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Quantitative Real-time Polymerase Chain Reaction (qPCR) is an important tool for molecular biology and biotechnology research, widely used to determine the expression levels of mRNA. Two main methods to performing qPCR are largely used: The absolute quantification, in which the mRNA levels are determined by using a standard curve and the relative method, which is based on the use of reference genes. Reference genes are widely expressed in cells of animal and plant tissues and their expression pattern are theoretically unchanged within several situations, which makes them an excellent choice to normalize mRNA quantification data in relative qPCR studies. However, several reports are increasingly showing that the use of only one reference gene in relative qPCR studies should be avoided, because in the real world their expression levels can significantly change from tissue to tissue. Several softwares, such as geNorm, BestKeeper and NormFinder, have been developed to perform data normalisation, and these programs may assist in choosing the most stable reference genes. The aim of this review was to describe the current normalisation strategies used in qPCR assay, as well as to establish essential rules to perform reliable mRNA quantification. Finally, this review show some innovations in the advances on qPCR.
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Journal of Molecular Biology Research; Vol. 5, No. 1; 2015
ISSN 1925-430X E-ISSN 1925-4318
Published by Canadian Center of Science and Education
45
Real Time PCR: the Use of Reference Genes and Essential Rules
Required to Obtain Normalisation Data Reliable to
Quantitative Gene Expression
Antônio J. Rocha1, José E. Monteiro-Júnior2, José E.C. Freire1, Antônio J.S. Sousa1 & Cristiane S.R. Fonteles3
1 Departamento de Bioquímica e Biologia Molecular, Avenida Humberto Monte, s/n - Pici - CEP 60440-900,
Fortaleza - CE, Brasil
2 Departamento de Biologia, Avenida Humberto Monte, s/n - Pici - CEP 60440-900, Fortaleza - CE, Brasil
3 Departmento de Clínica Odontológica, Universidade Federal do Ceará, Rua Monsenhor Furtado, s/n - Rodolfo
Teófilo - CEP 60430-350, Fortaleza - CE, Brasil
Correspondence: Antônio J. Rocha, Departamento de Bioquímica e Biologia Molecular, Avenida Humberto
Monte, s/n - Pici - CEP 60440-900, Fortaleza - CE, Brasil. E-mail: antonionubis@gmail.com
Received: July 1, 2015 Accepted: July 15, 2015 Online Published: July 20, 2015
doi:10.5539/jmbr.v5n1p45 URL: http://dx.doi.org/10.5539/jmbr.v5n1p45
Abstract
Quantitative Real-time Polymerase Chain Reaction (qPCR) is an important tool for molecular biology and
biotechnology research, widely used to determine the expression levels of mRNA. Two main methods to
performing qPCR are largely used: The absolute quantification, in which the mRNA levels are determined by
using a standard curve and the relative method, which is based on the use of reference genes. Reference genes
are widely expressed in cells of animal and plant tissues and their expression pattern are theoretically unchanged
within several situations, which makes them an excellent choice to normalize mRNA quantification data in
relative qPCR studies. However, several reports are increasingly showing that the use of only one reference gene
in relative qPCR studies should be avoided, because in the real world their expression levels can significantly
change from tissue to tissue. Several softwares, such as geNorm, BestKeeper and NormFinder, have been
developed to perform data normalisation, and these programs may assist in choosing the most stable reference
genes. The aim of this review was to describe the current normalisation strategies used in qPCR assay, as well as
to establish essential rules to perform reliable mRNA quantification. Finally, this review show some innovations
in the advances on qPCR.
Keywords: primer design, DNA binding dyes, probes, normalisation
1. Introduction
The polymerase chain reaction (PCR) technique was first introduced by Kary Mullis (Saiki et al., 1985). PCR is
historically used as a sensitive method for the detection and amplification of specific sequences of nucleic acids
in a sample. Advances in the specificity and sensibility of PCR reactions gave birth to a more sensitive PCR
technique, namely quantitative PCR (qPCR-quantitative real-time polymerase reaction), which utilizes mainly
cDNA as template, a complementary DNA from RNA molecules through of the reverse transcriptase reaction. In
these reactions, fluorescent reporters used include double-stranded DNA (dsDNA) binding dyes or probes that
are incorporated into the product during amplification. The increase in fluorescent signal is directly proportional
to the number of PCR product molecules generated in the reaction.
qPCR is amongst the best available methods to determining changes in gene expression, due their ability to
rapidly and accurately quantify target genes, even in the presence of very low expression levels (Holland, 2002).
Prior to the analysis of gene expression, the selection of an appropriate normalisation strategy is essential to
control for non-specific variations between samples of cDNA. The most commonly used method to normalising
qPCR data is relied on the use of one or more endogenous reference genes (Hamalainen et al., 2001; Rebouças et
al., 2013).
Reference genes have uniform and stable expression in a wide variety of tissues and cell types, at different
developmental stages, and comprise all genes that express protein products involved in basic cellular processes
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(Reid et al., 2006), showing none or only minimal changes in the expression levels between individual samples
and experimental conditions (Rebouças et al., 2013). These genes are largely used as internal controls for
normalisation in gene expression studies in different tissues and/or condition as in plants and animals (Wong et
al., 2005; Kumar et al., 2013; Sara et al., 2013; Rocha et al., 2013; Nakayama et al., 2004). Several reference
genes, including those coding for biological products such as tubulins, actin, glyceraldehyde-3-phosphate
dehydrogenase (GAPDH), phosphatases, albumin, cyclophilin, micro-globulin, ribosomal units (18S rRNA) or
ubiquitin (UBQ) have been described in the literature (Foss et al., 2003; Rocha et al., 2013). The correct choice
of reference genes is crucial to properly analyze the results of qPCR (Suzuki et al., 2000) and to measure and
reduce the errors from variations among the samples (Barsalobres-Cavallari et al., 2009).
Several research groups have developed software tools to identify the most stable expressed genes across a set of
samples in order to perform data normalisation. These tools include geNorm, NormFinder and BestKeeper
(Vandesompele et al., 2002; Pfaffl et al., 2004; Andersen & Orntoft, 2004), which is freely available on the web and
allows researchers to find the best reference gene for their experiments. A great number of studies describing the
identification of multiple reference genes for normalisation of qPCR data using these algorithms have been
performed on the animal and human health fields (Hong et al., 2008; De Boever et al., 2008), but similar reports are
scarce in plant research (Jain et al., 2006; Ransbotyn et al., 2006; Exposito-Rodriguez et al., 2008).
The aim of this review was to evaluate the importance of the application of reference genes in normalisation
strategies of qPCR assays, in different tissues or experimental conditions, as well as to describe essential rules
necessary to conduct successful qPCR experiments. Besides, we pointed out several precautions required for a
good qPCR. Finally, this review shows some innovations in the advances on qPCR in the last years.
2. DNA Binding Dyes Versus Hydrolysis Probes in qPCR
PCR is one of the most versatile technologies in molecular biology. The PCR reaction consists of 3 different
stages which involve, (a) the DNA denaturation; (b) the primer annealing and (c) the extension phase (Mullis et
al., 1987). In traditional (endpoint) PCR, the detection and quantification of amplified target sequences are
performed at the end of the reaction, and it involves additional work, such as gel electrophoresis and image
analysis. Nevertheless, in qPCR, the amount of PCR product is measured along each reaction cycle. The ability
to monitor the reaction during its exponential phase enables users to determine the initial amount of target gene
with great precision (Wong et al., 2005).
In qPCR, the amount of DNA is measured by the use of fluorescent markers that are incorporated into the PCR
product. The increase in fluorescent signal is directly proportional to the number of PCR product molecules
(amplicons) generated in the exponential phase of the reaction. Fluorescent reporters used include
double-stranded DNA (dsDNA) binding dyes or probes that are incorporated into the product during
amplification (Bustin et al., 2002). SYBR Green is an example of a fluorescent dye which binds to the
double-stranded DNA and emits light upon excitation. Once the reaction proceeds and the PCR product is
accumulated, the fluorescence levels increase proportionally to the amount of DNA present in the original
sample (Livak et al., 1995; Pabla et al., 2008; Bustin et al., 2002). This dye is used to monitor the amplification
of any DNA sequences and dispenses the use of a probe, thus reducing the cost of amplification and providing a
great advantage in its application. On the other hand, since the dye binds not only to the target DNA, but to all
dsDNA formed during qPCR, the use of SYBR Green, while simple lacks specificity (Figure 1a). The specificity
of the reactions, however, can be easily accessed by the use of melting curve analysis (Dheda et al., 2004).
In addition to DNA binding dyes, there are probes, such as TaqMan®, which are designed to binds to specific
DNA sequences. TaqMan® probes primarily consist in a oligonucleotide sequence complementary to some
regions of the target DNA. The probe is complexed with a quencher and a reporter fluorophore dye at its 3' and
5' ends, respectively (Livak et al., 1995). During the amplification step the probe is associated to its
complementary target DNA and then is cleaved by Taq DNA polymerase 5’-3' exonuclease activity (Figure 1b).
This cleavage releases the reporter dye and generates a fluorescent signal that increases with each cycle (Bustin
et al., 2002). TaqMan® provides higher specificity than DNA intercalating dyes, such as SYBR Green. In
addition, these probes can also be labeled with distinct and distinguishable reporter dyes, which allows the
amplification of two different sequences in the same reaction tube, eliminating the post-PCR processing, and
reducing hand labor. The main drawback of this system is the requirement to synthesize specific probes to each
target sequences, increasing the cost of the assay (La Cruz et al., 2013).
Another type of probe which is largely used in qPCR assay is molecular beacon. When free in solution molecular
beacon probes assume a hairpin structure consisting of a quencher and a reporter dye (Tyagi et al., 1996). The
reporter fluorescent dye and the quencher remain extremely close and therefore no fluorescence is detected when
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this structure is formed (Figure 1c). However, during the annealing step, Molecular Beacon hybridizes to the
target sequence generating conformational changes leading to the separation of reporter and quencher dyes,
which results in the emission of fluorescence (Tyagi et al., 1996; VanGuilder et al., 2008). The greater
specificity for mismatch discrimination is due to structural constraints. However, the main disadvantage
associated with Molecular Beacons is the accurate design of the hybridization probe. Optimal design of the
Molecular Beacon stem annealing strength is crucial (Wong et al., 2005).
Scorpions consist of a single-stranded oligonucleotide probe of approximately 20 to 25 nt carrying a reporter
fluorophore at its 5' end and a quencher at its 3' end. Their tridimensional conformation resembles a stem and
loop structure, in which a PCR primer is attached (figure 1d). The stem-and-loop structure acts as a blocker to
prevent DNA polymerase activity during the interaction of the probe with the target DNA (Bustin et al., 2002;
Ng et al., 2005). The close proximity of the reporter to the quencher leads to a continuous suppression of the
fluorescence emitted by the reporter. At the beginning of the PCR, TaqDNA polymerase extends the PCR primer
and synthesizes the complementary strand of the target sequence (Whitcombe et al., 1999). During the next
cycle, the stem-and-loop structure unfolds and the loop region of the probe hybridizes intra-molecularly to the
newly synthesized target sequence. The reporter is excited by light from the qPCR instrument (Bustin et al.,
2002). Once the reporter dye is no longer in close proximity to the quencher dye, fluorescence emission may
take place. The significant increase of the fluorescent signal is detected by the qPCR instrument and it is directly
proportional to the amount of target DNA (Holland, 2002; Wong et al., 2005; Kumar et al., 2013). Scorpions
have the advantage to providing a stronger signal and lower level of background when in compared to other
probes, such as molecular beacons (Bustin et al., 2002).
Figure 1. Probes and Dyes used in Real time PCR assay; a) SYBR Green; b) TaqMan; c) Molecular Beacon; d)
Scorpions
3. The Use of Reference Genes to Normalize qPCR Data
Reference genes in qPCR are critical for normalisation of expression levels, thus, avoiding misinterpretation of
results obtained by real time PCR data. In recent years, it has become clear that no single gene is constitutively
expressed in all cell types and under all experimental conditions. This implies that the expression stability of a
putative control gene (reference gene) must be verified before each qPCR assay and that the use of only one
reference gene is generally not enough to normalize the expression data (Livak et al., 2001; Lee et al., 2010).
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The choice of several reference genes to normalize and validate the final results may significantly influence the
accuracy of gene expression. Consequently, the use of inappropriate reference genes for normalisation of
expression data may lead to erroneous results and data misinterpretation (Suzuki & Higgins, 2000), because
normalisation is a pivotal step that provides the Cq values-based differences between the reference and target
genes, avoiding misinterpretation of the results and providing reliable Cqs, thus rendering a more accurate and
reliable gene expression (Vandesompele et al., 2002). The Ct or threshold cycle value is the cycle number at
which the fluorescence generated within a reaction crosses the fluorescence threshold, a fluorescent signal
significantly above the background fluorescence. Therefore, the selection of appropriate reference genes is a
critical step before evaluating gene expression in new species and/or tissues (Condori et al., 2001; Cordoba et al.,
2001). The best candidate genes are those selected by programs used to establish reference genes, such as
geNorm, BestKeeper and NormFinder. Therefore, the normalisation using appropriated reference genes are
pivotal to acquire suitable data and avoid and misinterpretation of the experiments.
4. Algorithms Used to Normalize qPCR Data
In the last decade, relevant tools to select genes for normalisation have become available. Several research
groups have developed softwares to identify the most stably expressed genes across a set of samples. Among
these tools we will focus on the most cited articles as geNorm, NormFinder and Bestkeeper (Vandesompele et
al., 2002; Andersen & Orntoft, 2004; Pfaffl et al., 2004), which are freely available on the web and allow
researchers to find the best reference gene for their experiments. These programs allow the calculation of a
normalisation factor over multiple reference genes, which improve the robustness of the normalisation even
further (Dekkers et al., 2012). Different manners to access the stability of putative reference genes are available
using the upon mentioned software. Hence, BestKeeper employs quantification cycle (Cq) values directly for
stability calculations, whereas geNorm and NormFinder have these values transformed to relative quantities
using normalisation factor (NF) (Mallona et al., 2004).
3.1 Genorm Analysis
The geNorm program has been recently reported to be one of the best statistical methods to identify stably
expressed genes for qPCR analysis. The geNorm calculates a gene-stability measure M as the Average pairwise
variation V of a particular gene reported to all other control genes. Genes with the lowest M values have the most
stable expression. Stepwise exclusion of the gene with the highest M value allows ranking of the tested genes
according to the stability (Condori et al., 2001; Cordoba et al., 2001; Vandesompele et al., 2002; Zhong et al.,
2009). The analysis relies on the principle that the expression ratio of two proper control genes should be identical
in all samples, regardless of the experimental conditions or cell type, and the M values below cutoff (< 1.5) are
regarded the most stable genes among all candidate reference genes (Vandesompele et al., 2002).
The geNorm program estimates also the number of genes required to be used as appropriate controls for
normalisation by evaluation of variation in pairs (V values), checking the variation of the expression of two by
two possible genetic combinations . The optimal number of reference genes that should be used for accurate
normalisation also depends on the specific experimental condition, which is determined by calculating V-values
as a pairwise variation (Vn/Vn+1) between two consecutively ranked normalisation factors (NF) after the
stepwise addition of the subsequent more stable reference gene (NFn and NFn+1) (Vandesompele et al., 2002).
Actually, the geNorm is part of qBASEPlus (Biogazelle) program as tool important to provide the reference
genes more stables (M value) and the number of genes suitable to normalisation (V value). Furthermore, the
qBASEPlus (Biogazelle) also provides the relative expression on qPCR experiments based on the normalisation
factor (NF). The use of the qBASEPlus (Biogazelle) is needed at least 8 reference genes and at least 2 samples
(control and conditions) for to analyze the qPCR data.
3.2 Normfinder Analysis
The NormFinder is an algorithm used to identify the optimal normalisation gene among a set of candidates. It
ranks the set of candidate normalisation genes according to their expression stability in a determined sample set
and given experimental design. This algorithm is rooted in a mathematical model of gene expression and uses a
solid statistical framework to estimate not only the overall expression variation of the candidate normalisation
genes, but also the variation between sample subgroups of the sample set e.g. normal and cancer samples
(Andersen & Orntoft, 2004). Notably, “NormFinder” provides a stability value for each gene, which is a direct
measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced
when using the gene for normalisation (Dekkers et al., 2012; Selim et al., 2012).
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3.3 Bestkeeper Analysis
The BestKeeper calculates standard deviation (SD) and the coefficient of variation (CV) based on Cq values of
all reference candidate genes. Genes with SD less than 1 are considered stable. Subsequently, the program
calculates a pairwise correlation coefficient between each gene and the BestKeeper index–geometric mean
between Ct values of stable genes grouped together. Genes with the highest coefficient of correlation with the
BestKeeper Index indicates the highest stability (Pfaffl et al., 2004). The BestKeeper use raw Ct data and
determines the most stably expressed genes based on a correlation coefficient (r) of the BestKeeper Index (BI)
and standard deviation, whereas BI is the geometric mean of Ct values of best reference genes. Hence, this
program relies on the “r” and “SD” values, and the higher the "r" value, the most stable is the gene; otherwise,
the lower the standard deviation value, the most stable is the gene (Pfaffl et al., 2004; Demidenko et al., 2011;
Niu et al., 2011; Petit et al., 2012).
These statistical algorithms have been developed for the evaluation of best suited reference gene(s) for
normalisation of qPCR data in a set of biological samples. Recognizing the importance of reference genes in
normalisation of qPCR data, various reference genes have been evaluated for their stable expression under
specific conditions in various organisms. Many studies have been conducted in the animal and human health (De
Boever et al., 2008; Hong et al., 2008) fields that describe the identification of multiple reference genes for
normalisation of qPCR data, but similar reports are scarce in plant research (Ransbotyn et al., 2006;
Exposito-Rodriguez et al., 2008).
The three algorithms are important for reference gene stability and normalisation data during qPCR experiments;
however, geNorm is the best tool since in addition to providing the best reference genes in geNorm M, this
software supplies the V value, which delivers the number of genes needed for use in normalisation data in a
qPCR experiment. The algorithms NormFinder and BestKeeper will only identify the most stable genes.
Generally, all three algorithms are used to render more reliable results for normalisation.
5. Essential Rules Required to Perform a Reliable qPCR
The efficiency and specificity of quantitative PCR depends on several parameters related to quantification of
mRNA, which must be controlled to avoid errors of interpretation: purification of RNA, efficiency of primer
specifics, normalisation of reference genes, tissue inhibitory factors, enzyme loading error (Rocha, Miranda, &
Cunha, 2014), pipetting errors, among others (Thellin et al., 1999; Livak et al., 2001; Suzuki et al., 2000;
Vandesompele et al., 2002) as described below.
5.1 Primers and Probes Design Considerations
The primers and probes design are essentials for amplification efficiency, specificity and fluorescence, respectively.
The primers are specially needed in junction exon-exon to avoid an amplification of DNA genomic, ensuring the
amplification of only a target gene specific cDNA sequence. In addition, it may be necessary to digest input DNA
with an RNase free DNA in the following circumstances: (1) to avoid DNA amplification during qPCR; (2) to use
primers that either flank an intron that is not present in the mRNA sequence or that span an exon-exon junction; (3)
when the gene of interest has no introns; (4) if the intron positions are unknown; (5) when there are no suitable
primers that span or flank introns (Udvardi et al., 2004). There are several programs used to design automatic
primers, such as Perl Primer (Marshall, 2004) that will require previous annotation of genes, establishing the introns
and exons of each sequence to input the program. Other programs are available in the web as primer BLAST, a tool
available at http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_ LOC=BlastHome in the GenBank of
NCBI, as well as Primer3 Plus available at http://www.bioinformatics.nl/ cgi-bin/primer3plus/primer3plus.cgi/.
Moreover, an absence of primer-dimer and non-specific amplification is especially important to suitable data of
qPCR. Therefore, the presence of homo-dimers, hetero-dimers, as well as self-dimers must be avoided, and the
formation of harpin of a forward or reverse primer (Condori et al., 2001; Rocha et al., 2013).
The probes, such as TaqMan®, Molecular beacon and scorpions are primers marked with fluorophores to emit
fluorescence. These probes are designed in different forms, but are used with the common purpose of emitting
fluorescence to assess the increase on gene expression due to the number of probes that bind a double-stranded
DNA (Bustin et al., 2002; Pabla et al., 2008; VanGuilder et al., 2008; Hwang et al., 2013). There are programs,
such as primer BLAST, available in the GenBank of NCBI, as well as Primer3 Plus both available in the web.
These are the same programs used to design primers and probes (Condori et al., 2001).
5.2 RNA Quality
The quality of RNA also is very important to provide accurate qPCR data. The quality of RNA depends on
extraction and purification of RNA, for example, during the extraction of RNA contaminants such as proteins,
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carbohydrate, as well as phenolic compounds that will affect the PCR reaction by inhibiting the action of
polymerases as reverse transcriptase and DNA polymerases, during qPCR must be avoided. Therefore, RNA of
good quality is needed for further experiments. According to Sambrook et al. (1989), the best relations
absorbance by spectrophotometer are as follows: RNA relation to A260/280: 1.8-2.0, which is the acceptable limit
of contamination with proteins, and A260/230: > 2,0 for contamination with carbohydrates. These data have also
been reported by Sambrook et al. (1989) and Romano (1998). Furthermore, to avoid contamination with
genomic DNA, Digest purified RNA with DNase I is needed to remove contaminating genomic DNA, which can
act as template during PCR and may lead to spurious results. It may also be necessary to perform PCR on the
treated RNA, using gene-specific primers, to confirm absence of genomic DNA (Udvardi et al., 2008).
To complete a reliability of the extracted and purified RNA, the integrity of the RNA requires evaluation. The
measure of RNA reliability is based on the integrity of 28S and 18S ribosomal RNA and the lack thereof shows a
smear in the agarose gel, indicating that the total RNA is degraded. Thus, an electrophoresis in agarose gel at
0.8% to 1.0% is recommended to observe the integrity of the ribosomal RNA bands (Sambrook et al., 1989).
5.3 Optimization and Efficiency Curve of Primers
Other parameters such as the optimization of primer concentrations and efficiency primer curves that might be done
in serial dilutions or standard curves are important to perform qPCR assays. A dilution series of known template
concentrations can be used to establish a standard curve for determining the initial starting amount of the target
template or for assessing the reaction efficiency. The log of each known concentration in the dilution series is
plotted against the Cq value for that concentration. Information on the performance of the reaction as well as
various reaction parameters (including slope, y-intercept, and correlation coefficient) can be derived from this
standard curve. The slope is obtained by the linear equation of the graph constructed by plotting the Cq values on
the y-axis and the log values of the dilutions on the x-axis. The concentrations chosen for the standard curve should
encompass the expected concentration range of the target (Pfaffl et al., 2004). At the end of the qPCR assay, primer
efficiency must be calculated, and the formula most frequently described in the literature for this purpose is as
follows: Efficiency = 10(–1/slope)-1, in which the slope corresponds to the Cq value of the first dilution (concentration
dilution) minus the Cq value of the last dilution divided by the number of dilutions. Hence, if the PCR is 100%
efficient, the amount of PCR product will double with each cycle and the slope of the standard curve will be –3.33
(100 = 100% = 10(–1/–3.33)-1). The ideal slope is approximately -3.33 cycles; however, a slope between –3.9 and –3.0
(80-110% efficiency) is generally acceptable (Livak et al., 2001; Pfaffl et al., 2004). Calculated levels of target
input may not be accurate if the reaction is not efficient. In order to improve efficiency, one must consider either (1)
optimize primer concentrations or (2) design alternative primers.
Since SYBR Green binding dye is a non-specific dye that will detect any double-stranded DNA, it is important
to verify if the qPCR is producing only the desired product. This can often be detected when PCR efficiencies
are larger than 120% (Bustin et al., 2002; Bustin et al., 2009). Melting or dissociation curve is expressed during
the last step of qPCR, following 40 cycles that only show one peak, revealing that a single multigene family
isoform was amplified. These analyses can also be used to determine the approximate product size (Udvardi et
al., 2004). If the melting curve has more than one major peak, the identities of the products should be determined
by fractionating them on an ethidium DNA agarose gel electrophoresis to check for the presence of non-specific
annealing. It must also be mentioned that lowering the primer concentrations will often reduce the amount of
non-specific products. If the use of low primer levels still allow the detection of non-specific products in
significant amounts, primer redesign may be a necessary measure. Once all cycles have been completed, the
melting curve is added to evaluate the specificity of the primers. Melting curves with peaks lower than 78°C
could indicate the presence of primer dimmers in the reaction or alternatively smaller non-specific amplicon
products (Condori et al., 2001).
5.4 Normalisation and Analysis of qPCR Results
In gene-expression profile quantification, an assessment of the reliability of qPCR assay is required to normalize the
target gene expression data. One of the most frequently used methods is the utilization of reference genes. Previous
to the qPCR assay, it is necessary to design primers that amplify constitutive genes. The groups of reference genes
are checked for stability to identify the most stable reference genes among all the selected genes that will be used to
normalize the qPCR data, using programs such as geNorm, BestKeeper and NormFinder. Once the best reference
genes are identified, data normalisation is required to ensure gene expression reliability (Zhong et al., 2011).
Likewise, to confirm reliability of the results, biological and technical replicates must be obtained to provide data
statistics, and evaluate the significance levels of gene expression analysis (Udvardi et al., 2004).
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Relative quantification describes a real-time PCR experiment in which the gene of interest in one sample (i.e.,
treated) is compared to the same gene in another sample (i.e., untreated). The results are expressed as fold up- or
down-regulation of the treated in relation to the untreated gene. Reference genes such as β-actin, GAPDH,
elongation factor, among others are used as a control for experimental variability in this type of quantification
(Tong et al., 2009). The most frequently used method for relative mRNA quantification by real time PCR has been
described by Livak et al. (2001). This is a convenient method which presents the advantage of eliminating the need
for standard curves. Thus, mathematical equations are used to calculate the relative expression levels of target
relative to reference control or calibration, such as an untreated sample or RNA from normal tissue or a sample at
time zero at qPCR experiments in time-course study. The amount of target gene in the sample normalized to a
reference gene, relative to the normalized calibrator, is then given: 2-∆∆Cq, where ∆∆Cq= Cq (sample)- Cq
(calibrator), and Cq is the Cq of the target gene subtracted from the reference gene Cq, as describe by (Livak et al.,
2001; Schmittgen et al., 2000; Schmittgen et al., 2008). In order to obtain reliable results, the target and reference
gene must be approximately equal, and preferably at a percentage greater than 90%. This level of sequence equality
is necessary to plot an efficiency curve based on the dilution serial method to given suitable results in experimental
data, as described above. Finally, statistics methods, including student t-test, ANOVA, among others, must be
applied to the concluding analysis. However, this method of Livak et al (2001) is limited due the use of only one
reference gene. Actually, has been used more than one reference genes to normalisation data qPCR using algorithm
that is based in the normalisation factor (NF) method, as geNorm, BestKeeper and NormFinder (Vandesompele et
al., 2002; Andersen & Orntoft, 2004; Pfaffl et al., 2004) as described above.
6. Several Advances on the Real Time PCR
Several researchers are developing techniques to improve the quality of detection of DNA fluorescence.
Recently, the manufacturer ELITE MGB™ has done a revolutionary advance in qPCR chemistry. The principle
is based in the proprietary of the protein called Minor Groove Binder (MGB), Superbases™ and Eclipse®Dark
Quencher technologies. These overlapping probes are much efficiently and accurately detect target DNA
sequences, while offering greater sensitivity and specificity. According to manufacturer, the MGB protein is a
synthetic molecule that binds to the minor groove of double stranded DNA molecules. In qPCR applications,
MGB increases the stability of double stranded DNA complexes, specifically, the hybridization between the
probe and the amplified DNA target. The increased DNA-DNA hybrid stability allows the design of shorter
detection probes with higher specificity. Furthermore, The Eclipse®Dark Quencher is a proprietary fluorophore
and dye quencher chemistry resulting in low background signals. Its key benefit is to ensure that every ELITe
MGB™ assay will have the highest sensitivity by minimizing background signal interference. Together, show
Real-Time PCR results of high accuracy.
Other works have shown important improving in the qPCR. Zheng et al. (2011) designed an aptamer-based sensing
platform using a triple-helix molecular switch (THMS). The THMS consists of a central, target-specific aptamer
sequence flanked by two arm segments and a dual-labeled oligonucleotide serving as a signal-transduction probe
(STP). The STP is doubly labeled with pyrene at both ends and designed as a hairpin-shaped structure. Initially, the
loop sequence of the STP binds with two arm segments of the aptamer, which forces the STP to form an ‘‘open’’
configuration and separate the two end labeled pyrene molecules, thus only emitting monomer fluorescence signal.
The formation of the aptamer/target complex releases the STP, which switches to a ‘‘closed’’ hairpin configuration,
bringing two pyrene molecules in close proximity and emitting excimer fluorescence signal. Hu et al. (2014)
developed a modified Molecular Beacons–based multiplex qPCR Assay. In their work, all sets of primers and
probes were combined, and the concentration of each reagent including primers, probes, magnesium, and Taq
polymerase concentrations in the reaction mix were optimized. These modifications helped the sensitivity and
specificity of the qPCR multiplex that were 100% and 99%, respectively.
Any need for fast and precise measurement of small amounts of nucleic acids represents a potential future niche
for real-time PCR-based innovations. As machines become faster, cheaper, smaller, and easier to use through
competition, standardized assay development, and advances in microfluidics (Mitchell et al., 2001), optics, and
thermocycling, more in-field application needs are likely to be filled. In the commercial food industry and
agriculture, real-time PCR will likely see expanded use for the detection and identification of microbes,
parasites, or genetically modified organisms. Forensics will benefit from real-time PCR's sensitivity, specificity,
and speed, especially because time is crucial to many criminal investigations and specimen size may be limited.
Reduced cost and increased portability open the door for the diagnosis of diseases in remote areas along with
on-site epidemiological studies and may facilitate the transfer of needed scientific technologies to developing
countries, thereby contributing to their “scientific capacity”.
www.ccsenet.org/jmbr Journal of Molecular Biology Research Vol. 5, No. 1; 2015
52
7. Final Considerations
The polymerase chain reaction (PCR) is one of the most powerful technologies in molecular biology. qPCR is an
efficient tool to measure the levels of mRNA expression in different types of samples; their use together with the
reference genes are ideal for decreasing the possible errors in RNA extraction and contamination during sample
manipulation, thus increasing the quality of cDNA. The qPCR has the sensible technical power to amplify target
specific genes, but it is necessary to obtain reliable results in the gene expression profile. Several parameters must
be considered, including good design of primers, evaluating their specificity and efficiency. In addition, the RNA
extracted must be free of contaminants, such as carbohydrates, proteins and phenols, because these may interfere
with the polymerases during PCR reaction. For the normalisation of qPCR data, the use of reference gene is needed
to provide suitable results and reproducibility. Thus, the selection of a reference gene for each experimental
condition is crucial. These precautions are pivotal to render reliable results during gene expression analysis.
Acknowledgements
The authors are grateful to CNPq and CAPES. This work was supported by the Universidade Federal do
Ceará-UFC.
References
Andersen, C. L., Jensen, J. L., & Ørntoft, T. F. (2004). 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, 64(15), 5245-5250.
http://dx.doi.org/10.1158/0008-5472.CAN-04-0496
Barsalobres-Cavallari, C. F., Severino, F. E., Maluf, M. P., & Maia, I. G. (2009). Identification of suitable internal
control genes for expression studies in Coffea arabica under different experimental conditions. BMC
molecular biology, 10(1), 1. http://dx.doi.org/10.1186/1471-2199-10-1
Bustin, S. A. (2002). Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and
problems. Journal of molecular endocrinology, 29(1), 23-39. http://dx.doi.org/10.1677/jme.0.0290023
Bustin, S. A., Benes, V., Garson, J. A., Hellemans, J., Huggett, J., Kubista, M., ... & Wittwer, C. T. (2009). The
MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clinical
chemistry, 55(4), 611-622. http://dx.doi.org/10.1373/clinchem.2008.112797
Condori, J., Nopo-Olazabal, C., Medrano, G., & Medina-Bolivar, F. (2011). Selection of reference genes for qPCR
in hairy root cultures of peanut. BMC research notes, 4(1), 392. http://dx.doi.org/10.1186/1756-0500-4-392
Cordoba, E. M., Die, J. V., González-Verdejo, C. I., Nadal, S., & Román, B. (2011). Selection of reference genes
in Hedysarum coronarium under various stresses and stages of development. Analytical biochemistry, 409(2),
236-243. http://dx.doi.org/10.1016/j.ab.2010.10.031
De Boever, S., Vangestel, C., De Backer, P., Croubels, S., & Sys, S. U. (2008). Identification and validation of
housekeeping genes as internal control for gene expression in an intravenous LPS inflammation model in
chickens. Veterinary immunology and immunopathology, 122(3), 312-317. http://dx.doi.org/10.1016/j.
vetimm.2007.12.002
Dekkers, B. J., Willems, L., Bassel, G. W., van Bolderen-Veldkamp, R. M., Ligterink, W., Hilhorst, H. W., &
Bentsink, L. (2012). Identification of reference genes for RT–qPCR expression analysis in Arabidopsis and
tomato seeds. Plant and Cell Physiology, 53(1), 28-37. http://dx.doi.org/10.1093/pcp/pcr113
Demidenko, N. V., Logacheva, M. D., & Penin, A. A. (2011). Selection and validation of reference genes for
quantitative real-time PCR in buckwheat (Fagopyrum esculentum) based on transcriptome sequence data.
PLoS One, 6(5), e19434. http://dx.doi.org/10.1371/journal.pone.0019434
Dheda, K., Huggett, J. F., Bustin, S. A., Johnson, M. A., Rook, G., & Zumla, A. (2004). Validation of
housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques, 37, 112-119.
Expósito-Rodríguez, M., Borges, A. A., Borges-Pérez, A., & Pérez, J. A. (2008). Selection of internal control
genes for quantitative real-time RT-PCR studies during tomato development process. BMC Plant Biology,
8(1), 131. http://dx.doi.org/10.1186/1471-2229-8-131
Foss, D. L., Baarsch, M. J., & Murtaugh, M. P. (1998). Regulation of hypoxanthine phosphoribosyltransferase,
glyceraldehyde3phosphate dehydrogenase and βactin mRNA expression in porcine immune cells and
tissues. Animal biotechnology, 9(1), 67-78. http://dx.doi.org/10.1080/10495399809525893
www.ccsenet.org/jmbr Journal of Molecular Biology Research Vol. 5, No. 1; 2015
53
Hamalainen, H. K., Tubman, J. C., Vikman, S., Kyrölä, T., Ylikoski, E., Warrington, J. A., & Lahesmaa, R.
(2001). Identification and validation of endogenous reference genes for expression profiling of T helper cell
differentiation by quantitative real-time RT-PCR. Analytical biochemistry, 299(1), 63-70. http://dx.doi.org/
10.1006/abio.2001.5369
Holland, M. J. (2002). Transcript abundance in yeast varies over six orders of magnitude. Journal of Biological
Chemistry, 277(17), 14363-14366. http://dx.doi.org/10.1074/jbc.C200101200
Hong, S. Y., Seo, P. J., Yang, M. S., Xiang, F., & Park, C. M. (2008). Exploring valid reference genes for gene
expression studies in Brachypodium distachyon by real-time PCR. BMC plant biology, 8(1), 112.
http://dx.doi.org/10.1186/1471-2229-8-112
Hwang, S., Kang, B., Hong, J., Kim, A., Kim, H., Kim, K., & Cheon, D. S. (2013). Development of duplex real
time RT PCR based on Taqman technology for detecting simultaneously the genome of pan
enterovirus and enterovirus 71. Journal of medical virology, 85(7), 1274-1279. http://dx.doi.org/10.1002/
jmv.23588
Jain, M., Nijhawan, A., Tyagi, A. K., & Khurana, J. P. (2006). Validation of housekeeping genes as internal
control for studying gene expression in rice by quantitative real-time PCR. Biochemical and biophysical
research communications, 345(2), 646-651. http://dx.doi.org/10.1016/j.bbrc.2006.04.140
Kumar, K., Muthamilarasan, M., & Prasad, M. (2013). Reference genes for quantitative real-time PCR analysis in
the model plant foxtail millet (Setaria italica L.) subjected to abiotic stress conditions. Plant Cell, Tissue and
Organ Culture (PCTOC), 115(1), 13-22. http://dx.doi.org/10.1007/s11240-013-0335-x
La Cruz, S., Lopez-Calleja, M. I., Alcocer, M., González, I., Martín, R., & García, T. (2013). TaqMan real-time
PCR assay for detection of traces of Brazil nut (Bertholletia excelsa) in food products. Food control, 140,
382-389. http://dx.doi.org/10.1016/j.foodcont.2013.01.053
Lee, J. M., Roche, J. R., Donaghy, D. J., Thrush, A., & Sathish, P. (2010). Validation of reference genes for
quantitative RT-PCR studies of gene expression in perennial ryegrass (Lolium perenne L.). BMC Molecular
Biology, 11(1), 8. http://dx.doi.org/10.1186/1471-2199-11-8
Livak, K. J., & Schmittgen, T. D. (2001). Analysis of relative gene expression data using real-time quantitative
PCR and the 2 ΔΔCT method. methods, 25(4), 402-408. http://dx.doi.org/10.1006/meth.2001.1262
Mallona, I., Lischewski, S., Weiss, J., Hause, B., & Egea-Cortines, M. (2010). Validation of reference genes for
quantitative real-time PCR during leaf and flower development in Petunia hybrida. BMC Plant Biology,
10(1), 4. http://dx.doi.org/10.1186/1471-2229-10-4
Marshall, O. J. (2004). PerlPrimer: cross-platform, graphical primer design for standard, bisulphite and real-time
PCR. Bioinformatics 20(15): 2471–2472. http://dx.doi.org/10.1093/bioinformatics/bth254
Mitchell, P. (2001). Microfluidics-downsizing large-scale biology. Nature biotechnology, 19(8), 717-721.
http://dx.doi.org/10.1093/bioinformatics/bth254
Mullis, K. B., & Faloona, F. A. (1987). Specific synthesis of DNA in vitro via a polymerase-catalyzed chain
reaction. Methods in enzymology, 155, 335-350. http://dx.doi.org/10.1016/0076-6879(87)55023-6
Ng, C. T., Gilchrist, C. A., Lane, A., Roy, S., Haque, R., & Houpt, E. R. (2005). Multiplex real-time PCR assay
using Scorpion probes and DNA capture for genotype-specific detection of Giardia lamblia on fecal samples.
Journal of clinical microbiology, 43(3), 1256-1260. http://dx.doi.org/10.1128/JCM.43.3.1256-1260.2005
Niu, J. Z., Dou, W., Ding, T. B., Yang, L. H., Shen, G. M., & Wang, J. J. (2012). Evaluation of suitable reference
genes for quantitative RT-PCR during development and abiotic stress in Panonychus citri (McGregor)(Acari:
Tetranychidae). Molecular biology reports, 39(5), 5841-5849. http://dx.doi.org/10.1007/s11033-011-1394-x
Pabla, S. S., & Pabla, S. S. (2008). Real-time polymerase chain reaction. Resonance, 13(4), 369-377.
http://dx.doi.org/10.1007/s12045-008-0017-x
Petit, C., Pernin, F., Heydel, J. M., & Délye, C. (2012). Validation of a set of reference genes to study response to
herbicide stress in grasses. BMC research notes, 5(1), 18. http://dx.doi.org/10.1186/1756-0500-5-18
Pfaffl, M. W., Tichopad, A., Prgomet, C., & Neuvians, T. P. (2004). Determination of stable housekeeping genes,
differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise
correlations. Biotechnology letters, 26(6), 509-515. http://dx.doi.org/10.1023/B:BILE.0000019559.84305.47
www.ccsenet.org/jmbr Journal of Molecular Biology Research Vol. 5, No. 1; 2015
54
Ransbotyn, V., & Reusch, T. B. (2006). Housekeeping gene selection for quantitative realtime PCR assays in
the seagrass Zostera marina subjected to heat stress. Limnology and Oceanography: Methods, 4(10),
367-373. http://dx.doi.org/10.4319/lom.2006.4.367
Rebouças, E. D. L., Costa, J. J. D. N., Passos, M. J., Passos, J. R. D. S., Hurk, R. V. D., & Silva, J. R. V. (2013).
Real time PCR and importance of housekeepings genes for normalization and quantification of mRNA
expression in different tissues. Brazilian Archives of Biology and Technology, 56(1), 143-154.
http://dx.doi.org/10.1590/S1516-89132013000100019
Reid, K. E., Olsson, N., Schlosser, J., Peng, F., & Lund, S. T. (2006). An optimized grapevine RNA isolation
procedure and statistical determination of reference genes for real-time RT-PCR during berry development.
BMC plant biology, 6(1), 27. http://dx.doi.org/10.1186/1471-2229-6-27
Rocha, A. J., Miranda, R., & Cunha, R. M. S. (2014). Avaliação de DNA polimerases em ensaios de amplificação
de microssatélites através do PowerPlex® 16 BIO System. BBR-Biochemistry and Biotechnology Reports,
3(2), 1-8. http://dx.doi.org/10.5433/2316-5200.2014v3n2p1
Rocha, A. J., Soares, E. L., Costa, J. H., Costa, W. L., Soares, A. A., Nogueira, F. C., ... & Campos, F. A. (2013).
Differential expression of cysteine peptidase genes in the inner integument and endosperm of developing
seeds of Jatropha curcas L.(Euphorbiaceae). Plant science, 213, 30-37. http://dx.doi.org/10.5433/2316-5200.
2014v3n2p1
Romano, E., Brasileiro, A. C. M. (1998). Extração de DNA de Tecidos Vegetais. In A. C. M. Brasileiro, & V. T.
C. (Eds.), Carneiro Manual de transformações Genéticas De Plantas. Editora Embrapa: Brasília v. 40-43,
1998.
Saha, G. C., & Vandemark, G. J. (2013). Stability of expression of reference genes among different Lentil (Lens
culinaris) genotypes subjected to cold stress, white mold disease, and aphanomyces root rot. Plant Molecular
Biology Reporter, 31(5), 1109-1115. http://dx.doi.org/10.1007/s11105-013-0579-y
Saiki, R. K., Scharf, S., Faloona, F., Mullis, K. B., Horn, G. T., Erlich, H. A., & Arnheim, N. (1985). Enzymatic
amplification of beta-globin genomic sequences and restriction site analysis for diagnosis of sickle cell
anemia. Science, 230(4732), 1350-1354. http://dx.doi.org/10.1126/science.2999980
Sambrook, J., Fritsch, E. F., & Maniatis, T. (1989). Molecular cloning (Vol. 2, pp. 14-9). New York: Cold spring
harbor laboratory press.
Schmittgen, T. D., & Livak, K. J. (2008). Analyzing real-time PCR data by the comparative CT method. Nature
protocols, 3(6), 1101-1108. http://dx.doi.org/10.1038/nprot.2008.73
Schmittgen, T. D., & Zakrajsek, B. A. (2000). Effect of experimental treatment on housekeeping gene expression:
validation by real-time, quantitative RT-PCR. Journal of biochemical and biophysical methods, 46(1), 69-81.
http://dx.doi.org/10.1016/S0165-022X(00)00129-9
Selim, M., Legay, S., Berkelmann-Löhnertz, B., Langen, G., Kogel, K. H., & Evers, D. (2012). Identification of
suitable reference genes for real-time RT-PCR normalization in the grapevine-downy mildew pathosystem.
Plant cell reports, 31(1), 205-216. http://dx.doi.org/10.1007/s00299-011-1156-1
Suzuki, T., Higgins, P. J., & Crawford, D. R. (2000). Control selection for RNA quantitation. Biotechniques,
29(2), 332-337.
Thellin, O., Zorzi, W., Lakaye, B., De Borman, B., Coumans, B., Hennen, G., ... & Heinen, E. (1999).
Housekeeping genes as internal standards: use and limits. Journal of biotechnology, 75(2), 291-295.
http://dx.doi.org/10.1016/S0168-1656(99)00163-7
Tong, Z., Gao, Z., Wang, F., Zhou, J., & Zhang, Z. (2009). Selection of reliable reference genes for gene
expression studies in peach using real-time PCR. BMC Molecular Biology, 10(1), 71. http://dx.doi.org/10.
1186/1471-2199-10-71
Udvardi, M. K., Czechowski, T., & Scheible, W. R. (2008). Eleven golden rules of quantitative RT-PCR. The
Plant Cell Online, 20(7), 1736-1737. http://dx.doi.org/10.1105/tpc.108.061143
Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., & Speleman, F. (2002).
Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal
control genes. Genome biology, 3(7), research0034. http://dx.doi.org/10.1186/gb-2002-3-7-research0034
VanGuilder, H. D., Vrana, K. E., & Freeman, W. M. (2008). Twenty-five years of quantitative PCR for gene
expression analysis. Biotechniques, 44(5), 619-626. http://dx.doi.org/10.2144/000112776
www.ccsenet.org/jmbr Journal of Molecular Biology Research Vol. 5, No. 1; 2015
55
Wong, M. L., & Medrano, J. F. (2005). Real-time PCR for mRNA quantitation. Biotechniques, 39(1), 75-85.
http://dx.doi.org/10.2144/05391RV01
Zheng, J., Li, J., Jiang, Y., Jin, J., Wang, K., Yang, R., & Tan, W. (2011). Design of aptamer-based sensing
platform using triple-helix molecular switch. Analytical chemistry, 83(17), 6586-6592. http://dx.doi.org/10.
2144/05391RV01
Zhong, H. Y., Chen, J. W., Li, C. Q., Chen, L., Wu, J. Y., Chen, J. Y., ... & Li, J. G. (2011). Selection of reliable
reference genes for expression studies by reverse transcription quantitative real-time PCR in litchi under
different experimental conditions. Plant cell reports, 30(4), 641-653. http://dx.doi.org/10.1007/s00299-010
-0992-8
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This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution
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... Reference genes are used for expression level normalization across multiple tissues or experimental conditions and should therefore be invariable (at least in those defined conditions). Hence, variation of reference gene expression levels can lead to under-or overestimation of the genes under study and consequently to incorrect conclusions (Rocha et al., 2015). ...
... Internal controls are selected from protein-coding genes that are involved in basic cellular processes and are expected to be uniformly expressed across different tissues, developmental stages and experimental conditions. However, evidence suggests that some of the most widely used references genes are not reliable internal controls, because their expression levels are variable (Livak and Schmittgen, 2001;Rocha et al., 2015;Vandesompele et al., 2002). With respect to R. communis, there are reports on reference gene validation for seed endosperm, cotyledon, roots and leaves. ...
... We selected candidate reference genes based on a literature search for reference genes commonly used in R. communis and other plant species, such as Arabidopsis thaliana, Solanum lycopersicum and Jatropha curcas L. (Arroyo-Caro et al., 2013;Cagliari et al., 2010;Chen et al., 2007;Eastmond, 2004;Fan et al., 2013;Gu et al., 2012;Le et al., 2012;Li et al., 2012;Loss-Morais et al., 2013;Rapacz et al., 2012;Rocha et al., 2013Rocha et al., , 2015, 2017. We selected nine of those genes for analysis in R. communis seed tissues, namely actin-11, cliclofilin, elongation factor 1-alpha (EF1-α), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), polyubiquitin (PUB), serine/threonine protein phosphatase 2a2 regulatory subunit A (PP2A2), tubulin alpha-2 (Tα2), tubulin beta (Tβ2) and ubiquitin-conjugating protein (UCP). ...
... However, with careful consideration of the above factors, the rabbit hypercholesterolaemia model can be very valuable for use in human disease research. Quantitative PCR (qPCR) is the most widely used experimental method for detecting gene expression, and is currently one of the most accurate, sensitive, and specific methods available [6]. However, many factors can influence the reliability of qPCR analysis, including purity and quality of RNA, reverse-transcriptase efficiency, and PCR efficiency [7]. ...
... Although all five algorithms are suitable, RefFinder and GeNorm are considered the best algorithms for evaluation of the most stable reference gene(s) because RefFinder yields more accurate results and GeNorm determines the optimal number of reference genes needed for normalization of qPCR data. The other three algorithms are not comprehensive enough or will only identify one stable reference gene, as has been covered by previous studies and reviews related to the evaluation of stable reference gene(s) in various organisms [6,[22][23][24][25]. ...
... The stability of reference genes is affected by several factors, including tissue type and specific experimental conditions [6,27]. As shown in many previous studies, these factors may have different impacts on the expression of reference genes, meaning that some reference genes are more stable than others in a particular tissue under specific conditions [7,8,27,28]. ...
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... As a result, the minimum (min) value of miR-16 in Berkshire is the lowest being 21.33 and the highest value of miR-107 is 34.84 (Fig. 1a). The reference normalizer should present high gene expression and the standard deviation should be < 1 [22]. In Fig. 1, the mean value of miRNAs is highest for miR-16 of 22.79 in the Berkshire, but not as a candidate reference miRNA, because the standard deviation is 1.18. ...
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... Any error in selecting a suitable reference gene may lead to misleading results. Hence, selecting a reliable reference gene is necessary for molecular biology-oriented studies 9, [14][15][16][17] . The most commonly used references genes for normalisation of plant gene expression studies are ubiquitin (UBQ), β-tubulin (β-TUB), ribosomal RNA genes (18S rRNA and 25S rRNA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), eukaryotic elongation factor (eEF), eukaryotic translation initiation factor 1 (eIF1), actin (ACT ), acetyl-CoA carboxylase (ACCase) etc 9,18 . ...
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... Any error in selecting a suitable reference gene may lead to misleading results. Hence, selecting a reliable reference gene is necessary for molecular biology-oriented studies 9, [14][15][16][17] . The most commonly used references genes for normalisation of plant gene expression studies are ubiquitin (UBQ), β-tubulin (β-TUB), ribosomal RNA genes (18S rRNA and 25S rRNA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), eukaryotic elongation factor (eEF), eukaryotic translation initiation factor 1 (eIF1), actin (ACT ), acetyl-CoA carboxylase (ACCase) etc 9,18 . ...
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Bromus sterilis is an annual weedy grass, causing high yield losses in winter cereals. Frequent use of herbicides had led to the evolution of herbicide-resistance in this species. Mechanisms underlying herbicide resistance in B. sterilis must be uncovered because this problem is becoming a global threat. qRT-PCR and the next-generation sequencing technologies can contribute to elucidation of the resistance mechanisms. Although qRT-PCR can calculate precise fold changes, its preciseness depends on the expression of reference genes. Regardless of stable expression in any given condition, no gene can act as a universal reference gene. Hence, it is necessary to identify the suitable reference gene for each species. To our knowledge, there are no reports on the suitable reference gene in any brome species so far. Thus, in this paper, the stability of eight genes were evaluated using qRT-PCR experiments followed by expression stability ranking via five most commonly used softwares for reference gene selection. Our findings suggest using a combination of 18S rRNA and ACCase to normalise the qRT-PCR data in B. sterilis . Besides, reference genes are also recommended for different experimental conditions. The present study outcomes will facilitate future molecular work in B. sterilis and other related grass species.
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Real-time RT-PCR method was exploited to identify endogenous reference genes in differentiating human T helper cells. When using this technology in our experimental system, finding a set of genes whose mRNA expression levels would not change appeared to be very challenging. Our initial plan to use the expression level of GAPDH in normalizing the results failed, because the mRNA expression of GAPDH underwent significant changes during the cell culture. Additional studies on the transcription of several other classical housekeeping genes led to similar results. Our second approach was to use results from an extensive survey of gene expression done by oligonucleotide microarrays and to select another panel of genes for testing. This resulted in the identification of three genes whose expression was relatively stable in our experimental system and, therefore, suitable as endogenous reference genes in these cells. The results indicate that the expression level of a constitutively expressed gene may change during the cell culture in vitro, which emphasizes again the importance of carefully validating endogenous control genes for comparative quantification
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