1University of Chester, Chester Medical School, England.
2Department of Molecular Biology, University of Okara, Pakistan.
3Sheffield Hallam University Service Sector Management, England.
4University of Northampton, Faculty of Arts, Science and Technology, England.
Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a housekeeping gene involved in glycolysis, catalyzing the oxidative phosphorylation of glyceraldehyde-3-phosphate. Given its consistent expression across various biological cases, GAPDH is often used as a reference gene for normalizing gene expression in molecular studies. This study aimed to quantify the expression of GAPDH in three different cancer cell lines comprising MG-63 (human osteosarcoma), HL-60 (human promyelocytic leukemia), and U937 (histiocytic lymphoma) using RT-qPCR. GAPDH expression was compared against the reference gene 18S rRNA to provide relative expression levels across 20 samples. RNA extraction was performed, but the quality was suboptimal, with a mean 260/230 nm ratio of 0.942 ± 0.293, indicating possible contamination, which may have impacted the accuracy of the results. The reverse transcription process yielded primer dimer artifacts instead of the expected cDNA bands, suggesting issues such as RNA degradation or subpar reverse transcriptase quality. Despite the RNA quality concerns, the expression levels of GAPDH were found to be consistent across the three cell lines, with no significant differences in [removed]p = 0.258). The mean expression ratios approached 1.0, supporting GAPDH reliability as a reference gene in these cell lines. However, the observed Ct values for GAPDH were higher than those reported in previous studies, potentially due to the compromised RNA quality and the resulting low expression levels. This study highlights the importance of optimizing RNA extraction and PCR conditions to ensure reliable results. Additionally, it recommends the use of multiple reference genes for more accurate normalization in RT-qPCR experiments. To fully examine GAPDH as a reference gene in a wider range of experimental situations, more studies with a bigger sample size and more diverse cell lines are required.
To evaluate and study the changes in gene expression in different cell lines and mRNA transcript abundance, RT-qPCR is considered to be a beneficial technique (González-Bermúdez et al., 2019). The results obtained from RT-qPCR need to be normalized against some data sets or references to eliminate any experimental background noise and to estimate results accurately (Li et al., 2011). Relative quantification of gene expression in multiple cell lines through RT-qPCR requires an internal reference gene (Gonzalez-Bermudez et al., 2019). Reference genes usually housekeeping genes, are internal reaction controls whose sequences differ from the target nucleotide sequences (Kozera & Rapacz, 2013). They are required for the comparison of gene expression levels under different experimental conditions, hence acting as endogenous controls for normalization (Bustin et al., 2013). Therefore, reference genes are considered crucial for the development of RT-qPCR assays. An ideal reference gene is one whose expression remains stable in all cells and tissues, irrespective of the cells/tissues developmental stage, and is not affected by any biotic and abiotic factors, such as different environments and sample treatments (Wang et al., 2019). Most of the currently used reference genes are associated with basic cellular functions; for instance, the actin gene is involved in muscle contraction, cell division, regulation of transcription, and maintenance of cell shape (Perrin & Ervasti, 2010), and the tubulin gene forms microtubules that are important during mitosis (Sadritdinova et al., 2013). Additionally, an ideal reference gene should have a similar threshold cycle (Ct) value to the target gene (Kozera & Rapacz, 2013) so that the level of expression of the target gene can be accurately compared with the reference gene (Gimeno et al., 2014). Cycle threshold (Ct) is the number of cycles needed for the fluorescent signal to surpass contextual noise in a real-time PCR reaction. It inversely correlates with the amount of target gene in the sample, hence higher Ct value indicates low signal and vice versa (Kamboj et al., 2018). Even though many housekeeping genes are currently being used as reference genes (Piazza et al., 2017), no universal reference gene, which can be used in all experimental conditions, has been identified so far (Gutierrez et al., 2008). Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH), a housekeeping gene, is one of the ten enzymes that catalyze the oxidative phosphorylation of glyceraldehyde-3-phosphate during the process of glycolysis (Sikand et al., 2012). Further research has shown that GAPDH is a multi-functional gene involved in cellular functions including DNA repair, transport and fusion of membrane, export of tRNA, and even programmed cell death (Tristan et al., 2011). In a study, the comparison of 6 housekeeping genes (GAPDH, ACTB, HPRT-1, ?2M, RPLP-1, and TBP) showed GAPDH to be the most consistently expressed gene, thus making it a good candidate for normalizing gene [removed]Tan et al., 2012). Furthermore, GAPDH was shown to have stable and unaffected gene expression during different experimental and physiological conditions, implying its usefulness in normalizing gene [removed]Guo et al., 2013). Some studies show conflicting evidence, reporting variations of GAPDH expression levels in different cell lines and individuals during various stages of the cell cycle, which limits the use of GAPDH as an ideal reference gene (Adeola, 2018). One such research showed that GAPDH did not have stable expression levels in breast cancer cell lines and hence was not recommended for breast cancer studies (Liu et al., 2015). In phylogenetic and molecular analyses, the 18S rRNA gene is commonly used. The ribosomal subunit in concern remains intact and consists of both variable and conserved regions. The ability to employ 18S rRNA as primers is due to the conserved area (Qin et al., 2020). The 18S rRNA gene is a viable option for a reference gene and normalizing gene expression in RT-qPCR since it is expressed constitutively in subcutaneous and visceral fat tissues and contributes to fundamental cellular processes including cell survival and maintenance (Ebrahimi et al., 2018). Another characteristic of 18S rRNA is that it is not significantly affected by the cell’s internal and external environment and the functional state of the cell. Rather, it has stable expression levels, thus making it an attractive candidate for normalizing gene [removed]Qin et al., 2020). Furthermore, 18S rRNA and beta-actin (ACTB), were validated to be good reference genes across all breast cancer cell lines (Liu et al., 2015). However, there are certain drawbacks to using 18S rRNA. Firstly, the expression of 18S rRNA is higher than that of the gene of interest, bringing upon the fundamental theoretical presumptions for reference genes and making it difficult to compare data (Lindseth, 2018). RT-qPCR, also known as reverse transcription real-time quantitative PCR, is a very effective technique that can precisely detect and quantify very minute amounts of nucleic acid sequences (Sanders et al., 2014). Owing to its highly specific, sensitive, simple, and cost-effective characteristics, RT-qPCR is widely used in examining expression differences between various genes under different conditions (Jacob et al., 2013). Gene expression is defined as the process by which information from a gene is translated into protein and non-protein (structural and housekeeping RNA) products (Finotello & Di Camillo, 2014). Characterization of gene expression levels by quantifying mRNA to form a “picture” of the cell’s metabolism is one of the greatest applications of RT-qPCR (Sanders et al., 2014). RT-qPCR also helps in investigating gene expression by measuring the increase or decrease in the production of a gene-specific transcript, which has practical applications such as in comprehending responses to a specific drug or the presence or absence of certain genes (Gong et al., 2014). MG-63 is a human osteoblast-like cell line that has been derived from human or animal-based osteosarcoma, and because of its adherent growth mode, it can be used in 3D cell culture experiments (Staehlke et al., 2019). HL-60 is a human promyelocytic leukemia cell line that was established by a 36-year-old patient with acute promyelocytic leukemia. This cell line is widely used in the laboratory in suspension culture to study how different types of blood cells are formed and to examine karyotype abnormalities (Ramachandran et al., 2014). Meanwhile, U937 is a suspension cell line that was isolated from a patient with histiocytic lymphoma and is the most widely used in-vitro model for researching human monocytes and macrophages (Galindo & Clavijo-Ramírez, 2020). Research shows that there is a need to develop reference genes for using MG-63 in orthopedic research (Chang et al., 2014), HL-60 for research in immunology (Birnie, 1988), and U937 for studying monocytes (Chanput et al., 2015). This study aimed to quantify the expression level of the housekeeping gene GAPDH in three different cell lines (MG-63, HL-60, and U937) using RT-qPCR. The expression of GAPDH was compared against the reference gene 18S rRNA to provide a relative measure of its expression across the cell lines.
METHODOLOGY
Cell lines
In this study, three different cell lines comprising MG-63 (human osteosarcoma cell line), HL-60 (human promyelocytic leukemia cell line), and U937 (histiocytic lymphoma) were used. A total of 20 samples were used in this experiment containing the three cell lines. These 20 samples were divided into groups of 5 (A, B, C, D, E), containing 4 samples each.
RNA isolation
The first step to examine the expression of the GAPDH gene in each of the three cell lines was to extract RNA from the cell lines. Cell line pellets containing 5–10 × 106 cells were thawed and resuspended in TRIsure Reagent (Bioline code BIO-38033). Further steps were performed according to the manufacturer’s (Bioline) instructions. The extracted RNA was then eluted in 10?l nuclease-free water.
RNA quantification and quality assurance
A sample from the extracted RNA was quantified via a Thermo Scientific Multiskan GO Microplate Spectrophotometer at the absorbance of 260nm (40?g/ml of RNA solution considered 1.0), using absorbance at 230nm as a reference. The integrity and quality of extracted RNA were then observed by gel electrophoresis on 1% agarose gel containing Gel Red stain (Promega code H5041) as tracking dye. The size of extracted RNA was compared with HyperLadder 100bp (Bioline code BIO-33025), which served as the size standard.
Reverse transcription / Synthesis of cDNA
To synthesize cDNA, which would serve as a reactant later in the RT-qPCR step, the extracted RNA was first treated with an RQ1 RNase-free DNase kit (Promega code: M6101) to remove any traces of genomic DNA to avoid contamination. The DNase-treated RNA was then reverse-transcribed by random hexamer priming using a Tetro cDNA synthesis kit and the manufacturer’s (Bioline) protocol was followed to yield cDNA. Table 2.1 shows the reagents used in the reverse transcription mix.
Table 1: Reverse transcription mix
18S RT-PCR of cDNA
To verify that reverse transcription had worked effectively, RT-PCR (Techne Prime Thermal Cycler) was performed using primers (MWG) that amplified specific regions of 18S rRNA. The primer sequence used is given in Table 2.2. DreamTaq PCR Master Mix (Thermo Scientific) was used to make a 25 ?l mix, with a no-template-control (NTC) tube as reference. The reagents, along with their volumes, are mentioned in Table 2.3, followed by Table 2.4, which mentions the PCR conditions for the 18S RT-PCR.
Table 2: Primer sequences for 18S rRNA gene
Table 3: PCR mix for 18S RT-PCR
Table 4: 18S RT-PCR condition
The Amplicon product of RT-PCR was then verified by gel electrophoresis on 2% agarose gel containing Gel Red DNA stain (Cambridge Bioscience code BT41003) as tracking dye. The size of the amplicon was compared with HyperLadder 100bp, which served as the size marker.
Relative Quantification by Real-time PCR – TaqMan assay
Real-time PCR (PCRmax Eco 48) was performed to analyse the expression of GAPDH about the expression of the housekeeping gene 18S rRNA. For relative quantification, real-time PCR was performed by using Taq Man Gene Expression Assays (Thermo Scientific), which was specific for both the GAPDH and 18S rRNA gene. Real-time PCR mix and cycle conditions are mentioned in Table 2.5 and Table 2.6 below.
Table 5: PCR mix for Real-time PCR
Table 6: Real-time PCR conditions
To compare the expression of GAPDH and 18S rRNA genes, Ct values (number of cycles required by the fluorescent signals to exceed background levels) for both genes’ expression levels were recorded. Results were obtained by EcoTM Real-time PCR System, which was then analyzed on EcoStudy software V5.2.16. GAPDH mRNA:18SrRNA expression ratio was then calculated. To provide a visual representation of the differences in expression between the two genes, a box-and-whisker graph was plotted.
Statistical Analysis
Using Microsoft Excel, the mean and standard deviation of the raw data were determined. One-way ANOVA was used in the statistical analysis using the Jamovi software. If p was less than 0.05, the data was deemed substantially different.
RESULTS
Quantification and quality assurance
Extracted RNA from all 20 samples of the three cell lines was quantified using the 260nm absorbance. The purity for these samples was then deduced via the calculation of the 260nm/230nm ratio of absorbance. All 20 samples showed a purity ratio between 0.51 and 1.53, with a mean value of 0.942 ± 0.293, which showed successful extraction of RNA. However, the ratio was considerably less than the ratio for pure RNA (2.0-2.2).
The quality of RNA assessed via 1% agarose gel showed high-quality bands for most of the samples as shown in Figure 3.1 below. Intact bands were observed for Groups A, B, C and E, where the relative intensity of 2:1 was observed for 28S bands and 18S bands respectively. Lighter bands indicated relatively smaller amounts of RNA present than the brighter bands. No bands were observed in Group D, indicating failure of RNA extraction.
Figure 3.1: Gel images taken following RNA isolation. Lanes for the annotated gel are as follows: 1 = HyperLadder 100bp, 2 = MG-63, 3 = empty, 4 = HL-60, 5 = empty, 6 = HL-60, 7 = empty, 8 = HL-60. Bands are indicating 28S, 18S and a smaller RNA fragment. 28S bands are showing approximately double the intensity than that of 18S bands. Well 1 and 8 show lighter bands compared to Well 6, indicating weaker signal / smaller concentration of RNA present. Well 4 shows a smear between 28S and 18S bands. Groups A, B, C and E showed high quality bands, whereas Group D showed no presence of RNA bands.
Quality assurance and quantification of synthesized cDNA
RNA samples that were extracted previously now underwent reverse transcription to produce cDNA, which was later amplified by RT-PCR. The agarose gel (2%)was used for the evaluation of cDNA quality as shown in Figure 3.2. None of the 5 groups showed the expected 206bp bands, indicating that reverse-transcription was not successful. Instead, bands of primer dimer artifacts were observed further below the 100bp mark from the HyperLadder 100bp, with Groups B, C, D, and E also showing random non-specific amplification in the NTC samples as shown in Figure 3.2.
Figure 3.2: Gel images taken following the 18S RT-PCR. Lanes for the annotated gel are as follows: Upper lanes: 1 = 100bp ladder, 2 = cDNA of MG-63, 3 = empty, 4 = NTC of MG-63, 5 = empty, 6 = cDNA of HL-60, 7 = empty, 8 = cDNA of HL-60; Lower lanes: 1 = 100bp ladder, 2 = cDNA of HL-60, 3 = empty, 4 = NTC of U937, 5 = empty, 6 = cDNA of HL-60, 7 = empty, 8 = NTC of HL-60. No expected 18S rRNA bands (206bp) were observed in any of the cDNA samples across the 5 groups
Analysis of GAPDH expression in synthesized cDNA
An amplification plot generated through RT-qPCR was used to calculate the mean Ct values of GAPDH expression in the three cell lines. The mean Ct values were 32.86 ± 2.70 for HL-60, 33.03 ± 4.59 for MG-63 and 33.42 ± 2.86 for U937. GAPDH expression was estimated by calculating the ratio of GAPDH:18S rRNA gene expression. In comparison to 18S rRNA gene, which was the reference housekeeping gene, GAPDH was most expressed in U937 (mean = 0.964 ± 0.076), followed by MG-63 (mean = 0.942 ± 0.108) and least expressed in HL-60 (mean = 0.889 ±0.078). For a better understanding of the ratios, a box-and-whisker graph was plotted, as shown in Figure 3.3, which represented similar levels of GAPDH expression in U937 and MG-63, with slightly lower expression ratio for HL-60. However, in general, results close to the ratio of 1.0 indicated that there was little to no variation of expression between the three cell lines.To further confirm whether the gene expression across the three cell lines was significantly different, especially the slightly lower expression in HL-60, statistical analysis was conducted. One-way ANOVA test showed that the gene expression differences among the three cell lines (U937, MG-63 and HL-60) were not significantly different (p = 0.258).
Figure 3.3: Box-and-whisker plot indicating the expression of GAPDH in U937, MG-63 and HL-60 cell lines. All three cell lines have a GAPDH:18S expression ratio close to 1.0, with HL-60 cell line having a slightly lower expression ratio.
DISCUSSION
Gene expression analysis is gaining immense importance in molecular biology procedures, owing to the vast dynamic range, high sensitivity, and reproducibility of quantitative real-time PCR. It is considered one of the most efficient methods to analyse gene [removed]Sun et al., 2015). Certain limitations, such as differences in RNA isolation methods, reverse transcription, cDNA quantity, and efficacy of the RT-qPCR procedure itself make analysis susceptible to errors (Bustin et al., 2013). To avoid variations and to obtain reliable gene expression profiles, controls known as reference genes are required for the normalisation of gene [removed]Derveaux et al., 2010). An ideal reference gene should have adequate gene expression level and its expression should be stable in all experimental conditions (Sun et al., 2015). In RT-qPCR analysis, quality RNA isolation is the first essential step for obtaining gene expression data, as different quality of RNA present different results (Bustin et al., 2010). A ratio of 2.0-2.2 for 260/230nm is generally regarded as pure RNA (Alabi et al., 2019). In this study, the RNA was extracted successfully from all three cell lines (MG-63, HL-60, and U937), but it was not pure or ideal (mean value = 0.942 ± 0.293). This low 260/230nm ratio could suggest the presence of contaminants, for instance, guanidine and phenol, which are absorbed at 230nm wavelength (Hansen et al.).
Further verification of RNA quality on 1% agarose gel showed intact 28S and 18S bands in Groups A, B, C, and E. The smaller RNA bands observed between 100bp and 200bp in Figure 3.1 could be 5S bands, as 5S rRNA is 120 nucleotides long (Ciganda & Williams, 2011). Meanwhile, the absence of bands in Group D meant that RNA extraction was not successful, possibly due to human error. Smears present in some of the samples suggested the attachment of contaminants with RNA and/or also degradation of RNA in those samples since RNA is readily biodegradable (Sangha et al., 2010). A possible reason for low RNA yield could be due to multiple individuals working on RNA extraction, hence variability in techniques, especially pipetting, which could cause variation in the results (Wong & Medrano, 2005). After the analysis of the quality of isolated RNA, the next important step is the synthesis of cDNA by reverse transcription (Mo et al., 2012). The 18S RT-PCR gel did not show the expected 206bp cDNA bands in any group, suggesting the failure of the reverse-transcription reaction. The possible reasons that could explain such results are degradation of RNA during the process of reverse transcription, poor quality of reverse transcriptase enzyme used, or issues in the PCR conditions (Matz, 2002). Instead of the expected cDNA bands, primer dimer artifacts were observed, possibly due to the presence of low amounts of cDNA. The unutilized primers formed dimers as they may have had self-complementarity, using each other as templates for extension (Poritz & Ririe, 2014). Since reverse transcription was not successful, fresh samples were provided by the laboratory to continue the experiments to the next stage. The box-and-whisker graph constructed from GAPDH, 18S rRNA gene expression ratios calculated after conducting the real-time PCR showed similar levels of [removed]close to ratio 1.0) between U937 (mean= 0.964), MG-63 (mean=0.942), with slightly less expression in HL-60 (0.889). Further statistical analysis confirmed that the minor differences in GAPDH expression across U937, MG-63, and HL-60 were not significant (p=0.258); all three cell lines had no significant variation in expression. The Ct values obtained in this study were quite higher compared to the studies reported by Chen et al. (2019) and Mathur (2014), in which the ideal Ct value for GAPDH expression was reported to be between 18 to 22. For MG-63 cell lines, moderate expression of GAPDH (Ct value=18-22) was been reported by Rienzo et al. (2013). Meanwhile, mean Ct value for GAPDH expression reported by Niaz et al. (2019) in HL-60 cell lines was 15.56, and Ct value for GAPDH expression in U937 cell line was 19-22 (Attri et al., 2018). A possible reason that could explain such higher Ct values in this study is that GAPDH was not expressed above the detection level due to poor quality of extracted RNA, hence less template for the real-time PCR. The limitations of this study could be addressed firstly by improving the RNA extraction method by using fresh reagents, including more washing steps, or using elution columns to remove contaminants. Primer dimers and other non-specific amplification could be avoided by optimization of PCR conditions, such as by designing better primers that have minimum self-complementarity, increasing the annealing temperature, and decreasing primer concentration (Pfaffl, 2004). Moreover, better validation of normalization could be done via the use of multiple reference genes rather than one, since normalization by one reference gene can falsely introduce bias in the results (Wong & Medrano, 2005). Finally, for more valid results, a larger sample size is necessary, which can be determined by power analysis (Bustin et al., 2009).
CONCLUSION
In conclusion, the observed Ct values were higher than those reported in previous studies, the expression of GAPDH was consistent across the MG-63, HL-60, and U937 cell lines, confirming its suitability as a reference gene for these cell lines. Despite conflicting reports that suggest variability in GAPDH expression as qPCR technology becomes more sensitive, reference genes continue to be the preferred method for normalizing gene expression. The most effective way to address potential discrepancies is to validate the stability of the reference gene's expression within one’s samples rather than relying solely on previously published data. While this study supports the use of GAPDH as a reference gene in the three cancer cell lines analyzed, further research involving a broader range of cell lines and larger sample sizes is needed to fully confirm GAPDH’s reliability as a reference gene in various experimental contexts.
ACKNOWLEDGMENTS
We thank all the authors who have contributed to current paper.
AUTHOR CONTRIBUTION
All authors contributed to the final manuscript.
FUNDING STATEMENT
No specific funding
CONFLICT OF INTEREST
No conflict of interest
PARTICIPANT CONSENT AND ETHICAL APPROVAL
Not applicable.
COMPETING INTERESTS
The authors disclose that they have no conflicts of interest.
REFERENCES
Ali Ahmad Alvi , Haris Munir , Moeen Zulfiqar , Muhammad Waqas , Abdul Basit , Ali Moazzam , Exploring The Impact Of Lifestyle Factors On Gestational Diabetes, Risk And Management: Insights From A Preclinical Model Study, Int. J. of Pharm. Sci., 2024, Vol 2, Issue 10, 435-447. https://doi.org/10.5281/zenodo.13904990
10.5281/zenodo.13904990
