A method for identifying the functional integrity of macrophage immune response of cd163 gene edited pig transformants

Abstract

The present application belongs to the technical field of genetic engineering, and particularly relates to a method for identifying the immune response function integrity of CD163 gene edited pig transformant macrophages. The present application provides a biomarker combination, which comprises a first primer pair for detecting the expression level of a TOX gene or a CD207 gene in an LPS stimulation group and a second primer pair for detecting the expression level of a SMPDL3B gene or a SLIT2 gene in a Poly(I:C) stimulation group, with RPS9 as the only internal reference gene. The present application does not require a wild type control individual, and can identify the immune response function integrity of macrophages of a single CD163 gene edited pig individual only by the gene activation amplitude of peripheral blood macrophages of the individual before and after immune stimulation.

Description

Technical Field

[0001] This invention belongs to the field of genetic engineering technology, and in particular relates to a method for identifying the integrity of the immune response function of macrophages in CD163 gene-edited pig transformants. Background Technology

[0002] In the industrial application of gene-edited animals, especially in the breeding of CD163 gene-edited pigs for disease resistance, the core requirement is not only to confirm that CD163 editing confers PRRSV resistance, but also to verify whether the macrophage immune response function of the transformed organisms remains intact. However, current DNA-based identification technologies in the industry cannot meet this precise screening requirement at the functional level.

[0003] In the industrial application of gene-edited animals, especially in the field of disease-resistant breeding of CD163 gene-edited pigs, the core requirement is not only to confirm that CD163 editing confers PRRSV resistance, but also to verify whether the macrophage immune response function of the edited transformants remains intact.

[0004] It is particularly important to note that even with the confirmed PRRSV resistance in pigs, current identification techniques still cannot solve a core technical problem: after CD163 gene editing, whether the immune response function of macrophages is intact and whether the immune metabolic network is unobstructed when facing invasions from different types of pathogens such as bacteria and viruses. Currently, genotyping of CD163 gene-edited pigs relies entirely on polymerase chain reaction (PCR) and sequencing technologies targeting the edited site. This method can only verify whether the genome sequence has been modified; it is a static, sequence-based physical detection method, and its fundamental flaw lies in its inability to determine whether the editing has led to functional silencing of the target gene.

[0005] In actual production practice, due to endogenous DNA repair and compensation mechanisms within cells (such as incomplete frameshift, alternative splicing, and upstream open reading frame activation), some individuals identified as "successfully edited" by PCR may still express functional proteins in their CD163 gene, becoming "invalidally edited" individuals. These individuals do not possess the expected resistance to porcine reproductive and respiratory syndrome virus (PRRSV). Secondly, while viral challenge experiments can directly verify the resistance phenotype, they are limited by biosafety risks, long cycles, high costs, and the need for specialized animal experimental facilities, failing to meet the rapid screening needs of large-scale breeding scenarios. Therefore, the industry urgently needs a technical solution that can directly and reliably identify the integrity of the macrophage immune response in CD163 gene-edited pig transformants at the functional activity level. Summary of the Invention

[0006] To overcome the aforementioned challenges in "functional verification," this invention abandons the traditional approach of analyzing only DNA sequences and innovatively proposes a functional identification method based on the amplitude of immune stress activation in a single sample. We recognize that whether CD163 gene editing leads to impaired macrophage immune function should ultimately be reflected in its transcriptional remodeling and immune metabolic response capabilities in the face of pathogen challenges. In an immune-intact CD163-edited pig, macrophages should be able to initiate an immune transcriptional remodeling program comparable to that of wild-type individuals when faced with bacterial or viral stimulation; that is, specific immune response genes should be significantly upregulated after stimulation. Conversely, if improper editing operations impair macrophage immune function, this transcriptional remodeling capability will be significantly weakened or lost.

[0007] Therefore, this invention innovatively employs peripheral blood single-cell transcriptome sequencing technology as a core tool for discovering high-fidelity functional biomarkers. Through single-cell transcriptome analysis, we can systematically analyze the transcriptional changes in peripheral blood macrophages from CD163 gene-edited pigs before and after stimulation with LPS (bacterial lipopolysaccharide, simulating bacterial infection) and Poly(I:C) (polyinosinic-cytosine monophosphate, simulating viral double-stranded RNA infection) at the precision of a single cell. By comparing the activation amplitude of specific genes before and after stimulation, we screened a group of marker genes that showed specific and significant upregulation in functionally intact CD163-edited pigs. These marker genes maintain only a basal resting state in wild-type macrophages in response to immune stimulation, but exhibit a unique and significant transcriptional activation pattern in functionally intact CD163-edited macrophages—a characteristic that allows us to accurately determine whether the macrophage immune function remains intact based on self-reference data from a single individual.

[0008] This invention provides a biomarker combination for identifying the integrity of the immune response function of macrophages in CD163 gene-edited pig transformants, comprising a first primer pair for detecting TOX gene expression levels or a fourth primer pair for detecting CD207 gene expression levels, and a second primer pair for detecting SMPDL3B gene expression levels or a fifth primer pair for detecting SLIT2 gene expression levels; wherein the first primer pair consists of the forward primer shown in SEQ ID No. 1 and the reverse primer shown in SEQ ID No. 2, the second primer pair consists of the forward primer shown in SEQ ID No. 3 and the reverse primer shown in SEQ ID No. 4, the fourth primer pair consists of the forward primer shown in SEQ ID No. 11 and the reverse primer shown in SEQ ID No. 12, and the fifth primer pair consists of the forward primer shown in SEQ ID No. 13 and the reverse primer shown in SEQ ID No. 14.

[0009] Furthermore, it also includes a third primer pair for detecting the internal reference gene RPS9, which consists of the forward primer shown in SEQ ID No. 9 and the reverse primer shown in SEQ ID No. 10. RPS9 (ribosomal protein S9) has a highly stable basal expression level in macrophages and is unaffected by LPS and Poly(I:C) immune stimulation, making it suitable as an internal reference gene for calculating the activation amplitude using the ΔΔCt method in this invention.

[0010] Furthermore, the assay includes the probe shown in SEQ ID No. 5 for detecting TOX gene expression levels, and / or the probe shown in SEQ ID No. 6 for detecting SMPDL3B gene expression levels, and / or the probe shown in SEQ ID No. 7 for detecting CD207 gene expression levels, and / or the probe shown in SEQ ID No. 8 for detecting SLIT2 gene expression levels. The use of probes can improve the specificity and sensitivity of RT-qPCR detection.

[0011] This invention provides a reagent for detecting the expression levels of the TOX gene, CD207 gene, SMPDL3B gene and / or SLIT2 gene in a biomarker combination, the reagent comprising a biomarker combination.

[0012] Furthermore, the reagent is used to detect total RNA from porcine peripheral blood macrophages.

[0013] This invention provides a method for identifying the integrity of the immune response function of macrophages from CD163 gene-edited pig transformants using a combination of biomarkers, comprising the following steps: (1) Sample processing: Peripheral blood was collected from the CD163 gene-edited pigs to be tested, macrophages were separated, and the obtained macrophages were divided into three tubes: untreated tube, LPS treated tube and Poly(I:C) treated tube; the cells in the three tubes were treated accordingly and then total RNA was extracted. (2) RT-qPCR detection: Using the primer pairs in the marker combination, the total RNA in the three tubes in step (1) was detected by RT-qPCR to obtain the Ct values ​​of TOX or CD207 gene, SMPDL3B or SLIT2 gene and RPS9 gene in each tube. (3) Result determination: The activation amplitude of each target gene before and after immune stimulation is calculated based on the ΔΔCt method, and the integrity of macrophage immune response function is determined based on the activation amplitude.

[0014] Furthermore, the specific method for calculating ΔΔCt in step (3) is as follows: baseline value (ΔCt baseline) = Ct value of target gene in untreated tube - Ct value of RPS9 in untreated tube; activation value (ΔCt activation) = Ct value of target gene in treated tube (LPS or Poly(I:C) tube) - Ct value of RPS9 in treated tube; activation difference (ΔΔCt) = ΔCt activation - ΔCt baseline. It should be noted that since the mRNA abundance of the target gene increases after it is activated and upregulated by immune stimulation, it is reflected as a decrease in Ct value in RT-qPCR. Therefore, a qualified ΔΔCt should be negative, and the larger the negative value, the higher the activation fold.

[0015] Furthermore, the determination method in step (3) is as follows: First step, bacterial stress compensation determination (LPS tube): detect TOX or CD207 gene. If ΔΔCt≤-3 (i.e., the target gene expression level in the treated group is upregulated by at least 2 compared to the untreated group), 3 =8 times), then it is determined that a large-scale transcriptional remodeling compensation has been successfully achieved in the face of bacterial crisis, and the bacterial stress compensation is qualified, and proceed to the next step; if ΔΔCt>-3, it proves that the compensation network has not been effectively activated, and it is determined to be an abnormal immune function; the second step, viral stress compensation judgment (Poly(I:C) tube): detect SMPDL3B or SLIT2 gene, if ΔΔCt≤-2 (that is, the target gene expression level in the treatment group is upregulated by at least 2 compared with the untreated group), the expression level is determined to be qualified. 2 =4 times), then the immune metabolic fine-tuning network is effectively activated in the face of a viral crisis, and the viral stress compensation is qualified; if ΔΔCt>-2, it proves that antiviral remodeling has failed and is judged as abnormal immune function; the final comprehensive judgment is: if and only if the above two conditions are met at the same time, the macrophage immune response function of the CD163 gene-edited pig transformant is finally judged to be complete; if only one of them is met or neither is met, it is judged as abnormal immune function.

[0016] This invention provides a kit for identifying the integrity of the immune response function of macrophages from CD163 gene-edited pig transformants, the kit comprising primer pairs and / or probes in a combination of biomarkers.

[0017] This invention provides an application of a kit in preparing a detection product for identifying the integrity of the immune response function of macrophages from CD163 gene-edited pig transformants, wherein the sample for identification is porcine peripheral blood macrophages. Compared with the prior art, this invention has the following advantages: 1. A pioneering single-sample self-referenced identification model eliminates reliance on wild-type controls. Existing technologies require wild-type pigs of the same breed as controls for functional identification, which not only increases the workload of sample collection and testing but also introduces interference from individual genetic background differences in result determination. This invention, through an innovative "self-pre- and post-control" design, uses the unprocessed tube data of each individual as its own baseline, and functional integrity determination can be completed using only three tubes of data from the same individual. This design fundamentally eliminates interference from inter-individual differences, improves the accuracy and reliability of result determination, and also greatly simplifies the operation process and reduces testing costs.

[0018] 2. This invention represents a revolutionary technological shift from static sequence validation to dynamic functional assessment. By introducing an immune stimulation experiment (LPS and Poly(I:C) to simulate bacterial and viral infections, respectively), it moves beyond simply detecting static changes in the expression levels of specific biomarkers after gene editing. Instead, it actively stimulates the immune response program of macrophages, directly assessing the patency and compensatory capacity of the macrophage immune metabolic network by quantifying the amplitude of transcriptional activation before and after immune stimulation. This dynamic functional assessment model more accurately reflects the immune functional status of macrophages than static expression detection, thus providing stronger functional evidence for the safe introduction of CD163 gene-edited pig transformants into breeding systems.

[0019] 3. Simultaneously covering both bacterial and viral immune stress dimensions, enabling more comprehensive functional assessment. Existing biomarker combinations can only identify macrophages from a single dimension (i.e., whether expression changes after editing). This invention, by separately setting up LPS stimulation tubes and Poly(I:C) stimulation tubes, can independently assess the immune response capacity of macrophages in both bacterial and viral stress dimensions. This dual assessment mode ensures the comprehensiveness of the identification conclusions—macrophages are only considered to have intact immune function when they exhibit normal immune compensation capacity in both dimensions. This design provides a more stringent screening standard for the safety assessment of gene-edited pig transformants.

[0020] 4. Standardized, high-throughput applications suitable for large-scale breeding scenarios. The method of this invention only requires collecting peripheral blood samples from the individuals to be tested. Identification can be completed through standardized cell separation, immune stimulation, and RT-qPCR procedures. Once the biomarker combination and judgment threshold are validated with large-scale samples, a standardized detection kit can be developed, enabling functional screening of a large number of individuals at a low cost in the early stages of growth. This significantly improves the efficiency of selecting superior breeding stock and opens up a key translational pathway from gene editing research to commercial disease-resistant breeding. Attached Figure Description

[0021] Figure 1The average expression level of the TOX gene in macrophages of the WT group and the CD163-edited pig group after LPS stimulation in Example 1 is shown.

[0022] Figure 2 The average expression level of the CD207 gene in macrophages of the WT group and the CD163-edited pig group after LPS stimulation in Example 1 is shown.

[0023] Figure 3 The average expression level of the SMPDL3B gene in macrophages of the WT group and the CD163-edited pig group after Poly(I:C) stimulation in Example 1 is shown.

[0024] Figure 4 The average expression level of the SLIT2 gene in macrophages of the WT group and the CD163-edited pig group after Poly(I:C) stimulation in Example 1 is shown.

[0025] Figure 5 The average expression level of the RPS9 gene in macrophages in the three experimental groups (untreated control group and CD163-edited pigs) in Example 1 is shown. Detailed Implementation

[0026] Example 1 Screening and identification of a combination of biomarkers for the integrity of the immune response in macrophages from CD163 gene-edited pig transformants Experimental Design and Grouping CD163 gene-edited pigs with the CD163 SRCR5 domain deletion confirmed by genotyping and wild-type pigs of the same breed were selected as experimental animals. All experimental animals were housed in a biosafety facility under standard conditions, and the experimental procedures were approved by the animal ethics committee.

[0027] This study employed a completely randomized design with three treatment groups: a blank control group (Control), an LPS stimulation group (LPS), and a Poly(I:C) stimulation group (Poly(I:C)). Four biological replicates (from four different pigs) were established for each treatment condition, and two genotypes were included: CD163-edited pigs and wild-type (WT) pigs. Peripheral blood PBMCs from each pig under each genotype were separated and divided into three aliquots, each receiving one of the three different treatment conditions. This resulted in six experimental groups: CD163-Control (n=4), CD163-LPS (n=4), CD163-Poly(I:C) (n=4), WT-Control (n=4), WT-LPS (n=4), and WT-Poly(I:C) (n=4), totaling 24 samples, which were then subjected to single-cell transcriptome sequencing.

[0028] Isolation of porcine peripheral blood mononuclear cells (PBMCs) 1) Blood dilution: Gently mix 10 mL of fresh anticoagulated peripheral blood with an equal volume of sterile PBS at a 1:1 ratio; 2) Density gradient preparation: Slowly add diluted blood to 15 mL of Ficoll separation solution, ensuring clear separation, and centrifuge horizontally at 2000 rpm for 40 minutes at room temperature; 3) Cell collection: After centrifugation, carefully aspirate the white, cloudy PBMCs layer at the interface between the plasma and the separation solution; 4) Washing: Resuspend the collected cells in RPMI 1640 medium containing 10% FBS and centrifuge (2000 rpm, 10 minutes), discarding the supernatant; 5) Repeat the wash once (2000 rpm, 10 minutes); 6) Resuspension: Resuspend the precipitated cells in an appropriate amount of complete culture medium, count them, and use them for single-cell sequencing.

[0029] Stimulation treatment The isolated PBMCs were resuspended in RPMI 1640 complete medium containing 10% FBS, and the cell concentration was adjusted to 5 × 10⁶ cells / mL. 5 cells / mL. The stimulation treatment steps are as follows: 1) Inoculate 5 mL of PBMC suspension (5 × 10⁻⁶) into each well of a 10 cm culture dish. 5 (cells / mL), pre-cultured at 37℃ in a 5% CO2 incubator for 2 h to allow PBMCs to fully adhere to the incubator and return to their physiological state; 2) After the pre-culture, the LPS stimulation group was given 1 mL of serum-free RPMI 1640 medium containing a final concentration of 100 ng / mL LPS, the Poly(I:C) stimulation group was given 1 mL of serum-free RPMI 1640 medium containing a final concentration of 10 ng / mL Poly(I:C), and the control group was given 1 mL of serum-free RPMI 1640 medium. 3) Continue incubation at 37℃ and 5% CO2 for 2-4 hours; 4) After the culture is completed, collect the cells for single-cell suspension preparation: collect the suspended cells directly, and gently scrape off the adherent cells with a cell scraper. Combine the suspended cells and adherent cells and transfer them into centrifuge tubes.

[0030] Preparation of single-cell suspension: 1) Centrifuge the cells (300×g, 5 minutes) and discard the supernatant; 2) Wash twice with PBS, centrifuge at 300×g for 5 minutes each time; 3) Resuspend the cells in PBS containing 0.04% BSA, filter through a 40 μm cell sieve to remove cell clumps; 4) Trypan blue staining and counting, adjust the cell concentration to 700-1200 cells / μL, the viable cell rate should be ≥85%, and the cell clumping rate should be <5%.

[0031] Single-cell transcriptome library building and sequencing PBMC cell suspensions were adjusted to approximately 1000 cells / μL, and controlled within the range of 700-1200 cells / μL. Libraries were constructed using the MobiCube® High-Throughput Single-Cell 3' Transcriptomics Kit, capturing approximately 8000-10000 cells per group. Sequencing was performed on the Illumina NovaSeq 6000 platform at a target depth of approximately 50000 reads / cell. The libraries were then subjected to high-throughput sequencing to obtain raw sequence data in FASTQ format, and preprocessed using MobiVision software (v3.2).

[0032] Quality control and preliminary filtering of raw single-cell transcriptome data The various quality control and filtering thresholds were determined by comprehensively referencing the default recommended parameters of bioinformatics analysis software (such as Seurat and Scanpy), combined with the theoretical expectations of the 10x Genomics microfluidic capture platform and the data distribution characteristics of actual samples. The specific filtering criteria are as follows: 1) Gene Filtering and Preliminary Cell Filtering: After reading the original gene expression matrix, the CreateSeuratObject() function is called to construct a single-cell data object. This function simultaneously sets the preliminary filtering parameters for genes and cells: setting min.cells=3 removes genes expressed in fewer than 3 cells (removing low-expression genes); setting min.features=200 removes cells with a total detected gene count (nFeature_RNA) of less than 200 (removing dead cells and empty droplets); The function returns a Seurat object containing the filtered gene and cell matrix. 2) Mitochondrial gene ratio calculation and filtering: In the Seurat object above, call the PercentageFeatureSet() function to calculate the mitochondrial gene ratio of each cell (mitochondrial gene UMI number / total UMI number), and store the result in the percent.mt column of the result object metadata. Call the subset() function and set the filtering condition subset=percent.mt<20 to retain cells with a mitochondrial gene ratio <20% (remove damaged or apoptotic cells). 3) Double-cell filtering: Double cells (based on cells with abnormal expression patterns) are identified using DoubletFinder (v2.0). The expected double-cell rate is <5%. The percentage is converted into the actual absolute number of double cells to be predicted using nExp_poi<-round(0.05 ncol(seurat_obj)) (where ncol(seurat_obj) is the total number of cells in the current sample). The percentage is then converted into the actual absolute number of double cells to be predicted in this experiment using the code nExp_poi<- round(0.05 ncol(seurat_obj)) (where ncol(seurat_obj) is the total number of cells in the current sample). The doubletFinder_v3() function is called, with the first 15 principal components (PCs=1:15) as input, and artificial double-cell simulation and classification prediction are performed in combination with the nExp_poi.adj parameter calculated above. After execution, a new category result column will be added to the object metadata. By extracting the cell barcode marked "Doublet" from this column and calling the subset() function to remove it from the Seurat object, the double cell filtering is completed. 4) Data Standardization: To reduce the impact of sequencing depth and technical noise on single-cell expression data, the SCTransform() function from the Seurat package was used for standardization. This method is based on a regularized negative binomial regression model, which regresses the UMI count of each gene to the sequencing depth, thereby correcting for bias caused by differences in sequencing depth. Simultaneously, variance stabilization transformation ensures comparability of genes with different expression levels and effectively removes non-biological variations such as batch effects. The standardized data exhibits a more uniform variance distribution and significantly reduced technical noise, which can then be used for subsequent screening of highly variable genes, dimensionality reduction, clustering, and differential expression analysis.

[0033] High-variant gene screening and dimensionality reduction analysis Hypervariable gene screening was performed using the VST method in Seurat (v4.3.0) to identify genes with significant expression variations among cells (reflecting cellular heterogeneity). The steps are as follows: 1) Call the FindVariableFeatures function, set the selection method to vst (selection.method = "vst"), and extract the top 5000 genes as hypervariable genes for subsequent analysis.

[0034] 2) Dimensionality reduction analysis includes two steps: PCA and UMAP. PCA dimensionality reduction: Principal component analysis was performed on the hypervariable gene expression matrix, and the first 15 principal components were retained (determined based on Elbow Plot, the principal components in this interval explained more than 80% of the data variation, and were in the plateau period after the inflection point). UMAP dimensionality reduction: Based on PCA results, UMAP dimensionality reduction is performed to achieve two-dimensional visualization. The RunUMAP function is used, with PCA as the dimensionality reduction basis (reduction = "PCA"), and the input dimension is limited to the 1st to 15th principal components (dims = 1:15). At the same time, the local neighborhood size parameter n.neighbors = 30 and the minimum distance parameter min.dist = 0.3 are set.

[0035] 3) Cell Clustering: After quality control and standardization, the single-cell transcriptome expression matrix is ​​used to calculate the nearest neighbors of each cell using the FindNeighbors function. A shared nearest neighbor (SNN) graph is constructed based on neighborhood overlap (Jaccard index) to analyze the local structural relationships of cells in high-dimensional space. The FindClusters function is used to execute a modular optimization algorithm (Louvain algorithm) based on the shared nearest neighbor graph, with the resolution parameter set to resolution=0.1, dividing the samples into multiple cell clusters with different gene expression characteristics for cell type annotation and functional enrichment analysis.

[0036] The core parameters determined in the above steps, such as the number of hypervariable genes (5000), the number of principal components retained (the first 15 PCs), and the cluster resolution (0.1), remain strictly consistent in all subsequent downstream analysis steps of this invention, ensuring the rigor, stability, and reproducibility of the entire single-cell data analysis process.

[0037] Macrophage annotations 1) Candidate differentially expressed gene screening: Based on screening for hypervariable genes in a single treatment group, cells with similar expression profiles corresponding to hypervariable genes are clustered. For each cell cluster obtained from the clustering, differential expression analysis (such as Wilcoxon rank-sum test) is used to screen out genes in the same cell cluster that have a large fold difference in expression relative to other clusters and are statistically significant, which are then used as candidate marker genes for each cell cluster.

[0038] 2) Validation of macrophage marker genes: By reviewing relevant articles and screening candidate marker genes, CD172α and CD68 were identified as specific marker genes for macrophage identification. The reliability of the marker genes was verified by the expression level and cell number of the marker genes in different cells. Then, UMAP dimensionality reduction was performed on the cells screened by the marker genes to verify whether the spatial distribution of the cell population with high expression of CD172α and CD68 was highly consistent with the cluster boundary of the specific cell cluster.

[0039] 3) Macrophage annotation and extraction: Based on the above validation results, cell clusters that specifically highly express CD172α and CD68 were directly annotated as macrophages, and the macrophage subpopulation was extracted from all single-cell data for subsequent differential gene analysis and biomarker screening.

[0040] Analysis of Transcriptional Differences between Gene-Effectively Edited Pigs and Wild-Type Pigs Based on the cell clustering and annotation results described above, cross-sample differential expression analysis was performed between groups. The Wilcoxon rank-sum test algorithm was used to calculate the expression difference of each gene between the two groups, and the Benjamini-Hochberg (BH) method was used to correct the p-values ​​generated by multiple hypothesis testing to control the false discovery rate (FDR). Genes with significant differential expression were screened based on a set significance threshold (e.g., corrected p-value < 0.05 and |log2FoldChange| > 1). GO functional enrichment analysis and KEGG pathway enrichment analysis were performed on these differentially expressed genes. Combining the enrichment analysis results with the fold change in gene expression, a marker gene combination for detecting the effective editing effect of gene function was finally screened and constructed.

[0041] Samples: Peripheral blood samples were used from 4 CD163-edited pigs and 4 wild-type pigs.

[0042] Results: By comparing differentially expressed genes in macrophages, in the LPS-stimulated group, the TOX gene showed the most significant and consistent upregulation in CD163-edited porcine macrophages (log2FoldChange>3, corrected p<0.001), and the CD207 gene, as a candidate marker, also showed a significant upregulation trend (log2FoldChange>2, corrected p<0.01). In the Poly(I:C)-stimulated group, the SMPDL3B gene showed the most significant specific upregulation (log2FoldChange>2, corrected p<0.001), and the SLIT2 gene, as a candidate marker, also showed a significant upregulation trend (log2FoldChange>1.5, corrected p<0.01). The mean expression level of the RPS9 gene in macrophages of the WT and CD163-edited porcine groups in the untreated control group was analyzed. The results showed that the expression level of RPS9 was consistent and stable between the two groups, with coefficients of variation (CV) of less than 5%. Under both LPS and Poly(I:C) immunostimulation conditions, the Ct value of RPS9 remained highly stable. Based on the above analysis, TOX and CD207 were identified as candidate biomarkers for the LPS-stimulated group, and SMPDL3B and SLIT2 were identified as candidate biomarkers for the Poly(I:C)-stimulated group. Combined with the highly stable expression of RPS9 as the sole internal reference gene, a candidate biomarker combination for subsequent RT-qPCR validation was constructed.

[0043] Example 2 A method for identifying CD163 gene function-edited pigs using the aforementioned biomarker combination Step 1 (Sample Processing): Peripheral blood of the pigs to be tested was collected, and macrophages were specifically separated by flow cytometry (FACS) or immunomagnetic beads and subjected to three parallel immune stimulation treatments.

[0044] 1.1 Sample collection: Collect 10 mL of peripheral blood from the anterior vena cava or marginal ear vein of the pigs to be tested (gene-functionally edited pigs and wild-type pigs), place it in an EDTA-K2 anticoagulant tube, gently invert and mix the blood 8-10 times after collection to ensure that the blood and anticoagulant are fully combined, store at 4℃ and perform separation processing on the same day.

[0045] 1.2 Mononuclear Cell Isolation: Peripheral blood mononuclear cells (PBMCs) were isolated using density gradient centrifugation. The specific procedure was as follows: First, peripheral blood was diluted 1:1 by volume and gently mixed. 15 mL of Ficoll separation buffer (SoLarbio, P4420) was added to a 50 mL centrifuge tube. Approximately 30 mL of diluted blood was carefully spread evenly on the surface of the separation buffer using a sterile disposable Pasteur tube. Centrifugation was performed at 2000 rpm for 40 minutes using a horizontal rotor (acceleration setting 8, deceleration setting 5; pausing during centrifugation was strictly prohibited). After centrifugation, the second white PBMC layer was transferred to a new tube, washed with RPMI 1640 medium containing 10% FBS, and centrifuged at 2000 rpm for 10 minutes. The supernatant was discarded, and the cells were resuspended in fresh RPMI 1640 medium containing 10% FBS and centrifuged at 1500 rpm for 10 minutes. The washing process was repeated once under the above conditions. The cell pellet was then incubated with ACK lysis buffer to remove residual red blood cells, followed by washing with PBS to terminate lysis. Finally, the cells were resuspended in 10% FBS medium. Cells were resuspended in FBS RPMI 1640 medium, counted by trypan blue staining, and the cell concentration was adjusted to 5 × 10⁶. 5 cells / mL, ensuring a viable cell rate of ≥95%.

[0046] 1.3 Target cell sorting: Based on monocyte / macrophage specific surface markers, target cell subpopulations were sorted from PBMCs using magnetic immunomagnetic bead sorting (MACS). Magnetic beads conjugated with anti-pig CD14 antibody were added and incubated at 4 °C for 15 minutes. After washing, the cell suspension was passed through a MACS separation column. Unbound cells were washed with PBS, the separation column was removed from the magnetic field, and elution buffer was added to quickly wash the positively bound target cells into a collection tube.

[0047] 1.4 Parallel Immunostimulation Treatment with Three Tubes: The sorted macrophages were resuspended in complete culture medium and the cell concentration was adjusted to 1×10⁶ cells / mL. The cells were divided into three tubes, 2 mL each: untreated tubes: containing only an equal volume of culture medium, serving as a baseline control; LPS-treated tubes: containing LPS to a final concentration of 100 ng / mL, incubated at 37°C and 5% CO₂ for 2–4 hours; Poly(I:C)-treated tubes: containing Poly(I:C) to a final concentration of 10 ng / mL, incubated at 37°C and 5% CO₂ for 2–4 hours. After incubation, the cells were collected from each tube.

[0048] 1.5 RNA Extraction and Purification: 1 mL of Trizol lysis buffer was added to the sorted target cells, and after mixing by pipetting, total RNA was extracted according to the standard reagent instructions (TIANGEN, DP405-02). To eliminate the influence of residual genomic DNA (gDNA) on the accuracy of subsequent RT-qPCR, 1 μL of RNase-Free DNase I was added during the RNA dissolution stage, and digestion was carried out at room temperature for 15 minutes. Subsequently, RNA was purified and eluted using an RNA purification column. RNA concentration and purity were measured; OD260 / 280 ratios were all between 1.8 and 2.0. The RNA was stored at -80℃ for later use.

[0049] Step 2 (RT-qPCR detection): Using specific primers and probes for TOX, CD207, SMPDL3B, SLIT2 and the internal reference gene RPS9, the expression levels of target genes in each tube cell were detected by RT-qPCR.

[0050] 2.1 Primer Information: TOX gene (LPS primary biomarker): Forward primer SEQ ID No. 1: CCCATCACGCAGCCCAC; Reverse primer SEQ ID No. 2: CACTGTAGACAGCATCACGGAG; Probe SEQ ID No. 5: TCGGGACACACAGACAGAC; SMPDL3B gene (primary biomarker for the Poly(I:C) ensemble): Forward primer SEQ ID No. 3: GCTGCCTGTGCTGCTGA; Reverse primer SEQ ID No. 4: CCAGCTCCCTGCCTTGC; Probe SEQ ID No. 6: CCTGGCCCACTGGGGAGTCACC; CD207 gene (LPS genome candidate biomarker): Forward primer SEQ ID No. 11: CCCTGAATGCCCTCGGC; Reverse primer SEQ ID No. 12: CTGGGAATGGGGGACTCTCT; Probe SEQ ID No. 7: TCCCCTCGGCCAGCCG; SLIT2 gene (Poly(I:C) group candidate biomarker): Forward primer SEQ ID No. 13: AGCTAGGAGGCGGCG; Reverse primer SEQ ID No. 14: CGCCAGCCCGTGACAGT; Probe SEQ ID No. 8: GGAAGATGCGCGGCGTCGGC; Internal reference gene (RPS9): Forward primer SEQ ID No. 9: TCCGTCCGGGAGTCTGT; Reverse primer SEQ ID No. 10: GTGAGACTCTCCCCTGAAGC; 2.2 Reverse transcription (cDNA synthesis): Take 1 μg of the extracted and purified total RNA and synthesize cDNA using a reverse transcription kit (TaKaRa, RR092A). The reaction volume is 20 μL. This kit uses a two-step method to remove residual genomic DNA and perform reverse transcription. The specific reaction system and procedure are as follows: Step 1: Genomic DNA removal reaction (10 μL) 5×gDNA Eraser Buffer…………2.0μL gDNA Eraser…………1.0μL Total RNA…………1.0 μg (approximately x μL) RNase-Free dH2O…………Add to a final volume of 10.0 μL Note: Calculate the required volume based on the RNA concentration. Make up any amount less than 10 μL with RNase-free dH2O.

[0051] Reaction procedure: 42℃ for 2 min, then maintain at 4℃.

[0052] Step 2: Reverse transcription reaction (add more fluid to 20 μL) In the reaction solution from step 1 above, add the following reagents sequentially and mix well: The reaction solution for step 1 is 10.0 μL. PrimeScript RT Enzyme Mix I…………1.0μL RT Primer Mix4…………1.0μL 5×PrimeScript Buffer 2…………4.0μL RNase Free dH2O…………4.0μL Reaction procedure: 37 ℃ for 15 min (reverse transcription), 85 ℃ for 5 sec (enzyme inactivation), 4 ℃ for maintenance.

[0053] The obtained cDNA solution was stored at -20℃ for later use in qPCR amplification.

[0054] 2.3 qPCR reaction system and procedure: Amplification was performed using a real-time PCR kit (TaKaRa, RR047A). Using the cDNA synthesized in step 2.2 as a template, qPCR reaction systems were prepared for TOX, CD207, SMPDL3B, SLIT2, and the internal reference gene RPS9, respectively.

[0055] Reaction system (total volume 20 μL): 2× TaqMan Probe qPCR Master Mix…………10.0 μL Forward primer (10 μM)…………0.5 μL Reverse primer (10 μM)…………0.5 μL Fluorescent probe (10 μM)…………0.2 μL cDNA template…………2.0 μL RNase Free dH2O…………6.8 μL Total…………20.0 μL Note: The primers and probes mentioned above should be replaced according to the detection target. For example, when detecting the TOX gene, add SEQ ID No.1, SEQ ID No.2 and SEQ ID No.5.

[0056] 2.4 Reference Settings: For each RT-qPCR assay, the following control was set up: a reaction system using nuclease-free water instead of cDNA template was set up as a blank control (NTC). It should be noted that, unlike traditional methods, this invention does not require wild-type pig samples as negative controls; the untreated tube of each individual being tested serves as their baseline control.

[0057] Step 3 (Result Determination): Calculate the activation amplitude based on the ΔΔCt method to determine the integrity of immune function.

[0058] 3.1 Calculation rules for ΔΔCt: The baseline value (ΔCt baseline) = Ct value of target gene in untreated tube - Ct value of RPS9 in untreated tube; the activation value (ΔCt activation) = Ct value of target gene in treated tube (LPS or Poly(I:C) tube) - Ct value of RPS9 in treated tube; the activation difference (ΔΔCt) = ΔCt activation - ΔCt baseline.

[0059] It is important to note that because the mRNA abundance of target genes increases after activation and upregulation by immune stimulation, this is reflected in a decreased Ct value in RT-qPCR detection. Therefore, a qualified ΔΔCt should be negative; the larger the negative value, the higher the fold increase in target gene activation and upregulation. For example, ΔΔCt = -3 means that the expression level of the target gene in the treated group was upregulated by 2% compared to the untreated group. 3 =8 times; ΔΔCt=-2 means it was increased by 2. 2 =4 times.

[0060] 3.2 Two-step judgment criteria: Step 1, Bacterial Stress Compensation Assessment (LPS tube): The target gene is either TOX (primary choice) or CD207 (secondary choice). The assessment threshold is set at ΔΔCt ≤ -3, meaning that the expression level of the target gene in the treated group must be upregulated by at least 2 compared to the untreated group. 3 =8 times. If ΔΔCt≤-3, it is judged as "bacterial stress compensation qualified" and proceeds to the second step of judgment. If ΔΔCt>-3, it suggests that there may be a defect in macrophage immune function, and it is directly judged as "abnormal immune function", and does not proceed to the next step.

[0061] The second step is to determine viral stress compensation (Poly(I:C) tubes): the target gene is either SMPDL3B (primary choice) or SLIT2 (alternative). The determination threshold is set to ΔΔCt≤-2, meaning that the expression level of the target gene in the treated group must be upregulated by at least 2 compared to the untreated group. 2 =4 times. If ΔΔCt≤-2, it is judged as "viral stress compensation qualified". If ΔΔCt>-2, it indicates that macrophages have an immune deficiency in the viral stress dimension, and is judged as "abnormal immune function".

[0062] 3.3 Final comprehensive judgment conclusion: For the CD163 gene-edited pig individuals to be tested, based solely on the qPCR data from their own three tubes, the following two conditions must be met simultaneously: (1) ΔΔCt of the TOX (or CD207) gene in the LPS tube ≤ -3; (2) ΔΔCt of the SMPDL3B (or SLIT2) gene in the Poly(I:C) tube ≤ -2; the final identification report shall be issued as: "The immune response function of the CD163 gene-edited pig transformed into peripheral blood macrophages is intact." If only one of these conditions is met or neither is met, it shall be judged as "abnormal immune function".

[0063] This invention abandons the static cross-sectional comparison model relying on wild-type controls, and innovatively shifts to a dynamic immune stress assessment level based on single-sample self-reference. By introducing dual simulated stimuli of bacteria and viruses, it precisely quantifies the magnitude of transcriptional changes in macrophages when facing pathogen challenges. This transforms the identification criterion from simply "whether the edited site sequence has changed" to "whether the individual's autoimmune metabolic network is stable." Theoretically, if a pig is genotyped as having "successfully edited sequences," but fails to trigger threshold-level upregulation of marker genes in the dual immune stress test of this invention, it can be reasonably inferred that the editing operation caused latent damage to the immune response function. Therefore, this invention provides, for the first time, a standardized solution for identifying "immunodefective edited individuals" at the functional activity level, fundamentally avoiding the industrialization safety risks caused by mistakenly selecting individuals with "antiviral potential but impaired overall immune networks" as core breeding stock.

[0064] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A combination of biomarkers for identifying the integrity of the immune response function of macrophages in CD163 gene-edited pig transformants, characterized in that, This includes a first primer pair for detecting the expression level of the TOX gene or the CD207 gene, or a fourth primer pair for detecting the expression level of the CD207 gene, and a second primer pair for detecting the expression level of the SMPDL3B gene, or a fifth primer pair for detecting the expression level of the SLIT2 gene. The first primer pair consists of the forward primer shown in SEQ ID No. 1 and the reverse primer shown in SEQ ID No. 2; the second primer pair consists of the forward primer shown in SEQ ID No. 3 and the reverse primer shown in SEQ ID No. 4; the fourth primer pair consists of the forward primer shown in SEQ ID No. 11 and the reverse primer shown in SEQ ID No. 12; and the fifth primer pair consists of the forward primer shown in SEQ ID No. 13 and the reverse primer shown in SEQ ID No.

14.

2. The biomarker combination according to claim 1, characterized in that, It also includes a third primer pair for detecting the internal reference gene RPS9, which consists of the forward primer shown in SEQ ID No. 9 and the reverse primer shown in SEQ ID No.

10.

3. The biomarker combination according to claim 1 or 2, characterized in that, It also includes the probe shown in SEQ ID No. 5 for detecting TOX gene expression level, and / or the probe shown in SEQ ID No. 6 for detecting SMPDL3B gene expression level, and / or the probe shown in SEQ ID No. 7 for detecting CD207 gene expression level, and / or the probe shown in SEQ ID No. 8 for detecting SLIT2 gene expression level.

4. A reagent for detecting the expression levels of the TOX gene, CD207 gene, SMPDL3B gene, and / or SLIT2 gene in the biomarker combination of claim 1, characterized in that, The reagent comprises the biomarker combination as described in claim 1.

5. The reagent according to claim 4, characterized in that, The reagent is used to detect total RNA from porcine peripheral blood macrophages.

6. A method for identifying the integrity of the immune response function of macrophages from CD163 gene-edited pig transformants using a combination of markers according to any one of claims 1 to 3, characterized in that, Includes the following steps: 1) Sample processing: Peripheral blood was collected from the CD163 gene-edited pigs to be tested, macrophages were isolated, and the obtained macrophages were divided into three tubes: untreated tube, LPS-treated tube and Poly(I:C)-treated tube; the cells in the three tubes were treated accordingly and total RNA was extracted. 2) RT-qPCR detection: Using the primer pairs in the marker combination described in claim 1 or 2, the total RNA in the three tubes in step (1) is detected by RT-qPCR to obtain the Ct values ​​of the TOX or CD207 gene, SMPDL3B or SLIT2 gene and RPS9 gene in each tube. 3) Result determination: The activation amplitude of each target gene before and after immune stimulation was calculated based on the ΔΔCt method, and the integrity of macrophage immune response function was determined based on the activation amplitude.

7. The method according to claim 6, characterized in that, The specific method for calculating ΔCt in step (3) is as follows: ΔCt baseline = Ct value of target gene in untreated tube - Ct value of RPS9 in untreated tube; ΔCt activation = Ct value of target gene in treated tube - Ct value of RPS9 in treated tube; ΔΔCt = ΔCt activation - ΔCt baseline; where, when a gene is activated and upregulated, the Ct value of the target gene in the treatment tube decreases, and the qualified ΔΔCt is a negative number. The larger the negative value, the higher the activation fold.

8. The method according to claim 6 or 7, characterized in that, The determination method in step (3) is as follows: First step, bacterial stress compensation determination: If the ΔΔCt of the TOX gene or CD207 gene in the LPS-treated tube is ≤-3, then the bacterial stress compensation is deemed qualified and proceed to the next step. The second step is to determine viral stress compensation: if the ΔΔCt of the SMPDL3B gene or SLIT2 gene in the Poly(I:C) treatment tube is ≤-2, then the viral stress compensation is deemed qualified; the final comprehensive judgment is: if and only if the above two conditions are met at the same time, the macrophage immune response function of the CD163 gene-edited pig transformant is determined to be intact; otherwise, it is determined to be immune dysfunction.

9. A kit for identifying the integrity of the immune response function of macrophages in CD163 gene-edited pig transformants, characterized in that, The kit contains primer pairs and / or probes from the biomarker combination of any one of claims 1 to 3.

10. The use of the reagent according to any one of claims 4-5 or the kit according to claim 9 in the preparation of a detection product for identifying the integrity of the immune response function of macrophages in CD163 gene-edited pig transformants, characterized in that, The sample being identified was porcine peripheral blood macrophages.

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