geNorm ranking of 13 reference genes from blueberry series.

<p><b>A.</b> Floral bud series <b>B.</b> Fruit development series. Vertical numbers at the top indicate the CV values of the reference genes involved in the normalization. References showing highly stable expression (<i>M</i> values<0.5) are represented as orange bars.</p

Superior Cross-Species Reference Genes: A Blueberry Case Study

<div><p>The advent of affordable Next Generation Sequencing technologies has had major impact on studies of many crop species, where access to genomic technologies and genome-scale data sets has been extremely limited until now. The recent development of genomic resources in blueberry will enable the application of high throughput gene expression approaches that should relatively quickly increase our understanding of blueberry physiology. These studies, however, require a highly accurate and robust workflow and make necessary the identification of reference genes with high expression stability for correct target gene normalization. To create a set of superior reference genes for blueberry expression analyses, we mined a publicly available transcriptome data set from blueberry for orthologs to a set of <i>Arabidopsis</i> genes that showed the most stable expression in a developmental series. In total, the expression stability of 13 putative reference genes was evaluated by qPCR and a set of new references with high stability values across a developmental series in fruits and floral buds of blueberry were identified. We also demonstrated the need to use at least two, preferably three, reference genes to avoid inconsistencies in results, even when superior reference genes are used. The new references identified here provide a valuable resource for accurate normalization of gene expression in <i>Vaccinium</i> spp. and may be useful for other members of the <i>Ericaceae</i> family as well.</p></div

Coefficients of variation predominantly decrease after omission of fruit series.

<p>Change of the CV after the omission of the transcriptome fruit libraries over 451 unigenes.</p

Digital expression level of blueberry genes in dormant floral buds and fruits at different stages of development.

<p><b>A.</b> Relative virtual expression of unigenes from the 454 transcriptome database representing putative orthologs to <i>Arabidopsis</i> genes <i>UBC9</i> (contig02681 and contig14017 with 96% and 93% identity at the amino acid level, respectively; red and blue), <i>UBC28</i> (contig13874 with 92% identity; green) and <i>RH8</i> (contig01609 with 45% identity; purple). <b>B.</b> Relative virtual expression of former frequently used blueberry reference genes in qPCR analyses, i.e. <i>ACT</i> (contig00039, red), <i>metallothionein</i> (contig0266, purple), <i>GAPDH</i> (contig00502, blue) and <i>EF1a</i> (contig14280, green). Contig Ids are from the ‘All’ assembly from the transcriptome database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073354#pone.0073354-Rowland2" target="_blank">[22]</a>. The digital expression for each contig in every sample is expressed relative to the average expression level over all the libraries.</p

Normalization of pectate lyase (<i>PL</i>) mRNA expression in blueberry at green and pink fruit developmental stages using 13 different reference genes.

<p>The <i>PL</i> mRNA ratio in pink fruit: green fruit (x-axis) was calculated using the 13 different reference genes shown on the y-axis. The ratio was calculated taking the gene specific amplification efficiencies into account (target gene and reference gene) according to the quantification model proposed by Pfaffl <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073354#pone.0073354-Pfaffl1" target="_blank">[15]</a>.</p

Representative gels of the experiment. 2DE gel analyses of proteins extracted from leaves.

<p><b>(A)</b> non-acclimated <i>R</i>. <i>catawbiense</i>, Cata.NA. <b>(B)</b> acclimated <i>R</i>. <i>catawbiense</i>, Cata.CA. <b>(C)</b> acclimated <i>R</i>. <i>ponticum</i>, Pont.CA.</p

Proteome dynamics of cold-acclimating <i>Rhododendron</i> species contrasting in their freezing tolerance and thermonasty behavior

<div><p>To gain a better understanding of cold acclimation in rhododendron and in woody perennials in general, we used the 2D-DIGE technique to analyze the rhododendron proteome during the seasonal development of freezing tolerance. We selected two species varying in their cold acclimation ability as well as their thermonasty response (folding of leaves in response to low temperature). Proteins were extracted from leaves of non-acclimated (NA) and cold acclimated (CA) plants of the hardier thermonastic species, <i>R</i>. <i>catawbiense</i> (Cata.), and from leaves of cold acclimated plants of the less hardy, non-thermonastic <i>R</i>. <i>ponticum</i> (Pont.). All three protein samples (Cata.NA, Cata.CA, and Pont.CA) were labeled with different CyDyes and separated together on a single gel. Triplicate gels were run and protein profiles were compared resulting in the identification of 72 protein spots that consistently had different abundances in at least one pair-wise comparison. From the 72 differential spots, we chose 56 spots to excise and characterize further by mass spectrometry (MS). Changes in the proteome associated with the seasonal development of cold acclimation were identified from the Cata.CA—Cata.NA comparisons. Differentially abundant proteins associated with the acquisition of superior freezing tolerance and with the thermonastic response were identified from the Cata.CA—Pont.CA comparisons. Our results indicate that cold acclimation in rhododendron involves increases in abundance of several proteins related to stress (freezing/desiccation tolerance), energy and carbohydrate metabolism, regulation/signaling, secondary metabolism (possibly involving cell wall remodeling), and permeability of the cell membrane. Cold acclimation also involves decreases in abundance of several proteins involved in photosynthesis. Differences in freezing tolerance between genotypes can probably be attributed to observed differences in levels of proteins involved in these functions. Also differences in freezing tolerance may be attributed to higher levels of some constitutive protective proteins in Cata. than in Pont. that may be required to overcome freeze damage, such as glutathione peroxidase, glutamine synthetase, and a plastid-lipid-associated protein.</p></div

Spot abundance profiles.

<p>Different patterns of protein induction (blue) or suppression (red) are shown for each pair-wise comparison. Grey color denotes non-significant regulation (effect factor <|2-fold|, <i>P</i> >0.05, or both).</p

Volcano plot of significance against effect.

<p>Each dot represents one of the reproducible protein spots, with the –log₁₀ of the <i>P</i> value plotted against the abundance difference between two biological conditions (log₂ on the abscissa). Blue color denotes increased protein levels; red color denotes decreased protein levels; grey color denotes spots in meaningful sectors (effect factor <|2-fold|, <i>P</i> >0.05, or both).</p

Representative gels of the experiment. 2DE gel analyses of proteins extracted from leaves.

<p><b>(A)</b> non-acclimated <i>R</i>. <i>catawbiense</i>, Cata.NA. <b>(B)</b> acclimated <i>R</i>. <i>catawbiense</i>, Cata.CA. <b>(C)</b> acclimated <i>R</i>. <i>ponticum</i>, Pont.CA.</p