Evaluation and validation of reference genes for RT-qPCR gene expression in Naegleria gruberi

doi:10.21203/rs.3.rs-2984765/v1

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Evaluation and validation of reference genes for RT-qPCR gene expression in Naegleria gruberi

https://doi.org/10.21203/rs.3.rs-2984765/v1

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published 05 Oct, 2023

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Naegleria fowleri is the etiologic agent of Primary Amoebic Meningoencephalitis (PAM). PAM occurs world-wide and it is considered a rare disease, but its fatality rate is high (96%) due to the delay in initiation of treatment due to misdiagnosis and lack of a specific treatment. The analysis of gene expression by quantitative real-time PCR (qRT-PCR) could be a highly efficient means to understand the pathogenicity and also to find drug targets. Accurate RT-qPCR analysis requires correct normalization of gene expression data using reference genes (RG), whose expression should be constant under different experimental conditions. In this study, six genes, representing the most frequently used housekeeping genes, were selected for evaluation as reference genes in N. gruberi. The expression and stability of these genes was evaluated employing four algorithms (GeNorm, NormFinder, BestKeeper and RefFinder). Moreover, in order to validate normalization with the two most promising reference genes, a target gene was used (HSP90), and its expression was studied.

Biological sciences/Biological techniques/Bioinformatics

Biological sciences/Biological techniques/Gene expression analysis/Reverse transcription polymerase chain reaction

Biological sciences/Genetics/Gene expression

Biological sciences/Microbiology/Parasitology

The free-living amoebae of the genus Naegleria occur world-wide, being found in soil and bodies of freshwater like lakes, ponds, wastewater sewage, hydrotherapy pools and spas ^1,2. Although N. fowleri is one of numerous species in the genus Naegleria, to date, it remains the only species known to produce an acute fulminant, necrotizing and hemorrhagic meningoencephalitis called Primary Amoebic Meningoencephalitis (PAM). PAM is considered a rare disease which is under-reported^3,4. In the past years, an increase of the number of reported cases has been observed, mostly due to raised awareness of the disease and better diagnostic tools. PAM has a very high fatality rate, around 96 % andmost cases have occurred in healthy children following recreational water activities. In the recent past, cases related to the ablution rituals practiced by Muslims have been described, especially when cleansing the nose using so-called neti pots⁵. The main reasons for the high fatality rate is delayed diagnosis and thus delayed initiation of treatment, for which, due its rarity, no specific drug is available. The lack of specific treatment is also related to the fact that many biological mechanisms of Naegleria are unknown, such as the factors that contribute to its pathogenicity, the components of the redox chain, its mechanism to transform into cysts or to the flagellar form. To understand all these mechanisms, it is necessary to evaluate the expression of the respective genes, real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) being the method of choice for the quantification of mRNA⁶. The RT-qPCR is a powerful and accurate tool and can give reliable and reproducible results, but it is important to select a robust normalization approach. The most common and simple method for normalizing the mRNA levels of a target gene is comparing it to reference genes (RGs) whose expression levels are constant under different experimental conditions⁷. Nevertheless, several studies have pointed out that there is no single gene that fulfills the criteria required from a universal reference gene because its expression stability varies depending on the conditions or the type of cell^8,9. The classic reference genes or housekeeping genes (for example β-actin, glyceraldehyde-3-phosphate dehydrogenase, 18S ribosomal RNA) have been used as references in Northern blots, RNase protection assays and conventional RT-PCR assays. It has been proven that the expression levels of these classic RG can greatly vary and in some experimental conditions are not valid for normalization and could led to biased findings. As far as we know, in the numerous studies that have investigated changes in gene expression in the genus Naegleria, none of them have demonstrated the stability, suitability and reliability of reference genes for qRT-PCR standardization.

For this reason, in the current study, the stability of six reference genes (RGs) of Naegleria gruberi: 18S rRNA gene (18S), actin (ACT), glucose-6-phosphate dehydrogenase (G6PD), glycerol-3-phospahte dehydrogenase (GAPDH), hypoxanthine-guanine phosphoribosyltransferase (HPRT) and TATA-binding-protein (TBP) was evaluated under different experimental conditions such as heat-shock and exposure to hydrogen peroxide. The RGs were analyzed with four algorithms, including geNorm¹⁰, NormFinder¹¹, BestKeeper¹² and RefFinder¹³.

Primer specificity and amplification efficiency

Each primer set of the RGs produced a single amplicon in agarose gel electrophoresis. The amplification efficiency and R² of the RT-qPCR assays of RGs were calculated with the slope of the standard curve. The amplification efficiency ranged from 95–122%, the primers of each RG with best amplification efficiency were selected for further analysis (Table 1). The amplification efficiency and coefficients of determination (R²) of the primer set selected ranged from 95–105% and 0.948–0.999, respectively.

Table 1

Primer details and parameters derived from a 10-fold standard curve of the six references genes and target gene. Underlined primers were chosen for further analysis.
Primer	Primer sequence (5’ – 3’)	Amplicon length (bp)	Average Tm (°C)	Amplification efficiency (%)	Slope	Correlation coefficient (R²)	GenBank accession no.
18S (1)	F: GCCTGAGAAATCGCTACCAC R: CAGAAGACAATACCTCCCCAC	125	60.1	94.80	-3.50	0.949	M18732.1
18S (2)	F: ATAACGAACGAGACCTAAGCC R: TCCGACAAACTAACCCTTCCC	70	62.5	95.96	-3.43	0.948	M18732.1
ACT (1)	F: CCTCGTGCTGTTTTCCCATC R: GCTTCATCTCCGACATAGGC	95	57.2	105.95	-3.19	0.998	XM_002672078.1
ACT (2)	F: TGGAATGGAAGCTGCTGGTA R: GGTTGTACCGCCTGAAAGTA	111	56.8	122.60	-2.88	0.976	XM_002672078.1
G6PD (1)	F: GTTCAACCAGAGCCATATCC R: TGCTTGCTACTAAACCATAACG	84	56.9	102.94	-3.26	0.991	XM_002681275.1
G6PD (2)	F: GGGAGTTGAAGGAAGAGGTG R: GTTCCATAGCAATGAGAGCC	101	55.0	101.50	-3.29	0.999	XM_002681275.1
GAPDH (1)	F: TGTCCACGCTGTTACTGCTA R: ATTGTAACCAGCAGCACGAC	84	56.9	96.78	-3.44	0.986	XM_002669943.1
GAPDH (2)	F: TGGTCGTGAAATCCACGTTT R: TAGCACCACCCTTCAAGTGC	140	59.2	101.34	-3.29	0.999	XM_002669943.1
HPRT (1)	F: CTTGTGTCTTGTTGACTGCCC R: CCAAACCGTATCCAACAACGA	107	60.7	117.45	-2.97	0.976	XM_002673756.1
HPRT (2)	F: CGTGTCAAGGAATTGGCTCA R: ACCCTTCAAAACTGGAACCA	93	57.0	104.44	-3.23	0.998	XM_002673756.1
TBP (1)	F: ACGGTAAATCTTGCTTGCGA R: ACGCATAATCACAGCAGCAA	98	55.8	105.11	-3.21	0.999	XM_002679885.1
TBP (2)	F: GACACCAGTGCCAGGTACAC R: GAAGAGGTGTTGATGTCGGC	101	58.4	99.44	-3.35	0.998	XM_002679885.1
Target gene
HSP90 (1)	F: TCTAATAGACTCTCCTCTGCAC R: TTCATCATCCAATCCATACCAC	127	57.1	100.54	-3.31	0.997	XM_002682845.1
HSP90 (2)	F: AATAGACTCTCCTCTGCACC R: TTCATCATCCAATCCATACCAC	124	55.1	97.05	-3.40	0.999	XM_002682845.1

Quantification cycle (Cq) values and expression profiles of the six reference genes

RT-qPCR assays were performed with the selected primers for the six candidate reference genes using the RNA as template extracted from different N. gruberi growth phase conditions: logarithmic (LOG) and stationary (STAT), and under stressful conditions: heat shock (HS) and oxidative stress (OS). Based on the Cq values obtained from RT-qPCR (Supplementary table S1), the efficiency correction (CqE) was calculated and is shown in a box diagram (Fig. 1 and Supplementary table S2). The average Cq values of the reference genes ranged from 5.98 to 26.23 and 4.12 to 24.95 for growth stages and stressful conditions, respectively. The results showed that GAPDH had the smallest variation, followed by TBP, while ACT had the largest variation, for different growth phases. In the case of stress conditions, the smallest variation was observed in ACT, followed by 18S, whereas HPRT showed the largest variation. Regardless of expression levels, the 18S RNA-gene is the gene with highest expression (lower CqE value) while ACT the least expressed (higher CqE value) in both experimental conditions.

Stability of candidate reference genes

Four algorithms were used to calculate the expression stability of RGs under different experimental conditions (i.e. logarithmic, stationary, heat-shock and oxidative stress). The outcomes of all analyses revealed that the rankings of the reference genes varied depending on the conditions tested (Table 2, Supplementary Table S3). Across all conditions combined (Fig. 2), ACT and G6PD were ranked equally as most stable with geNorm and NormFinder, in RefFinder analysis ACT and G6PD were also the most stable RG, but ACT was ranked first. While the analysis with BestKeeper was remarkably different, with 18S and TBP being the most stable RGs. When the analysis was performed for N. gruberi cells at different growth phases (LOG and STAT in combination), the results obtained were more inconsistent with G6PD and GAPDH, HPRT and G6PD, 18S and GAPDH and, HPRT and G6PD being the two most stable RGs when the analysis was carried out with geNorm, NormFinder, BestKeeper and RefFinder, respectively. The stability of the RG under stress conditions (SC – HS and OS combined) was also different depending on the software employed. When geNorm was used 18S and ACT genes were the most stable, whereas with NormFinder GAPDH and G6PD were the most stable ones, while the analysis with Bestkeeper revealed that G6PD and 18S were the most stable. Finally, the overall ranking given by RefFinder showed that ACT and also like in the last two previous cases, G6PD were the most stable RGs.

Table 2

Ranking of RGs for all conditions of *N. gruberi* cultures based on geNorm, NormFinder, BestKeeper and RefFinder.
	Rank	geNorm	NormFinder	BestKeeper	RefFinder
AC	1	ACT/G6PD	ACT/G6PD	18S	ACT
	2			TBP	G6PD
	3	HPRT	HPRT	G6PD	HPRT
	4	18S	18S	HPRT	GAPDH
	5	TBP	GAPDH	ACT	18S
	6	GAPDH	TBP	GAPDH	TBP
GP	1	G6PD/GAPDH	HPRT	18S	HPRT
	2		G6PD	GAPDH	G6PD
	3	ACT	TBP	TBP	GAPDH
	4	HPRT	GAPDH	G6PD	ACT
	5	TBP	ACT	HPRT	TBP
	6	18S	18S	ACT	18S
SC	1	18S/ACT	GAPDH	G6PD	ACT
	2		G6PD	18S	G6PD
	3	G6PD	ACT	GAPDH	18S
	4	HPRT	18S	HPRT	GAPDH
	5	GAPDH	HPRT	TBP	HPRT
	6	TBP	TBP	ACT	TBP
LOG	1	18S/GAPDH	G6PD/TBP	TBP	TBP
	2			ACT	G6PD
	3	TBP	HPRT	18S	GAPDH
	4	G6PD	GAPDH	GAPDH	18S
	5	HPRT	18S	G6PD	HPRT
	6	ACT	ACT	HPRT	ACT
STAT	1	18S/GAPDH	18S/GAPDH	G6PD	GAPDH
	2			18S	18S
	3	TBP	TBP	TBP	TBP
	4	ACT	ACT	HPRT	ACT
	5	G6PD	G6PD	GAPDH	G6PD
	6	HPRT	HPRT	ACT	HPRT
HS	1	TBP/G6PD	TBP	GAPDH	TBP
	2		G6PD	TBP	G6PD
	3	GAPDH	ACT/GAPDH	G6PD	18S
	4	18S		18S	ACT
	5	ACT	18S	HPRT	GAPDH
	6	HPRT	HPRT	ACT	HPRT
OS	1	ACT/HPRT	G6PD	18S	ACT/G6PD
	2		TBP	G6PD
	3	G6PD	ACT	TBP	HPRT
	4	18S	18S	HPRT	TBP
	5	TBP	HPRT	GAPDH	18S
	6	GAPDH	GAPDH	ACT	GAPDH

The analysis of RG stability in trophozoites in the exponential phase (LOG) revealed that NormFinder, BestKeeper and RefFinder matched for the most stable gene (TBP) while the most stable genes obtained with geNorm (18S and GAPDH) did not match any of the previous programs. When the analysis was carried out in stationary trophozoites (STAT), geNorm, NormFinder and RefFinder concurred regarding the most stable genes (18S/GAPDH) while the results of BestKeeper only agreed in one of the genes (18S) while the other most table gene was G6PD which the other algorithms ranked it as less stable. The most stable genes in heat-shocked trophozoites (HS) were common in geNorm, NormFinder and RefFinder (TBP/G6PD), but again BestKepeer agreed only on the stability of one of the genes (TBP) while the most stable gene (GAPDH) was considered less stable by the other three programs. When the analysis was done with trophozoites under oxidative stress conditions (OS), the RG were ranked differently in each algorithm: ACT and HPRT, G6PD and TBP, 18S and G6PD, ACT and G6PD, when geNorm, NormFinder, BestKeeper and RefFinder were respectively used. As in most of the cases studied, the results given by geNorm, NormFinder and RefFinder were the same for the most and least stable RG, the results of BestKeeper where not used for validation.

The least stable RG results varied for most conditions studied, but identical for STAT and HS conditions being HPRT. Also, when only one condition was analyzed with the four different algorithms, it revealed that geNorm, NormFinder and RefFinder had the same ranking for the least stable genes, except when all the conditions were combined. Interestingly, some RGs as ACT and TBP were ranked as the least stable RG under LOG and SC conditions, but considered the most stable RGs in other conditions.

In addition, geNorm software was used to determine the optimal number of RGs needed for RT-qPCR standardization by calculating the paired variation value Vn/Vn + 1 (n represents the reference gene number). The results showed that the Vn/Vn + 1 value of all the conditions studied were all lower than 0.15 (Fig. 3), suggesting that two reference genes were sufficient to complete the RT-qPCR normalization in N. gruberi under these test conditions.

Validation of the reference genes by RT-qPCR

The relative HSP90 expression of N. gruberi was studied after 6 hours of heat-shock treatment, untreated N. gruberi cells were used as control. The relative expression was calculated based on normalization with different RGs for validation. The two most stable RGs, ACT/G6PD (all conditions in combination-AC-) and G6PD/TBP (after heat-shock-HS-) were selected for normalization and also the most stable RGs under stress conditions. These were different depending on the software used, being 18S/ACT (geNorm), G6PD/GAPDH (NormFinder) and ACT/G6PD (RefFinder). Moreover, the least stable RGs were employed, which were GAPDH and TBP when all conditions were analyzed in combination, HPRT under HS and TBP under SC. Although HSP90 relative expression patterns had a similar tendency, which was an increase of expression after the heat-shock (Fig. 4), normalization with different RGs, led to considerable dissimilarities. HPS90 expression normalized with ACT/G6PD in combination and alone (Fig. 4a and Fig. 4b), was similar and no statistical differences were observed. The relative HPS90 expression was slightly lower if it was normalized with 18S/ACT the most stable RGs according to geNorm under SC (Fig. 4b) and G6PD/TBP the most stable RGs in agreement with geNorm, NormFinder and RefFinder after HS treatment (Fig. 4c. TBP was considered the least stable RG under AC showing a low HSP90 relative gene expression, so it makes sense that the relative expression of HPS90 expression was also lower when it was normalized with G6PD/TBP, leading to an inaccurate HSP90 relative expression. Whereas when GAPDH also ranked as one of the least stable RG under AC, the relative expression of HSP90 was significantly higher.

Hence for an appropriate normalization of HSP90 expression after heat-shock treatment, the use of the combination of ACT/G6PD, which was suggested by geNorm, NormFinder and RefFinder as the ideal pair of RG for N. gruberi under all conditions, was the best option.

In the present study, the stability of different genes was analyzed to determine their appropriateness as RGs for normalization of gene expression RT-qPCR experiments in N. gruberi under different conditions. Choosing the most stable RG to normalize RT-qPCR data is crucial for obtaining accurate quantification of gene expression and preventing bias by variations that can be introduced from RNA extractions, cDNA synthesis, PCR protocol and sample loadings^7,9. In the last decades, the genes known as housekeeping genes were broadly used as internal controls for the normalization of RNA levels for Northern blotting, RNAse protection and RT-qPCR analyses, because it was assumed that these genes are constitutively expressed and regulated, but no studies indeed evaluated the stability of expression of these genes under various experimental conditions^7,14. Moreover, it has been shown that the expression levels of these housekeeping genes vary depending on the conditions and tissues in which are expressed^15–19. Therefore, it is hard to select a perfect RG that would be consistently expressed in all tissues and cell types without being influenced by internal or external factors. As a result, recent studies have aimed to determine the stability of RGs under various conditions for several different species ^20–23.

However, to date, only very few gene expression studies have been performed for the genus Naegleria, which includes the brain-eating amoeba N. fowleri and in these studies, various genes were used as internal control, such β-actin²⁴, α-actin²⁵, glyceraldehyde 3-phosphate dehydrogenase^26–28. However, the stability of these genes in the Naegleria genus under different conditions is unknown. Given the lack of a study analysing the stability of the most commonly used RGs in the genus Naegleria, we decided to carry out a study analysing these genes in N. gruberi. The genes analysed in this study included the 18S rRNA gene, and the genes for actin, glucose-6-phosphate dehydrogenase, glycerol-3-phospathe dehydrogenase, hypoxanthine-guanine phosphoribosyltransferase and the TATA-binding-protein. The study was done using the four widely used programs geNorm¹⁰, NormFinder¹¹, BestKeeper¹² and RefFinder¹³ on N. gruberi trophozites under different conditions. Additionally, the geNorm¹⁰ algorithm enables the determination of the number of RGs required for the normalization of gene expression, which in all the conditions investigated for this study is two (Fig. 3). As pointed out in several studies, for accurate and trustworthy results, two or more RGs are needed¹⁰. Moreover, our findings are in agreement with those found in the genus Acanthamoeba, another free-living amoeba, for which geNorm analysis likewise recommended the use of two RGs²⁹ and also with the results in other protozoan parasites like Trichomonas vaginalis³⁰ or Trypanosoma brucei³¹.

The results of this study revealed slight variances amongst the 4 programs (Table 2), which was reported previously and is comprehensible since each software employs a different algorithm to determine the gene stability^{23, 32–34}. Moreover, also differences in gene stability under the different conditions tested here were observed, which was also found in other protozoan parasites^{17, 29–31}. For this reason, the analysis was also conducted combining the findings from all the conditions (AC) confirming that geNorm, NormFinder and RefFinder concur on the most stable genes (ATC/G6PD), whereas BestKeeper once more provided a completely different outcome (Table 2). In studies on different organisms it has been shown that geNorm, NormFinder and RefFinder give a similar result while BestKeeper gives a different ranking of gene stability^29,35,36 which can be partially explained by BestKeeper uses raw Ct values as input while RQ values are used by geNorm and NormFinder.

In order to validate that ACT and G6PD can be used as RGs in all the conditions studied in this work, the expression of HSP90 in N. gruberi trophozoites after six hours of heat-shock was analyzed, as it is known that the expression of this protein is high under heat-shock conditions^{29, 37–39} and the relative gene expression obtained when it was normalized with ACT/G6PD was compared with the one obtained when it was normalized with TBP/G6PD (most stable genes under heat-shock conditions). Our findings demonstrate that fold change values for relative gene expression normalized with ACT/G6PD are comparable to those normalized with TBP/G6PD (no statistical difference). Actin is the most abundant protein in many eukaryotic cells and is crucial for a wide variety of cellular processes, including cell division, migration, transcriptional regulation, and cell shape regulation, among many others^40,41. For this reason, ACT is widely used as internal control. This RG showed greater or lesser expression stability depending on the organism and conditions under study⁴². For instance, the gene is stable in protozoan human pathogen T. vaginalis under nutrient restriction³⁰ or in the agricultural pest Diabrotica undecimpunctata howardi²⁰, but it is not stable in hepatic fibrosis caused by Schistosoma japonicum⁴³ or in the Siberian giant trout Hucho taimen under heat stress³⁴. The enzyme glucose-6-phosphate dehydrogenase is an important enzyme in virtually all species from bacteria to mammals and its metabolic function is to catalyze the first step in the pentose phosphate pathway and to provide the NADPH that it needed in various biosynthetic and detoxification reactions^44–46. And similar to ACT, it is being used as internal control and its expression varies depending on the organism and condition^29,47,48. This work therefore reaffirms the importance of analyzing the expression stability of RGs on a case-by-case basis.

In conclusion, this is the first study to validate RGs in N. gruberi under different conditions. Under most conditions and with most algorithms ACT and G6PD appeared promising options for accurate expression. Interestingly, the GAPDH gene, one of the most often used genes in Naegleria gene expression research, was, depending on conditions and algorithm, not always the most promising choice. Therefore, we propose to use at least two RGs, e.g. ACT and G6PD, to obtain reliable results in Naegleria gene expression analyses.

Biological samples and experimental conditions

N. gruberi NEG-M ATCC 30224 was used as non-pathogenic counterpart for N. fowleri. It was cultured at 25°C in M7 media⁴⁹ in 75 cm² tissue culture flasks with weekly medium changes. Initial experiments were undertaken with amoebae in the logarithmic growth phase (LOG cells) and in the stationary growth phase (STAT cells). For logarithmic and stationary cells, 10 ml amoebae-containing medium from the culture flasks were transferred into fresh flasks and filled up with fresh medium to 20 ml and grown for another 4 days and for fourteen days for STAT cells, respectively, before harvesting. N. gruberi culture in logarithmic phase was exposed to heat shock (HS) and oxidative stress (OS). For generation of HS cells, growth temperature was shifted for 1 h from 25°C to 37°C, and for generation of OS cells, cultures were exposed to 250 µM hydrogen peroxide (H₂O₂) during 6 h prior to harvesting the cells for RNA isolation. For all preparations, amoebae were counted using a Fuchs-Rosenthal counting chamber. The inoculum was adjusted to 3 x 10⁶ amoeba and washed two times with PBS 1x.

Selection of Reference genes and PCR efficiency study

Referring to general recommendations from other publications and sequence availability, six candidate RGs were chosen for primer design. The genes selected were the 18S rRNA gene (18S), and the genes for actin (ACT), glucose-6-phosphate dehydrogenase (G6PD), glycerol-3-phosphate dehydrogenase (GAPDH), hypoxanthine-guanine phosphoribosyltransferase (HPRT) and the TATA-binding-protein (TBP). Two pairs of primers of each RG were designed using Primer3 and initially tested in conventional PCR using genomic DNA. All primers were synthesized by Eurofins. Then, standard curves were generated with 5 points of 10-fold serial dilutions of RNA to calculate the primer efficiency (E) and the correlation coefficients (R²). Efficiency was calculated according to the formula E =(10^{− 1/slope} – 1)*100. The primer pair with better efficiency in RT-qPCR was selected for further experiments (Table 1).

RNA extraction and quantitative real-time PCR (RT-qPCR)

The RNA was isolated using the GeneJET RNA Purification kit (Thermo Scientific # K0732) following the manufacturer’s protocol. The concentration and purity of RNA was measured with a NanoDrop spectrophotometer ND1000 (NanoDrop Technologies). All RNA samples were diluted to 10 ng/µl using nuclease free water and stored at − 80°C until use.

RT-qPCR was performed in a CFX96 thermocycler (Bio-Rad) using Luna® Universal One-Step RT-qPCR kit (New England BioLabs® Inc., E3005L). The reaction mixture (20 µl per reaction) contained 10 µl of Luna Universal One-Step Reaction Mix 2x, 1 µl of Luna WarmStart® RT Enzyme Mix 20x, 400 nM of each primer and 50 ng of RNA (5 µl of 10 ng/µl). The RT-qPCR profile included a reverse transcription step at 55°C for 10 min, initial denaturation step at 95°C for 1 min, followed the 40 cycles of denaturation at 95°C for 10 s and extension at 60°C for 60 s, melting curve was performed at the end of the run by stepwise (0.5°C per 5 s) increasing the temperature from 60 to 95°C. All experiments were carried out in three technical and three biological replicates.

Expression stability analysis of candidate reference genes

The expression stabilities of the RGs in LOG, STAT, HS and OS cells were individually assessed, as well as the combination of all these conditions (AC, GP and SC), was determined employing geNorm¹⁰, NormFinder¹¹ and BestKeeper¹², which are Microsoft Excel tools, and RefFinder¹³, which is a web-based tool. For geNorm and NormFinder analysis, the raw Cq values were transformed in relative quantities (RQ) with the following formula RQ = E^{(Cq min − Cq sample)}(where E is the primer efficiency and Cq min is the lowest Cq value across the sample pool) (Supplementary Table S4). Raw Cq values were used directly for BestKeeper and RefFinder. The geNorm program selects the most stable reference gene by calculating the average expression stability (M-value) of each reference gene. NormFinder calculates the standard deviation for each gene and compares it with the expression of the other genes. The gene with the lowest variation between intra- and inter-group comparisons is then considered the most stable. BestKeeper showed stability based on the Pearson coefficient of correlation (r) and standard deviation (SD) Finally, RefFinder generates a comprehensive ranking by calculating the geometric mean of each reference gene in the above three methods and delta-Ct method, in which the smaller the ranking, the more stable is the reference gene.

Pair-wise variation (V) was calculated with geNorm to identify the optimal number of RGs required for an accurate normalization for conditions tested. Here, the cut-off V value is 0.15, below which the addition of another internal control gene does not result in a significant improvement of normalization.

Validation of reference gene expression

The relative gene expression of the target gene HSP90 was analyzed using the Pfaffl method⁵⁰, employing all RGs separately for normalization. Additionally, based on pair-wise variation, expression patterns of the target gene were normalized with the two most stable RGs using the Vandersompele method¹⁰. The relative change of expression upon normalization was compared to the calculated RQ of the target gene based on the formula RQ = E^−ΔCq, by comparing Cq values of logarithmically cells with HS and OS cells. This calculation was based on the fact that each RT-qPCR was done with the same amount of RNA.

Statistical analysis

Statistical analysis was performed with GraphPad Prism 8 (GraphPad Software Inc., USA). To determine statistical significance among investigated groups, one-way analysis of variance (ANOVA) was performed. Statistical difference was considered when p < 0.05.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files]

Acknowledgements

This research was funded in whole by the Austrian Science Fund (FWF) [M 3095-B].

Author Contributions

All authors designed the study. T.M.P. performed the experiments. T.M.P and M.K. analyzed the data. T.M.P wrote the manuscript. M.K. and J.W. supervised the research. All authors reviewed and edited the manuscript.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Evaluation and validation of reference genes for RT-qPCR gene expression in Naegleria gruberi

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Abstract

Figures

Introduction

Results

Primer specificity and amplification efficiency

Quantification cycle (Cq) values and expression profiles of the six reference genes

Stability of candidate reference genes

Validation of the reference genes by RT-qPCR

Discussion and conclusion

Materials and methods

Biological samples and experimental conditions

Selection of Reference genes and PCR efficiency study

RNA extraction and quantitative real-time PCR (RT-qPCR)

Expression stability analysis of candidate reference genes

Validation of reference gene expression

Statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

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Journal Publication

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