Internal reference gene for real-time quantitative PCR of yersinia enterocolitica, and primers therefor and use thereof

Abstract

Provided is an internal reference gene for the real-time quantitative PCR (RT-qPCR) of Yersinia enterocolitica, including one of or a combination of some of glnS, nuoB, glmS, gyrB, dnaK, and thrS genes. Further provided is a primer set for amplifying the internal reference gene for the RT-qPCR of Yersinia enterocolitica. Given that stable housekeeping genes of Yersinia enterocolitica remain unclear, internal reference genes with relatively stable expression at different culture temperatures are selected by screening for Yersinia enterocolitica. Specific detection primers and real and reliable data provide a reference basis for selecting housekeeping genes of Yersinia enterocolitica; moreover, the use thereof alone or in combination, as an internal reference gene, improves the data accuracy, stability, and reliability, thereby solving the problem that usually only empirically selecting 16s rRNA as a single internal reference gene in conventional RT-qPCR results in unstable research results.

Description

Yersinia enterocolitica real-time fluorescent quantitative PCR internal reference gene, primer and application thereof TECHNICAL FIELD

[0001] The present application belongs to the technical field of molecular biology, and particularly relates to a Yersinia enterocolitica real-time fluorescent quantitative PCR internal reference gene, primer and application thereof. BACKGROUND

[0002] Yersinia enterocolitica is a human pathogen of Yersiniaceae widely existing in the environment and animal hosts. It is a zoonotic bacterium that is transmitted through the fecal-oral route, and usually causes self-limiting enteritis, terminal ileitis or adenitis, and occasionally causes chronic inflammatory diseases such as arthritis and erythema nodosum. A common feature of Yersinia enterocolitica is strong temperature adaptability, and many physiological properties of the bacterium are affected by temperature. The optimal growth temperature of the bacterium is 22°C, at which it is a single-cell motile bacterium with the highest expression of lipopolysaccharide (LPS) O-polysaccharide (OPS) and the highest growth rate. When transferred to a mammalian host or the culture temperature is increased to 37°C, it changes into a non-motile and self-aggregated form, and increases the expression level of virulence proteins such as invasin (Inv), Yersinia adhesin A (YadA), and plasmid-encoded Yersinia exotoxin (Yops). Therefore, when research is conducted on Yersinia enterocolitica (as well as Yersinia pestis and Yersinia pseudotuberculosis), the culture temperature is an important aspect that needs to be considered or noted. In related research, researchers usually divide experiments into two groups according to temperature conditions: room temperature (about 22-25°C) and host temperature (37°C), and then conduct subsequent experiments to observe and compare the results obtained under different temperature environments.

[0003] In recent years, with the popularization of technology, more and more studies have begun to use transcriptome or even proteome methods to observe the differences in bacterial protein expression. However, real-time quantitative PCR (qRT-PCR) as a traditional and mature technology is still one of the most commonly used and most reliable technologies in microbiological laboratories. This is because it has low technical threshold, widely available equipment, low reagent cost, good repeatability, and especially because its results are intuitive and easy to analyze. When using this technology for relative quantitative experiments, a "reference gene" is needed as an "internal control" and is used as a "baseline" to evaluate the expression level changes of other genes. Selecting a suitable reference gene has always been a hot topic of research. In mammalian studies, commonly used reference genes include GAPDH (glycolytic enzyme), ACTB (β-actin), B2M (β-2 microglobulin), etc., which belong to either cytoskeletal protein genes or are involved in basic biological functions of cells, and are theoretically expressed relatively stably and consistently in various cells. However, in recent years, multiple studies have shown that the expression levels of these commonly used reference genes also fluctuate under different organs or under specific types of external interference. Therefore, there is now a consensus that caution should be exercised in the selection of reference genes, and reference genes should be carefully evaluated and selected according to specific experimental conditions to minimize experimental errors.

[0004] In microbiological research, due to the large number of microorganisms and the large differences between species and genera, there is a lack of overall evaluation, which leads researchers to often only rely on consulting published literature when selecting reference genes. In fact, most studies have chosen 16s rRNA as a reference gene without exception, and the reason is that it is relatively conserved in all bacteria and has a prominent position in microbial classification and identification. Although 16S rRNA is indeed expressed at a relatively stable level in most cases, in fact, it has been proven to be unrealistic to set a single gene as a universal reference gene to adapt to all experimental conditions.

[0005] Therefore, a more scientific way may be to systematically evaluate the target bacteria to determine the combination of reference genes under specific conditions. SUMMARY

[0006] The purpose of the present application is to provide a Yersinia enterocolitica real-time fluorescence quantitative PCR reference gene, primers thereof, and applications thereof, to solve the problems raised in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solution: a Yersinia enterocolitica real-time fluorescence quantitative PCR reference gene, which comprises a combination of one or more of the following genes: glnS, nuoB, glmS, gyrB, dnaK, and thrS;

[0008] The nucleotide sequence of the glnS gene is shown as SEQ ID NO. 1, in particular:

[0009] 。

[0010] The nucleotide sequence of the nuoB gene is shown as SEQ ID NO. 2, in particular:

[0011] 。

[0012] The nucleotide sequence of the glmS gene is shown as SEQ ID NO. 3, in particular:

[0013] 。

[0014] The nucleotide sequence of the gyrB gene is shown as SEQ ID NO. 4, in particular:

[0015] 。

[0016] The nucleotide sequence of the dnaK gene is shown as SEQ ID NO. 5, in particular:

[0017]

[0018] The nucleotide sequence of the thrS gene is shown as SEQ ID NO. 6, in particular:

[0019]

[0020] A second object of the present application is to provide a primer set for amplifying the above-mentioned internal reference genes, and the sequence of the primer set is as follows:

[0021] The amplification primer of the glnS gene is:

[0022]

[0023] The amplification primer of the nuoB gene is:

[0024]

[0025] The amplification primer of the glmS gene is:

[0026]

[0027] The amplification primer of the gyrB gene is:

[0028]

[0029] The amplification primer of the dnaK gene is:

[0030]

[0031] The amplification primer of the thrS gene is:

[0032]

[0033] A third object of the present application is to provide the use of the above-mentioned reference gene in the quantitative detection of the reference gene of Yersinia enterocolitica.

[0034] A fourth object of the present application is to provide the use of the above-mentioned primer set in the quantitative detection of the reference gene of Yersinia enterocolitica.

[0035] A fifth object of the present application is to provide the use of the above-mentioned primer set in the preparation of a kit for the gene expression analysis of Yersinia enterocolitica.

[0036] A kit comprising the above-mentioned primer set.

[0037] Preferably, other reagents for PCR are also included.

[0038] A fifth object of the present application is to provide the use of the above-mentioned reference gene, the above-mentioned primer set or the above-mentioned kit in the gene expression analysis of Yersinia enterocolitica.

[0039] The PCR amplification reaction procedure is: 50°C for 3 min to generate cDNA, 95°C for 30 s, 40 cycles of 95°C for 10 s and 60°C for 30 s, and then 95°C for 15 s, 60°C for 1 min and 95°C for 15 s.

[0040] The screening method is as follows:

[0041] 1. Sample collection

[0042] Yersinia enterocolitica strains were collected from the China Pathogenic Bacteria Identification Network project (Table 1). All strains were isolated from stool specimens of patients with diarrhea. Strain identification was performed using VITEK® 2 Compact (bioMérieux, France). The O serogroup of the strains was determined by slide agglutination test (Denka Seiken, Japan).

[0043] Table 1 Yersinia enterocolitica strains used in this study

[0044]

[0045] 2. RNA-Seq data set and transcript abundance analysis

[0046] Yersinia enterocolitica RNA-Seq datasets (BioProject: PRJNA264525 and BioProject: PRJNA277186) were obtained from the Sequence Read Archive (SRA) database, which were uploaded by research teams at the University of Helsinki. Both studies used Yersinia enterocolitica Y11 (Serotype: 03) as the wild-type strain, and all strains were cultured at 22 °C and 37 °C, respectively, before sequencing. To improve the robustness of the screening results, the RNA-Seq data of genetically engineered mutant strains in these studies were also included.

[0047] Raw FASTQ files were extracted using the prefetch and fasterq-dump tools in SRA Toolkit 3.02, and fastp 0.23.2 was used to trim low-quality reads and adapters with default settings. The Yersinia enterocolitica Y11 (RefSeq: GCF_000253175.1) genome was first annotated using Bakta (v1.8.1), and then used to build the transcriptome index. Transcript abundance was quantified using Kallisto v0.48.0, and the Transcripts Per Million (TPM) values were obtained.

[0048] 3. Identification of new candidate reference genes from the transcriptome

[0049] Candidate reference genes that stably expressed between 22 / 37 °C were identified using the method provided by Eisenberg and Levanon with slight modification. Briefly, gene expression level was represented using Transcripts per kilobase million (TPM) instead of Reads per kilobase million (RPKM), and the following four principles were used in the screening process of candidate reference genes: (1) low variability between samples cultured at 22 / 37 °C: standard deviation (SD) of log2(TPM) < 0.5; (2) no abnormal expression in any single sample: Max |log2(TPM) - mean [log2(TPM)]| < 1; (3) relatively high expression level: Mean [log2(TPM)] > 5; and (4) highly conserved in Y. enterocolitica genome. To evaluate gene conservation, genome data of Y. enterocolitica was obtained from NCBI using ncbi-genome-download tool (v0.3.1), and the conservation of candidate reference genes was evaluated by blastn (v2.12.0) comparison with a filtering condition of >95% identity match in at least 90% of the genome. Genes that met all the above criteria were ranked in descending order of CV value, and the top 10 genes were selected for qRT-PCR validation. The scope of target genes was limited to coding sequences (CDS) and ribosomal RNA (rRNA), specifically excluding non-coding RNA (ncRNA), small open reading frame (sORF), transfer RNA (tRNA), and transfer messenger RNA (tmRNA).

[0050] 4. Verification of target gene expression stability

[0051] The expression stability of the previously reported internal reference genes (a total of 6) and the candidate internal reference genes (a total of 10) screened in the present study was verified using qRT-PCR (Table 2). The primers were designed using the Primer3Plus website tool. Total RNA was extracted using a spin column bacterial total RNA purification kit (Sangon, B518655) and an RNase DNase-free kit (Sangon, B618253). The RNA concentration was measured using a NanoDrop Lite spectrophotometer (Thermo), and the RNA purity was distinguished by the OD260 / 280 ratio. qRT-PCR was performed in a 96-well plate using a HiScript II One Step qRT-PCR SYBR Green kit (Vazyme) and a BioRad CFX system, with each 20 μL reaction containing 10 μL 2×One Step SYBR Green mixture, 1 μL One-Step SYBR Green enzyme mixture, 0.4 μL of each 10 μM primer, and a total of 1 ng RNA. The reaction was run using the following cycle parameters: 50°C for 3 min to generate cDNA, 95°C for 30 s, 40 cycles of 95°C for 10 s and 60°C for 30 s, followed by a melt curve analysis of 95°C for 15 s, 60°C for 1 min, and 95°C for 15 s. Each qRT-PCR analysis was performed in triplicate, and the analysis was evaluated for whether a single, specific product was produced by analyzing the melt curve.

[0052] The expression stability of the selected genes was evaluated using RefFinder, a web-based tool that evaluates the stability and reliability of reference genes by integrating the results of major computational programs such as geNorm, NormFinder, BestKeeper, and the comparative delta-Ct method. Comprehensive ranking was performed using the Robust Rank Aggregation (RRA) package in R.

[0053] Technical effects and advantages of the present application: 1. The present application screens the internal reference genes glnS, nuoB, glmS, gyrB, dnaK and thrS expressed relatively stably by Yersinia enterocolitica at different culture temperatures in view of the lack of reliable internal reference genes for Yersinia enterocolitica, the detection primers have specificity, and the data are real and reliable, solving the current situation that Yersinia enterocolitica lacks internal reference genes;

[0054] 2. The application uses glnS, nuoB, glmS, gyrB, dnaK and thrS genes singly or in combination as internal reference genes for Yersinia research, improving data accuracy, stability and reliability, and solving the problem of unstable research results caused by the use of traditional single internal reference gene 16s as a correction standard in the past. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a scatter plot of the log2(TPM) gene expression levels at 22°C and 37°C, the size of the circle reflects the gene expression level, the larger the circle, the higher the expression level, the color gradient represents the level of conservation, the green tone represents higher conservation, otherwise it tends to be gray, and the red highlighted area represents the range of candidate reference genes examined in this study;

[0056] Figure 2 is a target gene expression difference plot at 22°C and 37°C, the gene expression levels of the target gene at 22°C and 37°C are represented by blue and red circles respectively, the small circles represent the values of individual strains, and the large circles represent the average values of the small circles, the six previously reported internal reference genes are grouped together, and their gene names are underlined;

[0057] Figure 3 is a cumulative bar chart of the candidate internal reference gene ΔCq values at 22°C-37°C, the absolute value of the difference between the Cq values at 22°C and 37°C for each strain is calculated and summed, and the six previously reported internal reference genes are underlined;

[0058] Figure 4 is a line graph of the M values calculated by the geNorm algorithm, the strains are divided into four groups according to the serotypes: O:3, O:5,27, O:8 and O:9, the individual values of the three strains in each group are plotted, then the average value of each gene is determined, and they are sorted in ascending order according to their average values, the smaller value indicates that the expression level is more stable, and the six previously reported internal reference genes are underlined;

[0059] Figure 5 is a graph of the gene stability index calculated using the NormFinder algorithm, the data is divided into four categories according to the serotypes of the bacteria: O:3, O:5,27, O:8 and O:9, the stability index of each gene within the three strains in each group is plotted, after calculating the average stability index of each gene, they are sorted in ascending order according to the average value, the smaller value indicates that the expression level is more stable, and the six previously reported internal reference genes are underlined;

[0060] Figure 6. CV values calculated by BestKeeper algorithm for the genes in the present application, data were divided into four categories according to the serotypes of the strains: O:3, O:5, 27, O:8 and O:9, after calculating the average CV value of each gene in the strains, genes were ranked in ascending order, lower ranking genes represent better stability of gene expression, six previously reported reference genes were underlined;

[0061] Figure 7. RRA algorithm was used to integrate the stability ranking of candidate reference genes in the present application, RRA algorithm was used to combine the ranking from the methods of ACq, GeNorm, NormFinder and BestKeeper, resulting in an overall ranking, lower RRA score indicates higher consistency of the gene ranking in multiple lists, reflecting the stable expression characteristics; in contrast, higher RRA score indicates large changes in ranking or always ranked lower in different lists, in this study, genes with higher ranking (marked in blue) have better overall stability than genes with lower ranking (marked in red). DETAILED DESCRIPTION

[0062] The specific embodiments of the present application will be further described with reference to the drawings. It is to be noted that the descriptions of these embodiments are intended to assist in understanding the present application, and are not intended to limit the present application. Furthermore, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0063] Selection of reference genes:

[0064] 1. Selection of new candidate reference genes based on transcriptome sequencing data

[0065] By analyzing the RNA-Seq data, we obtained the transcript per million (TPM) values of all genes in Y. enterocolitica Y11. We calculated the log2(TPM) values and the maximum absolute deviation of each gene, and plotted these values in a scatter plot (see Figure 1). In this plot, genes close to the lower left corner represent the smallest expression variation due to temperature and strain differences. To identify new candidate reference genes, we set a strict criterion (red box in Figure 1) and selected 10 genes that showed sufficient sequence conservation, relatively high expression levels, and the lowest coefficient of variation (CV) of log2(TPM) values. The identified genes are: rpoD, thrS, ftsY, rlmL, rpoN, dnaX, glmS, lepA, argS, and glnS. To clearly compare the expression stability of the six previously reported reference genes and the 10 newly identified candidate reference genes at 22°C and 37°C, we constructed Figure 2. The closeness between the red and blue circles represents the stability of gene expression at the two temperatures: the closer the circles, the more stable the expression.

[0066] 2. Cq values of the internal control genes were obtained in clinical strains incubated at 22°C / 37°C

[0067] The next step in our study was to verify the expression stability of the 16 genes identified in the previous work by using Y. enterocolitica strains at temperatures of 22°C and 37°C to mimic a real laboratory scenario. The primer sequences of these experiments are detailed in Table 2 and were designed using the Primer3Plus website tool.

[0068] Table 2. Primers used for qRT-PCR analysis in this study

[0069]

[0070] These primers have been confirmed to be able to efficiently amplify the target genes and their melting curves showed a single peak, indicating the specificity of amplification (data not shown). To make our experimental results more credible, we did not use standard strains, but used clinical samples isolated from the feces of patients with diarrhea. In addition, to reduce the influence of heterogeneity between different serotypes, we made efforts to collect a comprehensive set of strains of four serotypes: O:3, O5:27, O:8 and O:9, three strains of each serotype were obtained. In this way, a total of 12 strains were used for qRT-PCR verification.

[0071] After obtaining the Cq values of each strain and each gene, we first calculated the sum of the ACq values of each gene at 22°C and 37°C (see Figure 3). We initially concluded that the genes with smaller sums of ACq values are more stable in expression levels. When the sum of the ACq values of each gene was arranged in ascending order, the four (top 25%) genes with the smallest values were rpoB, nuoB, rpoD and glmS. However, when evaluating the stability of each of the four serotypes separately, nine unique genes appeared in total. Among them, nuoB, rpoB and rpoD each appeared three times, glmS appeared twice, and argS, dnaK, gyrB, lepA and 16s rRNA each appeared once.

[0072] 3. Analysis of gene expression stability by different algorithms

[0073] Next, we evaluated the expression stability of the 16 target candidate internal control genes by three specialized analysis tools: geNorm, NormFinder and BestKeeper algorithms.

[0074] GeNorm is the most commonly used algorithm for evaluating the stability of candidate internal control genes. It calculates a gene stability metric (M) by evaluating the average pairwise variation of each candidate gene with other genes. The lower the M value, the higher the stability. After calculating the M values ​​of all genes, the two genes with the lowest scores are recommended as internal control genes. The experimental strains were divided into four groups according to serotype. The M value of each gene of the three strains in each group was calculated and sorted in ascending order according to their average value (see Figure 4). The top 25% of genes with the lowest M values ​​in each serotype group (four genes per group) were selected, resulting in nine unique genes. Among them, glnS, rlmL, and thrS each appeared three times, rpoN appeared twice, and the other five genes ftsY, glmS, gyrB, lepA, and nuoB each appeared once.

[0075] NormFinder is another popular algorithm for assessing gene expression stability. It employs a model-based approach to evaluate stability, calculating a stability value for each gene by considering both overall variation and variation within a specific sample group. Similarly, a lower score indicates higher gene stability. Within each serotype group, the top 25% of genes with the lowest NormFinder scores were selected, resulting in 16 genes, 10 of which were unique (see Figure 5). Among these, glnS and gyrB each appeared three times, glmS and ropN each appeared twice, while the remaining six genes ftsY, lepA, nuoB, rlmL, rpoD, and thrS each appeared once.

[0076] The BestKeeper algorithm calculates the stability of candidate genes by analyzing the standard deviation (SD) of the Cq value, as well as the coefficient of variation (CV), correlation coefficient (r), and p-value (p), which are also important parameters. It calculates the top 25% of the most stable genes in each serotype group, resulting in 16 genes, 8 of which are unique (see Figure 6). Specifically, rpoD was calculated 4 times, ropB was calculated 3 times, ftsY, glmS, and nuoB were each calculated 2 times, and argS, gyrB, and lepA were each calculated 1 time.

[0077] 4. Use the Robust Rank Aggregation algorithm to comprehensively rank the stability of internal reference genes.

[0078] It is clear that even for the same qRT-PCR data, different algorithms can produce somewhat inconsistent rankings. To obtain a comprehensive ranking, we used the RRA algorithm to integrate the 48 rankings of the 12 clinical bacterial strains obtained by the four different evaluation methods. This approach allowed us to obtain a comprehensive ranking (see Figure 7). The results showed that the comprehensive ranking of the 16 candidate internal control genes can be roughly divided into three levels. The first level includes glnS, nuoB, glmS, gyrB, dnaK and thrS, which have the lowest RRA scores, indicating that they are consistently ranked and have good stability in various algorithms. In particular, glnS, with an RRA score of only 0.01, ranks first. The second layer of genes rlmL, rpoD and rpoB shows moderate overall ranking performance. The third layer includes lepA, ftsY, 16s rRNA, rpoN, argS, gmk and dnaX, which indicates that their rankings fluctuate significantly among different algorithms or they tend to be more consistently ranked lower.

[0079] Our results show that glnS, nuoB, glmS, gyrB, dnaK and thrS have less difference in expression levels when the culture temperature changes, and therefore are recommended as internal control genes.

[0080] The application provides application of the reagent for detecting the internal control genes described above in quantitative detection of the internal control genes of Yersinia enterocolitica.

[0081] The application provides application of the primer set described above in quantitative detection of the internal control genes of Yersinia enterocolitica, and the specific PCR procedure is that the reaction is run using the following cycle parameters: 50°C for 3 min to generate cDNA, 95°C for 30 s, 40 cycles of 95°C for 10 s and 60°C for 30 s, and then 95°C for 15 s, 60°C for 1 min and 95°C for 15 s.

[0082] A fifth object of the application is to provide application of the primer set described above in preparation of a kit for gene expression analysis of Yersinia enterocolitica.

[0083] A kit comprising the primer set described above further comprises other reagents for PCR.

[0084] The application provides application of the internal control genes described above, the primer set or the kit in gene expression analysis of Yersinia enterocolitica.

[0085] Although the embodiments of the application have been shown and described above, it should be understood that the above-described embodiments are exemplary and should not be construed as limiting the application, and those of ordinary skill in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the application.

Claims

1. A Yersinia enterocolitica real-time fluorescence quantitative PCR internal reference gene, characterized in that: The internal reference gene comprises one or more of the following genes: glnS, nuoB, glmS, gyrB, dnaK and thrS; The nucleotide sequence of the glnS gene is shown as SEQ ID NO. 1, the nucleotide sequence of the nuoB gene is shown as SEQ ID NO. 2, the nucleotide sequence of the glmS gene is shown as SEQ ID NO. 3, the nucleotide sequence of the gyrB gene is shown as SEQ ID NO. 4, the nucleotide sequence of the dnaK gene is shown as SEQ ID NO. 5, and the nucleotide sequence of the thrS gene is shown as SEQ ID NO.

6.

2. A primer set for amplifying the Yersinia enterocolitica real-time fluorescence quantitative PCR internal reference gene according to claim 1, characterized in that: The sequences of the primer set are as follows: The amplification primer of the glnS gene is as follows:

3. The amplification primer of the nuoB gene is as follows:

4. The amplification primer of the glmS gene is as follows:

5. The amplification primer of the gyrB gene is as follows:

6. The amplification primer of the dnaK gene is as follows:

7. The amplification primer of the thrS gene is as follows: 。 8. Use of the reagent for detecting the internal reference gene of claim 1 in the quantitative detection of the internal reference gene of Yersinia enterocolitica.

9. Use of the primer set of claim 2 in the quantitative detection of the internal reference gene of Yersinia enterocolitica.

10. Use of the primer set of claim 2 in the preparation of a kit for the gene expression analysis of Yersinia enterocolitica.

11. A kit characterized in that: The kit comprises the primer set of claim 2.

12. The kit of claim 6, wherein It further comprises other reagents for PCR.

13. Use of the internal reference gene of claim 1, the primer set of claim 2 or the kit of claim 6 in the gene expression analysis of Yersinia enterocolitica.

14. The primer set of claim 2, wherein: The RT-qPCR amplification reaction procedure is as follows: 50°C for 3 min and 95°C for 30 s for synthesizing cDNA; then 40 cycles, each cycle comprising 95°C for 10 s and 60°C for 30 s for amplification; finally, 95°C for 15 s, 60°C for 1 min and 95°C for 15 s for melting curve analysis.

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