Combination of Cyclamen persicum Mill. floral gene promoters and chimeric repressors for the modification of ornamental traits in Torenia fournieri Lind

Ectopic expression of AtNF-YA6-VP16 in petals results in a novel petal phenotype in Torenia fournieri

A novel Torenia phenotype having separate petals was obtained by the combination of NF-YA6-VP16 with a floral organ-specific promoter. Genetic engineering techniques helped in obtaining novel flower colors and shapes, in particular, by introducing functionally modified transcription factors (TFs) to ornamental flower species. Herein, we used functionally modified Arabidopsis TFs fused with the repression domain SRDX and the activation domain VP16 to screen for novel floral traits in Torenia fournieri Lind (torenia). We avoided undesired phenotypes unrelated to flowers by expressing these TFs through a floral organ-specific promoter belonging to the class-B genes, GLOBOSA (TfGLO). Fourteen constructs were produced to express functionally modified Arabidopsis TFs in which each of SRDX and VP16 was fused into 7 TFs that were used for the collective transformation of Torenia plants. Among the obtained transgenic plants, phenotypes with novel floral traits reflected in separate petals within normally gamopetalous flower lines. Sequencing analysis revealed that the transgenic plants contained nuclear factor-YA6 (NF-YA6) fused with the VP16. In the margin between the lips of the petals and tube in the TfGLOp:NF-YA6-VP16 plants, staminoid organs have been developed to separate petals. In the petals of the TfGLOp:NF-YA6-VP16 plants, the expression of a Torenia class C gene, PLENA (TfPLE), was found to be ectopically increased. Moreover, expression of TfPLE-VP16 under the control of the TfGLO promoter brought a similar staminoid phenotype observed in the TfGLOp:NF-YA6-VP16 plants. These results suggest that the introduction of the TfGLOp:NF-YA6-VP16 induced TfPLE expression, resulting in the formation of staminoid petals and separation of them.

Measuring plant colors

Plant colors such as 'green leaf' and 'red apple' are often described based on human sense, even in scientific papers. On the other hand, colors are measured based on colorimetric principles in some papers, especially in the studies of horticultural plants. The science of color measurements ('colorimetry') is not included in any of the popular lectures in schools and universities, thus the principles of color measurements would not be understood by most researchers. The present review will overview the principles of colorimetry, and will introduce colorimetric methods which can be used for scientific measurement of plant colors. That is to say, the reflection spectrum of visible light (380-780 nm) is measured at 5-nm intervals on the surface of leaves or petals in 'Spectrometric Color Measurement' (SCM). The spectral data is multiplied with RGB or XYZ color matching functions and integrated to obtain RGB or XYZ intensities. Alternatively, approximate RGB values are directly obtained in 'Photographic Color Measurement' (PCM). RGB/XYZ intensities are further calculated to obtain 'hue', 'saturation', and 'lightness', the three factors of colors. Colorimetric insights into genetic regulations (such as MYB gene) and physiological regulations (such as alexandrite effect) of plant colors are also described.

Transcriptome profiling reveals regulatory mechanisms underlying corolla senescence in petunia

The genetic regulatory mechanisms that govern natural corolla senescence in petunia are not well understood. To identify key genes and pathways that regulate the process, we performed a transcriptome analysis in petunia corolla at four developmental stages, including corolla fully opening without anther dehiscence (D0), corolla expansion, 2 days after anthesis (D2), corolla with initial signs of senescence (D4), and wilting corolla (D7). We identified large numbers of differentially expressed genes (DEGs), ranging from 4626 between the transition from D0 and D2, 1116 between D2 and D4, a transition to the onset of flower senescence, and 327 between D4 and D7, a developmental stage representing flower senescence. KEGG analysis showed that the auxin- and ethylene-related hormone biosynthesis and signaling transduction pathways were significantly activated during the flower development and highly upregulated at onset of flower senescence. Ethylene emission was detected at the D2 to D4 transition, followed by a large eruption at the D4 to D7 transition. Furthermore, large numbers of transcription factors (TFs) were activated over the course of senescence. Functional analysis by virus-induced gene silencing (VIGS) experiments demonstrated that inhibition of the expression of TFs, such as ethylene-related ERF, auxin-related ARF, bHLH, HB, and MADS-box, significantly extended or shortened flower longevity. Our data suggest that hormonal interaction between auxin and ethylene may play critical regulatory roles in the onset of natural corolla senescence in petunia.

Rapid identification of apple (Malus×domestica Borkh.) S alleles using sequencing-based DNA marker APPLid

All apple cultivars harbor the trait called self-incompatibility. Self-incompatibility represents that the pistils of the flowers are not successfully fertilized with own, the same cultivar's pollens. Compatibility or incompatibility of apple flowers are determined by S alleles. For example, the most popular apple cultivar 'Fuji' possesses the S₁ and S₉ alleles (S₁S₉ ). Thus, 'Fuji' is incompatible with S₁S₉ cultivars, but is compatible with the cultivars possessing different combinations of S alleles such as S₂S₇ and S₁S₇ . Apple S alleles have been identified by performing a series of allele-specific PCR amplifications, to detect more than ten different S alleles separately. Here, we developed a new type of sequencing-based DNA marker of the apple S-RNase gene, which identifies S alleles. This DNA marker was named APPLid (apple S-allele identifier). A 53-base region in the first coding sequence of S-RNase is the target of APPLid sequencing. Variation in nucleotide sequences in this APPLid sequence enables allele identifications. This region is amplified from apple genomic DNA by using a pair of degenerate primers. The forward primer is attached with 'DS5 adaptor.' After PCR amplification, electrophoresis and gel extraction of 177-bp DNA fragments, APPLid sequence is determined by direct sequencing with a sequencing primer. The APPLid sequences of 20 apple cultivars completely matched their S alleles, which include triploid cultivars. In conclusion, APPLid identifies apple S alleles (S₁ , S₂ , S₃ , S₄ , S₅ , S₇ , S₉ , S₁₀ , S₂₀ , S₂₄ , S₂₅ , S₂₆ , S₂₇ and S₂₈ , so far) just by a single sequencing analysis.