Application of genetic marker associated with intramuscular fat in pig genetic breeding and kit

Abstract

The invention belongs to the technical field of biology, and discloses application of a genetic marker associated with intramuscular fat in genetic breeding of pigs, and the genetic marker is a WNT16 gene. Through verification of a pig IMF deposition regulation model which is established on the basis of the genetic marker and takes the WNT16 gene as a core, the fat deposition mode of pigs is accurately controlled by regulating the expression level of the WNT16 gene, the IMF content is increased, and then the pork quality is optimized. In the genetic breeding process of pigs, the regulation and control model is used for screening and breeding boars, and a pig variety with an ideal fat deposition mode and high-quality meat quality is bred. Meanwhile, the invention also provides a kit for realizing the application.

Description

Technical Field

[0001] This application belongs to the biological field, and specifically relates to the application of genetic markers associated with intramuscular fat in pig genetic breeding and kits. Background Art

[0002] Pork quality is restricted by various factors. Among them, the content, distribution, and fatty acid composition of fat play a decisive role in the tenderness, juiciness, and flavor of meat. IMF is of great significance in improving meat quality. An appropriate amount of IMF can significantly improve the taste and flavor of meat. However, in actual pig production, increasing the IMF content often leads to excessive accumulation of subcutaneous fat and visceral fat. This not only reduces feed utilization rate and increases breeding costs, but may also cause metabolic diseases in pigs and affect their health. At present, the research on the regulation mechanism of fat deposition in different parts of pigs is not sufficient, and there is a lack of effective means to precisely regulate IMF deposition without affecting the fat in other parts. Summary of the Invention

[0003] The object of the present invention is to provide an application of a genetic marker associated with intramuscular fat in pig genetic breeding. Through the verification of a pig IMF deposition regulation model established based on this genetic marker with the WNT16 gene (Genebank: ID: 100511484) as the core: by regulating the expression level of the WNT16 gene, precisely control the fat deposition pattern of pigs, achieve an increase in the IMF content, and thus optimize the pork quality. During the pig genetic breeding process, use this regulation model to screen and breed breeding pigs, and cultivate pig breeds with an ideal fat deposition pattern and high-quality meat.

[0004] Meanwhile, the present invention also proposes a kit for realizing this application.

[0005] To achieve the above object, this application discloses an application of a genetic marker associated with intramuscular fat in pig genetic breeding, and the genetic marker is the WNT16 gene.

[0006] In addition, the present invention also discloses the application of the WNT16 gene in constructing a pig adipocyte model; the pig adipocyte model is used to detect the expression levels of other genes related to adipocyte differentiation in this model.

[0007] In the above application, the cells used to construct the pig adipocyte model are porcine preadipocytes.

[0008] In the above application, the other genes are the PPARγ, C / EBPα, and AdipoQ genes.

[0009] In the above application, a WNT16 gene inhibitor was constructed based on the WNT16 gene, and a porcine adipocyte model was constructed using the WNT16 gene inhibitor; the nucleotide sequences of the WNT16 gene inhibitor are shown in SEQ ID NO.5 and SEQ ID NO.6.

[0010] In addition, the present invention also discloses a kit for constructing a porcine adipocyte model, which contains a WNT16 gene inhibitor, and the nucleotide sequences of the WNT16 gene inhibitor are shown in SEQ ID NO.5 and SEQ ID NO.6; the porcine adipocyte model is used to detect the expression levels of other genes related to adipocyte differentiation in this model. Preferably, the genes related to adipocyte differentiation are the PPARγ gene, the C / EBPα gene, and the AdipoQ gene.

[0011] The beneficial effects of this application are as follows: 1. In the present invention, Tibetan pigs with unique genetic characteristics were selected as the research object, and their IMF, BF, and PF samples were collected. Using histological analysis methods, the morphological differences of adipocytes in different parts were observed; transcriptome sequencing technology was used to comprehensively detect gene expression; metabolomics technology was used to analyze the changes in metabolites. Through the combined analysis of multi-omics data, differentially expressed genes and metabolites related to IMF deposition were screened, and key regulatory pathways and candidate genes, such as the WNT16 gene, were determined.

[0012] 2. For the key candidate gene WNT16 screened out in the present invention, specific siRNAs were designed and synthesized. The siRNAs were transfected into porcine preadipocytes to inhibit the expression of the WNT16 gene. The expression changes of key genes related to adipocyte differentiation, such as PPARγ, C / EBPα, and AdipoQ, were detected by real-time fluorescence quantitative PCR (qRT-PCR) technology; the oil red O staining method was used to observe the accumulation of lipid droplets in the cells, and the absorbance (OD value) was measured to verify the effect of the WNT16 gene on the adipogenic differentiation of porcine preadipocytes. 3. The present invention established a regulatory model for porcine IMF deposition with the WNT16 gene as the core. By regulating the expression level of the WNT16 gene, the fat deposition pattern of pigs can be precisely controlled, so as to increase the IMF content and further optimize the pork quality. During the genetic breeding process of pigs, this regulatory model can be used to screen and breed breeding pigs to cultivate pig breeds with ideal fat deposition patterns and high-quality meat.

[0013] The significance of this application research lies in: Precise regulation of fat deposition: The present invention first reveals the key regulatory role of the WNT16 gene in porcine IMF deposition, providing a new molecular target for precisely regulating the fat deposition pattern of pigs. By regulating the expression of the WNT16 gene, precise control of IMF deposition can be achieved.

[0014] Improve pork quality: effectively increase the IMF content, improve the tenderness, juiciness and flavor of pork, and enhance the edible quality and market value of pork.

[0015] Promote the development of genetic breeding: provide innovative technical means and theoretical basis for pig genetic breeding, help cultivate pig breeds with excellent meat quality traits, and promote the sustainable development of the pig industry. Description of the Drawings

[0016] Figure 1 It is a HE staining result diagram of tissue sections of BF (back fat), PF (perienteric fat) and IMF (intramuscular fat) of Tibetan pigs; Figure 2 It is a statistical result chart of the adipocyte area of tissue sections of BF, PF and IMF of Tibetan pigs; Figure 3 It is a statistical result chart of the adipocyte diameter of tissue sections of BF, PF and IMF of Tibetan pigs; Figure 4 It is a composition ratio diagram of fatty acids in IMF, PF and BF of Tibetan pigs; Figure 5A It is a distribution density diagram of the expression levels of IMF, BF and PF of Tibetan pigs in the transcriptome; Figure 5B It is a volcano diagram of the expression difference between IMF and BF of Tibetan pigs in the transcriptome; Figure 5C It is a volcano diagram of the expression difference between IMF and PF of Tibetan pigs in the transcriptome; Figure 5D It is a statistical chart of the expression level differences of IMF, BF and PF of Tibetan pigs in the transcriptome; Figure 5E It is a gene set upset diagram (gene set collection diagram) of IMF, BF and PF of Tibetan pigs in the transcriptome; Figure 5F It is a statistical bar chart of the EggNOG classification of the gene sets of IMF, BF and PF of Tibetan pigs in the transcriptome; Figure 5G It is a statistical bar chart of the GO classification of the gene sets of IMF, BF and PF of Tibetan pigs in the transcriptome; Figure 5H It is a GO enrichment analysis bubble diagram of IMF and BF of Tibetan pigs in the transcriptome; Figure 5I It is a GO enrichment analysis bubble diagram of IMF and PF of Tibetan pigs in the transcriptome; Figure 5J It is a KEGG pathway enrichment chord diagram of IMF and BF of Tibetan pigs in the transcriptome; Figure 5KIt is a chord diagram of KEGG pathway enrichment for IMF and PF in the transcriptome of Tibetan pigs; Figure 5L It is a gene expression map of WNT16 in IMF and BF of Tibetan pigs; Figure 6A It is a sample correlation heat map of IMF, BF and PF in the metabolome of Tibetan pigs; Figure 6B It is a PCA 3D map of IMF, BF and PF in the metabolome of Tibetan pigs; Figure 6C It is a differential statistical bar chart of IMF, BF and PF in the metabolome of Tibetan pigs; Figure 6D It is a differential statistical Venn diagram of IMF, BF and PF in the metabolome of Tibetan pigs; Figure 6E It is a vip analysis map of IMF and BF in the metabolome of Tibetan pigs; Figure 6F It is a bubble chart of KEGG enrichment analysis of IMF and BF in the metabolome of Tibetan pigs; Figure 6G It is a pie chart of HMDB compound classification of IMF and BF in the metabolome of Tibetan pigs; Figure 6H It is a vip analysis map of IMF and PF in the metabolome of Tibetan pigs; Figure 6I It is a bubble chart of KEGG enrichment analysis of IMF and PF in the metabolome of Tibetan pigs; Figure 6J It is a pie chart of HMDB compound classification of IMF and PF in the metabolome of Tibetan pigs; Figure 7A It is a correlation network diagram of IMF and BF in Tibetan pigs in the combined transcriptome-metabolome analysis; Figure 7B It is a correlation network diagram of IMF and PF in Tibetan pigs in the combined transcriptome-metabolome analysis; Figure 7C It is a Venn diagram of KEGG pathway annotation of IMF and BF in Tibetan pigs in the combined transcriptome-metabolome analysis; Figure 7D It is a Venn diagram of KEGG pathway annotation of IMF and PF in Tibetan pigs in the combined transcriptome-metabolome analysis; Figure 7E It is a bar chart of KEGG pathway enrichment of IMF and BF in Tibetan pigs in the combined transcriptome-metabolome analysis; Figure 7F It is a bar chart of KEGG pathway enrichment of IMF and PF in Tibetan pigs in the combined transcriptome-metabolome analysis; Figure 8A It is the gene expression of WNT16 after siRNA silencing in porcine preadipocytes; Figure 8B It is the oil red staining map after induction of differentiation by NC-siRNA silencing in Tibetan pig preadipocytes; Figure 8C It is the oil red staining map after induction of differentiation by WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 8D It is the OD value map after induction of differentiation by WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 8E It is the expression level of PPARγ gene after WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 8F It is the expression level of C / EBPα gene after WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 8G It is the expression level of AdipoQ gene after WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 9 It is the WNT16 gene expression level map of IMF, BF and PF in Tibetan pigs. Specific implementation manners

[0017] The following will combine the embodiments of the present invention to clearly and completely describe the present invention. In the description of the present invention, it should be noted that for those not specified in the embodiments, they are carried out according to conventional conditions or the conditions recommended by the manufacturer. For the reagents or instruments not specified for the manufacturer, they are all conventional products that can be obtained by purchasing in the market.

[0018] Embodiment (I) Animals and sample collection Select healthy 8-month-old female Tibetan pigs and raise them in the breeding environment of the Institute of Animal Science, Guangdong Academy of Agricultural Sciences. Slaughter them according to the national slaughter standard. Select 3 pigs at the transcriptome level and 6 pigs at the non-targeted metabolomics level. Collect IMF, BF and PF samples from each pig, and collect 5 samples from each part. Fix 1 of the samples with 4% paraformaldehyde for preparing tissue sections; quickly freeze the remaining 4 samples in a -80°C refrigerator for subsequent experiments such as fatty acid determination, transcriptome analysis, and metabolomics analysis.

[0019] (II) Adipocyte-related analysis Isolation and culture of preadipocytes: Adipose tissues were collected from the groin and back of 7-day-old Tibetan pigs, disinfected with 75% ethanol under sterile conditions, and muscle tissues were carefully removed. The adipose tissues were placed in PBS solution containing 3% penicillin-streptomycin and processed in a laminar flow hood. The adipose tissues were cut into small pieces and digested with 1 mg / mL type I collagenase at 37°C for 1 hour, with shaking every 10 minutes. The digested tissues were filtered through 70 μm and 40 μm cell sieves in sequence, washed by centrifugation multiple times, and the cells were resuspended with complete F12 medium and seeded in six-well plates for culture. Subsequent experiments were carried out after the cell confluence reached ≥70%.

[0020] Preparation of adipose tissue sections and measurement of cell size: The fixed adipose tissue samples were sent to a professional company for the preparation of tissue sections. Photographs were taken under a 10×10 microscope with the aid of computer-assisted photography technology, and the scale unit of the scale bar was calibrated using ImageJ software. At least 30 adipocytes were randomly selected from the field of view of each section to measure their area and diameter. The measured data were statistically analyzed using GraphPad Prism 10 software, and one-way ANOVA was used for inter-group comparison.

[0021] Result reference Figure 1 and Figure 2 ; Figure 1 are the HE staining results of adipose tissue sections of BF (back fat), PF (perienteric fat), and IMF (intramuscular fat) of Tibetan pigs; Among them: Figure 1 The picture corresponding to icon A of is the HE staining result of a 100-fold magnified adipose tissue section of BF (back fat) of Tibetan pigs; Figure 1 The picture corresponding to icon a of is the HE staining result of a 400-fold magnified adipose tissue section of BF (back fat) of Tibetan pigs; Figure 1 The picture corresponding to icon B of is the HE staining result of a 100-fold magnified adipose tissue section of PF (perienteric fat) of Tibetan pigs; Figure 1 The picture corresponding to icon b of is the HE staining result of a 400-fold magnified adipose tissue section of PF (perienteric fat) of Tibetan pigs; Figure 1 The picture corresponding to icon C of is the HE staining result of a 100-fold magnified adipose tissue section of IMF (intramuscular fat) of Tibetan pigs; Figure 1 The picture corresponding to icon c of is the HE staining result of a 400-fold magnified adipose tissue section of IMF (intramuscular fat) of Tibetan pigs; Figure 2Statistical result charts of adipocyte areas in BF, PF, and IMF tissue sections of Tibetan pigs; Figure 3 Statistical result charts of adipocyte diameters in BF, PF, and IMF tissue sections of Tibetan pigs; The results showed that the areas and diameters of IMF adipocytes were significantly smaller than those of BF and PF adipocytes, indicating obvious morphological differences in adipocytes from different parts, and this difference might be related to their functional differentiation.

[0022] Fatty acid determination: The fatty acid content was determined according to the method of national standard GB5009.168 - 2016. Accurately weigh 150 mg of IMF, BF, and PF samples and place them in beakers respectively. After a series of treatments such as hydrolysis, condensation, saponification, and fatty acid methylation, samples suitable for instrumental analysis are prepared. The data obtained from instrumental analysis are entered into Excel 2013 for sorting, and then one-way ANOVA statistical comparison is carried out using SPSS 20 software. The results are presented in the form of mean ± standard error.

[0023] The determination results are shown Figure 4 ; Figure 4 Composition ratio diagrams of fatty acids in IMF, PF, and BF of Tibetan pigs; From Figure 4 it can be seen that the content of saturated fatty acids in IMF is lower than that in BF and PF, while the content of monounsaturated fatty acids is higher than the latter two. This fatty acid composition characteristic gives IMF a unique advantage in improving meat quality, providing a basis for explaining the impact of IMF on pork quality.

[0024] (III) Multi-omics experiments Transcriptome sequencing: Entrust a professional company (Shanghai Majorbio Bio-pharm Technology Co., Ltd.) to perform RNA extraction and sequencing on the Illumina NovaSeq X Plus platform (PE150).

[0025] First, quality control is performed on the sequencing data, and low-quality data are removed using the fastp software. Then, the quality-controlled data are mapped to the Sscrofa11.1 reference genome using the HISAT2 software, and RSEM software is used for quantitative analysis of gene expression. The DESeq2 software is used for differential expression analysis, and differential expression genes (DEGs) are determined with the screening criteria of |log2FC|≥1 and FDR<0.05. Finally, GO and KEGG databases are used for functional enrichment analysis of differential expression genes.

[0026] Specifically visible Figures 5A to 5L ; Figure 5AIt is the density plot of the expression levels of IMF, BF, and PF in the transcriptome of Tibetan pigs; Figure 5B It is the volcano plot of the expression differences between IMF and BF in the transcriptome of Tibetan pigs; Figure 5C It is the volcano plot of the expression differences between IMF and PF in the transcriptome of Tibetan pigs; Figure 5D It is the statistical chart of the expression differences of IMF, BF, and PF in the transcriptome of Tibetan pigs; Figure 5E It is the upset plot (gene set collection plot) of the gene sets of IMF, BF, and PF in the transcriptome of Tibetan pigs; Figure 5F It is the bar chart of the EggNOG classification statistics of the gene sets of IMF, BF, and PF in the transcriptome of Tibetan pigs; Figure 5G It is the bar chart of the GO classification statistics of the gene sets of IMF, BF, and PF in the transcriptome of Tibetan pigs; Figure 5H It is the bubble plot of the GO enrichment analysis of IMF and BF in the transcriptome of Tibetan pigs; Figure 5I It is the bubble plot of the GO enrichment analysis of IMF and PF in the transcriptome of Tibetan pigs; Figure 5J It is the chord diagram of the KEGG pathway enrichment of IMF and BF in the transcriptome of Tibetan pigs; Figure 5K It is the chord diagram of the KEGG pathway enrichment of IMF and PF in the transcriptome of Tibetan pigs; Figure 5L It is the gene expression level diagram of WNT16 in IMF and BF of Tibetan pigs; Figures 5A to 5L It shows the results of the transcriptome analysis, including the gene expression distribution of the samples, the number of differential genes in the comparison of different adipose tissues, and the functional annotation and enrichment analysis of the differential genes. These results indicate that there are significant gene expression differences between different adipose tissues, and signal pathways such as PI3K-Akt and Wnt may be closely related to fat deposition, providing clues for screening key regulatory genes.

[0027] Untargeted metabolite detection: Similarly, Shanghai Majorbio Bio-Pharm Technology Co., Ltd. was commissioned to conduct untargeted metabolomics analysis, and an UHPLC-QExactiveHF-X (Thermo) system was used for LC-MS / MS detection. During the detection process, quality control samples were inserted regularly to ensure the stability and repeatability of the experiment. The raw data was processed to remove variables with a relative standard deviation (RSD) > 30%, and the remaining data was log10-transformed. Metabolites were identified by comparing the mass spectrometry and tandem mass spectrometry data with HMDB, METLIN, and an in-house database. Using the ropls R package (v1.6.2), differential expressed metabolites (DEMs) were identified with the screening criteria of VIP > 1 and p < 0.05. The KEGG database was used for pathway annotation of differential metabolites, and scipy (Python v1.0.0) was used for pathway enrichment analysis. Details can be seen in Figures 6A to 6J ; Figure 6A is the sample correlation heatmap of IMF, BF, and PF in Tibetan pigs in the metabolome; Figure 6B is the PCA 3D map of IMF, BF, and PF in Tibetan pigs in the metabolome; Figure 6C is the differential statistics bar chart of IMF, BF, and PF in Tibetan pigs in the metabolome; Figure 6D is the differential statistics Venn diagram of IMF, BF, and PF in Tibetan pigs in the metabolome; Figure 6E is the VIP analysis chart of IMF and BF in Tibetan pigs in the metabolome; Figure 6F is the KEGG enrichment analysis bubble chart of IMF and BF in Tibetan pigs in the metabolome; Figure 6G is the HMDB compound classification pie chart of IMF and BF in Tibetan pigs in the metabolome; Figure 6H is the VIP analysis chart of IMF and PF in Tibetan pigs in the metabolome; Figure 6I is the KEGG enrichment analysis bubble chart of IMF and PF in Tibetan pigs in the metabolome; Figure 6J is the HMDB compound classification pie chart of IMF and PF in Tibetan pigs in the metabolome; Figures 6A to 6J Presents the results of metabolomics sample correlation and differential analysis, including sample repeatability, metabolic differences, and the screened differential metabolites. At the same time, VIP analysis, KEGG enrichment analysis, and compound classification were performed on the differential metabolites.

[0028] These results revealed the differences in metabolic characteristics among different adipose tissues and the metabolic pathways mainly involved in differential metabolites, which contributed to the in-depth understanding of the regulatory mechanism of fat metabolism.

[0029] Integrated transcriptome and metabolome analysis: The scipy library (V1.0.0) of Python was used to perform correlation analysis on the obtained differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs). Further, OmicsPLS (V2.0.2) and vegan (V2.6.4) software were used to analyze the expression relationship between DEGs and DEMs. The DEGs and DEMs were mapped to the KEGG pathway database for enrichment analysis, and the significantly enriched pathways were determined with a screening criterion of P<0.05. The data analysis was completed with the help of the Majorbio Cloud Platform (cloud.majorbio.com), and the specific results are referred to Figures 7A to 7F ; Figure 7A is the correlation network diagram of IMF and BF in Tibetan pigs for integrated transcriptome and metabolome analysis; Figure 7B is the correlation network diagram of IMF and PF in Tibetan pigs for integrated transcriptome and metabolome analysis; Figure 7C is the Venn diagram of KEGG pathway annotation of IMF and BF in Tibetan pigs for integrated transcriptome and metabolome analysis; Figure 7D is the Venn diagram of KEGG pathway annotation of IMF and PF in Tibetan pigs for integrated transcriptome and metabolome analysis; Figure 7E is the bar chart of KEGG pathway enrichment of IMF and BF in Tibetan pigs for integrated transcriptome and metabolome analysis; Figure 7F is the bar chart of KEGG pathway enrichment of IMF and PF in Tibetan pigs for integrated transcriptome and metabolome analysis; Figures 7A to 7F showed the results of the integrated transcriptome and metabolome analysis, including the correlation network of genes and metabolites, KEGG pathway annotation, and enrichment results.

[0030] The results showed that genes such as WNT10B and WNT16 and L-tyrosine were significantly co-enriched in the melanogenesis pathway, providing a new perspective and key clues for studying the regulatory mechanism of fat deposition.

[0031] (IV) Gene function verification siRNA interference: Three pairs of siRNAs (si-WNT16-512, si-WNT16-663, si-WNT16-749) and a negative control siRNA were designed and synthesized against the WNT16 gene, which were synthesized by Shanghai GenePharma Co., Ltd., and the sequence information can be seen in Table 1.

[0032] Table 1 siRNA Sequence Information Table siRNA number siRNA sequence SEQ ID NO. si-WNT16 -512 5′-GCACCAAGGAAACAGCAUUTT-3′ 5′-AAUGCUGUUUCCUUGGUGCTT-3′ 1，2 si-WNT16- 663 5′-GGGCUGCUCUGAUGAUGUUTT-3′ 5′-AACAUCAUCAGAGCAGCCCTT-3′ 3，4 si-WNT16- 749 5′-GCAAAGUACUGUUAGCAAUTT-3′ 5′-AUUGCUAACAGUACUUUGCTT-3′ 5，6 Negative control 5′-UUCUCCGAACGUGUCACGUTT-3′ 5′-ACGUGACACGUUCGGAGAATT-3′ 7，8

[0033] When the confluence of porcine preadipocytes reaches 100%, the cells are seeded in 6-well plates at a density of 4×10 4 . When the cell confluence reaches 80%, the medium is changed to a basal medium without antibiotics (DMEM + 10% FBS), and then transfection is carried out using Lipofectamine™ 3000 transfection reagent (Invitrogen). Three replicates are designed for each group. The transfection system is Solution A: 125 μl of opti-MEM medium + 5 μL of Lipofectamine™ 3000; Solution B: 125 μl of opti-MEM medium + 2.5 μg of siRNA. After mixing Solution A and B, incubate for 15 minutes and then add it dropwise to the cell medium.

[0034] Six hours after transfection, the basal medium is changed, and the interference efficiency of si-WNT16 is verified by qRT-PCR technology. The si-WNT16-749 with the best interference effect is selected for subsequent experiments.

[0035] Adipogenic differentiation experiment: Twenty-four hours after transfection with si-WNT16-749, the standard medium of the cells is changed to an adipogenic induction medium (DMEM medium containing 10% FBS, 0.5 mM 3-isobutyl-1-methylxanthine, 1 μM dexamethasone, and 10 μg / ml insulin, Sigma) for differentiation induction. After 2 days of culture, it is changed to a maintenance medium (DMEM medium containing 10% FBS and 10 μg / ml insulin) and cultured for another 6 days. After the induction is completed, the medium is discarded, the cells are washed with PBS, and then fixed and stained with OilRed O for 30 minutes, followed by washing after staining. Finally, 50 μL of the treated isopropanol is taken from the cells and added to 4 wells of a 96-well plate, and the absorbance (OD value) is measured at a wavelength of 490 nm. At the same time, cell RNA is extracted, reverse transcribed into cDNA, and the expression levels of key genes related to adipogenic differentiation, PPARγ, C / EBPα, and AdipoQ, are detected by qRT-PCR technology using β-actin as an internal reference.

[0036] Result reference Figures 8A to 8G ; Figure 8A is the gene expression level of WNT16 after siRNA silencing in Tibetan pig preadipocytes; Figure 8B is the OilRed O staining map after induction of differentiation by NC-siRNA silencing in Tibetan pig preadipocytes; Figure 8CIt is the oil red staining map after the induction of differentiation by WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 8D It is the OD value map after the induction of differentiation by WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 8E It is the expression level of PPARγ gene after WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 8F It is the expression level of C / EBPα gene after WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figure 8G It is the expression level of AdipoQ gene after WNT16-749-siRNA silencing in Tibetan pig preadipocytes; Figures 8A to 8G It shows the results of the WNT16 gene interference and adipogenic differentiation experiment.

[0037] After silencing the WNT16 gene, the number of lipid droplets in porcine preadipocytes decreased significantly, the OD value decreased, and at the same time, the expression of key genes related to adipogenic differentiation, PPARγ and C / EBPα, was down-regulated, while the expression of AdipoQ was up-regulated.

[0038] This result strongly verifies the important regulatory role of the WNT16 gene in the adipogenic differentiation process of porcine preadipocytes.

[0039] (V) Verification of the expression level of WNT16 gene in different adipose tissues Select IMF, BF, and PF of Tibetan pigs to detect the expression level of the WNT16 gene.

[0040] The results are shown in Figure 9 ; In IMF, the expression level of the WNT16 gene is much higher than that in BF and PF. This result strongly verifies the important regulatory role of the WNT16 gene in porcine IMF deposition.

[0041] Figure 9 It is the expression level map of the WNT16 gene in IMF, BF, and PF of Tibetan pigs.

[0042] Summary: Through the above studies from different perspectives, the impact of the WNT16 gene on meat quality can be clearly determined. Specifically, the correlation between IMF and pork quality can be determined through the distribution of fatty acid content in different tissues; through multi-omics experiments, the differences in gene expression and metabolites in different adipose tissues are determined, proving the possible impact pathways of key genes; through gene knockout experiments, the impact of the WNT16 gene on key genes and the relationship between the number of intracellular lipid droplets and OD values are verified, proving its close association with adipocyte differentiation and being a key gene affecting meat quality. Among them, the more intracellular lipid droplets, the better the meat quality. Adipocyte differentiation genes affect the differentiation of adipocytes. When the expression of PPARγ and C / EBPα is up-regulated, it indirectly indicates an improvement in meat quality. In addition, we also verified the expression level of the WNT16 gene in different adipose tissues, proving the association between IMF and the WNT16 gene. Through double verification at the gene level and tissue level, it is proved that the WNT16 gene is a key gene affecting meat quality.

[0043] This enables the WNT16 gene to be a key gene for breeding and constructing a porcine adipocyte model.

Claims

1. Application of a genetic marker associated with intramuscular fat in pig genetic breeding, characterized in that: The genetic marker is the WNT16 gene.

2. Application of WNT16 gene in constructing a pig adipocyte model; the pig adipocyte model is used to detect the expression levels of other genes related to fat differentiation in the model.

3. The use according to claim 2, characterized in that: The cells used to construct the porcine adipocyte model are porcine preadipocytes.

4. The use according to claim 2, characterized in that: The other genes are PPARγ, C / EBPα and AdipoQ genes.

5. The use according to claim 2, characterized in that: A WNT16 gene inhibitor is constructed according to the WNT16 gene, and a pig adipocyte model is constructed by using the WNT16 gene inhibitor; the nucleotide sequence of the WNT16 gene inhibitor is shown in SEQ ID NO.5 and SEQ ID NO.

6.

6. A kit for constructing a pig adipocyte model, characterized in that: Contains a WNT16 gene inhibitor, the nucleotide sequence of the WNT16 gene inhibitor is shown in SEQ ID NO.5 and SEQ ID NO.6; the porcine adipocyte model is used to detect the expression levels of other genes related to fat differentiation in the model.

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