Selection of Reference Genes for qPCR- and ddPCR-Based Analyses of Gene Expression in Senescing Barley Leaves

<div><p>Leaf senescence is a tightly regulated developmental or stress-induced process. It is accompanied by dramatic changes in cell metabolism and structure, eventually leading to the disintegration of chloroplasts, the breakdown of leaf proteins, internucleosomal fragmentation of nuclear DNA and ultimately cell death. In light of the global and intense reorganization of the senescing leaf transcriptome, measuring time-course gene expression patterns in this model is challenging due to the evident problems associated with selecting stable reference genes. We have used oligonucleotide microarray data to identify 181 genes with stable expression in the course of dark-induced senescence of barley leaf. From those genes, we selected 5 candidates and confirmed their invariant expression by both reverse transcription quantitative PCR and droplet digital PCR (ddPCR). We used the selected reference genes to normalize the level of the expression of the following senescence-responsive genes in ddPCR assays: <i>SAG12, ICL, AGXT, CS</i> and <i>RbcS</i>. We were thereby able to achieve a substantial reduction in the data variability. Although the use of reference genes is not considered mandatory in ddPCR assays, our results show that it is advisable in special cases, specifically those that involve the following conditions: i) a low number of repeats, ii) the detection of low-fold changes in gene expression or iii) series data comparisons (such as time-course experiments) in which large sample variation greatly affects the overall gene expression profile and biological interpretation of the data.</p></div

Time-course expression changes of senescence marker genes.

<p>The profiles were calculated separately for each biological replicate. The changes are presented in relation to Day 0. The left plots represent raw data, the right plots represent data normalized to geometric mean of Ref A—Ref E genes. The associated tables show the coefficients of variation (CV) for digital PCR assays before (CV_raw) and after normalization (CV_norm). The values were determined by calculating the s.d. within each time- point from biological replicates a, b and c (n = 3) and dividing each of these by their respective mean values. The reduction in CV (CV_reduce) was calculated as the difference between the raw and normalized data CV divided by raw data CV and expressed as a percentage: CV_reduce = (CV_raw—CV_norm)/(CV_raw).</p

Optimization of the ddPCR sensitivity range and template cDNA input.

<p>The presented data are for gene Ref B. The cDNA amount is presented as [μl of RT reaction]. For the assay optimization, the 7-point dilution series (up to 120 x cDNA dilution) was prepared starting from 2 μl cDNA. The error bars indicate the Poisson 95% confidence intervals. The results were similar for all reference genes.</p

Effect of data normalization on mean Ref C transcript level.

<p>Effect of data normalization on mean Ref C transcript level.</p

Expression stability of candidate reference genes calculated with geNorm.

<p>Genes with M value ≤ 1.5 are considered highly stable across analyzed samples [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118226#pone.0118226.ref042" target="_blank">42</a>]</p><p>Expression stability of candidate reference genes calculated with geNorm.</p

The expression of Ref A—Ref E genes during leaf senescence, presented as Cq values in qPCR assay.

<p>Each Cq is the mean from three biological replicates, and each replicate Cq value is averaged from three technical replicates. The whiskers present the distribution of the Cq values between the biological replicates. The scales are identical on all plots.</p

Transcript amount and senescence-associated expression changes of Ref A—Ref E genes in barley leaf.

<p>a—Mean transcript amount, averaged from 18 samples (6 time-points × 3 biological replicates); b—Time-course profiles. The data for each time-point were averaged from 3 biological replicates and scaled to Day 0.</p

Effect of data normalization on variation of RefC transcript level in ddPCR assays.

<p>Coefficients of variation (CV) were determined by calculating the s.d. from replicates a, b and c (n = 3) and dividing each of these by their respective mean values. Reduction in CV (CV_reduce) was calculated as the difference between the raw data CV (CV_raw) and normalized data CV (CV_norm), divided by raw data CV and expressed as a percentage: CV_reduce = (CV_raw—CV_norm)/(CV_raw).</p><p>Effect of data normalization on variation of RefC transcript level in ddPCR assays.</p

Image_5_Insight into metabolic sensors of nitrosative stress protection in Phytophthora infestans.tif

Phytophthora infestans, a representative of phytopathogenic oomycetes, have been proven to cope with redundant sources of internal and host-derived reactive nitrogen species (RNS). To gain insight into its nitrosative stress resistance mechanisms, metabolic sensors activated in response to nitrosative challenge during both in vitro growth and colonization of the host plant were investigated. The conducted analyses of gene expression, protein accumulation, and enzyme activity reveal for the first time that P. infestans (avirulent MP946 and virulent MP977 toward potato cv. Sarpo Mira) withstands nitrosative challenge and has an efficient system of RNS elimination. The obtained data indicate that the system protecting P. infestans against nitric oxide (NO) involved the expression of the nitric oxide dioxygenase (Pi-NOD1) gene belonging to the globin family. The maintenance of RNS homeostasis was also supported by an elevated S-nitrosoglutathione reductase activity and upregulation of peroxiredoxin 2 at the transcript and protein levels; however, the virulence pattern determined the expression abundance. Based on the experiments, it can be concluded that P. infestans possesses a multifarious system of metabolic sensors controlling RNS balance via detoxification, allowing the oomycete to exist in different micro-environments flexibly.</p

Image_2_Insight into metabolic sensors of nitrosative stress protection in Phytophthora infestans.jpeg

Phytophthora infestans, a representative of phytopathogenic oomycetes, have been proven to cope with redundant sources of internal and host-derived reactive nitrogen species (RNS). To gain insight into its nitrosative stress resistance mechanisms, metabolic sensors activated in response to nitrosative challenge during both in vitro growth and colonization of the host plant were investigated. The conducted analyses of gene expression, protein accumulation, and enzyme activity reveal for the first time that P. infestans (avirulent MP946 and virulent MP977 toward potato cv. Sarpo Mira) withstands nitrosative challenge and has an efficient system of RNS elimination. The obtained data indicate that the system protecting P. infestans against nitric oxide (NO) involved the expression of the nitric oxide dioxygenase (Pi-NOD1) gene belonging to the globin family. The maintenance of RNS homeostasis was also supported by an elevated S-nitrosoglutathione reductase activity and upregulation of peroxiredoxin 2 at the transcript and protein levels; however, the virulence pattern determined the expression abundance. Based on the experiments, it can be concluded that P. infestans possesses a multifarious system of metabolic sensors controlling RNS balance via detoxification, allowing the oomycete to exist in different micro-environments flexibly.</p