Flavonoid Components of Different Color Magnolia Flowers and Their Relationship to Cultivar Selections

Wang, Ninghang; Zhang, Chao; Bian, Sainan; Chang, Pi-Fang Linda; Xuan, Lingjuan; Fan, Lijie; Yu, Qin; Liu, Zhigao; Gu, Cuihua; Zhang, Shouzhou; Wang, Yaling; Shen, Yamei

doi:10.21273/hortsci13462-18

Cited by 19 publications

(7 citation statements)

References 34 publications

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“…In orange, yellow, and white cultivars of Freesia , the anthoxanthin peaked at S0, followed by a rapid decline, which was similar to the case with Peony , Magnolia biondii , and Lonicera ( Yang et al, 2015 ; Wang et al, 2019 ; Liu et al, 2020 ), while in the red and purple Freesia cultivars, the anthoxanthin increased first up to S2 and then decreased at S3. Starting from S1, the anthoxanthins in the orange, yellow and white cultivars declined rapidly, while it increased in the red and purple cultivars.…”

Section: Discussionsupporting

confidence: 53%

Composition of Flavonoids in the Petals of Freesia and Prediction of Four Novel Transcription Factors Involving in Freesia Flavonoid Pathway

Zhu

Guo

et al. 2021

Front. Plant Sci.

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Freesia hybrida is rich in flower colors with beautiful flower shapes and pleasant aroma. Flavonoids are vital to the color formation of its flowers. In this study, five Freesia cultivars with different flower colors were used to study on the level of accumulation of their flavonoids and expression of flavonoid-related genes and further explore new novel transcription factor (TF). Ultra-high-performance liquid chromatography and VION ion mobility quadrupole time-of-flight mass spectrometer (UPLC-Q-TOF-MS) were used to determine the flavonoids. Combined with transcriptome sequencing technology, the molecular mechanism of the flavonoid metabolism difference in Freesia was revealed. A total of 10 anthoxanthin components and 12 anthocyanin components were detected using UPLC-Q-TOF-MS. All six common anthocyanin aglycones in high plants, including cyanidin, delphinidin, petunidin, peonidin, malvidin, and pelargonidin, were detected in Freesia at first time in this study. In orange, yellow, and white cultivars, anthoxanthins gradually decreased with the opening of the petals, while in red and purple cultivars, anthoxanthins first increased and then decreased. No anthocyanin was detected in yellow and white cultivars, while anthocyanins increased with the opening of the petals and reached their maximum at the flowering stage (S3) in other three cultivars. The correlation analysis revealed that the color of Freesia petals was closely related to the composition and content of anthoxanthins and anthocyanins. Petals of five cultivars at S3 were then selected for transcriptome sequencing by using the Illumina Hiseq 4000 platform, and a total of 100,539 unigenes were obtained. There were totally 5,162 differentially expressed genes (DEGs) when the four colored cultivars were compared with the white cultivar at S3. Comparing all DEGs with gene ontology (GO), KEGG, and Pfam databases, it was found that the genes involved in the flavonoid biosynthesis pathway were significantly different. In addition, AP2, WRKY, and bHLH TF families ranked the top three among all differently expressed TFs in all DEGs. Quantitative real-time PCR (qRT-PCR) technology was used to analyze the expression patterns of the structural genes of flavonoid biosynthesis pathway in Freesia. The results showed that metabolic process was affected significantly by structural genes in this pathway, such as CHS1, CHI2, DFR1, ANS1, 3GT1, and FLS1. Cluster analysis was performed by using all annotated WRKY and AP2 TFs and the above structural genes based on their relatively expression. Four novel candidate TFs of WRKY and AP2 family were screened. Their spatiotemporal expression patterns revealed that these four novel TFs may participate in the regulation of the flavonoid biosynthesis, thus controlling its color formation in Freesia petals.

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Section: Discussionsupporting

confidence: 53%

Composition of Flavonoids in the Petals of Freesia and Prediction of Four Novel Transcription Factors Involving in Freesia Flavonoid Pathway

Zhu

Guo

et al. 2021

Front. Plant Sci.

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“…K-means clustering or segmentation of the colour gamut into prede ned bins are two of the most common approaches to speed up the processing time of digital colorimetric analysis of images [39,40,41,42]. These methodologies output arrays in regular formats that are easy to compare, but have the drawback of requiring a-priori knowledge of a prede ned number of bins to which to assign each colour.…”

Section: Discussionmentioning

confidence: 99%

A Data Driven Approach to Assess Complex Colour Profiles in Plant Tissues

McAtee

Nardozza

Richardson

et al. 2021

Preprint

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BackgroundThe ability to quantify the colour of fruit is extremely important for a number of applied fields including plant breeding, postharvest assessment, and consumer quality assessment. Fruit and other plant organs display highly complex colour patterning. This complexity makes it challenging to compare and contrast colours in an accurate and time efficient manner. Multiple methodologies exist that attempt to digitally quantify colour in complex images but these either require a priori knowledge to assign colours to a particular bin, or average the colours present within an assayed region into a single colour value. As such, to date there are no published methodologies that assess colour patterning using a data driven approach. Results In this study we present a methodology to acquire and process digital images of biological samples that contain complex colour gradients. The CIE (Commission internationale de l'éclairage / International Commission on Illumination) ΔE2000 formula was used to determine the perceptually unique colours (PUC) within images of fruit containing complex colour gradients. This process, on average, resulted in a 98% reduction in colour values from the number of unique colours (UC) in the original image. This data driven procedure summarised the colour data values while maintaining a linear relationship with the normalised colour complexity contained in the total image. A weighted ΔE2000 distance metric was used to generate a distance matrix and facilitated clustering of summarised colour data.ConclusionsClustering showed that our data driven methodology has the ability to group these complex images into their respective binomial families while maintaining the ability to detect subtle colour differences. This methodology was also able to differentiate closely related images. We provide a high quality set of complex biological images that span the visual spectrum that can be used in future colorimetric research to benchmark method development.

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“…Fruit: 0.5 to 10.1 mg total carotenoids/g FW, 0.5 to 10.2 mg β-carotene/g FW (SM) [404] Flower: 1434.4 µg total carotenoids/g DW (SM) [214]; 33.9 µg total carotenoids/g DW, individual carotenoids (RRLC) [366] Flower: 179.1 mg GAE/g DW (SM) [214]; 146.9 mg total phenolics/g DW, individual phenolics (RRLC) [366]; 336.5 mg GAE/g [405], 15.2 to 25.9 mg GAE/g DW [275] Leaf: 79.7 mg GAE/100 g DW, and others (SM) [406] Seed: 0.7 mg GAE/g (SM) [405] Fruit: 4.0 to 11.8 mg total phenolics/g DW, and others (SM) [404] Magnoliaceae Magnolia grandiflora L. Flower: 32.6 µg total carotenoids/g DW (SM) [214]; 20.4 µg total carotenoids/g DW, individual carotenoids (RRLC) [366] Flower: individual phenolics, 32.6 to 140.6 mg sum of phenolics/g DW (HPLC-PDA-MS/MS-ESI) [277]; 1273.9 to 4836.9 µg total flavonols/g FW, and others (HPLC-ESI-MS) [407] [366] Whole plant: 55.0 to 69.0 µg total carotenoids/g [411]; 51.6 and 68.3 µg total carotenoids/g FW (SM) [258] Flower: 47.5 mg GAE/g DW (SM) [214]; 0.2 mg syringic acid/g DW, individual phenolics (RRLC) [366] Leaf: 25.7 mg GAE/g FW [108] Flower: 15.2 mg GAE/g FW [108] Whole plant: 7.2 and 8.1 mg GAE/g FW, 0.2 and 0.06 mg total anthocyanins/g FW (SM) [258] Malvaceae Hibiscus sabdariffa L.…”

Section: Koch Na Namentioning

confidence: 99%

Exploring Plants with Flowers: From Therapeutic Nutritional Benefits to Innovative Sustainable Uses

Coyago-Cruz,

Moya,

Méndez

et al. 2023

Foods

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Flowers have played a significant role in society, focusing on their aesthetic value rather than their food potential. This study’s goal was to look into flowering plants for everything from health benefits to other possible applications. This review presents detailed information on 119 species of flowers with agri-food and health relevance. Data were collected on their family, species, common name, commonly used plant part, bioremediation applications, main chemical compounds, medicinal and gastronomic uses, and concentration of bioactive compounds such as carotenoids and phenolic compounds. In this respect, 87% of the floral species studied contain some toxic compounds, sometimes making them inedible, but specific molecules from these species have been used in medicine. Seventy-six percent can be consumed in low doses by infusion. In addition, 97% of the species studied are reported to have medicinal uses (32% immune system), and 63% could be used in the bioremediation of contaminated environments. Significantly, more than 50% of the species were only analysed for total concentrations of carotenoids and phenolic compounds, indicating a significant gap in identifying specific molecules of these bioactive compounds. These potential sources of bioactive compounds could transform the health and nutraceutical industries, offering innovative approaches to combat oxidative stress and promote optimal well-being.

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Flavonoid Components of Different Color Magnolia Flowers and Their Relationship to Cultivar Selections

Cited by 19 publications

References 34 publications

Composition of Flavonoids in the Petals of Freesia and Prediction of Four Novel Transcription Factors Involving in Freesia Flavonoid Pathway

Composition of Flavonoids in the Petals of Freesia and Prediction of Four Novel Transcription Factors Involving in Freesia Flavonoid Pathway

A Data Driven Approach to Assess Complex Colour Profiles in Plant Tissues

Exploring Plants with Flowers: From Therapeutic Nutritional Benefits to Innovative Sustainable Uses

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