Mustard Yellow


Angiosperm diversity is marked by intra- and interspecific variation of flower color. The evolutionary forces that underlie the diversity of flower colors may be as varied as the actual colors. Non-pollinator selection agents can also have a significant influence on the evolution and diversity of flower color polymorphisms. This is especially true if the pigments are also expressed in vegetative tissue. In such cases, the first step in identifying the target(s), or agents of selection, is to determine the biochemical and molecular basis of the flower color variation. Next, examine any pleiotropic effects that may be manifested within vegetative tissues. We describe here a widespread purple-white color polymorphism of the mustard parrya nudicaulis flowers that spans Alaska. As stress tolerance is a key component of anthocyanin pigments, white-flowered individuals are more common with increasing temperatures. The early blockage of the anthocyanin biosynthetic pathway may have resulted in white petals not producing stress-responsive flavonoid intermediates (ABP). The cDNA sequences of petal petals did not show any blockages in any eight enzyme-coding genes found in white-flowered individuals or any color-differentiating SNPs. A qRTPCR analysis of white flowers revealed a 24-fold decrease Mustard Yellow chalcone synase ( CHS) at threshold level of the ABP. However, there was no change in CHS expressions in leaves or sepals. The arctic species avoided the negative effects of losing flavonoid intermediates within vegetative tissues. However, the correlation between climate and flower color suggests that white-flowered individuals may be less tolerant to colder environments if the CHS expression is not decoupled in the petals and leaves.


Angiosperms’ extraordinary variety of flower colors can often be attributed to the preferences – of their pollinators HTML3_. However, Mustard Yellow is important to consider the role of non-pollinators of selection and genetic drift. The frequency of certain color morphs can be due to genetic drift if pollinators do not prefer particular morphs among flower color polymorphic populations. Wright’s shifting balance theory, which relied on natural selection being absent, was prompted by a lack of pollinator preference for the flower color polymorphic Linanthus Parryae. Although subsequent studies have not found pollinator preference in the example, Mustard Yellow is possible that Mustard Yellow natural selection has been ruled out. However, flower color frequency is strongly correlated with spring precipitation. There are a variety of non-pollinator selection agents that can affect flower color morph frequency in polymorphic populations. These include biotic agents (pathogens, herbivores, and UV-light) HTML8_.Mustard Yellow is unclear whether these non-pollinator agent are directly affecting petal color, or on pleiotropic side effects of flower color variation in vegetative tissue.

Angiosperms are most well-known for their flower color polymorphism, which is the loss or whitening of floral anthocyanins. Variations in flower color can be subtle due to changes in epidermal cells shape, vacuolar pH or cofactors.

Materials and Methods

Alaskan flower color variations vary by the frequency

From 14 populations that covered the entire geographic area of P., we were able to estimate the proportion of white-flowered individuals to purple-flowered ones. Nudicaulis in Alaska. The selection of populations was based on their geographic uniqueness and accessibility.Mustard Yellow All the populations were more than 10km apart and about 250 km apart in average. We used the mean July temperature to determine the relationship between flower color frequency and climate. This Mustard Yellow the average of the three-month growing season for these plants. GDDs (growing degree days) are also used. These are the cumulative annual temperatures above which physiological function is inhibited. We compare GDDs with a baseline temperature of 4degC with those with a baseline temperature of 0degC. These three climate measures were calculated using monthly mean temperatures from 1961-1990 Climate Research Unit data and then downscaled to a 2-km grid (Nancy Fresco Scenarios Network for Alaska Planning University of Alaska Fairbanks). We used a Spearman rank-based correlation between climate and flower color because of uncertainties in climate predictions for particular geographical locations.

HPLC analysis flavonoids

This species has a wide range in pigmentation levels among purple and white morphs, but we chose to focus on purple- and/or white-flowered individuals for our biochemical and molecular analyses. The 12 Mile Summit population was given purple-flowered and white-flowered pet tissue (n = 5 and 4) and it was immediately flash-frozen. The samples were homogenized with 90% HPLC-grade methanol (Sigma-Aldrich St. Louis MO), centrifuged, and the supernatant passed through a Hewlett-Packard Lichrocart250-4 RP18e 5-um column using a Hewlett-Packard 1100-series HPLC-DAD instrument. Flavonoids were detected at 210 nm, 254, 330, and 529 nm. They were identified by their spectral similarity with known reference compounds.

An anthocyanin-producing tissue type by tissue type: A phenotypic study

In June 2008, a phenotypic survey of 12 Mile Summit’s (n=45) and Savage River’s (n=50) populations was done . Visually, each plant was classified as having either a pigmented or nonpigmented petal colour. Next, we measured the amount of pigmentation at the margins of the leaves. This is a common indicator for ABP loss in Arabidopsis. The data from both sites was pooled, and the differences in leaf, petal and sepal tissue pigmentation were analyzed with a chi square test.

RNA isolation and cDNA synthesis

The 12 Mile Summit and Savage River populations were able to collect tissue from white- and purple-flowered plants. Three to five people with the same petal color could be combined to get 50 to 100 mg of tissue. The field flash-frozen the tissue from the petals (n=12), leaves (n=13) and sepals (n=7). This represents a range developmental stages. Petals = early, late, opening, anthesis, and petals = leaves; young, medium, and old. Because there was no apparent change in pigmentation, the sepal tissue was pooled throughout all stages. The tissue was kept at -70°C until RNA extraction using the RNeasy Plant Mini Kit from Qiagen (Valencia, CA). Samples were treated with 750ng of RNA and amplified grade DNaseI (Invitrogen Carlsbad CA). To verify RNA integrity, agarose gels and a NanoDrop ND1000 spectrophotometer were used to determine the purity of the RNA. cDNA was synthesized with 750 ng of DNA and the SuperScript III First Strand Sythesis Kit (Invitrogen), using oligo(dT), and primers according to the manufacturer’s protocol.

Cloning ABP genes

We identified and characterized six core genes that are involved in anthocyanin production using Whittall et. al. previously published degenerate primers. For phenylalanine ammonia lyase ( PAL), new degenerate primers have been developed. The authors can provide all sequences of primers upon request. Mustard Yellow seven genes were amplified with degenerate primers via reverse-transcriptase polymerase chain reaction (RT-PCR). After gel purification, RTPCR products were cloned in the pCR-4TOPO TA vector by Invitrogen. The inserts were confirmed by PCR using M13 forward primers and reverse primers.Mustard Yellow Next, the sequence was done on an ABI3730xl DNA Analyzer, Sequetech, Santa Clara CA, following the BigDye protocol (Applied Biosystems Foster City, CA). On average, we sequenced 25 clones for each gene (range 21 to 31). Sequences were assembled using Sequencher software (version. 4.8; Gene Codes Corporation Ann Arbor, MI), and aligned in Bioedit (version . DnaSP (v. 5.07.07) was used to determine the minimum number of recombination event. All sequence data from cloning as well as all other sequencing methods were deposited in GenBank with accession numbers HQ215216 -HQ215513.

Illumina sequencing of the arctic mustard transcriptome

We compared one sample of each color morph’s early buds using mRNA–Seq and massively parallel sequence by synthesis according to the manufacturer’s protocol (Illumina). This was done in order to increase the ABP gene coverage and recover loci that were not covered with degenerate primers. We gained an average of 300 bps in open reading frame (ORF), and 50 bps UTR sequence for genes previously characterized using degenerate primers, including 93% of the ORF .

Genome mapping and ABP gene coverage expansion

We designed genome walking primers to increase ORF coverage for ABP genes. The primers amplified the 5′ and/or 3-foot ends of PAL and CHS genes using Clontech Laboratories Mountain View, CA. To obtain the most cDNA of each gene, new primers were created from the genome-walking sequences. Whittall and colleagues amplified cDNA from one-to-two purple-flowered and one-tofour white-flowered plants.

Analyse of ABP enzyme-coding gene genes

To determine if structural mutations can differentiate populations or create color morphs, we also characterized the color genes of flower color in genomic DNA. Eight locations in the geographical range of P. were used to collect samples from white- and purple-flowered plants. Figure 2: Nudicaulis was extracted using the NucleoSpin PlantII Kit (Macherey Nagel, Bethlehem PA). Primer3 [30] was used for designing primers for PAL and CHS, CHI. ANS, UF3GT, CHI. ANS, CHI. ANS, CHI. Whenever possible, the A. The thaliana genome. The F3H, DFR loci were not amplified because of the presence of multiple introns with multi-thymine tracts. We could not generate reliable sequences. We amplified between 30-40 individuals for each of the five other genes using crude Tq polymerase. After sequencing the samples, the Tajima’s D statistic and sequence were calculated.

Quantitative Real Time PCR

Primer Express 3.0 software from Applied Biosystems was used to design probe and primer sequences for quantitative Real Time PCR (qRTPCR) TaqMan assays. The probes were labeled using the 5′ reporter dye 6-FAM, and the 3′ quencher Iowa Black Q. qRTPCR was carried out on the StepOne Real Time PCR System by Applied Biosystems in 20 ul reaction volumes. The following reagents were used (and their final concentrations: 1X TaqMan gene expression master mix, 900 nM forward primers and reverse primers; 250 nM probe and 25 ng cDNA. To detect any genomic DNA contamination, 6.25 ng raw RNA extract was added to five samples. The thermal cycling conditions were 95degC (10 min), followed by 40 cycles at 95degC (15 sec) and 60degC (60 sec) for 60 seconds. Each sample was tested in duplicate. The specificity of the probes and primers was confirmed by running the qRTPCR products on an agarose gel.

To control for inter-assay variation, threshold levels for each gene were manually adjusted. The relative expression of samples was determined using the comparative DDC T method as described in Livak & Schmittgen. The DC Ts of each target and sample were calculated by normalizing their threshold cycle numbers (C T) with the constitutively expressed gene Glyceraldehyde-3-phosphate dehydrogenase, amplification efficiency = 95%. The relative quantification (RQ), or relative quantification, was calculated by the equation RQ = (1+E-DDCT), where E is the target’s amplification efficiency as determined by a standard curve. DDCT = DCT purple sample – DCT reference. Five dilutions were used to calculate the amplification efficiency. They were each run in duplicate and serially diluted twice from a starting amount of 20 ng/ul.

Because of the small sample sizes at each developmental stage, each color morph was pooled across stages to allow for statistical analyses. The DC values were converted first to a linear form using Livak and Schmittgen, by the (1+E) DCT calculation. The linear DC T value of each color morph in each tissue type was compared using a Student’s test with unmatched sample sizes and unequal variance. To determine significance, we compared the observed tstatistic with t-statistics taken from 10 and 4 bootstrapped data sets of similar sizes (sampled without replacement).

Pearson’s correlation coefficient was used to test for correlations between all the genes in each tissue type. As explained above, expression levels were also converted to a linear format for comparisons. To meet normality assumptions, data were naturally-log transformed. Statistical analyses were performed in Jmp 4.0 (SAS Institute Cary, North Carolina). 28 tests were performed on each tissue type for all eight ABP genes (including FLS). We used the Bonferroni correction, as described in Abdi’s equation 8 to control for multiple comparisons.

Sequence survey of CHS regulatory region cis

The Cis-regulation of CHS has been well documented in Brassicaceae [20] as well as in many other angiosperms[21], 33]. The CHS promoter contains the TATA box (-100bp from the start codon), and several petal-specific regulatory motifs (within 500bp of start codon). These are highly conserved . Genome walking was used (see above) to identify 589 bp downstream from the start codon. Then, we designed P. We then designed P.

FLS – Sequence and expression analysis

We also looked at this locus at both the expression and sequence levels, as several candidates for petal-specific trans-regulation CHS co-regulate flavonol synthesise ( FLS). We were able to sequence large amounts of FLS in the petals using Illumina transcriptome sequencing (841 bps). We used cDNA and genome DNA to amplify FLS and created qRTPCR assays for the ABP genes.


Alaskan flower color variations vary by the frequency

From zero to 24%, the percentage of white-flowered individuals in 14 populations varied. Five populations did not have white-flowered individuals. The four interior populations with higher mean July temperatures had the most white flowers. They also had greater growing-degree days and a more consistent amount of heat. We compared white-flowered individuals to the number of 4degC-GDDs calculated from 30 years of historical climate data. This revealed a significant positive correlation: the proportion of whiteflowered individuals drops as GDDs drop (Spearman rank Correlation, P = 0.021). The correlation between climate, flower color, and temperature was strong when 0degC was used as a baseline (rho=0.668, P=0.015) and using the average July temperature as the midpoint for most people’s three-month growing season (rho=0.659, P=0.016).


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