Identification of the regulatory networks and hub genes controlling alfalfa floral pigmentation variation using RNA-sequencing analysis

Abstract

Background: To understand the gene expression networks controlling flower color formation in alfalfa, flowers anthocyanins were identified using two materials with contrasting flower colors, namely Defu and Zhongtian No. 3, and transcriptome analyses of PacBio full-length sequencing combined with RNA sequencing were performed, across four flower developmental stages. Results: Malvidin and petunidin glycoside derivatives were the major anthocyanins in the flowers of Defu, which were lacking in the flowers of Zhongtian No. 3. The two transcriptomic datasets provided a comprehensive and systems-level view on the dynamic gene expression networks underpinning alfalfa flower color formation. By weighted gene coexpression network analyses, we identified candidate genes and hub genes from the modules closely related to floral developmental stages. PAL, 4CL, CHS, CHR, F3'H, DFR, and UFGT were enriched in the important modules. Additionally, PAL6, PAL9, 4CL18, CHS2, 4 and 8 were identified as hub genes. Thus, a hypothesis explaining the lack of purple color in the flower of Zhongtian No. 3 was proposed. Conclusions: These analyses identified a large number of potential key regulators controlling flower color pigmentation, thereby providing new insights into the molecular networks underlying alfalfa flower development.

Changes at a Critical Branchpoint in the Anthocyanin Biosynthetic Pathway Underlie the Blue to Orange Flower Color Transition in Lysimachia arvensis

“…Alternatively, transcriptomic approaches using RNA-Seq allow a much broader investigation of the underlying causes of flower color variation (e.g., across the broader flavonoid biosynthetic pathway). For non-model species, de novo transcriptome assembly can be used to estimate genes with significant differential expression (DEGs; Wang et al, 2009 ; Martin and Wang, 2011 ; Grabherr et al, 2011 ; Anders et al, 2013 ; Haas et al, 2013 ; Butler et al, 2014 ; Casimiro-Soriguer et al, 2016 ; Chen et al, 2018 ; Duan et al, 2020 ). Even using a transcriptome approach, most studies of anthocyanin loss have focused on a narrow group of ABP candidate genes ( Lulin et al, 2012 ; Lou et al, 2014 ; Casimiro-Soriguer et al, 2016 ; Gao et al, 2016 ; Lang et al, 2019 ; Li L. et al, 2019 ; Duan et al, 2020 ; Zhang et al, 2020 ).…”

Integrated metabolic profiling and transcriptome analysis of pigment accumulation in Lonicera japonica flower petals during colour-transition

“…To date, high-resolution mass spectrometry (MS)-based metabolomics represents an effective technique to detect the accumulation and dynamic changes of metabolites [ 14 – 16 ]. Furthermore, transcriptome analysis has developed into a powerful approach that provides abundant sequence resources to study the mechanisms regulating flower colour formation [ 17 , 18 ]. To identify pigment accumulation, endogenous hormone changes and related gene fluctuations in petal colour transitions, global analysis of the metabolome combined with the transcriptional levels of pigment biosynthesis genes is required.…”

Multi-Omics and miRNA Interaction Joint Analysis Highlight New Insights Into Anthocyanin Biosynthesis in Peanuts (Arachis hypogaea L.)

“…During the process, PAs are irreversibly converted to anthocyanin under high temperature conditions, which further deepens the colors of plants ( Deng, 2018 ). Color changes in various plants have been reported to be related to anthocyanin biosynthesis (e.g., white clover ( Trifolium repens ) ( Duan et al, 2020 ), alfalfa ( Medicago sativa ) ( Xue et al, 2019 ), white primrose ( Primula vulgaris ) ( Li et al, 2019 ), strawberry ( Fragaria × ananassa ) ( Zhang et al, 2018 ), and other plants.…”