Housekeeping gene validation for RT-qPCR studies on synovial fibroblasts derived from healthy and osteoarthritic patients with focus on mechanical loading

Ute Nazet; Agnes Schröder; Susanne Grässel; Dominique Muschter; Peter Proff; Christian Kirschneck

doi:10.1371/journal.pone.0225790

Abstract

Selection of appropriate housekeeping genes is essential for the validity of data normalization in reverse transcription quantitative PCR (RT-qPCR). Synovial fibroblasts (SF) play a mediating role in the development and progression of osteoarthritis (OA) pathogenesis, but there is no information on reliable housekeeping genes available. Therefore the goal of this study was to identify a set of reliable housekeeping genes suitable for studies of mechanical loading on SF from healthy and OA patients. Nine genes were evaluated towards expression stability and ranked according their relative stability determined by four different mathematical procedures (geNorm, NormFinder, BestKeeper and comparative ΔC_q). We observed that RPLP0 (ribosomal protein, large, P0) and EEF1A1 (eukaryotic translation elongation factor 1 alpha 1) turned out to be the genes with the most stable expression in SF from non-OA or OA patients treated with or without mechanical loading. According to geNorm two genes are sufficient for normalization throughout. Expression of one tested target gene varied considerably, if normalized to different candidate housekeeping genes. Our study provides a tool for accurate and valid housekeeping gene selection in gene expression experiments on SF from healthy and OA patients with and without mechanical loading in consistent with the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines and additionally demonstrates the impact of proper housekeeping gene selection on the expression of the gene of interest.

Citation: Nazet U, Schröder A, Grässel S, Muschter D, Proff P, Kirschneck C (2019) Housekeeping gene validation for RT-qPCR studies on synovial fibroblasts derived from healthy and osteoarthritic patients with focus on mechanical loading. PLoS ONE 14(12): e0225790. https://doi.org/10.1371/journal.pone.0225790

Editor: Ruslan Kalendar, University of Helsinki, FINLAND

Received: July 18, 2019; Accepted: November 12, 2019; Published: December 6, 2019

Copyright: © 2019 Nazet et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

For gene expression analysis there are currently three methods available: RNA-Seq, microarray analysis and RT-qPCR. RNA-Seq and microarray analysis were used to analyse many different gene expression profiles [1–5]. In contrast to these both methods reverse transcriptase quantitative PCR (RT-qPCR) allows to analyse the impact of various experimental conditions on the expression of one single gene [6–9]. Therefore RT-qPCR is still the method of choice for gene expression evaluation in most areas of molecular biology. Carefully chosen housekeeping genes guarantee precise gene expression quantification by accurate and valid data normalization [6,8,10]. One major feature of an optimal housekeeping gene is a small variation in expression across various experimental setups and cell types [8,11]. There are many studies dealing with the variation of expression stability of housekeeping genes across different experimental conditions or cell types [11,12]. The usage of not validated housekeeping genes in RT-pPCR studies can lead to potential bias and misinterpretation of experimental outcomes. To achieve conformity in RT-pPCR data evaluation, MIQE guidelines (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) were established several years ago [10]. Usage of these guidelines is proposed to enhance reproducibility of RT-qPCR results [10]. The choice of housekeeping genes has a direct impact on the results of target gene analysis by RT-qPCR. To investigate the stability of different housekeeping genes, some mathematical algorithms like geNorm [12], NormFinder [13], BestKeeper [14] and the comparative ΔC_q method [15,16] were developed.

The geNorm [12] algorithm calculates the mean pairwise variation of the C_q values of one tested housekeeping gene compared with all other tested genes and specifies that value as stability M. Housekeeping genes with elevated M values are suggested to have an higher pairwise variation. As enhanced pairwise variation correlates with instable expression ratios, housekeeping genes with higher M values are not ideal for normalization [12]. With geNorm it is also possible to define the minimum required amount of housekeeping genes for target gene normalization, as it computes the gene stability by average pairwise variation among internal control genes [12].

The NormFinder algorithm [13] calculates intra- and intergroup variation between the tested housekeeping genes. Furthermore it designates a conjoint stability value for each tested housekeeping gene applying a model-based approach [13]. In this mathematical algorithm increased gene expression stability is associated with decreasing stability values.

The comparative ΔC_q method evaluates housekeeping genes on the basis of the standard deviation of the average ΔC_q aberrations of each tested housekeeping gene to all other tested housekeeping genes. Therefore it collates relative housekeeping gene expression within groups of biological replicates and from all other tested housekeeping genes [15,16].

The BestKeeper [14] algorithm makes use of the standard deviation of mean C_q of each tested housekeeping gene and evaluates gene stability by pairwise bivariate correlations of C_q values of each gene using a “BestKeeper Index“. Stably expressed housekeeping genes are reported to have higher r values. Until now various studies were performed to assess appropriate housekeeping genes for different experimental setups and cell types, but there are still no reliable housekeeping genes for studies on human synovial fibroblasts published.

Synovial fibroblasts play a leading part in the maintenance of a healthy joint. The synovium encases articular joints throughout the human body and maintains the integrity of articular cartilage by regulating synovial fluid volume and composition producing lubricin and hyaluronic acid [17]. Among others, synovial fibroblasts make up the major cell population in the synovium of joints and play a critical mediating role in the development and progression of osteoarthritis (OA), since they are able to secrete proinflammatory cytokines [18] and express immune-receptors like toll-like receptors (TLR) [19,20]. The degenerative disease OA is associated with several afflictions such as chronic pain, articular cartilage degradation and subchondral bone remodelling as well as induced synovitis [21]. Aging, obesity, sport injuries, genetic predisposition [22] and mechanical overload [23,24] are reported to be risk factors for OA development. Also excessive mechanical loading on normal articular cartilage may lead to the development of OA by disruption of cartilage matrix homeostasis [24,25]. For several years, interest in the stromal-cell-like synovial fibroblasts has increased, as they were identified as key players in the innate immune-system-response, inflammation-related processes and intercellular actions and as principal performers involved in OA development and progression.

The study was designed to 1) identify a set of stably expressed housekeeping genes for human synovial fibroblasts derived from OA and non-OA patients, particularly in experiments with mechanical loading simulating OA pathogenesis and progression, 2) to assess the effects of different housekeeping genes used for normalization on target gene expression and 3) to compare various mathematical procedures used regarding their conformity. As mechanical stress loading is one major risk factor for the development of OA, we aimed to assess stable housekeeping genes for pressure application in both tested synovial fibroblast cell lines.

Materials and methods

In vitro cell culture experiment setup

Synovial fibroblasts from a healthy, non-OA patient were obtained directly from BioIVT (PCD-90-0645). Synovial fibroblasts from an OA patient were obtained and cultured from tissue to be discarded during knee surgery in the Department of Orthopedics at the University of Regensburg. The study was approved by the Ethics Committee of the Faculty of Medicine Regensburg (approval ID 12-170-0150) and written informed consent was obtained from the tissue donor.

Approximately 70,000 synovial fibroblasts per well, either derived from a non-OA or an OA patient, were seeded on a 6-well plate and preincubated under cell culture conditions for 24 h. Afterwards incubation was continued for another 48 h with or without mechanical loading according to an established and published model for inducing compressive force on adherently growing fibroblasts [8,26,27]:

1st group: synovial fibroblasts from a healthy, non-OA patient (N-SF), incubated under cell culture conditions for a total of 72 h (n = 6);
2nd group: synovial fibroblasts from a healthy non-OA patient (N-SF) exposed to static compressive force (2 g/cm² pressure, Fig 1) for 48 h after 24 h of preincubation (n = 6);
3rd group: synovial fibroblasts from an OA patient (OA-SF), incubated under cell culture conditions for a total of 72 h (n = 6);
4th group: synovial fibroblasts from an OA patient (OA-SF) exposed to static compressive force (2 g/cm² pressure, Fig 1) for 48 h after 24 h of preincubation (n = 6).

Download:

Fig 1. In vitro application of mechanical loading to synovial fibroblasts.

Application of a sterile glass disc of defined size and weight to the cell layer, exerting a compressive force of 2g/cm².

https://doi.org/10.1371/journal.pone.0225790.g001

RNA isolation and purity determination

In order to remove residual cell culture media, synovial fibroblasts were washed two times with phosphate-buffered saline (PBS). RNA was isolated using peqGOLD TriFast^™ (PEQLAB, 1 ml/well conforming with the manufacturer’s instructions [9,28,29]. The resulting RNA pellet was reconstituted in 25μl nuclease-free water (Carl-Roth) and immediately cooled on ice. Photometrical adsorption measurements at 280nm and 260nm revealed purity and quantity of the eluted RNA (NanoDrop, Implen). RNA integrity was determined using an Agilent 2100 Bioanalyzer (Agilent Technologies) based on the provided protocol of the manufacturer (S1 File). At least, one sample per group had to be excluded from further analysis due to poor or not measurable RNA integrity values (S2 File).

Reverse transcription (cDNA synthesis)

For cDNA synthesis we transcribed 100 ng RNA per sample using a combination of 0.1 nmol random hexamer primer (Life Technologies), 0.1 nmol oligo-dT18 primer (Life Technologies) mixed with 1×M-MLV-buffer (Promega), 40 nmol dNTP mix (Carl-Roth), 40 U RNase inhibitor (Life Technologies) and M-MLV reverse transcriptase (Promega) and added nuclease-free H₂O (Carl-Roth) to a final volume of 20 μl. We then incubated the samples at 37°C for 1 h and finally inactivated the reverse transcriptase at 95°C for 2 min. Experimental variation was reduced by simultaneous synthesis of cDNA for all samples.

Real-time quantitative RT-PCR

The used oligonucleotides were designed based on the gene sequences achieved from the Nucleotide database NCBI (GeneBank, National Centre for Biotechnology Information) and validated for absence of secondary structures, self and cross dimers as well as primer efficiency and specificity, as already described [8] (Table 1). Eurofins MWG Operon LLC (Huntsville; High Purity Salt Free Purification HPSF^®) was assigned for primer synthesis and purification.

Download:

Table 1. Information on tested genes, primer and target/amplicon for RT-qPCR.

https://doi.org/10.1371/journal.pone.0225790.t001

For each RT-qPCR reaction we used 7.5 μl SYBR^®Green JumpStart^™ Taq ReadyMix^™ (Sigma-Aldrich), 10 pmol/μl of the respective forward and reverse primer and 1.5 μl of the diluted cDNA (1:10). RNase-free H₂O (Carl-Roth) was added to a total volume of 15 μl. All cDNA samples were tested as three replicates per housekeeping gene and on the same 96 well PCR plate per biological replicate in 45 cycles (95°C for 5 min, per cycle 95°C for 10 s, 60°C for 8 s, 72°C for 8 s) to reduce possible inter-run variations on relative housekeeping gene stability assessment. Non-template controls and reverse transcription controls were additionally performed. For qPCR a Mastercycler^® ep realplex-S thermocycler (Eppendorf AG, Hamburg, Germany) was used in conjunction with 96-well PCR plates (Biozym Scientific) covered with BZO Seal Filmcover sheets (Biozym Scientific) [8,30,31].

Assessment of reference gene stability

We calculated C_q values with the realplex software (version 2.2, Eppendorf AG) using the CalqPlex algorithm. The arithmetic mean of each C_q triplet per tested housekeeping gene and sample was used for analysis. The stability for each tested potential housekeeping gene was assessed by four different mathematical procedures: geNorm [12], NormFinder [13], BestKeeper [14] and the comparative ΔC_q method [16]. We performed stability calculations with the official corresponding Microsoft-Excel-based software applets according to the developers’ instructions. The comparative ΔC_q method was executed by manual calculations [16]. The application of the geNorm and NormFinder algorithms requires a transformation of native C_q data to the linear scale expression quantities Q = E^{-(Cqmin-Cqsample)} computing the qPCR efficiency (E) of each gene [8,9,16]. For evaluation, the tested housekeeping genes were listed based on their stability values (geNorm: M, NormFinder: ρ_ig/σ_i, deltaCT: mean SD of ΔC_q; BestKeeper: Pearson’s r), determined by the chosen algorithms and set up. To assess the optimal number of houskeeping genes for solid RT-qPCR normalization, we used the geNorm [12] algorithm. A gene was considered superfluous for normalization, if the pairwise variation (V_n/V_n+1) of two pairs of housekeeping genes with one pair including an accessory gene was ≤ 0.15. By compilation of a bivariate correlation matrix (Pearson´s correlation coefficient r, two-sided, normality confirmed by Shapiro-Wilk tests and histogram evaluation) including the assessed stability values achieved by two respective algorithms, the variations between the tested mathematical procedures were ranked.

Normalization of target gene expression

To assess the impact of housekeeping gene stability on relative expression of the target gene prolyl-4-hydroxylase-alpha-1 (P4HA1), collagen-1-alpha-2 (COL1A), cylclooxygenase-2 (COX2) and interleukin-6 (IL6) we determined the relative gene expression as 2^-ΔCq [32] with ΔC_q = C_q (target gene)–C_q (housekeeping gene), divided by the respective arithmetic 2^-ΔCq mean of the untreated synovial fibroblast controls derived from a healthy subject with their relative gene expression normalized as 1. Using the software application SPSS^® Statistics 24 (IBM^®, Armonk, NY, USA), data were tested for normal distribution (Shapiro-Wilk test) and homogeneity of variance (Levene’s test). Experimental groups were compared by Welch-corrected one-way ANOVAs. We used Games–Howell post hoc tests for pairwise comparisons. All differences were considered statistically significant at p≤0.05. Descriptive statistics are given as arithmetic mean ± standard deviation.

Results

Quality and integrity of RNA samples

The mean concentration of harvested RNA (n = 20) was assessed by its optical density (260nm) as 25.5 ng/μl (standard deviation SD 10.8 / Min. 12.1 / Max. 46.4) with a mean OD_260nm/280nm ratio of 1.91 (SD 0.14 / Min. 1.82 / Max. 2.21) indicating a negligible contamination with protein (S1 Table). The RNA integrity number (RIN) algorithm allocates a RIN number score from 1 to 10 with a value of 10 representing completely intact RNA and a value of 1 degraded RNA [33]. Three samples displayed poor RIN values and one sample concentration was too low for RIN measurement. These samples were excluded from housekeeping gene analysis. For the other samples RIN values ranged from 9.3 to 9.8 (mean 9.2, SD 0.4), indicating negligible RNA degradation (S1 File). We also confirmed integrity of total RNA by assessing the ratio of 28S/18S ribosomal RNA, which ranged from 1.1 to 2.4 (S1 File). The negative controls (reverse transcription negative control, negative NTC reactions) did not show the presence of interfering genomic DNA and contamination, as the observed C_q values were substantially higher than those of of wells containing samples or reverse transcriptase (S2 Table).

Primer specificity and C_q expression levels

We confirmed primer specificity by agarose gel electrophoresis and melting curve analysis (S2 File). The range of observed C_q values of the tested genes was between 9.0 to 29.4 cycles (Fig 2, S3 Table). RNA18S showed the lowest values and TBP the highest.

Download:

Fig 2. Expression levels of tested housekeeping genes in the experimental set-up.

Values illustrate mean C_q (quantification cycle; n = 20). Gene names are listed in Table 1. Boxplots illustrate median, interquartile range as box and data range by whiskers. Circles show outliers and asterisms extreme values.

https://doi.org/10.1371/journal.pone.0225790.g002

Refinement of housekeeping gene number for normalization

For studies with synovial fibroblasts, two housekeeping genes in RT-qPCR in the tested experimental conditions were adequate for normalization according to the geNorm algorithm using their geometric mean (Fig 3a).

Download:

Fig 3. Analysis of expression stability of the tested housekeeping genes with geNorm.

(a) Ideal number of housekeeping genes for gene expression studies on synovial fibroblasts. V = variation (b) Average values of expression stability derived by successive exclusion of the most instable performed housekeeping gene across all specimens and experimental conditions (n = 20). Decreasing M values indicate increasing gene expression stability. Consult Table 1 for gene names.

https://doi.org/10.1371/journal.pone.0225790.g003

Relative performance as housekeeping gene

By using the geNorm algorithm, we found RPLP0 and GAPDH to be the most stably expressed housekeeping genes overall (Table 2, Fig 3b). Similarly, comparing N-SF and OA-SF fibroblasts without mechanical loading with the geNorm algorithm, we determined RPLP0, GAPDH and PPIB to have the lowest expression variation (Table 2). Additionally, we also analysed untreated N-SF and compressive-force-treated N-SF with this algorithm and revealed EEF1A1 and RPLP0 to be the most stably expressed housekeeping genes under these conditions (Table 2).

Download:

Table 2. Housekeeping gene stability ranking for human synovial fibroblast experiments with compressive force application (compressive force vs. untreated control), experiments on osteoarthritis (osteoarthritis vs. normal fibroblasts) and pooled/overall experimental conditions.

Calculations based on the algorithms geNorm, NormFinder, comparative ΔC_q and BestKeeper. A higher rank indicates reduced expression stability.

https://doi.org/10.1371/journal.pone.0225790.t002

The NormFinder algorithm identified RPLP0 and EEF1A1 as most stable genes overall (Table 2). NormFinder confirmed the geNorm findings for N-SF and OA-SF fibroblasts without compressive force application and for untreated and loaded N-SF (Table 2), as this algorithm also revealed RPLP0 and PPIB or EEF1A1 and RPLP0 as most stably expressed housekeeping genes.

The comparative ΔC_q method [16] was in line with NormFinder results (Table 2) regarding the combined conditions, as it also defined RPLP0 and EEF1A1 to be the most stably expressed genes. For the other tested conditions, the comparative ΔC_q method confirmed the findings of NormFinder and geNorm algorithms (Table 2).

The BestKeeper algorithm [14] was also in accordance with NormFinder and the comparative ΔC_q method suggesting EEF1A1 as the best housekeeping gene overall (Table 2). For experiments regarding N-SF and OA-SF experiments BestKeeper defined RNA18S and TBP as reliable housekeeping genes (Table 2). For N-SF treated with or without compressive force BestKeeper proposed EEF1A1 and TBP as suitable housekeeping genes (Table 2).

Conformity of used algorithms for housekeeping gene stability analysis

We performed bivariate correlations of gene rankings between the used algorithms to assess their conformity (Fig 4, n = 20). geNorm, NormFinder and the comparative ΔC_q method display significant correlations of the ranking of the tested housekeeping genes. Only BestKeeper algorithm showed discrepancies, as it did not correlate with the other algorithms used (Fig 4).

Download:

Fig 4. Correlation analysis of values for stability of the conducted algorithms for housekeeping gene assessment (geNorm, NormFinder, BestKeeper, comparative ΔC_q).

Bivariate correlations are displayed by scatterplots of the total stability values of the tested housekeeping genes, calculated by two algorithms. Correlation plots include linear regression lines.

https://doi.org/10.1371/journal.pone.0225790.g004

Effect of housekeeping gene stability on relative target gene expression

To assess the importance of the correct choice of housekeeping genes for experiments on N-SF and OA-SF with and without mechanical loading, we calculated relative gene expression of the target gene prolyl-4-hydroxylase-alpha-1 (P4HA1, Fig 5), collagen-1-alpha-2 (COL1A2), cylcooxygenase-2 (COX2) and interleukin-6 (IL-6; all in S3 File) using the candidate housekeeping genes tested in this study, which differ in expression stability. P4HA1 is responsible for the proper three-dimensional folding of newly synthesized procollagen chains. The expression levels of P4HA1 varied in a wide range depending on the housekeeping gene used for normalization (Fig 5). We observed significant pressure effects in N-SF with EEF1A1, GAPDH, POLR2A, RNA18S, RPL22, RPLP0 and YWHAZ used for normalization, but using PPIB as housekeeping gene, we determined no significant induction of P4HA1 (Fig 5). Differences between N-SF and OA-SF under physiological conditions seemed to be significant with GAPDH, PPIB and YWHAZ. With all other tested housekeeping genes there were no significant differences in P4HA1 expression between N-SF and OA-SF detectable (Fig 5). Variations between N-SF and OA-SF after compressive force treatment only appeared significant with EEF1A1, GAPDH, RPLP0, TBP and YWHAZ as housekeeping genes (Fig 5), but not with POLR2A, PPIB, RNA18S or RPL22.

Download:

Fig 5. Influence of the choice of housekeeping gene used for normalization and its stability on the fold-change expression of P4HA1 in N-SF and OA-SF without and with additional static pressure application.

Distinct differences in relative gene expression are evident with significance of pairwise comparisons varying across the individual housekeeping genes used for normalization (n = 6 per group). Statistics: Welch-corrected ANOVA with Games-Howell post-hoc tests.

https://doi.org/10.1371/journal.pone.0225790.g005

Furthermore, we calculated the statistical differences between results of target gene analysis for P4HA1, COL1A2, COX-2 and IL-6 normalized to the least stable housekeeping gene PPIB and the geometric mean of EEF1A1/RPLP0 as most stable housekeeping genes using an unpaired t test for the different conditions and groups (intragroup comparisons, S4 Table). We determined a significant difference only for IL-6 target gene expression in one experimental condition. Nonetheless, there are differences in significance levels due to normalization with different reference genes (Fig 5, S3 File).

Discussion

We identified RPLP0, EEF1A1, TBP and PPIB to be reliable housekeeping genes for normalization of target gene expression in RT-qPCR studies on human synovial fibroblasts derived from non-OA or OA patients by analysing the expression stability of at least nine potentially suitable housekeeping genes with four different mathematical algorithms. In general RPLP0 and EEF1A1 proved to be most stable throughout all tested experimental conditions and algorithms. As required by the MIQE guidelines, these genes have different functions in cell metabolism, which indicates that they are not co-regulated and may be used in conjunction for normalization of gene expression [10]. RPLP0 is involved in protein synthesis, as this gene encodes for one large 60S acidic ribosomal protein subunit [34]. EEF1A1 encodes for an isoform of the alpha subunit of the elongation factor-1 complex [35], which acts as GTPase and actin-bundling protein [36]. RPLP0, TBP and PPIB seem to be ideal for studies focusing on differences between non-OA and OA synovial fibroblasts. In contrast, PPIB encodes for a protein-binding cyclosporine in the endoplasmic reticulum, which is important in collagen type I folding [37]. PPIB is also associated with pathological conditions potentially affecting osteoarthritis, such as osteogenesis imperfecta [38]. TBP encodes for a TATA-box-binding protein, which is involved in transcription processes by the regulation of the RNA polymerase I [39]. A set of two internal control genes were determined to be adequate for reliable reference normalization using their geometric mean.

We confirmed high intraassay reliability and precision of the obtained data [40], based on the satisfactory quality of the obtained RNA samples and RT-qPCR analysis. We confirmed protein-free and qualitative adequate RNA by assessment of the purity and integrity of total isolated RNA, as protein contamination can inhibit cDNA synthesize and qPCR reaction and therefore lead to biased C_q values [41]. We also confirmed primer specificity in silico and in vitro.

Various studies on other tissues and experimental setups exist, which have determined stable housekeeping genes in the used conditions and tissues [42–44]. But most of them used only two algorithms for housekeeping gene stability assessment [11,45]. In this study we used four mathematical procedures and evaluated their conformity to assess, whether a combined usage of these algorithms adds reliability in housekeeping gene stability calculations. NormFinder, geNorm and comparative ΔC_q algorithms correlated to a a high degree with each other, which was mirrored by similar gene stability rankings. The BestKeeper algorithm on the other hand differed in its assessment from the other procedures. Originally, it was designed to assess general suitability of a housekeeping gene for RT-qPCR in a consecutive two-step assessment based on mean C_q standard deviation and correlation analysis and not to compare possible housekeeping genes. The other algorithms like geNorm or the comparative ΔC_q method implement either pairwise comparisons of housekeeping genes with linear quantities or apply an approach of linear quantity models, as it can be seen in the NormFinder algorithm [13].

To assess the impact of choosing appropriate housekeeping genes for normalization, we calculated P4HA1 gene expression normalized to the different candidate housekeeping genes. We observed distinct differences in the significance levels attributable to the relative stability of the respective housekeeping gene used for normalization. These results confirm the importance of housekeeping gene validation and of proper selection of stably expressed housekeeping genes in experiments on N-SF and OA-SF synovial fibroblasts.

Conclusions

We identified RPLP0, EEF1A1, TBP and PPIB to be reliable housekeeping genes for normalization of target gene expression in RT-qPCR studies on human synovial fibroblasts derived from non-OA or OA patients by analysing the expression stability of at least nine potentially suitable housekeeping genes with four different mathematical procedures. RPLP0 and EEF1A1 proved to be the most stably expressed housekeeping genes regarding studies on synovial fibroblasts focusing on experimental compressive force loading, whereas RPLP0, TBP and PPIB seem to be ideal for studies focusing on differences between non-OA and OA synovial fibroblasts. For accurate normalization, a set of two housekeeping genes was determined as sufficient, when using their geometric mean.

Supporting information

S1 Table. Yield (quantity) and quality of extracted total RNA per biological replicate (well).

https://doi.org/10.1371/journal.pone.0225790.s001

(DOCX)

S2 Table. MIQE checklist for authors, reviewers and editors.

https://doi.org/10.1371/journal.pone.0225790.s002

(DOCX)

S3 Table. Raw Cq values of RT-qPCR for the two experimental groups and nine potentially suitable housekeeping genes.

https://doi.org/10.1371/journal.pone.0225790.s003

(DOCX)

S4 Table. Statistical differences given as p values in each experimental group (intragroup comparisons) between results of target gene analysis for P4HA1, COL1A2, COX-2 and IL-6 normalized to a single reference gene and results normalized to the geometric mean of EEF1A1/RPLP0.

Statistics: unpaired t-test using GraphPad Prism version 8.0.

https://doi.org/10.1371/journal.pone.0225790.s004

(DOCX)

S1 File. RNA integrity analysis.

https://doi.org/10.1371/journal.pone.0225790.s005

(PDF)

S2 File. Primer specificity evaluation via (a) agarose gel and (b) melting curve analysis of all used samples (left) and a single sample (right).

https://doi.org/10.1371/journal.pone.0225790.s006

(PDF)

S3 File. Influence of the choice of housekeeping gene used for normalization on the fold-change expression of COL1A2, COX2 and IL6 in N-SF and OA-SF without and with additional static pressure application.

https://doi.org/10.1371/journal.pone.0225790.s007

(PDF)

Acknowledgments

The authors appreciate the technical support of Eva Zaglauer and Kathrin Bauer in RT-qPCR performance as well as Claudia Goettl for RNA integrity measurements.

References

1. Chen G, Ning B, Shi T. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Front Genet. 2019; 10: 317. pmid:31024627
- View Article
- PubMed/NCBI
- Google Scholar
2. Wang J, Dean DC, Hornicek FJ, Shi H, Duan Z. RNA sequencing (RNA-Seq) and its application in ovarian cancer. Gynecol Oncol. 2019; 152: 194–201. pmid:30297273
- View Article
- PubMed/NCBI
- Google Scholar
3. Rahmatallah Y, Emmert-Streib F, Glazko G. Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline. Brief Bioinformatics. 2016; 17: 393–407. pmid:26342128
- View Article
- PubMed/NCBI
- Google Scholar
4. Kierzek R, Turner DH, Kierzek E. Microarrays for identifying binding sites and probing structure of RNAs. Nucleic Acids Res. 2015; 43: 1–12. pmid:25505162
- View Article
- PubMed/NCBI
- Google Scholar
5. Keen HL, Sigmund CD. Microarray Analysis of Hypertension. Methods Mol Biol. 2017; 1527: 41–52. pmid:28116705
- View Article
- PubMed/NCBI
- Google Scholar
6. Jacob F, Guertler R, Naim S, Nixdorf S, Fedier A, Hacker NF, et al. Careful selection of housekeeping genes is required for reliable performance of RT-qPCR in human normal and cancer cell lines. PLoS ONE. 2013; 8: e59180. pmid:23554992
- View Article
- PubMed/NCBI
- Google Scholar
7. Kozera B, Rapacz M. Housekeeping genes in real-time PCR. J Appl Genet. 2013; 54: 391–406. pmid:24078518
- View Article
- PubMed/NCBI
- Google Scholar
8. Kirschneck C, Batschkus S, Proff P, Köstler J, Spanier G, Schröder A. Valid gene expression normalization by RT-qPCR in studies on hPDL fibroblasts with focus on orthodontic tooth movement and periodontitis. Sci Rep. 2017; 7: 14751. pmid:29116140
- View Article
- PubMed/NCBI
- Google Scholar
9. Kirschneck C, Proff P, Fanghänel J, Wolf M, Roldán JC, Römer P. Housekeeping genes for valid gene expression studies on rat dental, periodontal and alveolar bone tissue by means of RT-qPCR with a focus on orthodontic tooth movement and periodontitis. Ann Anat. 2016; 204: 93–105.
- View Article
- Google Scholar
10. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009; 55: 611–622. pmid:19246619
- View Article
- PubMed/NCBI
- Google Scholar
11. Tan SC, Carr CA, Yeoh KK, Schofield CJ, Davies KE, Clarke K. Identification of valid housekeeping genes for quantitative RT-PCR analysis of cardiosphere-derived cells preconditioned under hypoxia or with prolyl-4-hydroxylase inhibitors. Mol Biol Rep. 2012; 39: 4857–4867. pmid:22065248
- View Article
- PubMed/NCBI
- Google Scholar
12. Vandesompele J, de Preter K, Pattyn F, Poppe B, van Roy N, de Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002; 3: RESEARCH0034. pmid:12184808
- View Article
- PubMed/NCBI
- Google Scholar
13. Andersen CL, Jensen JL, Ørntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004; 64: 5245–5250. pmid:15289330
- View Article
- PubMed/NCBI
- Google Scholar
14. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper—Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004; 26: 509–515. pmid:15127793
- View Article
- PubMed/NCBI
- Google Scholar
15. Silver N, Best S, Jiang J, Thein SL. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol. 2006; 7: 33. pmid:17026756
- View Article
- PubMed/NCBI
- Google Scholar
16. Silver N, Cotroneo E, Proctor G, Osailan S, Paterson KL, Carpenter GH. Selection of housekeeping genes for gene expression studies in the adult rat submandibular gland under normal, inflamed, atrophic and regenerative states. BMC Mol Biol. 2008; 9: 64. pmid:18637167
- View Article
- PubMed/NCBI
- Google Scholar
17. Bhattaram P, Chandrasekharan U. The joint synovium: A critical determinant of articular cartilage fate in inflammatory joint diseases. Semin Cell Dev Biol. 2017; 62: 86–93. pmid:27212252
- View Article
- PubMed/NCBI
- Google Scholar
18. Wehmeyer C, Pap T, Buckley CD, Naylor AJ. The role of stromal cells in inflammatory bone loss. Clin Exp Immunol. 2017; 189: 1–11. pmid:28419440
- View Article
- PubMed/NCBI
- Google Scholar
19. Ospelt C, Brentano F, Rengel Y, Stanczyk J, Kolling C, Tak PP, et al. Overexpression of toll-like receptors 3 and 4 in synovial tissue from patients with early rheumatoid arthritis: toll-like receptor expression in early and longstanding arthritis. Arthritis Rheum. 2008; 58: 3684–3692. pmid:19035519
- View Article
- PubMed/NCBI
- Google Scholar
20. Carrión M, Juarranz Y, Pérez-García S, Jimeno R, Pablos JL, Gomariz RP, et al. RNA sensors in human osteoarthritis and rheumatoid arthritis synovial fibroblasts: immune regulation by vasoactive intestinal peptide. Arthritis Rheum. 2011; 63: 1626–1636. pmid:21337319
- View Article
- PubMed/NCBI
- Google Scholar
21. Zarb GA, Carlsson GE. Temporomandibular disorders: osteoarthritis. J Orofac Pain. 1999; 13: 295–306. pmid:10823044
- View Article
- PubMed/NCBI
- Google Scholar
22. Chen Di, Shen J, Zhao W, Wang T, Han L, Hamilton JL, et al. Osteoarthritis: toward a comprehensive understanding of pathological mechanism. Bone Res. 2017; 5: 16044. pmid:28149655
- View Article
- PubMed/NCBI
- Google Scholar
23. Herrero-Beaumont G, Roman-Blas JA, Castañeda S, Jimenez SA. Primary osteoarthritis no longer primary: three subsets with distinct etiological, clinical, and therapeutic characteristics. Semin Arthritis Rheum. 2009; 39: 71–80. pmid:19589561
- View Article
- PubMed/NCBI
- Google Scholar
24. Chang SH, Mori D, Kobayashi H, Mori Y, Nakamoto H, Okada K, et al. Excessive mechanical loading promotes osteoarthritis through the gremlin-1-NF-κB pathway. Nat Commun. 2019; 10: 1442. pmid:30926814
- View Article
- PubMed/NCBI
- Google Scholar
25. Tanaka E, Detamore MS, Mercuri LG. Degenerative disorders of the temporomandibular joint: etiology, diagnosis, and treatment. J Dent Res. 2008; 87: 296–307. pmid:18362309
- View Article
- PubMed/NCBI
- Google Scholar
26. Schröder A, Nazet U, Muschter D, Grässel S, Proff P, Kirschneck C. Impact of Mechanical Load on the Expression Profile of Synovial Fibroblasts from Patients with and without Osteoarthritis. Int J Mol Sci. 2019; 20: In press. pmid:30704030
- View Article
- PubMed/NCBI
- Google Scholar
27. Schröder A, Bauer K, Spanier G, Proff P, Wolf M, Kirschneck C. Expression kinetics of human periodontal ligament fibroblasts in the early phases of orthodontic tooth movement. J Orofac Orthop. 2018; 79: 337–351. pmid:30019109
- View Article
- PubMed/NCBI
- Google Scholar
28. Koretsi V, Kirschneck C, Proff P, Römer P. Expression of glutathione peroxidase 1 in the spheno-occipital synchondrosis and its role in ROS-induced apoptosis. Eur J Orthod. 2015; 37: 308–313. pmid:25312980
- View Article
- PubMed/NCBI
- Google Scholar
29. Kirschneck C, Meier M, Bauer K, Proff P, Fanghänel J. Meloxicam medication reduces orthodontically induced dental root resorption and tooth movement velocity: a combined in vivo and in vitro study of dental-periodontal cells and tissue. Cell Tissue Res. 2017; 368: 61–78. pmid:28044198
- View Article
- PubMed/NCBI
- Google Scholar
30. Kirschneck C, Fanghänel J, Wahlmann U, Wolf M, Roldán JC, Proff P. Interactive effects of periodontitis and orthodontic tooth movement on dental root resorption, tooth movement velocity and alveolar bone loss in a rat model. Ann Anat. 2017; 210: 32–43. pmid:27838559
- View Article
- PubMed/NCBI
- Google Scholar
31. Kirschneck C, Maurer M, Wolf M, Reicheneder C, Proff P. Regular nicotine intake increased tooth movement velocity, osteoclastogenesis and orthodontically induced dental root resorptions in a rat model. Int J Oral Sci. 2017; 9: 174–184. pmid:28960194
- View Article
- PubMed/NCBI
- Google Scholar
32. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001; 25: 402–408. pmid:11846609
- View Article
- PubMed/NCBI
- Google Scholar
33. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006; 7: 3. pmid:16448564
- View Article
- PubMed/NCBI
- Google Scholar
34. Kenmochi N, Kawaguchi T, Rozen S, Davis E, Goodman N, Hudson TJ, et al. A map of 75 human ribosomal protein genes. Genome Res. 1998; 8: 509–523. pmid:9582194
- View Article
- PubMed/NCBI
- Google Scholar
35. Lund A, Knudsen SM, Vissing H, Clark B, Tommerup N. Assignment of human elongation factor 1alpha genes: EEF1A maps to chromosome 6q14 and EEF1A2 to 20q13.3. Genomics. 1996; 36: 359–361. pmid:8812466
- View Article
- PubMed/NCBI
- Google Scholar
36. Becker M, Kuhse J, Kirsch J. Effects of two elongation factor 1A isoforms on the formation of gephyrin clusters at inhibitory synapses in hippocampal neurons. Histochem Cell Biol. 2013; 140: 603–609. pmid:23839781
- View Article
- PubMed/NCBI
- Google Scholar
37. Hoffmann H, Schiene-Fischer C. Functional aspects of extracellular cyclophilins. Biol Chem. 2014; 395: 721–735. pmid:24713575
- View Article
- PubMed/NCBI
- Google Scholar
38. Gagliardi A, Besio R, Carnemolla C, Landi C, Armini A, Aglan M, et al. Cytoskeleton and nuclear lamina affection in recessive osteogenesis imperfecta: A functional proteomics perspective. J Proteomics. 2017; 167: 46–59. pmid:28802583
- View Article
- PubMed/NCBI
- Google Scholar
39. Savinkova L, Drachkova I, Arshinova T, Ponomarenko P, Ponomarenko M, Kolchanov N. An experimental verification of the predicted effects of promoter TATA-box polymorphisms associated with human diseases on interactions between the TATA boxes and TATA-binding protein. PLoS ONE. 2013; 8: e54626. pmid:23424617
- View Article
- PubMed/NCBI
- Google Scholar
40. Taylor S, Wakem M, Dijkman G, Alsarraj M, Nguyen M. A practical approach to RT-qPCR-Publishing data that conform to the MIQE guidelines. Methods. 2010; 50: S1–5. pmid:20215014
- View Article
- PubMed/NCBI
- Google Scholar
41. Taylor SC, Mrkusich EM. The state of RT-quantitative PCR: firsthand observations of implementation of minimum information for the publication of quantitative real-time PCR experiments (MIQE). J Mol Microbiol Biotechnol. 2014; 24: 46–52. pmid:24296827
- View Article
- PubMed/NCBI
- Google Scholar
42. Taki FA, Abdel-Rahman AA, Zhang B. A comprehensive approach to identify reliable housekeeping gene candidates to investigate the link between alcoholism and endocrinology in Sprague-Dawley rats. PLoS ONE. 2014; 9: e94311. pmid:24824616
- View Article
- PubMed/NCBI
- Google Scholar
43. Robledo D, Ronza P, Harrison PW, Losada AP, Bermúdez R, Pardo BG, et al. RNA-seq analysis reveals significant transcriptome changes in turbot (Scophthalmus maximus) suffering severe enteromyxosis. BMC Genomics. 2014; 15: 1149. pmid:25526753
- View Article
- PubMed/NCBI
- Google Scholar
44. Mahanty A, Purohit GK, Mohanty S, Nayak NR, Mohanty BP. Suitable housekeeping gene for quantitative real-time PCR analysis of gene expression in gonadal tissues of minnow Puntius sophore under high-temperature stress. BMC Genomics. 2017; 18: 617. pmid:28810828
- View Article
- PubMed/NCBI
- Google Scholar
45. Reuther S, Reiter M, Raabe A, Dikomey E. Effect of irradiation on the expression of DNA repair genes studied in human fibroblasts by real-time qPCR using three methods of housekeeping gene validation. Radiat Environ Biophys. 2013; 52: 463–469. pmid:23884658
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Chen G, Ning B, Shi T. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Front Genet. 2019; 10: 317. pmid:31024627
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Wang J, Dean DC, Hornicek FJ, Shi H, Duan Z. RNA sequencing (RNA-Seq) and its application in ovarian cancer. Gynecol Oncol. 2019; 152: 194–201. pmid:30297273
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Rahmatallah Y, Emmert-Streib F, Glazko G. Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline. Brief Bioinformatics. 2016; 17: 393–407. pmid:26342128
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Kierzek R, Turner DH, Kierzek E. Microarrays for identifying binding sites and probing structure of RNAs. Nucleic Acids Res. 2015; 43: 1–12. pmid:25505162
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Keen HL, Sigmund CD. Microarray Analysis of Hypertension. Methods Mol Biol. 2017; 1527: 41–52. pmid:28116705
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Jacob F, Guertler R, Naim S, Nixdorf S, Fedier A, Hacker NF, et al. Careful selection of housekeeping genes is required for reliable performance of RT-qPCR in human normal and cancer cell lines. PLoS ONE. 2013; 8: e59180. pmid:23554992
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Kozera B, Rapacz M. Housekeeping genes in real-time PCR. J Appl Genet. 2013; 54: 391–406. pmid:24078518
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Kirschneck C, Batschkus S, Proff P, Köstler J, Spanier G, Schröder A. Valid gene expression normalization by RT-qPCR in studies on hPDL fibroblasts with focus on orthodontic tooth movement and periodontitis. Sci Rep. 2017; 7: 14751. pmid:29116140
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Kirschneck C, Proff P, Fanghänel J, Wolf M, Roldán JC, Römer P. Housekeeping genes for valid gene expression studies on rat dental, periodontal and alveolar bone tissue by means of RT-qPCR with a focus on orthodontic tooth movement and periodontitis. Ann Anat. 2016; 204: 93–105.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref10] 10. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009; 55: 611–622. pmid:19246619
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Tan SC, Carr CA, Yeoh KK, Schofield CJ, Davies KE, Clarke K. Identification of valid housekeeping genes for quantitative RT-PCR analysis of cardiosphere-derived cells preconditioned under hypoxia or with prolyl-4-hydroxylase inhibitors. Mol Biol Rep. 2012; 39: 4857–4867. pmid:22065248
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Vandesompele J, de Preter K, Pattyn F, Poppe B, van Roy N, de Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002; 3: RESEARCH0034. pmid:12184808
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Andersen CL, Jensen JL, Ørntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004; 64: 5245–5250. pmid:15289330
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref14] 14. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper—Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004; 26: 509–515. pmid:15127793
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref15] 15. Silver N, Best S, Jiang J, Thein SL. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol. 2006; 7: 33. pmid:17026756
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref16] 16. Silver N, Cotroneo E, Proctor G, Osailan S, Paterson KL, Carpenter GH. Selection of housekeeping genes for gene expression studies in the adult rat submandibular gland under normal, inflamed, atrophic and regenerative states. BMC Mol Biol. 2008; 9: 64. pmid:18637167
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref17] 17. Bhattaram P, Chandrasekharan U. The joint synovium: A critical determinant of articular cartilage fate in inflammatory joint diseases. Semin Cell Dev Biol. 2017; 62: 86–93. pmid:27212252
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref18] 18. Wehmeyer C, Pap T, Buckley CD, Naylor AJ. The role of stromal cells in inflammatory bone loss. Clin Exp Immunol. 2017; 189: 1–11. pmid:28419440
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref19] 19. Ospelt C, Brentano F, Rengel Y, Stanczyk J, Kolling C, Tak PP, et al. Overexpression of toll-like receptors 3 and 4 in synovial tissue from patients with early rheumatoid arthritis: toll-like receptor expression in early and longstanding arthritis. Arthritis Rheum. 2008; 58: 3684–3692. pmid:19035519
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref20] 20. Carrión M, Juarranz Y, Pérez-García S, Jimeno R, Pablos JL, Gomariz RP, et al. RNA sensors in human osteoarthritis and rheumatoid arthritis synovial fibroblasts: immune regulation by vasoactive intestinal peptide. Arthritis Rheum. 2011; 63: 1626–1636. pmid:21337319
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref21] 21. Zarb GA, Carlsson GE. Temporomandibular disorders: osteoarthritis. J Orofac Pain. 1999; 13: 295–306. pmid:10823044
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref22] 22. Chen Di, Shen J, Zhao W, Wang T, Han L, Hamilton JL, et al. Osteoarthritis: toward a comprehensive understanding of pathological mechanism. Bone Res. 2017; 5: 16044. pmid:28149655
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref23] 23. Herrero-Beaumont G, Roman-Blas JA, Castañeda S, Jimenez SA. Primary osteoarthritis no longer primary: three subsets with distinct etiological, clinical, and therapeutic characteristics. Semin Arthritis Rheum. 2009; 39: 71–80. pmid:19589561
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref24] 24. Chang SH, Mori D, Kobayashi H, Mori Y, Nakamoto H, Okada K, et al. Excessive mechanical loading promotes osteoarthritis through the gremlin-1-NF-κB pathway. Nat Commun. 2019; 10: 1442. pmid:30926814
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref25] 25. Tanaka E, Detamore MS, Mercuri LG. Degenerative disorders of the temporomandibular joint: etiology, diagnosis, and treatment. J Dent Res. 2008; 87: 296–307. pmid:18362309
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref26] 26. Schröder A, Nazet U, Muschter D, Grässel S, Proff P, Kirschneck C. Impact of Mechanical Load on the Expression Profile of Synovial Fibroblasts from Patients with and without Osteoarthritis. Int J Mol Sci. 2019; 20: In press. pmid:30704030
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref27] 27. Schröder A, Bauer K, Spanier G, Proff P, Wolf M, Kirschneck C. Expression kinetics of human periodontal ligament fibroblasts in the early phases of orthodontic tooth movement. J Orofac Orthop. 2018; 79: 337–351. pmid:30019109
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref28] 28. Koretsi V, Kirschneck C, Proff P, Römer P. Expression of glutathione peroxidase 1 in the spheno-occipital synchondrosis and its role in ROS-induced apoptosis. Eur J Orthod. 2015; 37: 308–313. pmid:25312980
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref29] 29. Kirschneck C, Meier M, Bauer K, Proff P, Fanghänel J. Meloxicam medication reduces orthodontically induced dental root resorption and tooth movement velocity: a combined in vivo and in vitro study of dental-periodontal cells and tissue. Cell Tissue Res. 2017; 368: 61–78. pmid:28044198
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref30] 30. Kirschneck C, Fanghänel J, Wahlmann U, Wolf M, Roldán JC, Proff P. Interactive effects of periodontitis and orthodontic tooth movement on dental root resorption, tooth movement velocity and alveolar bone loss in a rat model. Ann Anat. 2017; 210: 32–43. pmid:27838559
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref31] 31. Kirschneck C, Maurer M, Wolf M, Reicheneder C, Proff P. Regular nicotine intake increased tooth movement velocity, osteoclastogenesis and orthodontically induced dental root resorptions in a rat model. Int J Oral Sci. 2017; 9: 174–184. pmid:28960194
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref32] 32. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001; 25: 402–408. pmid:11846609
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref33] 33. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006; 7: 3. pmid:16448564
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref34] 34. Kenmochi N, Kawaguchi T, Rozen S, Davis E, Goodman N, Hudson TJ, et al. A map of 75 human ribosomal protein genes. Genome Res. 1998; 8: 509–523. pmid:9582194
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref35] 35. Lund A, Knudsen SM, Vissing H, Clark B, Tommerup N. Assignment of human elongation factor 1alpha genes: EEF1A maps to chromosome 6q14 and EEF1A2 to 20q13.3. Genomics. 1996; 36: 359–361. pmid:8812466
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref36] 36. Becker M, Kuhse J, Kirsch J. Effects of two elongation factor 1A isoforms on the formation of gephyrin clusters at inhibitory synapses in hippocampal neurons. Histochem Cell Biol. 2013; 140: 603–609. pmid:23839781
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref37] 37. Hoffmann H, Schiene-Fischer C. Functional aspects of extracellular cyclophilins. Biol Chem. 2014; 395: 721–735. pmid:24713575
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref38] 38. Gagliardi A, Besio R, Carnemolla C, Landi C, Armini A, Aglan M, et al. Cytoskeleton and nuclear lamina affection in recessive osteogenesis imperfecta: A functional proteomics perspective. J Proteomics. 2017; 167: 46–59. pmid:28802583
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref39] 39. Savinkova L, Drachkova I, Arshinova T, Ponomarenko P, Ponomarenko M, Kolchanov N. An experimental verification of the predicted effects of promoter TATA-box polymorphisms associated with human diseases on interactions between the TATA boxes and TATA-binding protein. PLoS ONE. 2013; 8: e54626. pmid:23424617
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref40] 40. Taylor S, Wakem M, Dijkman G, Alsarraj M, Nguyen M. A practical approach to RT-qPCR-Publishing data that conform to the MIQE guidelines. Methods. 2010; 50: S1–5. pmid:20215014
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref41] 41. Taylor SC, Mrkusich EM. The state of RT-quantitative PCR: firsthand observations of implementation of minimum information for the publication of quantitative real-time PCR experiments (MIQE). J Mol Microbiol Biotechnol. 2014; 24: 46–52. pmid:24296827
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref42] 42. Taki FA, Abdel-Rahman AA, Zhang B. A comprehensive approach to identify reliable housekeeping gene candidates to investigate the link between alcoholism and endocrinology in Sprague-Dawley rats. PLoS ONE. 2014; 9: e94311. pmid:24824616
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref43] 43. Robledo D, Ronza P, Harrison PW, Losada AP, Bermúdez R, Pardo BG, et al. RNA-seq analysis reveals significant transcriptome changes in turbot (Scophthalmus maximus) suffering severe enteromyxosis. BMC Genomics. 2014; 15: 1149. pmid:25526753
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref44] 44. Mahanty A, Purohit GK, Mohanty S, Nayak NR, Mohanty BP. Suitable housekeeping gene for quantitative real-time PCR analysis of gene expression in gonadal tissues of minnow Puntius sophore under high-temperature stress. BMC Genomics. 2017; 18: 617. pmid:28810828
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref45] 45. Reuther S, Reiter M, Raabe A, Dikomey E. Effect of irradiation on the expression of DNA repair genes studied in human fibroblasts by real-time qPCR using three methods of housekeeping gene validation. Radiat Environ Biophys. 2013; 52: 463–469. pmid:23884658
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

Figures

Abstract

Introduction

Materials and methods

In vitro cell culture experiment setup

RNA isolation and purity determination

Reverse transcription (cDNA synthesis)

Real-time quantitative RT-PCR

Assessment of reference gene stability

Normalization of target gene expression

Results

Quality and integrity of RNA samples

Primer specificity and Cq expression levels

Refinement of housekeeping gene number for normalization

Relative performance as housekeeping gene

Conformity of used algorithms for housekeeping gene stability analysis

Effect of housekeeping gene stability on relative target gene expression

Discussion

Conclusions

Supporting information

S1 Table. Yield (quantity) and quality of extracted total RNA per biological replicate (well).

S2 Table. MIQE checklist for authors, reviewers and editors.

S3 Table. Raw Cq values of RT-qPCR for the two experimental groups and nine potentially suitable housekeeping genes.

S4 Table. Statistical differences given as p values in each experimental group (intragroup comparisons) between results of target gene analysis for P4HA1, COL1A2, COX-2 and IL-6 normalized to a single reference gene and results normalized to the geometric mean of EEF1A1/RPLP0.

S1 File. RNA integrity analysis.

S2 File. Primer specificity evaluation via (a) agarose gel and (b) melting curve analysis of all used samples (left) and a single sample (right).

S3 File. Influence of the choice of housekeeping gene used for normalization on the fold-change expression of COL1A2, COX2 and IL6 in N-SF and OA-SF without and with additional static pressure application.

Acknowledgments

References

Primer specificity and C_q expression levels