New Results

Selection and validation of reference genes for quantitative real-time polymerase chain reaction in Serratia ureilytica DW2

Fenglin Bai, Bianxia Bai, Tingting Jin, Guiping Zhang, Jiahong Ren
doi: https://doi.org/10.1101/2022.05.03.490547

Abstract

Serratia ureilytica DW2 is a highly efficient phosphate-solubilizing bacterium isolated from Codonopsis pilosula rhizosphere soil that can promote the growth of C. pilosula. However, no validated reference genes from the genus Serratia for use in quantitative real-time polymerase chain reaction (RT–qPCR) normalization have been reported. To screen stable reference genes in S. ureilytica DW2, the expression of eight candidate reference genes (16S rRNA, ftsZ, ftsA, mreB, recA, slyD, thiC, and zipA) under different treatment conditions (pH, temperature, culture time, and salt content) was assayed by RT–qPCR. The expression stability of these genes was analyzed with different algorithms (geNorm, NormFinder, and BestKeeper). To verify the reliability of the data, the most stably expressed reference gene was used to quantify expression of the glucose dehydrogenase (gdh) gene under different soluble phosphate levels. The results showed that the zipA and 16S rRNA genes were the most stable reference genes, and the least stable were thiC and recA. The expression of gdh was consistent with the phosphate solubilization ability on plates containing National Botanical Research Institute phosphate (NBRIP) growth medium. Therefore, this study provides a stable and reliable reference gene for Serratia, which is vital for the accurate quantification of functional gene expression in future studies.

Introduction

Real-time quantitative polymerase chain reaction (RT–qPCR) is an established technique for the quantitative expression analysis of target genes under different treatment conditions [1]. This technique has many advantages, including high sensitivity, accuracy, repeatability, and low labor intensity [2–6]. However, RT–qPCR can be affected by RNA quality, amplification efficiency, and the stability of reference gene expression among different samples [7–9]. Therefore, reliable reference genes are vital to accurately compare the gene expression level between control and experimental groups [10]. The ideal reference genes are usually stable under different experimental conditions, with abundances closely correlated with total mRNA amounts [7]. These genes, such as gyrA, gyrB, recA, fabD, rpoB, and 16S rRNA, are usually used as reference genes, but they are not universal for all bacteria [11]. As there have been no reports related to the reference genes for the genus Serratia, it is necessary to explore potential reference genes for this genus.

Serratia ureilytica DW2 is an excellent phosphate-solubilizing bacterium (PSB) previously isolated from the Codonopsis pilosula rhizosphere by this research group. In greenhouse experiments, the strain DW2 could promote the growth of C. pilosula after inoculation in rhizosphere soil (unpublished). C. pilosula is a traditional Chinese medicine that is used widely in clinical therapeutics to enhance immunity, lower blood pressure, and improve microcirculation[12]. C. pilosula is cultivated in large quantities to meet high market demand. During cultivation, fertilizer is often used to improve yield, which affects the quality of C. pilosula and its soil environment. S. ureilytica DW2 could be used to develop biofertilizers for C. pilosula and thereby promote its sustainable cultivation.

To further study the growth-promoting characteristics of S. ureilytica DW2 at the molecular level, reliable reference genes need to be confirmed. Reference genes can be used to validate the gene expression among different samples. Therefore, in the present study, eight genes, namely, recombinase A (recA), 16S ribosomal RNA (16S rRNA), rod shape-determining protein MreB (mreB), FKBP-type peptidyl-prolyl cis-trans isomerase (slyD), hydroxymethylpyrimidine phosphate synthase (thiC), cell division protein (zipA), cell division protein FtsA (ftsA), and FtsZ (ftsZ), were selected as candidate reference genes. Their expression was detected by RT–qPCR, and their expression stabilities were evaluated by three methods (geNorm, NormFinder, and BestKeeper). This identification of reference genes in S. ureilytica DW2 is the first reported research on reference genes in the genus Serratia and could provide a basis for the quantification of functional gene expression in this genus.

Materials and methods

Selection of reference genes and design of primers

Eight genes (16S rRNA, ftsZ, ftsA, mreB, recA, slyD, thiC, and zipA) were selected for the real-time RT–qPCR-based relative expression analysis of target genes [13–15]. The sequences of these 8 candidate reference genes were obtained from S. ureilytica DW2 genome data. Primers for candidate reference genes were designed using Primer 3 software and synthesized by Suzhou Jinweizhi Biotechnology Co., Ltd. (China).

Bacterial strain and culture conditions

Sample preparation of candidate reference genes under different expression conditions. S. ureilytica DW2 was cultured in LB solid medium overnight, and single colonies were selected and activated in liquid LB at 30°C overnight 2 times. The cells were inoculated at 1% (volume fraction) in a triangular flask of 200 mL liquid LB, starting at pH 7, and cultured at 30°C for 9 h to logarithmic metaphase. At different temperatures (25, 30, and 37°C), NaCl concentrations (0.5%, 1.0%, and 1.5%), pH values (5, 7, and 9), and culture times (12, 24, and 36 h), single factor analysis was used for grouping. The samples were centrifuged at room temperature at 6 000 r/min for 10 min, frozen with liquid nitrogen and stored in a −80°C refrigerator for later use.

RNA extraction and cDNA synthesis

Two milliliters of DW2 cells was collected by centrifugation. RNA extraction was performed as follows: total RNA was extracted using TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. To ensure the complete removal of any contaminating DNA, all RNA preparations were subjected to a second DNase treatment using the genomic DNA (gDNA) Eraser (Perfect Real Time Kit, Takara Biochemicals, China). RNA concentrations were quantified on a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). The OD260/OD280 and OD230/OD260 ratios were calculated to evaluate the purity of the RNA extracts. The integrity of the RNA was checked by agarose gel electrophoresis. Complementary DNA (cDNA) was synthesized using the PrimeScript RT reagent kit (Perfect Real Time Kit, Takara Biochemicals, China). According to the vendor’s instructions, 1000 ng total RNA was reverse transcribed using Phusion RT–PCR Kit (Finnzymes, Finland) random primers.

RT–qPCR run conditions

RT–qPCR was performed on a 7500 Fast Real Timer PCR system (ABI, USA) using a SYBR® Premix Ex Taq™ kit (Takara, China). The standard curve was measured with serial 10-fold dilutions of cDNA to determine the associated amplification efficiency (E) and nonspecific products. The PCR consisted of 10 μL of SYBR Green PCR Master Mix, and the upstream and downstream primers were 0.5 μL each, 0.2 μL of ROX (passive reference dye), and 2.0 μL of diluted cDNA template in a total volume of 20 μL. The PCR amplification process was as follows: 95°C 5 min; 40 cycles of 95°C 5 s denaturation, 60°C 34 s annealing extension. All qRT–PCR products were analyzed in three technical replicates and validated by subsequent melting curve analysis.

Analyses of reference gene expression stability

Three software programs (geNorm [16], NormFinder [17], and BestKeeper [18]) were used to compare the expression stability of eight candidate reference genes under 12 treatments.

geNorm uses expression stability measurement values (M) to evaluate candidate reference genes. The most stable expression is indicated by the lowest M value. The algorithm defines the stability of a gene by calculating the average paired variation, whose cutoff value is 1.54 [18]. When the critical value is greater than 1.5, the gene is not suitable for use as an internal reference. In the range of m values less than 1.5, the smaller the value is, the better the gene stability and the more suitable it is for use as an internal reference gene.

With NormFinder calculation, two stable internal parameters can be selected. Unlike geNorm, NormFinder takes into account changes in expression within and between groups. Therefore, the misinterpretation caused by gene coregulation can be avoided by this algorithm. Similar to geNorm, NormFinder analyzes candidate reference genes according to their expression stability value, and the higher the expression stability, the lower the value, and vice versa [17].

BestKeeper calculates the coefficient of variation (CV), Pearson correlation coefficient (r) value, geometric mean (GM), arithmetic mean (AM) and other rich data. Suitable reference genes should meet both the normalization factor and pairwise correlation analysis limitations. This method compares the standard deviation of each gene, takes a standard deviation (SD) less than 1 as a necessary condition and does not consider those with values greater than 1 as suitable internal references. On this basis, the gene with the minimum CV (%Cq) and ±Cq value was considered the most stable. BestKeeper analysis compared pairs, and the correlation coefficient (r) was calculated. The closer the r value was to 1, the more suitable the gene was as an internal reference.

Phosphate-solubilizing ability under different concentrations of soluble phosphate

On NBRIP medium, the transparent circle formed by PBS bacteria can qualitatively represent the phosphorus solubilizing capacity of microorganisms, which is determined by the ratio of the transparent zone diameter and the colony diameter [19]. Ca3(PO4)2 was used as the only insoluble phosphate source in NBRIP solid medium. After adding soluble phosphate, K2HPO4, at three different concentrations (0, 5.0 and 10.0 mmol L-1), the transparent circles around colonies were observed after 7 days of culture to infer the phosphorus solubilizing ability of DW2. Three replicates were performed for each soluble phosphate level.

Validation of glucose dehydrogenase (gdh) gene expression

PSB converts insoluble phosphate to soluble phosphate by producing organic acids. One of its pathways is the conversion of glucose to gluconic acid, catalyzed by glucose dehydrogenase (GDH). GDH is a membrane-binding enzyme that participates in the direct oxidative pathway of glucose catabolism. This study analyzed the genomic sequence of S. ureilytica DW2 and successfully cloned the gdh gene. According to the average Cq value of the reference genes obtained by screening, the relative expression level of gdh at different soluble phosphate levels was calculated by the 2-ΔΔct method [20]. Biostatistics was used to analyze the significant difference in the relative expression levels of target genes under different levels of soluble phosphate. By comparing the qRT–PCR results of the target gene and the ratio of colony diameters, the expression stability of the reference genes obtained by screening was verified.

Results

Evaluation of experimental credibility

Information on the 8 candidate reference genes is shown in Table 1. The products of the 8 candidate reference genes were between 100 and 200 bp, which is consistent with the expected band size, and the bands were clear and single. (Fig. S1). Melting curve analysis showed that all primers were specific, as a single signal peak was obtained. The melting temperatures of the amplicons were in the range of 85.56– 90.17°C (Fig. S2). Standard curves showed that PCR efficiencies (E[%]) ranged from 91.398% (thiC) to 100.214% (recA). The correlation coefficient (R2) results ranged from 0.994 to 0.999, indicating that the data were reliable.

View this table:
Table 1. Primer list used for qPCR validation

Transcript levels of candidate reference genes

The expression levels of candidate genes in S. ureilytica DW2 were assessed by RT– qPCR. The expression of each candidate gene in the 12 processed samples is shown in Fig. 1. All genes were amplified prior to the amplification cycle of the PCR amplification profile. Among the eight candidate reference genes, the highest geometric mean of the Cq value was for thiC (32.57), and the lowest was for 16S rRNA (12.37). The Ct values of 16S rRNA ranged from 10.91 to 13.66, indicating higher expression levels than those of the other seven genes. Then, the data of 8 candidate reference genes were further analyzed.

Fig. 1.
Fig. 1. Quantification cycle (Cq) values of eight candidate reference genes.

Comparison of the expression stability of candidate reference genes according to normalization strategies

Three independent statistical algorithms were used to compare the expression stability. geNorm is an effective tool for selecting stable internal parameters. It selects two targets with the same expression ratio as the most stable pair in the test samples [21]. Figure 2 shows the M values of the 8 reference genes in the primary selection. The M values of seven candidate genes were < 1.5, which indicates that they can theoretically be used as reference genes, with the exception of thiC (M 1.54). Overall, the least stable genes were thiC and recA. The 16S rRNA and zipA genes showed the lowest M values (0.48). Therefore, 16S rRNA and zipA were considered the two most stable reference genes according to the geNorm calculation.

Fig. 2
Fig. 2 geNorm analysis of reference genes in S. ureilytica DW2.

Analyses were conducted with geNorm software. A low stability M value (y-axis) indicates that the gene was stably expressed under the 12 experimental conditions.

NormFinder was also used to analyze the expression stability of the reference genes. Generally, the smaller the S value is, the higher the expression stability of the reference genes and the higher the rank [22]. According to NormFinder analysis (Table 2), the top three genes with the lowest S values were slyD (S = 0.106), zipA (S = 0.128), and mreB (S = 0.155) under all conditions. By comparison, thiC (S = 0.290) and recA (S = 0.277) had higher S values and ranked as the least stable.

View this table:
Table 2. Stability values (S) of the eight candidate reference genes in S. ureilytica DW2 determined with NormFinder.

Comparing the operation results of geNorm and NormFinder under different treatment conditions (Table 2, Fig. 2), according to the geNorm algorithm, 16S and zipA could be considered reference genes under the different pH values. However, NormFinder advised against this, ranking 16S and zipA at positions eight and five, respectively. After salt treatment, zipA and ftsA showed the least overall variation under both algorithms. At different temperatures, ftsZ and zipA were the most stable under the two algorithms. The genes slyD and zipA performed the best at different culture times under both algorithms.

BestKeeper is a calculation method that mainly performs paired correlation and regression analyses on the selected genes [23]. The results of BestKeeper analysis are shown in Table 3. In general, ± Cq values for appropriate reference genes should < 1. In terms of this criterion, the genes 16S rRNA (0.8) and zipA (0.99) passed this screening. However, ftsZ has the highest SD [± Cq] (1.49). Moreover, the ± x-fold value should be < 2 [24], and only the 16S rRNA and zipA genes met this requirement. For the 16S rRNA and zipA genes, the CV (%Cq) values (3.60) of zipA were lower. A comparison of the r values of the reference genes shows that the r value of zipA (0.95) was closer to 1. The thiC gene was the least stable, with an r value of only 0.68. By BestKeeper’s calculation, zipA and 16S rRNA were considered the two best internal reference genes.

View this table:
Table 3. Descriptive statistics of candidate reference genes.

The pairwise variation (V value) method was used to calculate the optimal number of reference genes by geNorm. According to Vandesompele, no additional reference gene is required if Vn/Vn+1 is < 0.15 [16]. All samples were included to calculate the optimal number of reference genes (Fig. 3). Furthermore, geNorm indicated the recommended number of reference genes under various treatment conditions.

Fig. 3
Fig. 3 The pairwise variation method was used to calculate the optimal number of reference genes.

Under all conditions (a). Treatments with temperatures (b), NaCl concentrations (c), pH values (d), culture times (e).

Under the 12 treatment conditions, the V2/V3 value was much lower than the cutoff value, which suggests that the first two genes were sufficient for use as internal references. When analyzing all the different conditions, the values of Vn/Vn+1 ranged from 0.16 for V4/5 to 0.32 for V7/8. The value for V4/V5 (0.16) was higher than the cutoff of 0.15. However, the recommended V value (0.15) is only a recommended critical point and should not be used as a strict cutoff value [16].

According to the geometric mean of the ranking obtained by these three programs (Table 4), zipA and 16S rRNA had the smallest geometric mean and the highest expression stability.

View this table:
Table 4. Ranking of the candidate genes based on the results of analyses with geNorm, NormFinder and BestKeeper.

Validation of gdh gene variation under different soluble phosphate levels

Phosphate-solubilizing bacteria can dissolve the inorganic phosphorus fraction into available phosphorus for plants and thereby indirectly promote plant growth [25]. The phosphate-solubilizing activity of phosphate-solubilizing bacteria is affected by the amount of exogenous soluble phosphate.

zipA and 16S rRNA were selected as reference genes to validate gdh gene variation under the conditions of sufficient and insufficient soluble phosphate (Fig. 4). In the absence of soluble phosphate, the gdh gene showed the highest level, which was 199.21-fold higher than the gene expression under a soluble phosphate concentration of 10.0 mmol L-1. There was no significant difference (P < 0.01) in the gdh gene expression level between soluble phosphate concentrations of 5 and 10 mmol L-1.

Fig. 4
Fig. 4 Verification of gdh gene expression under different soluble phosphate levels.

Relative gdh expression levels (a). Colony ratio between the transparent zone diameter and the colony diameter at different phosphate levels (b). Phosphate solubilization on NBRIP plates with different concentrations of soluble phosphate: 0 mmol L-1 (c), 5.0 mmol L-1 (d), and 10.0 mmol L-1 (e).

The phosphate solubilization results on the NBRIP plate are shown in Fig. 4. The phosphate solubilization ability was determined by the ratio between the transparent zone diameter and the colony diameter. At a soluble phosphate concentration of 0 mmol-1, the average diameter of the transparent zone was 2.637-fold greater than the diameter of the colony. Compared with the level under soluble phosphate concentrations of 0 and 5 mmol L-1, the ratio between the transparent zone diameter and the colony diameter showed a significant decrease (P < 0.01). The ratio decreased with increasing soluble phosphate concentration. No transparent zone was observed at a 10.0 mmol L-1 soluble phosphate concentration.

Discussion

At present, RT–qPCR is used in almost all organisms, such as bacteria [24,26], viruses [21,27], animals [28] and humans [29], and is one of the most widely used methods for gene expression analysis. An increasing number of studies have confirmed that the expression of even the commonly used housekeeping genes may fluctuate in different species, different growing stages and different experimental conditions [27–33]. This suggests that reference genes should be selected according to species and experimental conditions. As the reference genes in the genus Serratia have not been reported to date, it is of great significance to confirm the internal reference genes of this genus.

Serratia ureilytica is a newly discovered species that was first isolated from the Torsa River in West Bengal, India, in 2005 by Bhaskar [34]. S. ureilytica DW2 was recently isolated from the rhizosphere soil of C. pilosula. S. ureilytica DW2 shows strong phosphorolytic properties and other plant growth-promoting properties, which means that strain DW2 has the potential to be used as a biofertilizer in C. pilosula cultivation. When analyzing its phosphate-solubilizing mechanisms based on whole genome sequencing data, it was found that there were no related reference genes for this species, which would limit the quantification of target gene expression under different conditions. The 16S rRNA gene is a common housekeeping gene used to verify the expression of functional genes[35, 36], but the Cq level of 16S rRNA may rely on the cell’s nutritional environment [37]. Furthermore, the expression levels of the target genes in cells were relatively low, but the 16S rRNA content was relatively high, resulting in large differences between the expression levels of 16S rRNA and target genes in the RT–qPCR experiment [38]. Therefore, it was necessary to screen more internal reference genes in S. ureilytica DW2.

In the present study, 12 candidate reference genes (16S rRNA, ftsZ, ftsA, mreB, recA, slyD, thiC, zipA, trpR, rpoD, secA, and pyrI) were selected and cloned for investigation based on previous reports [39–41]. Four genes, trpR, rpoD, pyrI, and secA, were not selected as candidate genes for internal reference due to poor standard curve data and low transcript abundance. However, according to the results of a study reported by Bai, rpoD was a stable internal reference gene in Pseudomonas brassicacearum GS20 [41]. Therefore, it could be inferred that the internal reference genes exhibited specificity [42].

Eight genes were finally selected for screening and operation under different conditions. Under the different conditions, the most unstable genes were uniformly identified as thiC and recA by geNorm and NormFinder, which was in accordance with the findings of previous studies [43, 44].

The most stable genes in this experiment were found to be consistent across all treatment conditions. The top three genes according to geNorm were zipA, 16S rRNA, and slyD, and the top three genes in NormFinder were slyD, zipA, and mreB. BestKeeper screened only two stable reference genes that met its screening conditions: zipA and 16S rRNA. The results of the present study showed that the inclusion of a third gene had roughly the same effect (V = 0.24) as the use of a second gene (V = 0.22). The use of more than two genes resulted in no significant improvement in the normalization factor. Similar results were reported by Guo [29]. Due to the small differences in V values, two pairs of genes were finally selected as internal references. Therefore, geometric average values were used to rank these candidate reference genes, and zipA and 16S rRNA were selected as the most suitable reference genes for strain DW2.

After confirming the reliability of the reference genes, the gdh gene was found to be related to the phosphate-solubilizing ability of strain DW2. The zipA and 16S genes were then used to validate gdh expression under three soluble phosphate concentrations. The expression of gdh was upregulated under 0 mmol-1 soluble phosphate, which could enhance the ability of DW2 to produce gluconic acid, resulting in the transformation of insoluble phosphate into soluble phosphate. gdh expression decreased with increasing soluble phosphorus concentration. When the soluble phosphate content reached 5 and 10 mmol-1, gdh expression significantly decreased (P < 0.01). This result was consistent with the data obtained from the NBRIP plate, which showed that when the soluble phosphate content was 0 mm, the largest transparent zone diameters were produced. Therefore, the expression of gdh could be objectively quantified by zipA and 16S, and these two genes could be used as reference genes for S. ureilytica DW2. This study was the first to identify reference genes and confirm their reliability in S. ureilytica DW2. This study may provide a reference for studies on other strains in the genus Serratia and provide a basis for research on phosphate solubilization by S. ureilytica DW2.

Supporting information

S1 Fig. Agarose gel electrophoresis of PCR products from candidate reference genes.

S2 Fig. Melting curve analysis of primer specificity for the amplification of genes from S. ureilytica DW2.

Acknowledgments

The authors thank the reviewers for their valuable comments on our paper. Yuhe Fan, Pengfei Zhang and Heng Xue helped conduct the S. ureilytica DW2 culture experiment. We thank them very much for their hard work.

Footnotes

  • Conflict of Interest Statement, We declare that we do not have any commercial or associative interest.

  • Fundings: This work was supported by the General program of Natural Science Foundation of China (32071770), the Fundamental Research Program of Shanxi Province (award No. 202103021223380),the Fund for Shanxi “1331 Project” Key Subjects Construction (1331KSC)

References

  1. 1.↵
    Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strate gies and considerations. Genes and Immunity. 2005; 6: 279–284. https://doi.org/10.1038/sj.gene.6364190
    OpenUrlCrossRefPubMedWeb of Science
  2. 2.↵
    Hong S. Y., Seo P. J., Yang M. S., Xiang F., Park C. M. Exploring valid referenc e genes for gene expression studies in Brachypodium distachyon by real-time PC R. BMC Plant Biol. 2008; 8(112):1–11. https://doi.org/10.1186/1471-2229-8-112
    OpenUrlCrossRefPubMedWeb of Science
  3. 3.
    Ginzinger D. G. Gene quantification using real-time quantitative PCR: an emergi ng technology hits the mainstream. Exp Hematol. 2002; 30(6): 503–512. https://doi.org/10.1016/S0301-472X(02)00806-8
    OpenUrlCrossRefPubMedWeb of Science
  4. 4.
    Bustin S. A., Benes V., Nolan T., Pfaffl M. W. Quantitative real-time RT-PCR— a perspective. J Mol Endocrinol. 2005 Jun;34(3):597–601. https://doi.org/10.1677/jme.1.01755
    OpenUrlAbstract/FREE Full Text
  5. 5.
    VanGuilder H. D., Vrana K. E., Freeman W. M. Twenty-five years of quantitative PCR for gene expression analysis. BioTechniques. 2008; 44(5): 619–626. https://doi.org/10.2144/000112776
    OpenUrlCrossRefPubMedWeb of Science
  6. 6.↵
    Thellin O., Zorzi W., Lakaye B., De Borman B., Coumans B., Hennen G., et al. H ousekeeping genes as internal standards: use and limits. Journal of Biotechnology. 1999; 75(2-3): 291–295.https://doi.org/10.1016/S0168-1656(99)00163-7
    OpenUrlCrossRefPubMedWeb of Science
  7. 7.↵
    Suzuki T., Higgins P. J., Crawford D. R. Control selection for RNA quantitation. BioTechniques. 2000 Aug; 29(2): 332–337. https://doi.org/10.2144/00292rv02.
    OpenUrlCrossRefPubMedWeb of Science
  8. 8.
    Shen GM, Jiang HB, Wang XN, Wang JJ. Evaluation of endogenous references for gene expression profiling in different tissues of the oriental fruit fly Bactrocera dorsalis (Diptera: Tephritidae). BMC molecular biology. 2010; 11(76):1–10. https://doi.org/10.1186/1471-2199-11-76
    OpenUrl
  9. 9.↵
    Tunbridge E. M., Eastwood S. L., Harrison P. J. Changed relative to what? House keeping genes and normalization strategies in human brain gene expression studies. Biol Psychiatry. 2011; 69(2):173–179. https://doi.org/10.1016/j.biopsych.2010.05.023
    OpenUrlCrossRefPubMedWeb of Science
  10. 10.↵
    Danilo J. P. Rocha. Carolina S. Santos. Luis G. C. Pacheco. Bacterial reference genes for gene expression studies by RT-qPCR: survey and analysis. Antonie van Leeuwenhoek. 2015; 108: 685–693. https://doi.org/10.1007/s10482-015-0524-1
    OpenUrlCrossRef
  11. 11.↵
    Rivera L., Lopez-Patino M.A., Milton D.L., Nieto T.P., Farto R. Effective qPCR methodology to quantify the expression of virulence genes in Aeromonas salmoni cida subsp. salmonicida. Journal of Applied Microbiology. 2014; 118: 792–802. https://doi.org/10.1111/jam.12740
    OpenUrl
  12. 12.↵
    Fei L, Yafei J, Lixia P, Qi L, Haijuan C, Yan Y, et al. Extraction, purification, structural characteristics and biological properties of the polysaccharides from Codo nopsis pilosula: A review. Carbohydrate Polymers. 2021 Jun 1;261:117863. https://doi.org/10.1016/j.carbpol.2021.117863
    OpenUrl
  13. 13.↵
    Savli H, Karadenizli A, Kolayli F, Gundes S, Ozbek U, Vahaboglu H. Expression stability of six house-keeping genes: A proposal for resistance gene quantification studies of Pseudomonas aeruginosa by real-time quantitative RT-PCR. Journal of Medical Microbiology. 2003 May; 52(Pt 5): 403–408. https://doi.org/10.1099/jmm.0.05132-0
    OpenUrlCrossRefPubMedWeb of Science
  14. 14.
    Zhao W, Li Y, Gao P, Sun Z, Zhang H. Validation of reference genes for real-time quantitative PCR studies in gene expression levels of Lactobacillus casei Zhang. Journal of Industrial Microbiology and Biotechnology. 2011; 38: 1279–1286. https://doi.org/10.1007/s10295-010-0906-3
    OpenUrl
  15. 15.↵
    Chang Q, Amemiya T, Liu J, Xu X, Rajendran N, Itoh K. Identification and valid ation of suitable reference genes for quantitative expression of xylA and xylE genes in Pseudomonas putida mt-2. Journal of Bioscience and Bioengineering. 2009 Feb; 107(2): 210–214. https://doi.org/10.1016/j.jbiosc.2008.09.017
    OpenUrlCrossRefPubMed
  16. 16.↵
    Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology. 2002; 3: research0034. https://doi.org/10.1186/gb-2002-3-7-research0034
    OpenUrlCrossRefPubMed
  17. 17.↵
    Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research. 2004 Aug; 64(15): 5245–5250. https://doi.org/10.1158/0008-5472.CAN-04-0496
    OpenUrlAbstract/FREE Full Text
  18. 18.↵
    Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable house keeping genes, differentially regulated target genes and sample integrity: BestKeeper—Excel-based tool using pair-wise correlations. Biotechnology Letters. 2004; 26: 509–515. https://doi.org/10.1023/b:bile.0000019559.84305.47
    OpenUrlCrossRefPubMedWeb of Science
  19. 19.↵
    Qingwei Z, Xiaoqin W, Xinyi W. Effects of Soluble Phosphate on Phosphate-Solubilizing Characteristics and Expression of gcd Gene in Pseudomonas frederiksbergensis JW-SD2. Curr Microbiol, 2016; 72: 198–206. https://doi.org/10.1007/s00284-015-0938-z
    OpenUrlCrossRef
  20. 20.↵
    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta C(T)) Method. Methods. 2001 Dec; 25(4): 402. https://doi.org/10.1006/meth.2001.1262
    OpenUrlCrossRefPubMedWeb of Science
  21. 21.↵
    Juanjuan L, Yang T, Xiaohong Y, Xiaohua C, Fuli L. Evaluation of Clostridium ljungdahlii DSM 13528 reference genes in gene expression studies by qRT-PCR. Journal of Bioscience and Bioengineering. 2013; 116 (4): 460–464. http://dx.doi.org/10.1016/j.jbiosc.2013.04.011
    OpenUrlCrossRefPubMed
  22. 22.↵
    SundaramID VK, Sampathkumar NK, Massaad C, Grenier J. Optimal use of statistical methods to validate reference gene stability in longitudinal studies. PLoS ONE. 2019; 14(7): e0219440. https://doi.org/10.1371/journal.pone
    OpenUrlCrossRef
  23. 23.↵
    Morley AA. Digital PCR: A brief history. Biomol Detect Quantif. 2014; 1(1):1–2. https://doi.org/10.1016/j.bdq.2014.06.001 eCollection Sep.
    OpenUrl
  24. 24.↵
    Ritz, M., Garenaux A., Berge M., Federighi M. Determination of rpoA as the most suitable internal control to study stress response in C. jejuni by RTqPCR and application to oxidative stress. J. of Microbiol Methods, 2009; 43: 429–435. https://doi.org/10.1016/j.mimet.2008.10.014
    OpenUrl
  25. 25.↵
    Chhetri G, Kang M, Kim J, Kim I, So Y, Seo T. Fuscibacter oryzae gen. nov., sp. nov., a phosphate-solubilizing bacterium isolated from the rhizosphere of rice plant. Antonie van Leeuwenhoek. 2021; 114: 1453–1463. https://doi.org/10.1007/s10482-021-01619-2
    OpenUrl
  26. 26.↵
    Piston F., Dorado G., Martin A., Barro F. Cloning and characterization of a gamma-3 hordein mRNA (cDNA) from Hordeum chilense (Roem. et Schult.), Theor. Appl. Genet. 2004; 108: 1359–1365. https://doi.org/10.1007/s00122-003-1548-x
    OpenUrlCrossRefPubMedWeb of Science
  27. 27.↵
    Doshi KM., Eudes F., Laroche A., Gaudet D. Anthocyanin expression in marker free transgenic wheat and triticale embryos, In Vitro Cell. Dev. Biol. Plant. 2007; 43: 429–435. https://doi.org/10.1007/s11627-007-9089-7
    OpenUrl
  28. 28.↵
    Xiaojuan C, Yun S, Panpan Z, Jianlong L, Haiping L, Caoying W, et al. Screening of stable internal reference genes by uantitative real-time PCR in humpback grouper Cromileptes altivelis. Journal of Oceanology and Limnology. 2021; 39(5): 1985–1999. https://doi.org/10.1007/s00343-020-0238-8
    OpenUrl
  29. 29.↵
    Yi G, Jia-xin C, Shu Y, Xu-ping F, Zheng Z, Ke-he C, et al. Selection of reliable reference genes for gene expression study in nasopharyngeal carcinoma. Acta Pharmacologica Sinica. 2010; 31: 1487–1494.
    OpenUrl
  30. 30.
    Chen X J, Zhang X Q, Huang S, Cao Z J, Qin Q W, Hu W T, et al. Selection of reference genes for quantitative real-time RT-PCR on gene expression in Golden Pompano (Trachinotus ovatus). Polish Journal of Veterinary Sciences. 2017; 20 (3): 583–594. https://doi.org/10.1515/pjvs-2017-0071.
    OpenUrl
  31. 31.
    Bin W. S., Wei L. K., Ping D. W., Li Z., Wei G., Bing L. J., et al. Evaluation of a ppropriate reference genes for gene expression studies in pepper by quantitative real-time PCR, Mol. Breed. 2012; 30: 1393–1400.
    OpenUrl
  32. 32.
    Pombo MA, Zheng Y, Fei Z, Martin GB, Rosli HG. Use of RNA-seq data to identify and validate RT-qPCR reference genes for studying the tomato-Pseudomonas pathosystem. Scientific Report. 2017 Mar; 7: 44905. https://doi.org/10.1038/srep44905
    OpenUrl
  33. 33.↵
    Sean S J Heng, Oliver Y W Chan, Bryan M H Keng, Maurice H T Ling. Glucan Biosynthesis Protein G is a Suitable Reference Gene in Escherichia coli K-12. International Scholarly Research Network. 2011 (5): 469053. https://doi.org/10.5402/2011/469053
  34. 34.↵
    Bhaskar B, Pradosh R, Ranadhir H. Serratia ureilytica sp. nov., a novel urea-utilizing species. International Journal of Systematic and Evolutionary Microbiology. 2005; 55, 2155–2158. https://doi.org/10.1099/ijs.0.63674-0
    OpenUrlCrossRefPubMed
  35. 35.↵
    Conte E, Catara V, Greco S, Russo M, Alicata R, Strano L, et al. Regulation of polyhydroxyalkanoate synthases (phaC1 and phaC2) gene expression in Pseudomon as corrugata. Applied Microbiology and Biotechnology. 2006; 72: 1054–1062. htt ps://doi.org/10.1007/s00253-006-0373-y
    OpenUrlPubMed
  36. 36.↵
    Schwartz T, Walter S, Marten SM, Kroschhö fer F, Nusser M, Obst U. Use of quantitative real-time RT-PCR to analyse the expression of some quorum-sensing regulated genes in Pseudomonas aeruginosa. Analytical and Bioanalytical Chemistry. 2006; 387: 513–521. https://doi.org/10.1007/s00216-006-0909-0
    OpenUrl
  37. 37.↵
    Condon C, Squires C, Squires CL. Control of rRNA Transcription in Escherichia coli. Microbiological Reviews. 1995; 59(4): 623–645. https://doi.org/10.1146/annurev.genet.38.072902.091347
    OpenUrlAbstract/FREE Full Text
  38. 38.↵
    Desroche N, Beltramo C, Guzzo J. Determination of an internal control to apply reverse transcription quantitative PCR to study stress response in the lactic acid bacterium Oenococcus oeni. Journal of Microbiology Methods. 2005; 60: 325–333, https://doi.org/10.1016/j.mimet.2004.10.010
    OpenUrl
  39. 39.↵
    Péricles S. Galisa, Helder A.P. da Silva, Aline V.M. Macedo, Verônica M. Reis, Márcia S. Vidal, José I. Baldani, et al. Identification and validation of reference genes to study the gene expression in Gluconacetobacter diazotrophicus grown in different carbon sources using RT-qPCR. Journal of Microbiological Methods. 2012; 91: 1–7.2. https://doi.org/10.1016/j.mimet.2012.07.005
    OpenUrlPubMed
  40. 40.
    Bin-Yan W, Jian-Ren Y, Lin H, Ling-Min H, De-Wei L. Validation of reference genes for RT-qPCR analysis in Burkholderia pyrrocinia JK-SH007. Journal of Microbiological Methods. 2017; 132: 95–98. http://dx.doi.org/10.1016/j.mimet.2016.10.004
    OpenUrl
  41. 41.↵
    Bai B, Ren J, Bai F, Hao L. Selection and validation of reference genes for gene expression studies in Pseudomonas brassicacearum GS20 using real-time quantitative reverse transcription PCR. PLoS ONE. 2020; 15(1): e0227927. https://doi.org/10.1371/journal.pone
    OpenUrl
  42. 42.↵
    Hoang VLT, Tom LN, Quek X-C, Tan J-M, Payne EJ, Lin LL, et al. RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers. PeerJ. 2017; 5:e3631. https://doi.org/10.7717/peerj.3631
    OpenUrlCrossRef
  43. 43.↵
    Artico S, Nardeli S, Brilhante O, Grossi-de-Sa M, Alves-Ferreira M. Identification and evaluation of new reference genes in Gossypium hirsutum for accurate normalization of real-time quantitative RT-PCR data. BMC Plant Biology. 2010 Mar; 10:49. https://doi.org/10.1186/1471-2229-10-49
    OpenUrl
  44. 44.↵
    Wan H., Zhao Z., Qian C., Sui Y., Malik A. A., Chen J. Selection of appropriate reference genes for gene expression studies by quantitative realtime polymerase chain reaction in cucumber. Anal. Biochem., 2010; 399: 257–261.
    OpenUrlCrossRefPubMed
Back to top
Posted May 03, 2022.
Download PDF
Email
Selection and validation of reference genes for quantitative real-time polymerase chain reaction in Serratia ureilytica DW2
Fenglin Bai, Bianxia Bai, Tingting Jin, Guiping Zhang, Jiahong Ren
bioRxiv 2022.05.03.490547; doi: https://doi.org/10.1101/2022.05.03.490547
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area