Gene expression studies which utilize lipopolysaccharide (LPS)-stimulated macrophages to model immune signaling are widely used for elucidating the mechanisms of inﬂammation-related disease. When expression levels of target genes are quantiﬁed using Real-Time quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR), they are analyzed in comparison to reference genes, which should have stable expression. Judicious selection of reference genes is, therefore, critical to interpretation of qRT-PCR results. Ideal reference genes must be identiﬁed for each experimental system and demonstrated to remain constant under the experimental conditions. In this study, we evaluated the stability of eight com- mon reference genes: Beta-2-microglobulin (B2M), Cyclophilin A/Peptidylprolyl isomerase A, glyceraldehyde-3- phosphatedehydrogenase (GAPDH), Hypoxanthine Phosphoribosyltransferase 1, Large Ribosomal Protein P0, TATA box binding protein, Ubiquitin C (UBC), and Ribosomal protein L13A. Expression stability of each gene was tested under different conditions of LPS stimulation and compared to untreated controls. Reference gene stabilities were analyzed using C value comparison, NormFinder, and geNorm. We found that UBC, closely followed by B2M, is the most stable gene, while the com- monly used reference gene GAPDH is the least stable. Thus, for improved accuracy in evaluating gene expression levels, we propose the use of UBC to normalize PCR data from LPS-stimulated macrophages. Keywords: PCR; inﬂammation; standardization; murine; J774A.1; housekeeping genes Introduction conditions. Therefore, it is necessary to evaluate potential nor- malization targets for each experimental protocol. Once a reli- Accurate normalization of quantitative real-time PCR data is critical for obtaining meaningful gene expression results . able gene or gene panel has been rigorously substantiated, it may then be employed as a standard for the specific experimen- This is most simply accomplished by scaling raw expression data from genes of interest to a stably expressed standard gene. tal approach, allowing better reproducibility of results both within and between laboratories. We sought to address this However, it is well understood that there is no single gene with invariant expression in all cell types under all experimental need in a widely used model of inflammation signaling. A Received: 25 May 2016; Revised: 12 October 2016; Editorial decision: 13 October 2016 V The Author 2016. Published by Oxford University Press. All rights reserved. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact firstname.lastname@example.org Downloaded from https://academic.oup.com/biomethods/article-abstract/1/1/bpw005/2743811 by Ed 'DeepDyve' Gillespie user on 08 June 2018 2| Kalagara et al. robust inflammatory response, including inflammasome activa- macrophage activation behavior with high fidelity . In addi- tion and release of cytokines, can be stimulated in the mouse tion, it is common to use LPS to stimulate macrophage differen- monocyte/macrophage cell line J774A.1 by treatment with lipo- tiation and cytokine secretion. This in turn triggers pyroptosis, polysaccharide (LPS) [2, 3]. Various reference genes have been a type of programmed cell death invoked in macrophages dur- used in this popular model [4–7], but, to our knowledge, ing cytokine-facilitated inflammation [3, 21, 22]. Extensive gene there are no published studies on the relative stability of these expression analysis has been conducted using these LPS- genes under typical experimental conditions. We analyzed the stimulated macrophages, employing qRT-PCR with various ref- expression stability of eight commonly used reference genes: erence genes [23–25]. There has been work done to evaluate ref- Ribosomal protein L13A (RPL13A), Beta-2-microglobulin (B2M), erence genes in a number of systems, such as in rat Ubiquitin C (UBC), glyceraldehyde-3-phosphate dehydrogenase oligodendrocytes  and bovine muscular tissues . (GAPDH), Hypoxanthine Phosphoribosyltransferase 1 (HPRT1), Reference genes have also been explored for J774A.1 cells under Cyclophilin A (Peptidylprolyl isomerase A) (PPIA), TATA box conditions of stimulation with laminin . However, to our knowledge, there are no similar reports focused on the stability binding protein (TBP), and large ribosomal protein P0 (RPLP0). Investigation of inflammation signaling in macrophages of reference genes in LPS-treated macrophages. relies heavily upon real-time quantitative reverse transcription In this study, following the guidance of Minimum PCR (qRT-PCR) (RRID: SCR_003089), which measures gene ex- Information for Publication of Quantitative Real-Time PCR pression based on mRNA quantity [8–10]. This approach is par- Experiments (MIQE), we have evaluated the stability of various ticularly relevant for establishing signaling pathways and reference genes in LPS-stimulated J774A.1 mouse macrophages networks. These gene expression measurements often provide . We carefully reviewed the literature to determine the most the basis for the development of diagnostic and therapeutic ap- widely used reference genes in this and similar systems. From plications. Normalization of qRT-PCR is accomplished by using this search, we chose to analyze the following reference genes: a reference gene, ideally a ubiquitously expressed housekeeping RPL13A, B2M, UBC, GAPDH, HPRT1, PPIA, TBP, and RPLP0 gene, as an internal control. However, normalization can be- (Table 1). Then, we evaluated the expression of these genes in come problematic due to unanticipated variability in expression macrophages under different conditions of LPS stimulation. We of the reference gene, particularly in response to the experi- then analyzed the expression data using statistical models such mental conditions under study. Indeed, the expression level of as cycle threshold (C ) value comparison, geNorm  reference genes can vary tremendously between different ex- (RRID:SCR_006763), and NormFinder  (RRID:SCR_003387). perimental designs and cell types. Because different studies use Both geNorm and NormFinder are commonly used programs for a variety of reference genes with varying expression stabilities, identifying the most stable reference gene among a group of target gene expression levels, and trends can be misinterpreted candidates. Both tools rank a set of candidate genes by stability, or unreproducible. Thus, one major task for gene expression but they employ different approaches. GeNorm ranks via se- analysis is to identify the most stable reference genes for each quential, pairwise comparisons between each gene and all other study system and to normalize data accordingly . genes in the test set. NormFinder generates a stability value for Macrophages were first documented in the context of their each gene based on its variability both within-sample groups ability to engulf foreign particles via phagocytosis. Subsequent and between groups. We found that the three methods yielded studies demonstrated that these cells play an important role similar results and all identified the same gene as the best refer- in innate immunity and are the primary mediators of inflam- ence for the system. Finally, we evaluated the performance of mation. Increasing appreciation for the role of inflammation the reference genes for evaluating the expression levels of NF- in the progression of cancer, heart disease, diabetes, and other jB1, an exemplar target gene that plays a role in the inflamma- clinically relevant conditions has spurred interest in this cell tory response in macrophages . type [12–17]. Initially, these cells exist as small, circulating monocytes in the bloodstream. However, when stimulated, they enter the tissues and undergo a series of significant Materials and Methods transformations, eventually differentiating into macrophages Cell culture and LPS treatments . Fully mature macrophages are found in all mammalian TM tissues and exhibit important functions in embryonic develop- J774A.1 macrophage cells (ATCC TIB-67 , Manassas, VA, USA) ment and tissue repair, in addition to immune system activa- were grown in Dulbecco’s Modified Eagle’s Medium (DMEM) tion . supplemented with 10% fetal bovine serum (FBS), 100 m/ml pen- Murine J774A.1 cells (RRID:CVCL_0358) are a popular model icillin G and 100 mg/ml streptomycin sulfate (all from Gibco, in gene expression studies because they recapitulate in vivo Grand Island, NY, USA) in T25 flasks (Sigma-Aldrich, St. Louis, Table 1: Reference genes and functions Gene Full name RefSeq Function References B2M Beta-2-microglobulin NM_009735.3 Beta-chain of major histocompatibility complex class I molecules PPIA Cyclophilin A (peptidylprolyl isomerase A) NM_008907.1 Protein metabolism and modiﬁcation GAPDH Glyceraldehyde-3-phosphatedehydrogenase NM_008084.2 Carbohydrate metabolism HPRT1 Hypoxanthine phosphoribosyltransferase 1 NM_013556.2 Purine synthesis RPLP0 Large ribosomal protein P0 NM_007475.5 Ribosome production and assembly TBP TATA box binding protein NM_013684.3 RNA polymerase II transcription factor UBC Ubiquitin C NM_019639.4 Protein degradation RPL13A Ribosomal protein L13A NM_009438.5 Structural component of ribosomal subunit Downloaded from https://academic.oup.com/biomethods/article-abstract/1/1/bpw005/2743811 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Stable reference genes in LPS-stimulated macrophages | 3 MO, USA) at 37˚C with 5% CO in a humidified incubator. Tissue Bioinformatics and NCBI Primer Blast (Table 2). Designed PCR culture flasks were passaged every 3 or 4 days by scraping and primers were purchased from Integrated DNA Technologies cells were counted for density and viability with a Countess (Coralville, IA, USA). Before use in analyzing reference gene ex- Automated Cell Counter (Life Technologies, Eugene, OR, USA) pression, primers were evaluated for their efficiency and specif- using the trypan blue dye exclusion assay. For LPS treatment as- icity. Primers were tested by qPCR using the ABI Step One Plus say, 1 10 cells were suspended in 1 ml DMEM, supplemented Real-Time PCR System (Applied Biosystems, Foster City, CA, TM with 10% FBS (Sigma-Aldrich). Cells were exposed to LPS USA). The reaction mixture used was: 5 ml of SYBR GreenER (Sigma-Aldrich) at 10 ng/ml, 1 mg/ml, or left untreated, and then qPCR SuperMix Universal (2) (Cat No. 11762100, Thermo Fisher incubated for 1 h or 4 h at 37˚C with 5% CO . After the LPS treat- Scientific), 1.0 ml of forward primer (4 mM), 1.0 ml of reverse primer V R ment, the macrophages were transferred into Corning 15-milli- (4 mM), 1 ml of cDNA template (1/20 dilution of RT reaction), and liter tubes (Sigma-Aldrich), followed by centrifugation at 300 g 2.0 ml of Nuclease-free water (Cat No. AM 9930, Thermo Fisher for 3 min. After discard the supernatants, the left cell pellet was Scientific). The PCR amplification profile consisted of 95 C for used for Ribonucleic acid (RNA) extraction as below. 30 s, followed by 40 cycles of 95 C for 5 s and 72 C for 30 s, and ending with a melt curve analysis according to the defaulted program of ABI. Following qPCR, the samples were purified us- RNA extraction and reverse transcription ing QIAquick Gel Extraction Kit (Cat No. 28704, QIAGEN), and then sequenced. The resulting chromatograms were compared RNA was extracted from the cells using the RNeasy Mini Kit (Cat to known sequences and the specificity of the primers was No. 74104, QIAGEN, Hilden, Germany) according to the following verified. modified version of the manufacturer’s protocol. First, 350 mlof RLT and 350 ml of 70% ethanol were added into a Corning 15-mil- liliter tube containing 1 10 cells, followed by vortexing for ho- qPCR mogenization for 1 min. Then, 700 ml of lysate was transferred to After primer validation, qPCR was conducted using template an RNeasy Mini Spin column sitting on a 2 ml collection tube and cDNA prepared from J774A.1 cells that were exposed to various centrifuged at 8000 g for 1 min. The flow-through was discarded LPS treatments. Reactions were prepared using reagent quanti- and the spin column was replaced. Then 700 mlof RW1 wasadded ties similar to those used in primer design and validation: 5 mlof in the spin column followed by centrifugation at 8000gfor SYBR GreenER SuperMix Universal (2), 1.0 ml of forward primer 1 min. Again, the flow-through was discarded and the spin col- (4 mM), 1.0 m of reverse primer (4 mM), 1 ml of cDNA template (1/20 umn was replaced. Five hundred microliters of RPE was added dilution of RT reaction), and 2.0 ml of nuclease-free water. An into the spin column, followed by centrifugation at 8000 gfor 1 ABI Step One Plus Real-Time PCR System (Applied Biosystems) min. The flow-through was then discarded and the spin column was used with an amplification profile of 95 C for 30 s, followed was replaced. The RPE washing procedure was repeated. Lastly, by 40 cycles of 95 C for 5 s, 72 C for 30 s, and a step and hold the spin column was transferred to a new 1.5 ml Eppendorf tube melt curve analysis. Each gene was analyzed in two indepen- and 50 ml of RNase-free water was added, followed by centrifuga- dent experiments, each conducted in triplicate and done on dif- tion at 8000 g for 1 min. After RNA was collected, it was quanti- ferent days. Moreover, qPCR using the same protocols and fied using the NanoDrop2000 (Thermo Fisher Scientific, amplification methods were run on NF-jB1, an example target Waltham, MA, USA) and RNA concentration was adjusted to 1 mg/ gene. ml. Complementary DNA (cDNA) was synthesized using the QuantiTect reverse transcription protocol (Cat No. 205311, QIAGEN). For each reaction, 4 ml of iScript reaction mix (5), 1 mlof Statistical analysis iScript reverse transcriptase, and 15 ml of the adjusted RNA were Expression data for NF-jB1 was analyzed by two-way ANOVA added together and mixed well with a pipette. Afterward, the RT with Dunnett’s post-hoc test for multiple comparisons, using was run with the following program at a thermal cycler (MJ Mini GraphPad Prism (GraphPad Software, San Diego, CA, USA) Personal Thermal Cycler, Bio-Rad, Hercules, CA, USA): 5 mins at (RRID:SCR_002798). P< 0.05 were considered significant. 25 C, 30 mins at 42 C, and 5 mins at 85 C. The obtained cDNA was stored at 20 C prior to use in qPCR. Results Primer design and validation Data were collected from qPCR runs and each gene was ana- Primers were designed and evaluated using two online genomic lyzed for utility as a normalization standard for LPS-stimulated information databases to ensure validity: UCSC Genome macrophages. J774A.1 macrophages were treated with LPS Table 2: Reference and target gene primers Gene Forward Primer 5’–3’ Reverse Primer 5’–3’ B2M ACCGTCTACTGGGATCGAGA TGCTATTTCTTTCTGCGTGCAT GAPDH AAGGGCTCATGACCACAGTC CAGGGATGATGTTCTGGGCA HPRT1 GATCAGTCAACGGGGGACAT ATCCAACAAAGTCTGGCCTGT PPIA CCAAGACTGAATGGCTGGATG TGTCCACAGTCGGAAATGGTG RPL13A GAAGCAGATCTTGAGGTTACGGA GCAGGCATGAGGCAAACAGT RPLP0 TCACTGTGCCAGCTCAGAAC ATCAGCTGCACATCACTCAGA TBP AAACTCTGACCACTGCACCG CTGCAGCAAATCGCTTGGGA UBC CCCAGTGTTACCACCAAGAAG CCCCATCACACCCAAGAACA NF-jB1 ATGGCAGACGATGATCCCTAC TGTTGACAGTGGTATTTCTGGTG Downloaded from https://academic.oup.com/biomethods/article-abstract/1/1/bpw005/2743811 by Ed 'DeepDyve' Gillespie user on 08 June 2018 4| Kalagara et al. C Value Comparison HPRT1 TBP RPLP0 RPL13A B2M UBC GAPDH PPIA Gene Figure 1:C value comparison. Relative expression levels from all treatment conditions for each reference gene, obtained through qRT-PCR, were combined and aver- aged. Each point represents the average of 30 C values with six replicates of each treatment. Error bars indicate the range of C values. t t under the conditions of 10 ng/ml for 1 h, 10 ng/ml for 4 h, 1 mg/ml a stability value of 0.081, followed by RPLP0 and PPIA. Together, for 1 h, and 1 mg/ml for 4 h, while control cells were left these analyses highlight the stability of UBC and TBP, as well as untreated. The success of this LPS activation protocol was previ- the inconsistency of GAPDH and RPL13A as normalization genes ously shown in our laboratory by both western blotting and cell in LPS-stimulated macrophages. death assay [40, 41]. Expression values from each condition were run a total of six times; two qPCR experiments were con- Gene expression stability determined by geNorm ducted in triplicate on two different days. Data were also analyzed using geNorm, an algorithm that deter- mines the most stable reference gene from a panel by calculating C value comparison of reference genes M-values for each gene. M-values are determined by averaging C values for eight genes from the J774A.1 cells were calculated the pairwise variation between the gene of interest and all other using raw PCR data and found to range from 13.9 to 25.2 (Fig. 1). reference genes . Similar to the NormFinder stability values, a The values presented for each gene are the average C from all t lower M-score indicates more stable gene expression. In treatment conditions for each of the genes. The genes PPIA and untreated control cells, the average M-value for this data was RPLP0 were determined to have the highest expression levels. A 0.685. GAPDH was the least stable gene with an M-value of 0.698, delta C value comparison of the variation in C values revealed t t and UBC was the most stable with an M-value of 0.124 (Fig. 3A). that B2M and UBC had the lowest standard deviation in expres- Combining data across all treatments for each gene resulted in sion while RPLP0 and GAPDH had the highest deviation. The error similar ranking with M-values of 0.746 and 0.311 for GAPDH and bars in Fig. 1 indicate the range of C values and, therefore, the t UBC, respectively (Fig. 3B). Data were also combined based on the variability of each gene across the panel of control and LPS treat- concentration of LPS used for stimulation. At 10 ng/ml LPS, the ments. These results highlight the need to validate stability of average stability value was 0.256. HPRT1, RPL13A, and RPLP0 reference genes in this experimental system since LPS treat- were the most stable, with M-values of 0.083, 0.075, and 0.062 ments caused large variations in some of the observed C values. t (Fig. 3C). GAPDH was the least stable gene in this condition with an M-value of 0.728. At 1 mg/ml LPS, the average gene stability value was 0.1375. UBC, B2M, and HPRT1 proved to be the most Gene expression stability determined by NormFinder stable genes, with M-values of 0.043, 0.033, and 0.033, respectively NormFinder is an algorithm that measures the stability of vari- (Fig. 3D). GAPDH again proved to be the least stable gene with an ous reference genes and produces raw stability values for genes, M-value of 0.508. Finally, data were grouped by duration of LPS where a lower stability value indicates a more stable gene. treatment. In the 1-h subcategory, the average M-value was 0.338 NormFinder calculates this value by combining the approxima- and the most stable reference genes were PPIA and UBC, with M- tion of group expression variations of target reference genes [30, values of 0.116 and 0.133, respectively (Fig. 3E). The least stable 42]. The resulting numbers represent the variation in expression gene was GAPDH, with an M-value of 0.978. In the 4-h subcate- across samples and between groups . Data was entered in gory, the average M-value was 0.148 and the most stable refer- the form of linear efficiency corrected quantities (Q). The equa- ence genes were B2M and UBC, both with M-values of 0.010 (Fig. Ct (min) Ct (sample) tion used for this calculation is : Q¼ E .C 3F). The least stable reference gene, again, was GAPDH with an (min) corresponds to the lowest C value for an assay and C t t M-value of 0.446. (sample) refers to the sample in question. NormFinder was used as a Microsoft Excel add-in and two analyses were conducted. Comparison of most and least stable reference genes In the first analysis, data were grouped based on the day the qRT-PCR was done. In the second analysis, data were organized GAPDH was generally the least stable reference gene, while UBC based on experimental conditions, such as LPS concentration was the most stable. GeNorm was used to compare the M- and treatment time. In the first test, the average stability value values of these two genes for each condition (Fig. 4). The M- was 0.055. The most stable gene was UBC followed closely by values of UBC, which were the lowest of any of the tested genes, TBP, which had stability values of 0.015 and 0.020, respectively. ranged from 0.100 to 0.311. The M-values of GAPDH, which were GAPDH, with a value of 0.087, and RPL13A, with a value of 0.084, consistently higher, ranged from 0.446 to 0.978. were the least stable reference genes (Fig. 2A). In the second analysis, which separated the LPS treatments as different sam- Effects of reference genes on calculated NF-jB1 ples, an average stability value of 0.042 was calculated. UBC and expression TBP were again found to be the most stable genes in the panel with stability values of 0.002 and 0.005, respectively (Fig. 2B). In order to show how selection of different reference genes im- However, in this analysis, RPL13A was the least stable gene with pacts gene expression results, we evaluated the expression of Downloaded from https://academic.oup.com/biomethods/article-abstract/1/1/bpw005/2743811 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Ct value Stable reference genes in LPS-stimulated macrophages | 5 A NormFinder by Day 0.100 0.087 0.084 0.076 0.070 0.080 0.054 0.060 0.033 0.040 0.020 0.015 0.020 0.000 GAPDH RPL13A RPLP0 PPIA B2M HPRT1 TBP UBC Gene NormFinder by Treatment 0.100 0.081 0.072 0.080 0.066 0.051 0.060 0.036 0.040 0.020 0.020 0.005 0.002 0.000 RPL13A RPLP0 PPIA GAPDH B2M HPRT1 TBP UBC Gene Figure 2: NormFinder Analysis. (A) NormFinder analysis of reference genes by day. Gene stability values and accumulated standard deviation analysis using NormFinder. Data were grouped together by the day the experiment was conducted. (B) NormFinder analysis of reference genes by treatment. Gene stability values and accumulated standard deviation analysis using NormFinder grouped together by treatment. Stability values were calculated between data sets from control cells and four LPS treatment protocols: 10 ng/ml for 1 h, 10 ng/ml for 4 h, 1 mg/ml for 1 h, and 1 mg/ml for 4 h. Genes are ordered by increasing stability from left to right. Average for Untreated Cells Average for All Conditions A B 0.746 0.698 0.8 0.8 0.7 0.7 0.533 0.6 0.6 0.454 0.424 0.427 0.5 0.5 0.386 0.367 0.335 0.327 0.318 0.311 0.4 0.4 0.225 0.3 0.3 0.16 0.128 0.124 0.2 0.2 0.1 0.1 GAPDH RPL13A TBP RPLP0 HPRT1 B2M PPIA UBC GAPDH RPL13A RPLP0 PPIA TBP HPRT1 B2M UBC Gene Gene Average for 10 ng/mL LPS Average for 1 µg/mL LPS C D 0.728 0.8 0.6 0.508 0.7 0.5 0.6 0.4 0.402 0.5 0.3 0.4 0.3 0.183 0.219 0.3 0.179 0.124 0.2 0.106 0.07 0.2 0.083 0.075 0.062 0.043 0.0330.033 0.1 0.1 0 0 GAPDH TBP B2M UBC PPIA HPRT1 RPL13A RPLP0 GAPDH RPLP0 RPL13A TBP PPIA UBC B2M HPRT1 Gene Gene Average for 1 Hour LPS Treatments Average for 4 Hour LPS Treatments E F 0.446 1.2 0.5 0.978 0.37 0.4 0.8 0.3 0.231 0.465 0.6 0.355 0.308 0.2 0.4 0.201 0.066 0.154 0.133 0.116 0.039 0.1 0.014 0.2 0.010.01 0 0 GAPDH RPLP0 RPL13A B2M TBP HPRT1 UBC PPIA GAPDH RPL13A TBP HPRT1 PPIA RPLP0 B2M UBC Gene Gene Figure 3: GeNorm analysis. Summary of geNorm analysis of reference genes grouped by LPS treatment. GeNorm was used to calculate M-values for the reference genes from qRT-PCR data for: (A) untreated controls, (B) all conditions combined, (C) 10 ng/ml LPS treatments, (D) 1 mg/ml LPS treatments, (E) 1 h LPS treatments, and (F) 4h LPS treatments. Genes are ordered by increasing stability from left to right. Downloaded from https://academic.oup.com/biomethods/article-abstract/1/1/bpw005/2743811 by Ed 'DeepDyve' Gillespie user on 08 June 2018 M Value M Value M Value Stability Values Stability Value M Value M Value M Value 6| Kalagara et al. Therefore, we set out to determine the stability of prevalent M Value Comparison 1.2 reference genes used in this LPS-stimulated, macrophage sys- tem. Macrophage gene expression has been studied in contexts such as autoimmune-related inflammation, coronary artery di- 0.8 lation, cancer progression, and wound healing [7, 47–51]. In 0.6 UBC each case, macrophage gene expression was critically linked to GAPDH 0.4 pathology. In these studies, reference genes with a wide range 0.2 of stability were used, including UBC, GAPDH, 18S, HPRT1, and PPIA. For example, GAPDH normalization was used in a study Untreated All 10 ng/mL 1 µg/mL 1 hr 4 hr that sought to characterize LPS-stimulated monocyte-to-macro- Condition phage differentiation in terms of gene expression . Our data suggest that GAPDH and PPIA are particularly poor references Figure 4: GeNorm analysis of UBC and GAPDH stability. Data are the M-values for LPS-stimulated J774A.1 gene expression analysis. In con- calculated by geNorm of data from: untreated control samples, all conditions, LPS concentrations, and LPS treatment times. trast, we find that UBC is a reliable reference gene for this model. This conclusion is supported by the results from both NormFinder and geNorm analyses. Because these two analyses rank using different methods, we have a high level of confi- dence in this finding. Further, C value analysis also shows that UBC expression remains consistent under the treatment conditions. In addition to calculating M-values, geNorm analysis also in- cludes a determination of the optimal number of reference genes needed to ensure accurate data normalization. Often the average expression of a panel of genes provides more reliable normalization than any single gene. However, this analysis showed that there was no subset of genes in the tested panel that would be expected to outperform UBC alone under our treatment conditions. We demonstrate here a comprehensive approach for deter- Figure 5: Calculated NF-jB1 expression changes. Values of NF-jB1 expression mining the best reference gene to use in any cell system. Our re- for each LPS treatment were calculated using the delta-delta C method. Expression was normalized to each of the eight reference genes. The resulting sults substantiate the importance of evaluating potential values are expressed as fold change in expression relative to untreated control. reference genes for use in specific gene expression studies. We Calculated expression values were compared to the most stable reference gene, show that there are measureable differences in stability among UBC. Data are the mean of two experiments, error bars represent standard devi- a number of commonly used housekeeping genes in an inflam- ation. ***P< 0.001, ****P< 0.0001. mation model of LPS-stimulated macrophages. We used expres- sion of the inflammation mediator NF-jB1 to show that use of NF-jB1 in our experimental system. NF-jB1 is a key mediator of the less stable reference genes produced significantly altered re- inflammation signaling and is highly upregulated in macro- sults relative to the most stable gene. phage in response to LPS stimulation . We used qRT-PCR Here, we evaluated eight metabolic housekeeping genes for DDCT and the 2 method to compare expression of this target their suitability as reference genes. It would be valuable to ex- gene when normalized to each reference gene (Fig. 5). pand this analysis to a larger pool of possible reference genes Expression values are presented relative to the untreated con- including targets such as the ribosomal protein, 18S. It is also trol. We found that the use of either GAPDH or HPRT1 resulted important to note that we evaluated reference gene stability on in significantly different expression values, across the panel of the bulk cell level. Single-cell gene expression measurements treatments, compared to the most stable reference gene UBC. are becoming increasingly accessible [52, 53]. It is likely that Results from normalization to the other reference genes were genes shown to be acceptably stable across cell populations not significantly different from UBC for NF-jB1 expression. would not be suitable as references at the single-cell level. Indeed, for this application, larger panels of standard genes may be required to produce robust datasets. Discussion Macrophages have been extensively studied because of their Acknowledgements importance in a wide range of normal and pathological pro- We thank Arizona State University for providing funding cesses, but the evaluation of reference genes used for normali- and support for this project to D. Meldrum, Director, the zation in macrophage studies has been less well explored. An Biodesign Center for Biosignatures Discovery Automation. exhaustive search of the literature revealed no published re- ports evaluating reference genes for the LPS-stimulated J774A.1 macrophage model. This is an important area of investigation, Author Contributions due to the known pitfalls in reference gene selection. For in- stance, previous studies have shown that reference genes like R.K., L.B., and C.Z. devised the project. R.K. and L.B. con- GAPDH and ACTB contain many pseudogenes, which skew the ducted experiments. R.K. analyzed the data. R.K., H.L.G., and measured expression levels . Generally, reference genes W.G. prepared the manuscript. D.R.M., W.G., and H.L.G. di- need to be evaluated independently for each experimental sys- rected the work and edited the manuscript. tem to ensure stability in the cell type of interest and under the specific experimental conditions to be tested . Conﬂict of interest statement. None declared. Downloaded from https://academic.oup.com/biomethods/article-abstract/1/1/bpw005/2743811 by Ed 'DeepDyve' Gillespie user on 08 June 2018 M Value Stable reference genes in LPS-stimulated macrophages | 7 20. Blonska M, Bronikowska J, Pietsz G et al. Effects of ethanol ex- References tract of propolis (EEP) and its ﬂavones on inducible gene expres- 1. Bustin SA, Benes V, Garson JA et al. The MIQE guidelines: min- sion in J774A.1 macrophages. J Ethnopharmacol 2004;91:25–30. imum information for publication of quantitative real-time 21. Ito K, Lim S, Caramori G et al. A molecular mechanism of ac- PCR experiments. Clin Chem 2009;55:1–12. tion of theophylline: induction of histone deacetylase activity 2. Shi J, Zhao Y, Wang Y et al. Inﬂammatory caspases are innate to decrease inﬂammatory gene expression. Proc Natl Acad Sci immune receptors for intracellular LPS. Nature 2014;514: USA 2002;99: 8921–26. 187–92. 22. Strieter ARM, Kunkel SL, Showell HJ et al. Endothelial cell 3. Meng F, Lowell CA. Lipopolysaccharide (LPS)-induced macro- gene expression of a neutrophil chemotactic factor by TNF- phage activation and signal transduction in the absence of alpha, LPS, and IL-1 beta. Science 1989;243:1467–69. Src-family kinases Hck, Fgr, and Lyn. J Exp Med 23. Peteroy-Kelly M, Venketaraman V, Connell ND. Effects of 1997;185:1661–70. Mycobacterium bovis BCG infection on regulation of L-argi- 4. Hirschfeld M, Weis JJ, Toshchakov V et al. Signaling by toll-like nine uptake and synthesis of reactive nitrogen intermediates receptor 2 and 4 agonists results in differential gene expres- in J774.1 murine macrophages. Infect Immun 2001;69:5823–31. sion in murine macrophages. Infect Immun 2001;69:1477–82. 24. Ishii T, Masuzaki H, Tanaka T et al. Augmentation of 11beta- 5. Ruffell D, Mourkioti F, Gambardella A et al. A CREB-C/EBPbeta hydroxysteroid dehydrogenase type 1 in LPS-activated J774.1 cascade induces M2 macrophage-speciﬁc gene expression macrophages—role of 11beta-HSD1 in pro-inﬂammatory and promotes muscle injury repair. Proc. Natl. Acad Sci U S A properties in macrophages. FEBS Lett 2007;581:349–54. 2009;106:17475–480. 25. Zhang Y, Lian F, Zhu Y et al. Cyanidin-3-O-beta-glucoside in- 6. Luyendyk JP, Schabbauer GA, Tencati M et al. Genetic analysis hibits LPS-induced expression of inﬂammatory mediators of the role of the PI3K-Akt pathway in lipopolysaccharide- through decreasing I kappa B alpha phosphorylation in THP- induced cytokine and tissue factor gene expression in mono- 1 cells. Inﬂamm Res 2010;59:723–30. cytes/macrophages. J Immunol 2008;180:4218–26. 26. Nelissen K, Smeets K, Mulder M et al. Selection of reference 7. Kim C, Sano Y, Todorova K.et al. The kinase p38-alpha serves genes for gene expression studies in rat oligodendrocytes using cell type–speciﬁc inﬂammatory functions in skin injury and quantitative real time PCR. JNeurosciMethods 2010;187:78–83. coordinates pro- and anti-inﬂammatory gene expression. Nat 27. Pe ´ rez R, Tupac-Yupanqui I, Dunner S. Evaluation of suitable Immunol 2008;9:1019–27. reference genes for gene expression studies in bovine muscu- 8. Chawla A, Barak Y, Nagy L et al. PPAR-gamma dependent and lar tissue. BMC Mol Biol 2008;9:1–6. independent effects on macrophage-gene expression in lipid 28. Ferraz FB, Fernandez JH. Selection and validation of reference metabolism and inﬂammation. Nat Med 2001;7:48–52. house-keeping genes in the J774A1 macrophage cell line for 9. Spinsanti G, Zannolli R, Panti C et al. Quantitative Real-Time quantitative real-time PCR. Genet Mol Res GMR 2016;15:5017720. PCR detection of TRPV1-4 gene expression in human leuko- 29. Vandesompele J, De Preter K, Pattyn F et al.Accurate normali- cytes from healthy and hyposensitive subjects. Mol. Pain zation of real-time quantitative RT-PCR data by geometric av- 2008;4:51. eraging of multiple internal control genes. Genome Biol. 10. Nackiewicz D, Dan M, He W et al. TLR2/6 and TLR4-activated 2002;3:RESEARCH0034. macrophages contribute to islet inﬂammation and impair 30. Andersen CL, Jensen JL, Orntoft TF. Normalization of real- beta cell insulin gene expression via IL-1 and IL-6. Diabetologia time quantitative reverse transcription-PCR data: a model- 2014;57:1645–54. based variance estimation approach to identify genes suited 11. Gutierrez L, Mauriat M, Gue ´ nin S et al. The lack of a systematic for normalization, applied to bladder and colon cancer data validation of reference genes: a serious pitfall undervalued in sets. Cancer Res 2004;64:5245–50. reverse transcription-polymerase chain reaction (RT-PCR) 31. Caamano ~ BJH, Rizzo CA, Durham SK et al. Nuclear factor (NF)- analysis in plants. Plant Biotechnol J 2008;6:609–18. kappa B2 (p100/p52) is required for normal splenic microarch- 12. Coussens LM, Werb Z. Inﬂammation and cancer. Nature itecture and B cell-mediated immune responses. J. Exp. Med. 2002;420:860–67. 1998;187:147–59. 13. Karin M, Greten FR. NF-kappaB: linking inﬂammation and im- 32. Smith LK, He Y, Park JS et al. b2-microglobulin is a systemic munity to cancer development and progression. Nat Rev pro-aging factor that impairs cognitive function and neuro- Immunol 2005;5:749–59. genesis. Nat Med 2015;21:932–37. 14. Yndestad A, Dama ˚ s JK, Øie E et al. Role of inﬂammation in the 33. Colgan J, Asmal M, Luban J. Isolation, characterization and progression of heart failure. Curr Cardiol Rep 2007;9:236–41. targeted disruption of mouse PPIA: cyclophilin A is not essen- 15. Donath MY, Shoelson SE. Type 2 diabetes as an inﬂammatory tial for mammalian cell viability. Genomics 2000;68:167–78. disease. Nat Rev Immunol 2011;11:98–107. 34. Nicholls C, Li H, Liu JP. GAPDH: a common enzyme with un- 16. Navarro-Gonzalez JF, Mora-Fernandez C. The role of inﬂam- common functions. Clin. Exp Pharmacol Physiol 2012;39:674–79. matory cytokines in diabetic nephropathy. J Am Soc Nephrol 35. Duan J, Nilsson L, Lambert B. Structural and functional analy- 2008;19:433–42. sis of mutations at the human hypoxanthine phosphoribosyl 17. Holmes C, Cunningham C, Zotova E et al. Woolford, J., Dean, transferase (HPRT1) locus. Hum Mutat 2004;23:599–611. C., Kerr, S., Culliford, D., Perry, V. H. Systemic inﬂammation 36. Artero-Castro A, Castellvi J, Garcia A et al. Expression of the ri- and disease progression in Alzheimer disease. Neurology bosomal proteins Rplp0, Rplp1, and Rplp2 in gynecologic tu- 2009, 73, 768–74. mors. Hum Pathol 201;42:194–203. 18. Saeed S, Quintin J, Kerstens HHD et al. Epigenetic program- 37. Hsu TC, Wang CK, Yang CY et al. Deactivation of TBP contrib- ming of monocyte-to-macrophage differentiation and utes to SCA17 pathogenesis. Hum Mol Genet 2014;23:6878–93. trained innate immunity. Science 2014;345:1251086. 38. Baker RT, Board PG. Unequal crossover generates variation in 19. Wynn TA, Chawla A, Pollard JW. Macrophage biology in de- ubiquitin coding unit number at the human UbC polyubiqui- velopment, homeostasis and disease. Nature 2013;496: tin locus. Am J Hum Genet 1989;44:534–42. 445–55. Downloaded from https://academic.oup.com/biomethods/article-abstract/1/1/bpw005/2743811 by Ed 'DeepDyve' Gillespie user on 08 June 2018 8| Kalagara et al. 39. Jia J, Arif A, Willard B et al. Protection of extraribosomal 47. Cua DJ, Sherlock J, Chen Y et al. Interleukin-23 rather than RPL13a by GAPDH and dysregulation by S-nitrosylation. Mol interleukin-12 is the critical cytokine for autoimmune in- Cell 2012;47:656–63. ﬂammation of the brain. Nature 2003;421:744–48. 40. Yaron JR, Rao MY, Gangaraju S et al. The oxindole Syk inhibi- 48. Sessa WC, Pritchard K, Seyedi N et al. Chronic exercise in dogs tor OXSI-2 blocks nigericin-induced inﬂammasome signaling increases coronary vascular nitric oxide production and en- and pyroptosis independent of potassium efﬂux. Biochem dothelial cell nitric oxide synthase gene expression. Circ Res Biophys Res Commun 2016;472:545–50. 1994;74:349–53. 41. Yaron JR, Gangaraju S, Rao MY et al. LK(þ) regulates Ca(2þ)to 49. Hagemann T, Wilson J, Burke F et al. Ovarian cancer cells po- drive inﬂammasome signaling: dynamic visualization of ion larize macrophages toward a tumor-associated phenotype. ﬂux in live cells. Cell Death Dis 2015;6:1–11. J Immunol 2006;176:5023–32. 42. Boda E, Pini A, Hoxha E et al. Selection of reference genes for 50. Martinez, FO, Gordon S, Locati M et al. Transcriptional proﬁl- quantitative real-time RT-PCR studies in mouse brain. J Mol ing of the human monocyte-to-macrophage differentiation Neurosci 2009;37:238–53. and polarization: new molecules and patterns of gene expres- 43. Cameron RC, Duncan EJ, Dearden PK. Biased gene expression in sion. J Immunol 2006;177:7303–11. early honeybee larval development. BMC Genomics 2013;14:903. 51. Aung HT, Schroder K, Himes SR et al. LPS regulates proinﬂam- 44. McCulloch RS, Ashwell MS, Nan ATO et al. Identiﬁcation of matory gene expression in macrophages by altering histone stable normalization genes for quantitative real-time PCR in deacetylase expression. FASEB J 2006;20:1315–27. porcine articular cartilage. J Anim Sci Biotechnol 2012;3:1–7. 52. Park J, Ogunnaike B, Schwaber J, et al. Identifying functional 45. Sun Y, Li Y, Luo D et al. Pseudogenes as weaknesses of ACTB gene regulatory network phenotypes underlying single cell (Actb) and GAPDH (Gapdh) used as reference genes in reverse transcriptional variability. Prog Biophys Mol Biol transcription and polymerase chain reactions. PLoS One 2015;117:87–98. 2012;7(8):e41659. 53. Marr C, Zhou JX, Huang S. Single-cell gene expression 46. Radonic A, Thulke S, Mackay IM et al. Guideline to reference proﬁling and cell state dynamics: collecting data, correlating gene selection for quantitative real-time PCR. Biochem Biophys data points and connecting the dots. Curr Opin Biotech Res Commun 2004;313(4):856–62. 2016;39:207–14. Downloaded from https://academic.oup.com/biomethods/article-abstract/1/1/bpw005/2743811 by Ed 'DeepDyve' Gillespie user on 08 June 2018
Biology Methods and Protocols – Oxford University Press
Published: Dec 27, 2016
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera