A biomarker associated with Salmonella enteritidis infection in chickens and its application

Abstract

This invention discloses a biomarker related to Salmonella enteritidis infection in chickens and its application, belonging to the field of poultry genetics, breeding, and reproduction technology. This invention is the first to discover that the RUNX2 transcription factor exhibits significant differences in content in cecal T cells after Salmonella enteritidis infection. The expression level of RUNX2 in the susceptible group is much higher than that in the resistant group, indicating that it is a susceptibility gene after Salmonella enteritidis infection. If chickens exhibit typical enteritis symptoms such as diarrhea and loss of appetite, and a significantly elevated RUNX2 expression level is detected, this may be a marker of chickens susceptible to Salmonella enteritidis infection. If chickens do not exhibit corresponding symptoms and the RUNX2 expression level is not significantly elevated, it indicates that the chickens are resistant to Salmonella enteritidis. Single-cell transcriptomics technology can be used to identify chicken resistance to Salmonella enteritidis infection at the molecular level, providing technical support for breeding poultry breeds resistant to Salmonella enteritidis infection.

Description

Technical Field

[0001] This invention relates to the field of poultry genetics, breeding and reproduction technology, specifically to a biomarker related to Salmonella enteritidis infection in chickens and its application. Background Technology

[0002] Salmonella enterica serovar Enteritidis, S. Enteritidis, is a common zoonotic Gram-negative foodborne pathogen. It is non-host-specific but highly invasive, primarily parasitizing the intestines of animals and humans. Salmonella enterica infection colonizes the intestines and invades intestinal epithelial cells, potentially causing damage to the intestinal wall, manifesting as diarrhea, loss of appetite, and weight loss, and even leading to serious complications such as septicemia. It interferes with the chicken's immune system, reducing its resistance to other pathogens and increasing the risk of infection. Salmonella enterica infection leads to increased mortality and reduced growth rate in chickens; it also affects egg production performance and can even cause a decline in the quality of chicken products. Furthermore, Salmonella enterica is a significant foodborne pathogen; humans can become infected by consuming infected poultry or eggs, leading to food poisoning, posing a significant threat to livestock and human health. Traditional methods for preventing and controlling Salmonella enteritidis, such as antibiotic injections and vaccines, cannot fundamentally solve the problem of disease in livestock and poultry. However, Salmonella enteritidis infection is genetically regulated. Analyzing the regulatory mechanism of Salmonella enteritidis infection in chickens and thereby improving their genetic resistance to Salmonella enteritidis infection is an effective way to prevent and control Salmonella enteritidis infection.

[0003] The host's genetic background has a profound impact on Salmonella resistance. Even after intensive selection in terms of production performance and viability, differences in immune responses and disease resistance to Salmonella remain among chicken breeds and strains, providing a possibility for genetically improving poultry disease resistance. Identifying chickens resistant to Salmonella is beneficial for reducing Salmonella contamination in poultry products (such as chicken meat and eggs), minimizing the spread of Salmonella to humans, ensuring food safety for consumers, and guaranteeing the healthy operation and sustainable development of the poultry industry.

[0004] Changes in individual transcription factor characteristics can reflect an animal's response to Salmonella enteritidis infection. Linking transcription factors with biophenotypes can provide clues for discovering new biomarkers. The cecum is the primary site of invasion for Salmonella enteritidis, and changes in cecal tissue characteristics can reflect whether an animal is infected with Salmonella enteritidis to some extent. Transcription factors can serve as potential biomarkers reflecting Salmonella enteritidis infection. Therefore, understanding the relationship between transcription factors and Salmonella enteritidis infection in animals is of great significance for chicken genetic breeding and reproduction. However, to date, no biomarkers associated with resistance to Salmonella enteritidis infection in chickens have been reported. Summary of the Invention

[0005] In view of the above-mentioned prior art, the purpose of this invention is to provide a biomarker related to Salmonella enteritidis infection in chickens and its application. The biomarker of this invention can identify the resistance of chickens to Salmonella enteritidis infection, exhibiting high sensitivity and specificity, and is expected to become a new method for identifying Salmonella enteritidis infection resistance.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the invention provides the use of RUNX2 as a target in the preparation of products for identifying resistance to Salmonella enteritidis infection in livestock and poultry.

[0008] In the above applications, the amino acid sequence of RUNX2 is shown in SEQ ID No. 1; specifically as follows:

[0009] MASNSLFSSVTPCQQNFFWDPSTSRRFSPPSSSLQPGKMSEVSPVVVAQQQQQQQQQQQEAAVPRLRPHDNRTMVEIIADHPAELVRTDSPNFLCSVLPSHWRCNKTLPVAFKVVAL GEVPDGTVVTVMAGNDENYSAELRNASAVMKNQVARFNDLRFVGRSGRGKSFTLTITVLTNPPQVATYHRAIKVTVDGPREPRRHRQKLDDSKPSLFPERLSDLGRIPHPSMRVGVPT QSPRPSLNSAPSPFNPQGQSQITDPRQAQSSPPWSYDQSYPSYLSQMTSPSIHSTTPLSSTRGTGLPAITDVPRRLSGASELGPFSDPRQFTSISSLTESRFSNPRMHYPATFTYTPP VTSGMSLGMSATTHYHTYLPPPYPGSSQNQSGPFQTSSTPYLYYGTSSGSYQFPMVPGGDRSPSRMLPPCTTTSNGSTLLNPNLPNQSDGVEADGSHSSSPTVLNSSGRMDESVWRPY.

[0010] In the above applications, the preferred livestock or poultry is chicken.

[0011] A second aspect of the invention provides the use of the RUNX2 gene as a target gene in either (1) or (2) below:

[0012] (1) Prepare products for identifying the resistance of livestock and poultry to Salmonella enteritidis infection;

[0013] (2) Develop livestock and poultry breeds resistant to Salmonella enteritis infection.

[0014] In the above applications, the nucleotide sequence of the RUNX2 gene is shown in SEQ ID No. 2; specifically as follows:

[0015]

[0016] This invention has found that RUNX2 is differentially expressed in chickens that are susceptible to and resistant to Salmonella enteritidis, and the expression level in susceptible chickens is significantly higher than that in resistant chickens. Therefore, the expression level of the RUNX2 gene can be detected to identify the infection resistance of chickens to Salmonella enteritidis, as well as to breed disease-resistant breeds resistant to Salmonella enteritidis infection.

[0017] A third aspect of the invention provides the use of a reagent for detecting RUNX2 in the preparation of products for identifying resistance of livestock and poultry to Salmonella enteritidis infection.

[0018] Preferably, the reagent is a reagent for detecting the RUNX2 content in cecal T cells. Examples include ELISA kits, Western blot kits, and immunohistochemical (IHC) reagents for detecting RUNX2 content.

[0019] In a fourth aspect, the present invention provides a kit for detecting the RUNX2 gene for use in either (1) or (2) below:

[0020] (1) Prepare products for identifying the resistance of livestock and poultry to Salmonella enteritidis infection;

[0021] (2) Develop livestock and poultry breeds resistant to Salmonella enteritis infection.

[0022] Preferably, the kit for detecting the RUNX2 gene is a real-time PCR kit.

[0023] A fifth aspect of the present invention provides a method for identifying resistance in chickens to Salmonella enteritidis infection, comprising the following steps:

[0024] The expression level of the RUNX2 gene in the test chickens was detected. Chickens with low RUNX2 gene expression levels were more resistant to Salmonella enteritidis infection than chickens with high RUNX2 gene expression levels.

[0025] The beneficial effects of this invention are:

[0026] This invention is the first to discover that the RUNX2 transcription factor exhibits significant differences in its content in cecal T cells after Salmonella enteritidis infection. The expression level of RUNX2 in the susceptible group is much higher than that in the resistant group, indicating that it is a susceptibility gene after Salmonella enteritidis infection. If chickens exhibit typical enteritis symptoms such as diarrhea and loss of appetite, and a significant increase in RUNX2 expression level is detected, this may be a marker of chickens susceptible to Salmonella enteritidis infection. If chickens do not exhibit corresponding symptoms and the RUNX2 expression level is not significantly increased, it indicates that the chickens are resistant to Salmonella enteritidis infection. Single-cell transcriptomics technology can be used to identify the resistance of chickens to Salmonella enteritidis infection at the molecular level, providing technical support for breeding poultry breeds resistant to Salmonella enteritidis infection. Attached Figure Description

[0027] Figure 1 Results of the second cell quality control.

[0028] Figure 2 UMAP nonlinear dimensionality reduction clustering (all cells in the cecal sample).

[0029] Figure 3 UMAP nonlinear dimensionality reduction clustering (6 samples from groups S and R).

[0030] Figure 4 : The relative expression level of a specific marker gene for each cell type.

[0031] Figure 5 Cell annotation results.

[0032] Figure 6 Heatmap of RAS activity in regimens at the R group cell level.

[0033] Figure 7 RAS activity heatmap of each cell population regiment in group R.

[0034] Figure 8 Heatmap of RAS activity of regimens at the S group cell level.

[0035] Figure 9 RAS activity heatmap of each cell population regiment in group S.

[0036] Figure 10 : Regulon specificity ordination plot for group R.

[0037] Figure 11 : Regulon specificity sorting plot for group S.

[0038] Figure 12 RAS-specific heatmap of each cell population regimens in group R.

[0039] Figure 13 RAS-specific heatmap of each cell population regimens in group S.

[0040] Figure 14 ROC curve of RUNX2 in T cells.

[0041] Figure 15 Results of the detection of RUNX2 gene expression in chickens. Detailed Implementation

[0042] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0043] To enable those skilled in the art to better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to specific embodiments.

[0044] The test materials used in the embodiments of this invention are all conventional test materials in the art and can be purchased through commercial channels. Experimental methods without specified detailed conditions are performed according to conventional test methods or the supplier's recommended operating instructions.

[0045] Example 1: Screening and identification of biomarkers associated with Salmonella enteritidis infection in chickens

[0046] 1. Experimental Method:

[0047] (1) Determination of whether chickens are infected with Salmonella enteritidis and sample collection

[0048] Seventy-seven one-day-old, Salmonella-negative Wenshang Luhua chickens of similar weight were selected and administered 0.3 mL of a 3.03 × 10⁻⁶ gavage to each chicken. 8 Salmonella enteritidis (CFU / mL, purchased from China Veterinary Microbial Culture Collection Center, CVCC3377) bacterial suspension was used to determine the bacterial content in feces by plating. Seven days post-infection, using a two-tail method, the 17 chickens with the highest bacterial count were selected as the susceptible group (S group), and the 17 chickens with the lowest bacterial count were selected as the resistant group (R group). The 3 chickens with the highest and 3 chickens with the lowest bacterial count were used as the selection set, and single-cell suspensions were prepared from their cecal tissue for single-cell transcriptome sequencing. The remaining chickens in the S and R groups were used as the validation set.

[0049] (2) Single-cell transcriptome sequencing

[0050] ①Preparation of single-cell suspension

[0051] Cut the tissue into 2 mm fragments and incubate in 20 mM EDTA-PBS on ice for 90 min, shaking once every 30 min. After incubation, shake the tissue vigorously and collect the supernatant into a new centrifuge tube as fraction 1. Add fresh EDTA-PBS to fresh tissue and incubate, collecting fractions every 30 min until the supernatant is almost completely occupied by crypts. Wash the last fraction (filled with crypts) twice with PBS, centrifuge at 300 g for 3 min, and dissociate at 37 °C using TrypLE express for 1 min. Use 40 μm Flowmi...TM Tip: Use a strainer to remove cell debris and cell clusters; after resuspending, prepare a cell suspension for subsequent experiments; use trypan blue staining and a hemocytometer to calculate the concentration and cell viability of the single-cell suspension. Once the viable cell count reaches 90%, proceed to the next step of the experiment.

[0052] ② Library construction and sequencing

[0053] This library construction and sequencing employed the 10x Genomics platform from Shanghai Ouyi Biomedical Technology Co., Ltd., utilizing microfluidic technology to encapsulate beads with cell barcodes and cells in droplets. The cell-containing droplets were collected, and the cells were then lysed within the droplets, allowing the mRNA in the cells to link with the cell barcodes on the beads, forming SingleCellGEMs. Reverse transcription was then performed within the droplets to construct a cDNA library. The sample source of the target sequence was identified by the sample index on the library sequence.

[0054] (3) Data preprocessing

[0055] After the sequencing was completed, the raw reads generated during high-throughput sequencing were in FASTQ format. The 10xGenomics official software Cell Ranger (version 7.0.1) was used to perform data quality statistics and compare the raw data with the reference genome (chicken: Gallus gallus_v5.0). This software quantifies high-throughput single-cell transcriptome data by identifying CellBarcode markers that distinguish cells in the sequence and UMI markers of different mRNA molecules in each cell, and obtains quality control statistics such as high-quality cell count, gene median, and sequencing saturation.

[0056] Based on the initial quality control using Cell Ranger, the Seurat (version 4.0.0) software package was used for further quality control of the data. The number of expressed genes, UMIs, and the percentage of mitochondrial transcript expression in most cells tend to cluster within a specific region. Based on the distribution of indicators such as nUMI, nGene, and percent.mito, low-quality cells were filtered out. The specific quality control protocol was as follows: cells with more than 200 genes, more than 1000 UMIs, log10GenesPerUMI greater than 0.7, a mitochondrial UMI percentage below 5%, and a erythrocyte gene percentage below 5% were considered high-quality cells. Double cells were removed using DoubletFinder (version 2.0.3). After quality control, the NormalizeData function in the Seurat package was used to standardize the data.

[0057] (4) Transcription factor identification and screening of key differentially expressed transcription factors

[0058] The FindVariableGenes function (mean.function = FastExpMean, dispersion.function = FastLogVMR) in the Seurat package was used to screen the top 2000 highly variable genes (HVGs). Principal component analysis (PCA) was performed using the expression profiles of these highly variable genes, and the results were visualized in two-dimensional space using UMAP (non-linear dimensionality reduction). Cell type annotation was performed using the SingleR (version 1.4.1) package based on the HPCA reference dataset and specific marker genes obtained from previous studies. The RcisTarget motif database and GRNboost (SCENIC version 1.2.4, RcisTarget version 1.10.0, and AUCell version 1.12.0) were run with default parameters. Potential target genes for each transcription factor were identified based on co-expression; the true transcription factors and their corresponding target genes were identified using motif analysis with the RcisTarget package; and the activity of each regulator in each cell was scored using the AUCell package (version 1.8.0).

[0059] To assess the cell type specificity of each regulator, the regulator specificity score (RSS) based on Jensen-Shannon divergence (JSD) and the correlation index (CSI) of all regulators were calculated using the scFunctions package (https: / / github.com / FloWuenne / scFunctions / ). Comparative analysis of the correlation results between the susceptible and resistant groups revealed differentially expressed transcription factors in different cell types.

[0060] 2. Experimental Results:

[0061] (1) Cell Overview

[0062] The results of the first cell quality control are shown in Table 1. After aligning the reads to the reference genome using Cell Ranger, the quality control results, such as the number of high-quality cells, the number of genes, and the genome alignment rate, were obtained from the original data, thereby evaluating the quality of each sample.

[0063] Table 1: Results of the first cell quality control

[0064]

[0065] The results showed that the Estimated Number of Cells was ≥7000, Sequencing Saturation was ≥40%, and Fraction Reads in Cells was ≥70%, which met the basic requirements.

[0066] The results of the second cell quality control are shown below. Figure 1 Based on the initial quality control using Cell Ranger, further quality control was performed on the experimental data, removing multi-cell, double-cell, or unbound cells before downstream analysis. The quality control criteria were: cells with more than 200 genes, more than 1000 UMIs, and a log10GenesPerUMI ratio greater than 0.7 were considered high-quality cells. Double-cell removal was then performed using DoubleFinder software before downstream analysis. A violin plot showing the number of genes (nGene), the number of UMIs (nUMI), and the proportion of genes per unit UMI in the corresponding sample (log10GenesPerUMI) before and after quality control reflects the complexity of the data. A concentrated distribution of points in the violin plot indicates that the cell stability and quality in the sample meet the requirements, and the experimental data are reliable.

[0067] (2) Single-cell transcriptome data analysis

[0068] ①UMAP (Nonlinear Dimensionality Reduction) Analysis

[0069] The dimensionality reduction algorithms used were PCA (Principal Component Analysis) and UMAP (Unified Manifold Approximation and Projection). The dimensionality reduction results based on PCA were visualized using UMAP to cluster single-cell populations. The clustering algorithm employed was a Sub-Neural Network (SNN). The optimal cell clusters were ultimately obtained, with closer points representing cells with similar gene expression patterns, while farther points represented cells with significantly different gene expression patterns. The horizontal and vertical axes represent the first and second principal components of the dimensionality reduction, respectively. Each point in the graph represents a cell, and cells from different populations are distinguished by different colors. Figure 2 Cells from different sample sources are distinguished by different colors. Figure 3 All cells in the cecal sample could be divided into 18 clusters (Figure 1). Figure 2 ), and appeared in all 6 samples. Figure 3 ).

[0070] ② Cell type annotation

[0071] The SingleR (version 1.4.1) package was used to develop specific marker genes based on the HPCA reference dataset and previous research. Figure 4 Cell type annotation was performed, with the horizontal axis representing different cell types and the vertical axis representing genes. The size of the dot reflects the expression ratio of that gene in the cell population, and the color of the dot reflects the expression level of that gene in the cell population: from light blue (low expression) to dark red (high expression). A total of 8 cell types were obtained. Figure 5 Each cell type is distinguished by a different color: Epithelium, Endothelium, Glia, Fibroblast, B cells, T cells, Myeloid, and Mast cells.

[0072] ③Scenic analysis

[0073] The SCENIC software can identify the regulons co-expressed between transcription factors (TFs) and potential target genes, as well as the regulon activity score (RAS) for each cell. Figures 6-9 The specific correspondence between predicted regimenls and each cell type is obtained by calculating the regimenlon specificity score (RSS). Figure 10-13 The rows represent different regimenls, the columns represent different cell populations, and the color changes from blue to red to indicate that the RAS activity score is from low to high. The higher the RAS score, the stronger the activity / specificity of the regimenl in that population.

[0074] Score analysis revealed that in group R, FOXO1 and IRF4 were highly expressed in T cells and were specifically associated with this cell population, while SOX5 was highly expressed in Mast cells and was specifically associated with this cell population. In group S, FLI1 was highly expressed in Endothelium and was specifically associated with this cell population, FOXA1 was highly expressed in Epithelium and was specifically associated with this cell population, while RUNX2, ETS1, and IRF4 were specifically highly expressed in T cells.

[0075] ④ Screening for differentially expressed transcription factors between susceptible and resistant groups

[0076] The comparison between the R group and the S group showed that RUNX2 was specifically highly expressed in T cells only in the S group, and this transcription factor may be related to S. enteritidis infection.

[0077] ⑤ Confirmation of disease resistance biomarkers for Salmonella enteritidis infection in chickens

[0078] The validation set was used to confirm the candidate biomarkers associated with Salmonella enteritidis infection in chickens. ROC curves for individual indicators of the candidate cecal differential biomarkers screened in this study were plotted using IBM SPSS Statistics 25 software, and sensitivity and specificity were calculated. The diagnostic efficacy of individual transcription factors was analyzed to confirm biomarkers associated with Salmonella enteritidis infection resistance in chickens. The ROC curves are shown below. Figure 14 As shown.

[0079] ROC curves of differentially regulated transcription factors in groups S and R were plotted using SPSS software. The ROC curve reflects the relationship between sensitivity and specificity. The x-axis represents 1 – specificity, also known as the false positive rate; the closer the x-axis is to zero, the higher the accuracy. The y-axis represents sensitivity, also known as the true positive rate; a larger y-axis indicates better accuracy. Sensitivity refers to the proportion of a screening method that correctly identifies an actually downregulated metabolite as downregulated. Specificity refers to the proportion of a screening method that correctly identifies an actually downregulated metabolite as downregulated.

[0080] The Youden index, also known as the accuracy index, is used to assess the reliability of screening tests when it is assumed that false negatives (missed diagnoses) and false positives (false diagnoses) are equally harmful. The Youden index = Sensitivity + Specificity - 1. A higher Youden index indicates greater accuracy and a better diagnostic method.

[0081] The results showed that the AUC value of the differential transcription factor RUNX2 was 1.000, with a sensitivity of 100.0% and a specificity of 100.0%. Figure 14 Therefore, the transcription factor RUNX2 has excellent ability to identify resistance to Salmonella enteritidis in chickens.

[0082] Example 2: Application of transcription factor RUNX2 in the identification of resistance to Salmonella enteritidis in chickens

[0083] 1. Experimental Method:

[0084] Another 12 one-day-old, Salmonella-negative Wenshang Luhua chickens of similar weight were taken and administered Salmonella enterica by gavage according to the method in Example 1. Seven days after infection, the three chickens with the highest bacterial count were selected as the susceptible group (S group) and the three chickens with the lowest bacterial count were selected as the resistant group (R group) by the two-tail method.

[0085] RNA was extracted from the cecum of chickens; the extracted RNA was reverse transcribed into cDNA; the expression level of RUNX2 transcription factor was monitored by designing specific primers (F: CAGACCAGCAGCACTCCATA; R: TTGGGCAAGTTTGGGTTTAG); the expression data were corrected by using internal reference genes (such as β-actin or GAPDH) as normalization genes, and the expression level of RUNX2 was detected by real-time quantitative PCR.

[0086] 2. Experimental Results:

[0087] The results are as follows Figure 15 As shown, the results indicate that the expression level of RUNX2 in group S was significantly higher than that in group R, suggesting that RUNX2 is a susceptibility gene for Salmonella enteritidis infection. Therefore, the expression level of RUNX2 can be used to identify the resistance of chickens to Salmonella enteritidis infection. If chickens exhibit typical enteritis symptoms such as diarrhea and loss of appetite, and a significantly elevated RUNX2 expression level is detected, this may be a marker of chickens susceptible to Salmonella enteritidis infection; if chickens do not exhibit corresponding symptoms and the RUNX2 expression level is not significantly elevated, then the chickens may be resistant to Salmonella enteritidis.

[0088] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. The application of a reagent for detecting RUNX2 in the preparation of products for identifying resistance to Salmonella enteritidis infection in livestock and poultry, characterized in that, The livestock or poultry is chicken; the reagent is a reagent for detecting the RUNX2 content in cecal T cells; the amino acid sequence of RUNX2 is shown in SEQ ID No.

1.

2. The application according to claim 1, characterized in that, The reagents are ELISA kits, Western blot kits, or immunohistochemical reagents for detecting RUNX2 content.

3. Testing RUNX2 The gene kit is used in the following (1) or (2): (1) Prepare products for identifying the resistance of livestock and poultry to Salmonella enteritidis infection; (2) To cultivate livestock and poultry breeds resistant to Salmonella enteritidis infection; The livestock and poultry mentioned are chickens; the kit is a kit for detecting the RUNX2 content in cecal T cells; RUNX2 The nucleotide sequence of the gene is shown in SEQ ID No.

2.

4. The application according to claim 3, characterized in that, The detection RUNX2 The gene assay kit is a real-time PCR kit.

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