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G3 (Bethesda). 2017 April; 7(4): 1085–1095.
Published online 2017 March 3. doi:  10.1534/g3.116.038075
PMCID: PMC5386857

A Whole-Transcriptome Approach to Evaluating Reference Genes for Quantitative Gene Expression Studies: A Case Study in Mimulus

Abstract

While quantitative PCR (qPCR) is widely recognized as being among the most accurate methods for quantifying gene expression, it is highly dependent on the use of reliable, stably expressed reference genes. With the increased availability of high-throughput methods for measuring gene expression, whole-transcriptome approaches may be increasingly utilized for reference gene selection and validation. In this study, RNA-seq was used to identify a set of novel qPCR reference genes and evaluate a panel of traditional “housekeeping” reference genes in two species of the evolutionary model plant genus Mimulus. More broadly, the methods proposed in this study can be used to harness the power of transcriptomes to identify appropriate reference genes for qPCR in any study organism, including emerging and nonmodel systems. We find that RNA-seq accurately estimates gene expression means in comparison to qPCR, and that expression means are robust to moderate environmental and genetic variation. However, measures of expression variability were only in agreement with qPCR for samples obtained from a shared environment. This result, along with transcriptome-wide comparisons, suggests that environmental changes have greater impacts on expression variability than on expression means. We discuss how this issue can be addressed through experimental design, and suggest that the ever-expanding pool of published transcriptomes represents a rich and low-cost resource for developing better reference genes for qPCR.

Keywords: expression stability, Mimulus guttatus, Mimulus luteus, quantitative RT-PCR, RNA-seq

qPCR is the premier method for quantifying gene expression because of its simplicity, accuracy, and low cost. However, the quantification accuracy of qPCR is dependent on normalization against reference genes to reduce the impact of technical noise and variation in sample preparation. qPCR data normalization is crucial for the reliable quantification of expression levels, so care must be taken to choose a reliable reference gene that has low variation in expression across diverse sample types (Dheda et al. 2005; Gutierrez et al. 2008). Traditionally, high expression housekeeping genes involved in basic cellular functions were used for qPCR normalization based on the assumption that they would be stably expressed (Thellin et al. 1999). Unfortunately, these traditional housekeeping reference genes, such as ubiquitin-conjugating enzyme (UBC), polyubiquitin (UBQ), β-actin, α- and β-tubulin, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH), can exhibit surprisingly high expression variance in some species, or among different environmental conditions (Brunner et al. 2004; Czechowski et al. 2005; Dheda et al. 2004; Suzuki et al. 2000).

In efforts to find alternatives to housekeeping genes, high-throughput technologies have been used to survey whole transcriptomes for novel, stably expressed genes. Microarrays have been successfully used for novel reference gene identification in a variety of plants, including Arabidopsis thaliana, Eucalyptus, and soybean (Czechowski et al. 2005; Libault et al. 2008; de Oliveira et al. 2012). However, RNA-seq, a potentially more effective high-throughput method, has rarely been employed. RNA-seq has many advantages over microarrays: it does not require an assembled genome (Haas and Zody 2010; Grabherr et al. 2011; Robertson et al. 2010), it has the power to identify novel transcripts and splice variants (Trapnell et al. 2010), and it is sensitive enough to quantify transcripts with very low expression levels (Marioni et al. 2008). In addition, RNA-seq is fast, relatively inexpensive, and shows minimal variation across technical replicates (Marioni et al. 2008; Wang et al. 2009; Mortazavi et al. 2008; Nagalakshmi et al. 2008). For all of these reasons, RNA-seq is an attractive, whole-transcriptome method for the detection of stably expressed genes and the identification of novel reference genes for qPCR normalization. This approach has rarely been used to evaluate potential qPCR reference genes [but see Chang et al. (2012), Yang et al. (2014), and Zhuang et al. (2015)].

A potential pitfall of both the microarray and the RNA-seq approach to reference gene selection is that there are no accepted practices for the analysis of expression variability within whole transcriptomes. Many methods for analyzing expression variability from qPCR data have been developed, including geNorm, BestKeeper, and NormFinder (Andersen et al. 2004; Vandesompele et al. 2002; Pfaffl et al. 2004), but these programs can only analyze the expression data from a handful of genes at a time and, thus, are not useful for exploring whole transcriptomes. Without an established method for analysis, many diverse methods have been adopted for estimating expression variability within whole transcriptomes, including coefficient of variation (CV) calculations (Czechowski et al. 2005), fold change cut-offs (Yang et al. 2014), and P-value cut-offs (Libault et al. 2008). However, no comparison of the different methods is currently available; each of the earlier studies included only a single whole-transcriptome measure of expression variability.

One system in which a transcriptomic approach to reference gene selection has great potential to advance gene expression studies is the monkeyflower genus Mimulus [recently split into genera Mimulus and Erythranthe (Barker et al. 2012)]. Mimulus has become a widely used model for evolutionary genetic studies because of its phenotypic, ecological, and genetic variation, with centers of species diversity in both North and South America (Wu et al. 2008; Sobel and Streisfeld 2013; Beardsley and Olmstead 2002; Twyford et al. 2015). Mimulus is a powerful system for genetic studies due to the interfertility of diverse species and the availability of genomic resources, including the genome sequence of Mimulus guttatus, M. cardinalis, M. lewisii, and M. luteus (Hellsten et al. 2013; Yuan et al. 2013; Edger et al. 2016). Yet, despite the utility of Mimulus for studying the evolution of genes and gene expression, the only evaluation of qPCR reference genes to date is a nonquantitative assessment of six housekeeping genes (Scoville et al. 2011). A rigorous and quantitative genome-wide analysis of candidate qPCR reference genes is therefore of special utility for advancing evolutionary genetic studies in Mimulus.

In this study, we systematically and quantitatively evaluate a panel of traditional reference genes and screen whole transcriptomes to identify a set of novel reference genes that can be used for qPCR expression studies in Mimulus. We utilize whole-transcriptome RNA-seq libraries from two species: M. guttatus, a North American diploid, and M. luteus var. luteus, a Chilean allotetraploid (Mukherjee and Vickery 1962; Vallejo‐Marín et al. 2015). We further develop the toolkit for transcriptome-enabled reference gene selection by comparing the utility of two distinct methods—the “CV method” and the “fold change cut-off method”—for identifying novel stably expressed genes from RNA-seq data.

In these two Mimulus species, which differ in their ecology, ploidy, and level of resource development, we find that both the CV and fold change methods identify a similar set of novel reference genes. We propose that these highly stable genes provide a good starting pool of candidate reference genes for qPCR expression studies in Mimulus, and report that some traditional reference genes are also satisfactory according to standard quantitative guidelines for qPCR. In addition, we propose a workflow that incorporates either the CV or the fold change method to screen whole transcriptomes for novel reference genes in other systems. Across environmentally and genetically different plants, we found that gene expression means were relatively similar but expression variability fluctuated dramatically. Based on this finding, we suggest that transcriptomes should either be specific to the samples used for the planned qPCR study or should cover a wide span of biological and environmental diversity, in order for reference genes to be selected with high confidence.

Materials and Methods

Plant materials

Two batches of each species were grown in separate greenhouses, providing the RNA samples for both RNA-seq and qPCR (Figure 1). M. guttatus genotype CG (Dublane, Scotland) and M. luteus var. luteus inbred line EY7 [El Yeso, Chile, see Cooley et al. (2008)]were grown at Duke University (NC). Transcriptome T1 was produced for M. l. luteus using stem, calyx, and petal tissues from a single individual grown at Duke University (NC). For M. guttatus, stem, calyx, petal, and leaf were sampled from two CG plants grown at Duke University. For transcriptome T2, M. guttatus inbred line IM767 [Iron Mountain, OR, see Willis (1999)] and M. l. luteus inbred line EY7 (El Yeso, Chile) were grown at Whitman College (WA). RNA from four tissue types of a single individual of each species grown at Whitman College was sequenced to produce the second set of transcriptomes (T2). Two different leaf samples were collected from M. l. luteus and processed separately (L2A and L2B), to compensate for the lack of a leaf transcriptome in T1. RNA from four tissue types of four individuals (one of which was the same individual used for the T2 transcriptomes) from each species grown at Whitman College was extracted for use in qPCR.

Figure 1
Sources of the plant materials that provided RNA for RNA-seq and qPCR. Because no leaf sample was available for M. l. luteus T1, two leaves were collected and sequenced for M. l. luteus T2, to enable leaf-to-leaf comparisons within M. l. luteus. M. guttatus ...

In the Whitman greenhouse, plants were grown with supplemental 14 hr lighting in Miracle-Gro potting soil (N:P:K = 0.21:0.11:0.16, The Scotts Company, Marysville, OH). Plants were maintained on “self-watering” capillary action flats with once-daily top-watering. Greenhouse temperatures, as recorded by a wall sensor, ranged from 16° to 36° daily. Plants were fertilized twice weekly with Miracle-Gro Bloom Booster (N:P:K = 15:30:15). In the Duke greenhouse, plants were grown with supplemental 16 hr lighting with twice-daily watering. Greenhouse temperatures ranged from 12° to 21° daily. Plants were fertilized with Peter’s Professional fertilizer every 2 wk, alternating between general purpose (N:P:K = 20:10:20) and low-phosphorus (N:P:K = 15:0:15) formulas, and fertilized with Jack’s Classic Blossom Booster (JR Peters INC, PA) (N:P:K = 10:30:20) every week to enhance flowering.

Tissue was harvested from young, budding plants, usually between the first and third flower. Four tissue types were collected: young leaf (<2.5 cm) near apical and lateral meristems, whole calyx from unemerged buds, petal (with stamen and pistil removed) from unemerged buds, and stem (~2.5 cm segments) from newer plant growth. Tissue samples were flash frozen in liquid nitrogen and stored at −80° until the date of RNA extraction.

Transcriptome preparation and assembly

Total RNA was isolated from four tissue types (stem, leaf, calyx, and petal) from both M. guttatus and M. l. luteus. At Whitman, the Agilent Plant RNA Isolation Kit (Santa Clara, CA) was used, and at Duke, the Zymo Research Direct-Zol RNA MiniPrep (Irvine, CA) was used, following the manufacturer’s protocol, with on-column DNase I and elution in nuclease free water heated to 65°. RNA concentration and integrity were assessed using a NanoDrop Lite spectrophotometer (Thermo Fisher Scientific, DE) or Qubit fluorometer (Thermo Fisher Scientific).

Whole-transcriptome, RNA-seq libraries were constructed for four tissue types from each of two biological replicates (T1 and T2) for both M. guttatus and M. l. luteus (see Figure 1). T1 transcriptomes were prepared using the TruSeq RNA kit (Illumina, San Diego, CA) and then sequenced with single-end 100 bp reads using one lane of an Illumina HiSeq-2000 at the University of Missouri DNA core. T2 transcriptomes were prepared using the Kapa Stranded mRNA-Seq kit (Kapa Biosystems, Wilmington, MA) and were sequenced using one lane of an Illumina Hiseq-2500 at the Duke University DNA core.

All Illumina reads were quality filtered using NextGENe v2.3.3.1 (SoftGenetics, State College, PA). Adapter sequences and reads with a median quality score of <22 were removed, reads were trimmed at positions that had three consecutive bases with a quality score of <20, and any trimmed reads with a total length <40 bp were removed. This resulted in ~87.9% of the reads passing the quality-score filter. Expression levels, in FPKM (fragments per kilobase per million reads), were determined for a total of 25,465 genes in M. guttatus (diploid) and 46,855 genes in M. l. luteus (tetraploid). Quality-filtered reads for each library were aligned to the respective genomes using NextGENe v2.3.3.1. Only uniquely mapped reads were counted, using the following parameters: A. matching Requirement: >40 Bases and >99%, B. Allow Ambiguous Mapping: FALSE, and C. Rigorous Alignment: TRUE. This resulted in the alignment of over 74.4 million reads to the diploid M. guttatus genome and 107.4 million reads to the tetraploid M. l. luteus genome.

Genome completeness of the allotetraploid M. l. luteus in terms of gene content was assessed using BUSCO (Simão et al. 2015) with the default setting and a set of universal single-copy orthologs. The vast majority of BUSCO groups, 931 of 956 (97.4%), were identified in the M. l. luteus genome assembly, and 837 of those had duplicates. The high percentage of duplicate genes in this analysis indicates that homeologs were not collapsed during the assembly of the genome. This is further supported by comparative genomic analyses of both Mimulus genomes (Edger et al. 2016), revealing a 2:1 genome-wide ratio of M. l. luteus (tetraploid): M. guttatus (diploid) syntenic blocks.

Analysis of RNA-seq libraries

Within each species, genes with expression levels lower than five FPKM in any of the eight transcriptomes were excluded from any of the further stability analyses. We reasoned that such low-expression genes would make poor qPCR references due to the difficulties in detecting and quantifying their expression. After their removal, a total of 7225 genes in M. guttatus and 10,755 genes in M. l. luteus were evaluated. Two methods were used for the analysis of expression stability: simple CV calculations and exclusion of differentially expressed genes [fold change method (Robinson et al. 2010)].

For the CV method:

Calculations for mean expression (mean), SD, and the CV were executed in Microsoft Excel or in R (Pumpkin Helmet, v.3.1.2). CV was calculated as SD/mean. Mean and SD were measured over the four tissue types of both biological replicates (eight samples in total) for each species. We adopted a CV cut-off for stable genes of 0.5, which was the cut-off for stable expression across heterogeneous samples advocated by Hellemans et al. (2007).

For the fold change method:

Log fold change was used to evaluate differential expression in pairwise sample comparisons. Genes with a high fold change (>0.4 in M. guttatus and 0.3 in M. l. luteus) in any pairwise sample comparison were eliminated until a final list of stably expressed genes was obtained (Supplemental Material, Table S1). The cut-off values used in this study were selected so as to obtain a short list of genes with low variation in expression; the appropriate cut-off value can vary depending on the samples being analyzed and the overall goal of the analysis. The edgeR program (v. 3.12.0) was used to calculate log fold change because the program normalizes expression values by library size for each sample, but any method of fold change calculation can be used. The edgeR program was accessed through Bioconductor and analysis was executed in R.

Gene annotation

Stably expressed genes were annotated based on the agreement between BLAST results from the NCBI nucleotide database (http://blast.ncbi.nlm.nih.gov/) and from the annotated M. guttatus v.2 genome in the Phytozome v.10 database (http://phytozome.jgi.doe.gov/). Traditional reference genes were identified in the RNA-seq datasets in a three-part method. First, known A. thaliana sequences for traditional reference genes 60s ribosomal protein L8 (L8), actin 2/7 (ACT), actin 11 (ACT1), β-tubulin 2 (TUB), ubiquitin 5 (UBQ), UBC 25, peroxin 4 (PEX), GAPDH-C1 (GAP), and EF1-α (EF1) (see Table S2) were used in a BLAST search against the M. guttatus v.2 genome in the Phytozome v.10 database in order to identify the appropriate M. guttatus homologs. Once a gene match with the correct annotation was identified in Phytozome, a short (~20 bp) sequence from the coding region was then used to identify transcripts from the M. guttatus and M. l. luteus RNA-seq libraries. The resulting M. guttatus and M. l. luteus transcripts were used in a BLAST search against the NCBI nucleotide database to ensure that they had been correctly identified.

qPCR genes

Eight genes were selected for validation via qPCR (Table S3). Four traditional reference genes were selected based on both their widespread use in qPCR reference gene literature and on the ease of designing copy-specific primers. The four traditional genes chosen were ACT, GAP, PEX, and UBC. See the above section on Gene annotation for methods of gene identification within the transcriptome. Four additional genes were chosen based on their apparent stability across T1 tissues in both species, but were later found to be unstably expressed across T2 tissues (see Table S3). However, these genes were retained for analysis in order to compare the qPCR and RNA-seq methods. The four genes chosen were mediator of RNA polymerase 12 (MRP), pectin acetylesterase (PAE), receptor-like kinase (RPK), and FYVE zinc-finger transcription factor (ZNF). The M. guttatus GenBank accession numbers for these eight genes, cataloged under Erythranthe guttata (Barker et al. 2012), are: ACT = XM_012974510.1, GAP = XM_012999102.1, PEX = XM_013002418.1, UBC = XM_012995233.1, MRP = XM_012984744.1, PAE = XM_012984356.1, RPK = XM_012985914.1, and ZNF = XM_013000433.1.

qPCR primer design

qPCR primers were designed using Primer3 (http://biotools.umassmed.edu/bioapps/primer3_www.cgi) with the following criteria: Tm of 60 ± 3°, PCR amplicon length of 130–250 bp, primer length of 18–25 bp, and GC content of 35–60%. The Tm criterion was relaxed for UBC to 55 ± 3° to enable the discovery of suitable primers. Primers were designed to optimally sit as close to the 3′-end of the transcript as possible and to span an intron, but these criteria were relaxed in an effort to design primers that are homeolog-specific in the allotetraploid M. l. luteus. M. l. luteus primers were aligned with BLAST against the M. l. luteus (Illumina masked v1.1) genome in CoGe (https://genomevolution.org/CoGe/) to ensure homeolog and paralog specificity. M. guttatus primers were aligned with BLAST against the M. guttatus genome (JGI hardmasked vV2) in CoGe to ensure copy specificity. Primers were synthesized by Invitrogen (Life Technologies). See Table S4 for the full list of primer pairs.

To verify primer specificity, PCR products were amplified by Taq DNA polymerase in a Mastercycler Nexus (Eppendorf, Germany), gel purified using the E.Z.N.A. kit (Omega Biotek), and Sanger sequenced by Eton Bioscience. Although all primers produced a single band on an agarose gel, the gel extraction step was included to produce cleaner and more concentrated sequencing products. Sequencing confirmed the copy specificity of all primer pairs except for the M. l. luteus RPK and PEX primer pairs, which targeted two and three paralogs, respectively.

cDNA synthesis

cDNA was synthesized from 1 μg of total RNA and a mixture of oligo dT and random primers using the Quanta qScript cDNA Synthesis kit (Quanta BioSciences, MD) and following the manufacturer’s protocol. cDNA was stored at 4° and unused RNA was stored at −80°.

Quality controls for cDNA were twofold. First, all RNAs and cDNAs were checked for the absence of genomic DNA contamination using primers that surround an actin intron (5′-CCCAAGGCTAACAGGGAGAA-3′ and 5′-GTGCTGGATTCTGGTGACG-3′). Second, gene expression estimates were obtained from the 3′- vs. 5′-ends of a single gene. A 3′/5′ ratio substantially greater or less than 1 may indicate degradation of the mRNA template, or incomplete processivity of the reverse transcription reaction. The MIQE guidelines (Bustin et al. 2009) suggest a range of 0.2–5.0 for samples to be used in qPCR. The 3′/5′ ratio of the receptor-like protein kinase cDNA was tested for all tissue types in each individual used in this study, using two primer pairs that amplify in the 5′ region (5′-TGGGCTCGAGTATTTTGCTT-3′ and 5′-TGCTTCCTAATCCAAAGATACCA-3′) or the 3′ region (5′-CCTGAGGGTGACAAGACACA-3′ and 5′-ATCAATGGACAAAAGCAGGC-3′) ~1 kb away from each other. Some 3′/5′ ratios were found to be >5 (see Table S5). This could result in an underestimation of expression for genes with primers in the 5′ region of the gene, which includes ACT in both species and the M. guttatus ZNF. The 3′/5′ ratios also had a tissue bias, with all stem cDNA samples and some of the calyx cDNA samples having values >5.

qPCR conditions

Comparative qPCR was performed for four biological replicates (all from plants grown at Whitman College, see Plant materials) and three technical replicates for each tissue type (leaf, stem, petal, and calyx) from each of the two species (M. guttatus and M. l. luteus). A total of eight genes were selected for qPCR validation (see section qPCR genes and Table S3) using the primers listed in Table S4. Reactions contained 1 × SYBR Green Master Mix (Brilliant III Ultra-Fast SYBR Kit, Agilent Technologies, CA), 400 nM of primer (except for when amplifying PEX4 from M. l. luteus, where 500 nM of primer was used), 1 μl of 1:500 diluted ROX dye, and 1 μl of cDNA (50 ng/μl), in a final volume of 12.5 μl. PCR reactions were performed in either optical eight-well PCR strips (Agilent Technologies) or optical 96-well plates (Greiner Bio-One, Belgium) using the Stratagene Mx3000P qPCR system (Agilent Technologies). Samples were amplified for 40 cycles of 10 sec at 95° and 20 sec at the appropriate annealing temperature (see Table S4), after an initial denaturation step at 95° for 3 min. An additional dissociation curve was recorded after cycle 40 by heating from 55° to 95° with a ramp speed of 0.01° per second (Figure S1). Raw qPCR fluorescence data were collected and analyzed by the default settings of the MxPro software v.4.10 (Agilent Technologies). Cq (“quantification cycle,” the cycle in which fluorescence from DNA amplification first exceeds background fluorescence) was determined at a fluorescence threshold of 0.23 for all runs; this fixed threshold was based on the average adaptive threshold of all individual runs. Amplification efficiencies for each primer pair were determined using the Cq values obtained from a 1/4 dilution series (1:4, 1:16, 1:64, 1:256, and 1:1024) where E = 10(1/−slope). Efficiency for each primer pair was calculated to be between 83 and 102% using the standard curve method (Table S6).

Analysis of qPCR expression data

Before analysis, the Cq values from qPCR were averaged over the three technical replicates, unless the replicates differed by >1 Cq. In that case, the outlier technical replicate was removed and Cq was averaged over the two remaining technical replicates. These averages were then both calibrator and efficiency normalized using the equation below. GAP amplified from the same sample of M. l. luteus young leaf cDNA acted as the interplate calibrator. Efficiency values for each gene are listed in Table S6. Relative expression of each gene was calculated as:

RelativeExpression=EfficiencyΔCq,whereΔCq=(Cqcalibrator−Cqsample)

In order to have a metric of gene stability that could be directly compared to stability estimates from RNA-seq data, the CV was calculated for each gene from the relative qPCR expression data. Calculations for mean expression (mean), SD, and for the CV (CV = SD/mean) were executed in Microsoft Excel. SD and mean were calculated from the relative expression of each of the four tissue types, averaged over the four biological replicates per tissue.

Statistical analyses

All statistical tests were run using R software (Pumpkin Helmet, v.3.1.2). Linear models were fitted to obtain t-test results and Pearson’s correlation coefficient.

Data availability

All transcriptomic expression data are provided in Table S7 and Table S8. Primer sequences are provided in Table S4. Raw reads from this study are deposited in Dryad (http://dx.doi.org/10.5061/dryad.84655) and are further analyzed in Edger et al. (2016).

Results

Identification of novel reference genes for Mimulus

In order to identify potential qPCR reference genes, we compared two simple methods for evaluating variation in expression across tissue types and growing environments: (a) genes with the lowest overall CV across all tissues from both transcriptome sets (T1 and T2; see Figure 1) and (b) exclusion of differentially expressed genes, determined through calculations of fold change, between pairwise comparisons of all tissue samples from both transcriptome sets. We identified 50 genes per species using the CV method and eight genes per species using the fold change method (Table S1 and Table S9) that have the potential to be good candidate reference genes for qPCR studies in Mimulus.

Although CV was not correlated with total expression level (Figure S2), we used a minimum expression cut-off of 5 FPKM in order to exclude genes that are expressed at levels too low to be useful for qPCR normalization. The 50 genes with the lowest CV across both biological replicates of each species are listed in Table S9. Genes on this list have CVs <0.14 for M. guttatus and <0.12 for M. l. luteus. Although a 0.50 CV cut-off has previously been recommended for choosing qPCR reference genes (Hellemans et al. 2007), we find that the majority of robustly expressed genes fall under this cut-off (Figure 2). In M. guttatus, 4106 genes out of 7225 had a CV of <0.50; in M. l. luteus, 6832 genes out of 10,755 were under this cut-off.

Figure 2
Distribution of CV for all reliably expressed genes (>5 FPKM in all samples) in (A) M. guttatus and (B) M. l. luteus. The dashed line marks the 0.50 CV cut-off for stably expressed genes and the arrows point to the two traditional reference genes ...

For the fold change method, any genes with a log fold change >0.4 in M. guttatus or 0.3 in M. l. luteus, in any pairwise sample comparison, were excluded. Eight M. guttatus and eight M. l. luteus genes were identified in this manner that had low variation in expression across the four tissue types from two biological replicates (Table S1). The fold change method was consistent with the CV method; five M. guttatus genes and one M. l. luteus gene identified by the fold change method are also found on the top 50 CV list, and all of the genes identified by the fold change method are listed within the top 200 genes with the lowest CV (Table S10).

Traditional reference genes in Mimulus

Since traditional reference genes can be inconsistently expressed in many biological systems (Brunner et al. 2004; Czechowski et al. 2005; Dheda et al. 2004; Suzuki et al. 2000), we investigated the expression variability of these traditional housekeeping reference genes in Mimulus using both transcriptomics and qPCR. We chose nine common traditional reference genes to analyze from the RNA-seq datasets: L8, ACT, ACT1, TUB, UBQ, UBC, PEX, GAP, and EF1 (see Table S2). We then corroborated the expression variability for four of these nine genes (ACT, PEX, UBC, and GAP) using qPCR (see Table S3).

In both M. guttatus and M. l. luteus, there were thousands of expressed genes with lower CVs than the traditional housekeeping genes (Figure 2 and Table S2), and none of the traditional housekeeping genes were among the 16 genes identified by the fold change method. Nevertheless, four traditional genes in M. guttatus (GAP, UBC, TUB, and PEX) and four in M. l. luteus (L8, GAP, ACT, and UBC) do have CVs <0.5, suggesting that they could be useful reference genes for qPCR normalization in these species (Figure 3).

Figure 3
Expression variability estimates for selected traditional reference genes, based on coefficient of variation (CV). Expression variability in M. guttatus (A) and M. l. luteus (B) measured via RNA sequencing (RNA-seq) on both T1 and T2 (left column) or ...

The follow-up qPCR validation reported much lower expression variability for the tested subset of traditional genes. This is most likely due to a less variable group of plants being measured for qPCR than were measured for RNA-seq (see Figure 1). Expression variability was even lower when measures from petal tissue were excluded (Figure 3), as expression levels for all four tested genes were substantially higher in petal tissue than in the other three tissue types (Figure S3). This is only the case for the qPCR data and there is no trend in the RNA-seq data when petal is excluded, even though transcriptome T2 was derived from one of the same RNA samples that was used for qPCR. When all tissues were included in the qPCR variability calculations, we found that GAP had the lowest variation in expression in M. guttatus and PEX was the least variable in M. l. luteus. When petal was excluded, UBC was the least variable traditional reference gene in both species.

Efficacy of transcriptomics for reference gene selection

Although environmental condition was not a purposeful manipulation in our study, the different growth histories of the genetically identical plants used for the two M. l. luteus transcriptome sets allowed us to evaluate the robustness of gene expression to moderate environmental variation. This was achieved by comparing both mean expression and expression variability (measured by CV) across the different tissue types between T1 and T2. For comparison, we also evaluated the two M. guttatus transcriptome sets, although the plants used in this comparison were genetically as well as environmentally different (see Figure 1).

The correlation in CV between T1 and T2 is weaker than the correlation in mean expression for both species, showing a stronger environmental effect on the variance than on the mean (Figure 4). Additionally, CV estimates were more closely correlated between the replicates of M. l. luteus than between the replicates of M. guttatus, as expected given that the M. l. luteus replicates came from the same highly inbred line of plants while the M. guttatus replicates came from different lineages.

Figure 4
Correlation in expression mean (FPKM) and variability (CV), as measured by RNA-seq, between the biological replicates (T1 and T2) of M. guttatus (A) and M. l. luteus (B). Values for Pearson’s correlation coefficient are given above each graph ...

The expression data collected via RNA-seq were validated for accuracy using qPCR expression data for a selected group of eight genes, including four traditional reference genes (Table S3). Mean expression values measured by RNA-seq transcriptomes T1 and T2 were both in agreement with values found via qPCR (Figure 5A). In contrast, expression variability estimated by qPCR was significantly correlated with T2 expression variability, but had no significant relationship to T1 expression variability (Figure 5B). This is most likely due to variation in plant lineage and plant growth conditions, as the T2 transcriptomes and the qPCR data derive from genetically identical plants that were grown in the same greenhouse, while the T1 transcriptomes derive from plants grown in a greenhouse at a separate institution. For M. guttatus, T1 and T2 also differed in the accession used (Figure 1). This pattern, particularly for the isogenic M. l. luteus transcriptomes, suggests that environmental factors may have a greater effect on the “noise” in gene expression than on the expression level itself.

Figure 5
Comparisons of relative gene expression and of expression variability as determined by RNA-seq and qPCR for a sample of four traditional reference genes (closed symbols) and four additional genes that had initially been found to be stably expressed in ...

Discussion

Identification of novel reference genes for Mimulus

While RNA-seq has the potential to accurately identify genes with low variation in expression, there is still not a universally accepted method for selecting reference genes from RNA-seq data. Most of the programs that are widely used for reference gene selection, such as geNorm, BestKeeper, and NormFinder, were designed specifically for qPCR data and can only process a handful of genes at a time (Vandesompele et al. 2002; Andersen et al. 2004; Pfaffl et al. 2004). We explored two different methods for identifying stably expressed genes from whole-transcriptome data: (1) ranking genes based on the CV of expression across different samples (CV method) and (2) excluding unstable genes using a log fold change cut-off value (fold change method). We find that both methods identify many stably expressed genes that have the potential to be novel reference genes for qPCR expression studies in M. guttatus and M. l. luteus (see Table S1 and Table S9).

Using the CV method, all expressed genes from M. guttatus and M. l. luteus were ranked based on the variability of their expression across different tissue types and growing conditions, and the top 50 genes with the lowest variability were identified (Table S9). Using the fold change method, we identified eight M. guttatus and eight M. l. luteus genes with low variability in expression across four different tissue types and two biological replicates. No traditional reference genes were identified as being among the top 50 most stably expressed genes by either of our methods. In addition, the novel reference genes we identified had much lower expression variability in our system than any of the most commonly used traditional reference genes (Figure 2), which highlights the utility of the whole-transcriptome approach to reference gene selection.

The advantage of using either of these methods for reference gene selection is their simplicity in calculation. While the fold change method has the benefit of producing a discrete list of genes with low variation in expression, the CV method has the benefit of quantifying expression variability in a way where genes can be ranked and directly compared. These methods have previously been used in other plant species to select novel reference genes from transcriptomic data (Czechowski et al. 2005; Chang et al. 2012), but we are the first to show that these two methods produce comparable results. All of the genes found on the fold change short-list were among the 200 genes with the lowest CV, which corresponds to the top 2–3% most stably expressed transcripts. Ideally, novel reference genes would be selected that score well according to both metrics.

The CVs of the novel reference genes we identified are all <0.20, whereas a previously suggested cut-off for valid reference genes is a CV of 0.50 (Hellemans et al. 2007). It is important to note that using a 0.50 CV cut-off in our system included the majority of expressed genes (Figure 2), and thus it was not a very discriminatory standard for determining expression variability.

Traditional reference genes in Mimulus

Many studies have pointed to the instability of traditional housekeeping reference genes (Brunner et al. 2004; Czechowski et al. 2005; Dheda et al. 2004; Suzuki et al. 2000). We find that some traditional reference genes in Mimulus have the potential to work well for qPCR normalization. Using a whole-transcriptome method, we identified four traditional reference genes that have somewhat low variation in expression (CV < 0.50) in M. guttatus and M. l. luteus (Figure 3). Two genes, UBC and GAP, were even identified as stably expressed in both species and could potentially be good universal reference genes for the Mimulus genus. We confirmed our findings for four of these traditional reference genes with qPCR and found that all four (GAP, ACT, UBC, and PEX) could be acceptable as reference genes for both species based on qPCR estimates of expression variability across tissues, although some of the genes were at or slightly above the recommended 0.5 CV cut-off when the relatively divergent petal tissue samples were included (Figure 3). However, these traditional reference genes were nowhere near the most stably expressed in the transcriptome as a whole (Figure 2), which highlights the opportunity to discover dramatically more stable reference genes using a transcriptome-guided approach.

Despite the widespread use of Mimulus as a model genus for genetics, very few papers have attempted to validate reference genes for use in this genus. As part of a larger study, Scoville et al. (2011) qualitatively ranked the expression variability of six traditional reference genes in M. guttatus and found that UBQ and EF1 were the most stably expressed. We quantitatively investigated four of these six traditional reference genes in our own study and found that UBQ and EF1 had higher expression variability than other traditional reference genes and that, in both species, the genes' CVs were >0.5 under our study conditions. Scoville et al. (2011) tested different lines of M. guttatus and included a wound treatment, which may have resulted in our differing reports of traditional reference gene stabilities. This again highlights the importance of reference gene validation for specific study conditions.

Although we found that some traditional reference genes can be used for qPCR normalization, they are not optimal reference genes; the variability in expression of the traditional reference genes is very high when compared to the variability of all robustly expressed genes (Figure 2 and Table S2). This indicates that whole-transcriptome approaches, such as RNA-seq, have great potential to discover novel reference genes that are stably expressed in the study system of interest. With the current speed and low cost of RNA-seq, as well as the online availability of multi-tissue and/or multi-environment RNA-seq data sets, we expect that the whole-transcriptome approach will be increasingly useful for reference gene identification and validation.

Efficacy of transcriptomics for reference gene selection

RNA-seq has been repeatedly shown to generate accurate measurements of gene expression (Marioni et al. 2008; Mortazavi et al. 2008; Nagalakshmi et al. 2008; Nookaew et al. 2012). We find similar results in Mimulus when comparing the relative expression determined by RNA-seq to the relative expression determined by qPCR for eight selected genes (Figure 5A). We also find that estimates of expression mean are robust to moderate environmental and genetic variation, but that estimates of expression variability across tissue types are only in agreement when the samples are obtained from a shared environment (Figure 4 and Figure 5). These results suggest that environmental changes may have a greater impact on expression variability than on expression means.

For the goal of reference gene selection, where expression variability must remain low, this difficulty can be solved in two ways. One approach is to use the same samples for both RNA-seq and the subsequent qPCR analysis, as in Chang et al. (2012) and Yang et al. (2014). This method would be highly accurate, but would be extremely specific to particular study conditions. A second approach would be to evaluate a large variety of genotypes or growth conditions to discover genes that are maximally stable across genetically and environmentally distinct samples, as was done for A. thaliana in Czechowski et al. (2005). This method would allow for the identification of a starting pool of “universally” stable genes.

Reference gene selection using RNA-seq

We show, using Mimulus as a case study, that RNA-seq is a promising tool for selecting genes with low gene expression variance that can be used as novel qPCR reference genes. As many research labs regularly use RNA-seq as a first approach to collecting expression data, already completed RNA-seq transcriptomes are a readily available tool that can be used to search for candidate qPCR reference genes in any study system. Although we find that the variance in expression is variable between environmental conditions, we propose that transcriptomes from diverse samples can be pooled in order to identify more universally stable genes. We show that two simple methods for identifying genes with low expression variance, the CV method and the fold change method, both result in comparable evaluations of expression variance. Thus, either of these methods can be used to identify a preliminary set of highly stable candidate reference genes for qPCR experiments.

Acknowledgments

We thank Mario Vallejo-Marín and John Kelly for seeds of M. guttatus CG and IM767 respectively. We thank Paul Whetstone and two anonymous reviewers for helpful comments on the manuscript. Funds for this research were provided by Murdock Life Sciences grant #2013265 to A.M.C.; a Franklin Research Grant to A.M.C.; and Michigan State University AgBioResearch funds to P.P.E.

Footnotes

Supplemental material is available online at www.g3journal.org/lookup/suppl/doi:10.1534/g3.116.038075/-/DC1.

Communicating editor: E. Grotewold

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