2.1. The Differential Landscape of Metabolites between Blue- and White-Colored Southwest Iris

Randomly selected fresh flower buds of Southwest iris and its white variant were collected and subjected to targeted metabolomics, revealing the differential landscape of metabolites, specifically anthocyanin and carotenoids.

Flavonoid profiling identified 297 metabolites with 69 differentially accumulated (25 downregulated and 44 upregulated in white flower samples compared to blue flowers) flavonoids between both flowers (Additional Files 1 and 2). A total of 24 anthocyanins were identified in the two groups of samples, and only 13 showed differential expression patterns between both flower types, and all 13 were downregulated in white flowers compared to blue flowers (Table 1). These anthocyanins included cyanidin 3-O-glucosyl-malonylglucoside, delphinidin O-malonyl-malonylhexoside, peonidin, cyanidin O-syringic acid, cyanidin 3-O-glucoside (kuromanin), delphinidin 3-O-glucoside (mirtillin), malvidin 3,5-diglucoside (malvin), delphinidin 3-O-rutinoside (tulipanin), pelargonidin 3-O-beta-D-glucoside (callistephin chloride), cyanidin 3-O-galactoside, peonidin 3, 5-diglucoside chloride, petunidin 3, 5-diglucoside, and peonidin 3-sophoroside-5-glucoside. Some of these anthocyanins were further validated using the LC-MS/MS standard-based quantification (Table 2). White variant showed less abundance of these three metabolites; particularly, delphinindin chloride and myrtillin (delphinidin 3-glucoside (Dp 3G)) chloride were not detected in white flowers. These results emphasized that either downregulation or blockage of anthocyanins in the white variant of Southwest iris is likely to be the major reason for differentiation from blue to white color.

Furthermore, carotenoid metabolites were also investigated in both groups. Eleven carotenoids were identified using targeted metabolomics (Additional Files 3, 4, and 5). There was no significant differential accumulation of carotenoids in blue and white flowers. However, α-carotene depicted higher accumulation in blue flowers compared to white, while zeaxanthin and xanthophyll (lutein) both up accumulated in white flowers. The changes in accumulation patterns of these three carotenoids were statistically nonsignificant, suggesting a neglected role of carotenoids in color differentiation from blue to white flowers. The accumulation pattern of carotenoids in purple and white flowers explained the conserved yellow stripes on both flowers.

2.2. Differential Landscape of Expressed Genes between Blue- and White-Colored Southwest Iris

In order to analyze the metabolism of anthocyanins in different colors, two libraries were constructed with blue and white perianths during the full bloom period for high-throughput sequencing. The clean data of each sample reached 8.91 Gb, and the Q30 base percentage was higher than 91.76%. The GC contents of the white and blue flowers were 46.32 and 46.49%, respectively (Additional File 6). Through the above sequencing quality control, high-quality clean data were obtained and used for downstream analysis. Subsequently, 370,387 transcripts and 299,827 unigenes were recombined and annotated against seven databases, viz., NT, NR, KOG, GO, and PFAM (Figure 2(a)). Principal component analysis (PCA) differentiated both color variants into two groups, and biological replicates were closely grouped (Figure 2(b)). PCA results suggested high reliability of transcriptome data for further analysis.

2.3. Differential Expression between Blue and White Flowers of Southwest Iris

Based on differential expression analysis in Southwest iris and its white variant, a total of 422 differentially expressed genes (DEGs) were identified, with 242 upregulated and 180 downregulated genes in the blue flowers compared to the white flowers (Additional File 7). The identified DEGs depicted significant enrichment in phenylpropanoid biosynthesis, flavonoid biosynthesis, pyrimidine biosynthesis, and photosynthesis. Accumulating KEGG annotation and DEGs, we identified 21 genes associated with flavonoid/anthocyanin biosynthesis and carotenoid biosynthesis (Table 3). Three genes, viz., c174379_g1 (3GT (anthocyanin 3-O-glucosyltransferase)), c178689_g1 (CHS (chalcone synthase)), and c134319_g1 (5GT (anthocyanin 5-O-glucosyltransferase)), showed downregulation expression pattern in the white variant as compared to the blue flowers. While other genes c165047_g2 (CHS2 (chalcone synthase 2)), c151362_g1 (CHI (chalcone-flavonone isomerase)), c173776_g1 (FNS (flavone synthase)), c145508_g1 (F3H (flavanone 3-hydroxylase)), c144091_g2 (F3 ′ H (flavonoid 3 ′-hydroxylase)), c144091_g1 (F3 ′ 5 ′ H (flavonoid 3′,5′-hydroxylase)), c144091_g1 (FLS (flavonol synthase)) c151171_g1 (DFR (dihydroflavonol-4-reductase)), c117196_g1 (ANS (anthocyanin synthase)), c161528_g2 (5,3GT (glucosyltransferase)), c167438_g1 (3AT (3-Amino-1,2,4-triazole)), c105331_g2 (URT-UDP-rhamnose: anthocyanidin 3-O-glucoside rhamnosyltransferase), c160759_g1 (PSY2 (phytoene synthase)), c172816_g1 (PDS (phytoene desaturase)), c168460_g1 (ZDS (Z-carotene desaturase)), c168442_g1 (LCYB (lycopene β-cyclase)), c144636_g1 (ZEP (zeaxanthin epoxidase)), and c143882_g1 (VDE (violaxanthin deepoxidase)) did not show a significant differential expression between the two variants. These results further confirm that the carotenoid biosynthesis pathway has no important effect on the white/blue flower coloration. Besides, the downstream product of CHS (chalcone substance) has little change between the two flower types, indicating that the flower color variation observed in the Southwest iris is mainly affected by the sharp downregulation of 3GT and 5GT genes.

Based on previously published reports suggesting the involvement of MYB and bHLH transcription factors as a key regulators in plant pigmentation [56–58], we identified 158 MYBs and 122 bHLHs. However, their expression was conserved between the Southwest iris and its white variant.

2.4. Proposed Mechanisms of Blue/White Color Formation in Southwest Iris

In the two Southwest iris variants, we identified 13 anthocyanins differentially accumulated. The initial anthocyanins are very unstable and can easily degrade [59, 60]; therefore, they need to be glycosylated and transferred into vacuoles for pigmentation. The 3GT and 5GT genes play this function [61], and because they were significantly downregulated in the white flower of Southwest iris, anthocyanin glucosides could hardly be produced, resulting in no blue coloration. In contrast, the high activity of 3GT and 5GT in the blue Southwest iris favored the formation and accumulation of anthocyanin glycosides, contributing to the blue color of the flowers (Figure 3). We did not observe any change in the carotenoid pathway, which explains the conserved yellow stripes in the flowers of both genotypes (Figure 3).

To further confirm the expression of identified genes in the development of flower color, we performed qRT-PCR for three groups of selected genes related to flower color regulation, viz., flavonoid biosynthesis, anthocyanin biosynthesis, and carotenoid biosynthesis. The qRT-PCR results have been presented in Figure 4. Interestingly, the genes 3GT and 5GT showed significantly lower expression patterns in white flowers compared to blue flowers (Figure 4(b)), which further confirms our hypothesis that downregulation of 3GT and 5GT genes resulted in white coloration. Besides, genes related to carotenoid synthesis did not show significant differential expression in both flowers (Figure 4(a)), supporting our transcriptome results.

4.1. Plant Materials and Sample Collection

Wild Southwest iris, Iris bulleyana Dykes, and its white variant I. bulleyana Dykes f. alba YT Zhao were used in this study. Iris bulleyana Dykes grows naturally in the outskirts of Shangri-La county, Yunnan province, China. No permissions were necessary to collect such samples. The formal identification of the plant materials was undertaken by the corresponding author of this article. No voucher specimen of this material has been deposited in a publicly available herbarium. During its flowering stage, random samples from plants grown under a controlled environment were selected with the same conditions as the degree of development, size, and length. Flower samples were collected when half of the flower parts appeared from the bud (Figures 1(b1) and 1(b2)) after quickly removing the stalks and bracts at the base of the buds and placed in liquid nitrogen. Samples were stored at -80°C. The samples were collected with three biological replicates for each flower color, viz., blue (Iris bulleyana Dykes) and white (I. bulleyana Dykes f. alba YT Zhao). A total of six samples were used for transcriptome sequencing analysis, metabolome analysis, and qRT-PCR analysis.

4.2. Metabolic Profiling

The targeted metabolite landscape for flavonoids/anthocyanins and carotenoids was explored and analyzed according to the standard procedure detailed by Yuan et al. [90]. The flower samples collected from Iris bulleyana Dykes and I. bulleyana Dykes f. alba YT Zhao were grounded to powder and subjected to LC-MS analysis. UPLC-MS/MS analysis was performed by Metware (http://www.metware.cn). Prior to further data analysis, quality control (QC) analysis was performed. VIP (variable importance in projection) values were identified utilizing PLS-DA. The metabolites were considered differentially expressed when the VIP ≥ 1, and fold change ≥ 2 or fold change ≤ 0.5. To validate the anthocyanin metabolome, three selected anthocyanins were further tested using HPLC-MS/MS performed by Metware (http://www.metware.cn), and their corresponding concentrations were identified in both variants of Southwest iris.

4.3. RNA Extraction, Library Preparation, and Sequencing

Transcriptome sequencing was performed by constructing six libraries corresponding randomly collected bud samples, each with three replicates, of Iris bulleyana Dykes and I. bulleyana Dykes f. alba YT Zhao. After extraction of total RNAs with TRIzol reagent (Takara, China), contamination and RIN (RNA integrity number) were checked using 1% agarose gel and Agilent 2100 Bioanalyzer system (Agilent Technologies, CA, USA), respectively. Pair-end sequencing libraries were constructed using 3 μg RNA for each sample. Further, libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s instructions. Illumina HiSeq platform was utilized for RNA sequencing and was performed by the company Novogene (https://en.novogene.com/). The libraries were sequenced by paired-end sequencing on Illumina HiSeq.

Low-quality reads and short sequence reads (<50 bp) were removed using FastQC and Perl program. Clean reads were de novo assembled using Trinity v2.11.0 (http://trinityrnaseq.sourceforge.net). The transcriptome data of Iris bulleyana Dykes and I. bulleyana Dykes f. alba YT Zhao have been deposited to the national center for biotechnology information (NCBI) sequence read archive (SRA) under accession number PRJNA676187.

4.4. Differential Expression Analysis of Identified Genes

The read numbers mapped to each gene were counted using featureCounts v1.5.0-p3 [55]. Then, calculating the expected number of FPKM (fragments per kilobase of exon model per million reads mapped) of each gene based on the length of each gene and reads count mapped to the gene. DEGs between blue and white groups of colored samples were identified using the DESeq R package (v1.18.0) [91] and edgeR package (v 3.24.3). The threshold p value in multiple tests to judge the significance of gene expression difference was based on the false discovery rate (FDR) method. When FDR ≤ 0.05 and FPKM values showed at least a 2-fold difference among samples, the gene was considered a significant DEG. DEGs commonly detected by both packages were used in this study.

4.5. Validation of Gene Expression Using qRT-PCR

To verify the RNA-seq data, qRT-PCR was used following total RNA extraction from flower bud samples in three replicates, using the Tiangen RNAprep Pure Plant kit (Tiangen Biotech, Beijing, China), following the manufacturer’s protocol. Twenty genes related to flavonoid/anthocyanin and carotenoid pathways of the transcriptome data were selected, and corresponding primers were designed for qRT-PCR using the Oligo-7 software (Additional File 8). The primers were synthesized by Sangon Biotech (Shanghai, China). Actin was used as an internal reference gene for qRT-PCR analysis of the target genes [92]. The cDNA was extracted from RNA and used as a template to make the reaction for qRT-PCR by using Takara qPCR kit SYBR Premix Ex TaqTM II (Tli RNaseH Plus). Three biological repeats were used for each qRT-PCR reaction.

4.6. KEGG Enrichment Analysis of DEGs

To test the statistical enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, the GOseq R package was used. The KEGG pathways enriched with DEGs (FDR < 0.05) were detected using KOBAS 2.0 software [62] based on the method of overrepresentation analysis (ORA). The adjusted p value of significantly corroborated KEGG terms was less than 0.05.