0065] 3.3 Oil Red O staining

[0066] After fixation of frozen sections, incubation with Oil Red O working solution in the dark was performed, cell nuclei were lightly stained with hematoxylin, and the sections were mounted for observation of lipid droplet distribution. Results are as follows: Figure 2 As shown in (GJ): compared with WT mice, MPKO mice showed a significant reduction in red lipid droplets in the masseter muscle, indicating a lower level of fat infiltration; however, there was no significant difference in fat infiltration in the quadriceps femoris muscle between the two genotypes.

[0067] 3.4 Immunofluorescence detection

[0068] Paraffin sections were dewaxed and rehydrated, then subjected to high-pressure antigen retrieval with 0.01M sodium citrate buffer for 8 min; endogenous peroxidase was blocked with 3% hydrogen peroxide and sheep serum at room temperature for 1 h; primary antibodies (Laminin, Col1a1, Pax7, Ki67) were added and incubated overnight at 4°C; after warming and washing, Goat Anti-Rabbit IgG (Alexa Fluor® 488) and Goat Anti-Mouse IgG (Alexa Fluor® 555) fluorescent secondary antibodies were added and incubated at room temperature for 1 h; incubated in TSA-488 staining working solution (Tyramide dilution 0.3% H2O2: iF488-Tyramide = 1000:10:1) for 10 min in the dark, then the bound antibodies were removed and a second primary antibody incubation was performed; finally, DAPI nuclear chromatographic staining was performed, and images were acquired under a fluorescence microscope. ImageJ was used for quantitative analysis of basement membrane integrity, collagen deposition, muscle stem cell number, and cell proliferation activity.

[0069] The results are as follows Figure 3 As shown in (A-F'): the basement membrane of the masseter muscle in MPK mice is significantly thickened (white arrow), and the basement membrane of the masseter muscle in MPKO mice is discontinuous or broken (white asterisk); while the basement membrane of the quadriceps femoris muscle shows less change. Figure 3 (GL) showed that Ki67-positive cells were mainly located in the interstitial space of myofibrils (white arrows), with the highest number of Ki67-positive cells in the masseter muscle of MPK mice. Quantitative analysis ( Figure 3 Further confirmation (MO) revealed that MPK mice had the largest muscle fiber diameter and cross-sectional area, and the number of Ki67-positive cells outside the basement membrane was significantly higher than that of WT and MPKO mice.

[0070] 3.5 Detection of Reactive Oxygen Species (ROS)

[0071] Fresh frozen sections were incubated with DHE probe dilution buffer at 37°C for 1 hour in the dark, followed by DAPI nuclear chromatographic staining. The in-situ ROS fluorescence intensity was then detected using a fluorescence microscope. Results are as follows: Figure 4 As shown in (GL), the ROS fluorescence intensity of the masseter muscle of MPK mice was the highest, indicating a significant increase in oxidative stress. The ROS level of the masseter muscle of MPKO mice was lower than that of WT mice. The ROS level of the quadriceps muscle showed little difference among the three groups.

[0072] 3.6 Observation using transmission electron microscopy

[0073] Tissue samples were fixed with 2.5% glutaraldehyde and 1% osmium tetroxide, dehydrated with gradient alcohols, and embedded in epoxy resin. Ultrathin sections were stained with uranium acetate and lead citrate, and mitochondrial morphology, sarcomere structure, basement membrane integrity, and autophagosome formation were observed by transmission electron microscopy.

[0074] The results are as follows Figure 4 As shown in (AF): MPK mouse masseter muscle mitochondria exhibit significant swelling and vacuolation (B, E, red boxes and high-magnification images), with disrupted mitochondrial cristae structure; MPKO mouse masseter muscle mitochondria show electron-dense concentric layered structures (C, F), suggesting abnormal mitochondrial morphology but different from the swelling phenotype; WT mouse mitochondrial morphology is normal. The degree of mitochondrial changes in the quadriceps femoris muscle is milder than that in the masseter muscle.

[0075] Example 4: Transcriptome Sequencing and Single-Cell Transcriptome Sequencing

[0076] 4.1 Transcriptome sequencing (RNA-seq)

[0077] Total RNA was extracted from muscle tissue of 3-month-old WT, MPK, and MPKO mice using the Tizol method. After passing quality control by Nanodrop and agarose gel electrophoresis, Illumina sequencing libraries were constructed. Paired-end sequencing at 150bp was performed using the NovaSeq platform to obtain raw FastQ data. Trim_galore software was used for quality control filtering, and STAR software was used to align to the mouse reference genome (version mm10). Deseq2 software was used to screen for differentially expressed genes, and GO and KEGG pathway enrichment analysis was performed, focusing on the inflammatory immune, mitochondrial metabolism, and extracellular matrix remodeling pathways.

[0078] The results are as follows Figure 5 (MPK mice) As shown: Compared to the quadriceps femoris muscle, MPK mice have a gene preference for the masseter muscle ( Figure 5 A) is enriched in inflammatory immune pathways (such as the TNF signaling pathway and the NF-κB pathway), mitochondrial metabolic pathways, and neural inhibitory pathways; the two sites share genes ( Figure 5 B) Involves muscle morphogenesis, protein stability, calcium homeostasis, and apoptosis; quadriceps femoris preference genes ( Figure 5 C) It accumulates in extracellular matrix remodeling, fibrosis and cell cycle pathways.

[0079] The results are as follows Figure 6 (MPKO mice) As shown: Compared to the quadriceps femoris muscle, MPKO mice have a gene preference for the masseter muscle ( Figure 6 A) Enriched in necrotic inflammation, immune, and mitochondrial metabolic pathways; the two sites share genes ( Figure 6 B) Involves muscle atrophy, iron metabolism, apoptosis, fibrosis, and autophagy; quadriceps femoris preference genes ( Figure 6 C) It is enriched in neural maintenance, vascular integrity maintenance and cell cycle pathways.

[0080] 4.2 Single-cell transcriptome sequencing (scRNA-seq)

[0081] Single-cell suspensions were prepared from the masseter and quadriceps femoris muscle tissues of 3-month-old WT, MPK, and MPKO mice. After trypan blue counting and a viability rate of ≥80%, the cells were used for library construction. Cellranger software was used for data quality control, genome alignment, and quantitative gene counting. The Seurat framework was used for data normalization, standardization, dimensionality reduction clustering, and cell subset annotation. The transcriptional characteristics of subsets such as fibroadipocyte progenitors (FAPs), myocyte stem cells, and immune cells were analyzed, and the Pdgfra regulatory network and cell differentiation trajectory were constructed.

[0082] Example 5: Virtual Drug Screening Using Artificial Intelligence

[0083] 5.1 Dataset Construction

[0084] Using Pdgfra and differentially encoded proteins as core targets, protein information was obtained from UniProt, and target structures were downloaded from RCSB PDB; active compounds (positive samples) were downloaded from ChEMBL, and irrelevant small molecules (negative samples) were randomly selected; 710,000 candidate compounds were extracted from the COCONUT natural product library.

[0085] 5.2 Model Training and Virtual Screening

[0086] A bimodal model combining Graph Convolutional Network (GCN) and Large Language Model (LLM) was constructed based on an open-source framework, and the model parameters were optimized using a training dataset. Molecules with a predicted activity of 1.0, confidence level ≥ 0.7, conforming to Lipinski rules (Lipinski Passes ≥ 4), molecular weight of 150-500, and LogP⁻²–⁵ were initially selected.

[0087] According to the formula

[0088] CompositeScore = Confidence × 0.4 + QED × 0.6 - max(0, (LogP-3) × 0.1) - max(0, (MW-400) × 0.01) Calculate the composite score and screen out candidate compounds that meet the basic requirements for small molecule drugs.

[0089] 5.3 ADMET Prediction and Molecular Docking

[0090] Absorption, distribution, metabolism, excretion, and toxicity were evaluated using the ADMETlab 3.0 platform, and the top 150 high-potential candidate molecules were screened. SMILES compounds were converted to PDBQT format using Open Babel, and virtual docking was performed using AutoDock Vina, retaining high-potential compounds with binding affinity >9 kcal / mol.

[0091] Example 6: In vivo efficacy verification (non-therapeutic purpose)

[0092] Taking artesunate as an example (the same applies to other high-potential compounds), MPK model mice were administered the drug via intraperitoneal injection (once daily for 14 consecutive days). Simultaneously, littermate WT mice served as a blank control, and untreated MPK mice served as a negative control, with all mice receiving an equal volume of physiological saline. After administration, the mice were sacrificed, and muscle tissue was harvested for Masson staining to assess the fibrosis area, and immunofluorescence was used to detect the expression of Col1a1 and α-SMA. The results showed that compared with untreated MPK mice, the artesunate-treated group exhibited a significantly reduced fibrosis area and decreased Col1a1 and α-SMA expression levels, indicating that artesunate can effectively alleviate muscle fibrosis in MPK mice.

[0093] All measurement data are expressed as mean ± standard deviation. Each experiment had at least 3 parallel samples (n≥3). The Student's t-test (for two-group comparison) or one-way ANOVA (for multiple-group comparison) was used to analyze the differences in means. P < 0.05 was considered statistically significant, and P < 0.01 or P < 0.001 was considered highly statistically significant. GraphPad Prism 9.5, ImageJ, and Seurat were used to perform data quantification, statistical analysis, and visualization.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing and intervening in the pathological heterogeneity of muscle fibrosis based on Pdgfra signal regulation of myogenic progenitor cells, characterized in that, Includes the following steps: Step 1: Construct a mouse model regulated by myogenic progenitor cell-specific Pdgfra signaling, including a Pdgfra conditional knockout model and a Pdgfra sustained activation model. Step 2: Take individual mice from the mouse model constructed in Step 1, and take samples from their craniofacial muscles and skeletal muscles of the limbs for histopathological examination, ultrastructural observation and oxidative stress level detection to determine site-specific differential phenotypes. Step 3: Transcriptome sequencing and single-cell transcriptome sequencing were performed on the craniofacial muscles and limb skeletal muscles to analyze differentially expressed genes and their encoded proteins, signaling pathways and changes in cell subsets, and to elucidate the molecular mechanism by which Pdgfra signaling regulates muscle pathological heterogeneity. Step 4: Using Pdgfra and differentially encoded proteins obtained through transcriptome sequencing and single-cell transcriptome sequencing in Step 3 as targets, perform artificial intelligence-based virtual drug screening to obtain candidate compounds; Step 5: Administer the candidate compound to the Pdgfra sustained activation model mice to verify its in vivo efficacy.

2. The method according to claim 1, characterized in that, In step 1: the Pdgfra conditional knockout model is the Myf5-cre mediated Pdgfra conditional knockout mouse model MPKO, which is obtained by crossing Pdgfrafl / fl mice with Myf5-cre tool mice; The Pdgfra sustained activation model is the Myf5-cre mediated Pdgfra sustained activation mouse model MPK, which is created by using Pdgfra mice carrying the D842V activation mutation. K / + The mice were obtained by crossing Myf5-cre mice.

3. The method according to claim 1, characterized in that, The histopathological examinations described in step 2 include: using HE staining to assess myofibril morphology, necrosis, inflammatory infiltration and tissue structure; using Masson staining to assess the degree of collagen deposition and fibrosis; and using Oil Red O staining to assess the level of fat infiltration. The oxidative stress level detection includes: using a DHE probe to detect the in-situ reactive oxygen species level in tissues; The ultrastructural observations include: using transmission electron microscopy to observe mitochondrial morphology, sarcomere structure, basement membrane integrity, and autophagosome formation.

4. The method according to claim 1, characterized in that, Step 2 also includes immunofluorescence detection, wherein the immunofluorescence markers include one or more of Laminin, Ki67, Pax7 and Col1a1, used to assess basement membrane integrity, cell proliferation activity, number of muscle stem cells and degree of fibrosis.

5. The method according to claim 1, characterized in that, The analysis methods for transcriptome sequencing described in step 3 include: alignment to the mm10 reference genome using STAR, screening for differentially expressed genes using Deseq2, and performing GO and KEGG pathway enrichment analysis. The analysis methods for the single-cell transcriptome sequencing include: data normalization, dimensionality reduction clustering, and cell subpopulation annotation using the Seurat framework.

6. The method according to claim 1, characterized in that, Step 4, which involves AI-powered virtual drug screening, includes: (a) Dataset construction: Obtain target structure information of Pdgfra and differentially expressed gene-encoded proteins, collect active compounds as positive samples and inactive compounds as negative samples from compound databases, and obtain candidate compounds from natural product libraries; (b) Construct a bimodal model based on graph convolutional networks and a large language model, train the model with the dataset, and predict the binding activity of compounds and targets; (c) Screening candidate compounds from natural product libraries and compound databases, and performing ADMET property predictions; (d) The candidate compounds were virtually docked using the molecular docking method, and the compounds with a binding affinity greater than 9 kcal / mol were retained as high-potential candidate compounds.

7. The method according to claim 6, characterized in that, The molecular docking was performed using AutoDock Vina software.

8. The method according to claim 1, characterized in that, Step 5 describes the in vivo efficacy verification, which includes: intraperitoneal injection of the candidate compound into MPK model mice, taking muscle tissue after administration, assessing the fibrosis area by Masson staining, and detecting the expression level of fibrosis-related proteins by Col1a1 and / or α-SMA immunofluorescence.

9. A method for target selection for precise intervention in muscle fibrosis, characterized in that, Includes the following steps: (a) Using steps 1 to 4 of the method according to any one of claims 1-8, the molecular mechanism by which Pdgfra regulates muscle pathological heterogeneity is analyzed and candidate compounds are screened. (b) To verify the in vivo efficacy of the candidate compounds and identify effective intervention targets.

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