Physiological and transcriptomic analyses reveal the mechanisms underlying leaf color variation in zebra-striped mutants of oat (Avena sativa)

Abstract

Background

Leaf color mutants serve as valuable models for investigating chloroplast development, chlorophyll metabolism, and photosynthetic regulation. However, research on oat mutagenesis remains limited, and the physiological and molecular effects of leaf color mutations in oats are not fully understood. The zebra-striped oat mutant, derived from the wild-type Everleaf via sodium azide (SA) mutagenesis, exhibits distinct white leaf stripes, impaired pigment accumulation, and reduced photosynthetic efficiency. This mutant serves as a model to investigate the underlying molecular mechanisms of pigment biosynthesis and photosynthetic efficiency, offering valuable resources for oat molecular breeding and the development of specialty varieties.

Results

The zebra-striped mutant displays a significant reduction in most pigments, particularly carotenoids (43.80% reduction), while chlorophyll b remains largely unaffected. Photosynthetic efficiency and chlorophyll fluorescence parameters are substantially impaired, resulting in stunted growth and poor agronomic performance, with a 16.95% decrease in main panicle seed number and a 26.59% reduction in seed weight of the main panicle. Transcriptome analysis revealed 1,958 DEGs between the mutant and wild-type, including 571 up-regulated and 1,387 down-regulated genes. These DEGs were enriched in GO terms related to chlorophyll biosynthesis, chloroplast function, and carbohydrate binding. KEGG pathway analysis implicated these genes in chlorophyll and carotenoid biosynthesis (e.g., CHLD, POR, ZDS, BCH), photosynthesis, and photosynthetic carbon fixation. Additionally, 36 DEGs in the starch and sucrose metabolic pathways were down-regulated, leading to inhibited starch and sucrose metabolism. qRT-PCR validated reduced expression of key genes (CHLD, POR, LHCB1), confirming their role in the mutant phenotype.

Conclusions

The zebra-striped phenotype results from disruptions in pigment synthesis, metabolic pathways, and photosynthetic carbon fixation, leading to reduced chlorophyll content, impaired photosynthesis, and decreased growth and yield. Leaf color variation leads to downregulation of related genes, thereby affecting chlorophyll and photosynthetic metabolism in oats, as well as starch and sucrose metabolism. These findings provide important genetic resources and phenotypic markers for oat breeding programs, contributing to our understanding of chloroplast development and photosynthetic efficiency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-07321-z.

Introduction

Leaves serve as the primary sites of photosynthesis in plants, playing a pivotal role in growth, development, and energy conversion [1]. Leaf coloration is determined by the spatial and temporal distribution of pigments, with their types and concentrations varying across developmental stages [2]. Chlorophyll, the dominant photoreceptor pigment, is indispensable for photosynthesis and energy storage, whereas carotenoids function as critical components of both the photosynthetic apparatus and photoprotective systems [3, 4]. Impairments in the biosynthesis of these key pigments—chlorophyll and carotenoids—can significantly reduce photosynthetic carbon fixation efficiency, ultimately leading to growth retardation and abnormal leaf coloration [5–7].

Leaf color mutations arise from structural and functional abnormalities in chloroplasts that disrupt chlorophyll metabolism, leading to distinct phenotypic variations [8, 9]. These mutations are collectively referred to as chlorophyll-deficient mutants [10] and are typically categorized into five phenotypic classes: albino, striped, light green, yellow, and stay-green [11, 12]. While such mutants can occur naturally, artificial mutagenesis techniques significantly enhance mutation frequencies [13]. To date, leaf color mutants have been extensively characterized in major crops, including maize [14], rice [15], wheat [16], barley [17], and alfalfa [18].

Comparative studies between mutants and wild-type plants reveal consistent patterns of pigment deficiency and photosynthetic impairment. For instance, the maize ygl-1 mutant (A novel yellow-green leaf mutant yellow-green leaf-1) exhibited reductions of 24.32%, 20.4%, 40.2%, and 1.96% in total chlorophyll (Chl), Chl a, Chl b, and carotenoid (Car) content, respectively, severely compromising photosynthetic capacity [19]. Similarly, the wheat Ygm mutant (The yellow-green leaf color mutant) showed altered pigment profiles, with leaf Chl a/b ratios 3.81–5.64 times higher than wild-type XINONG 1718 [20]. The EMS-induced rice wlp3 mutant (White leaf and panicle3 mutant is that the leaves and panicles are white or albino) demonstrated significantly lower carotenoid content [21], a phenomenon also observed in barley and wheat mutants [22].

These pigment deficiencies directly impact photosynthetic performance, which is fundamentally linked to dry matter accumulation. Consequently, affected plants typically show reduced biomass and economic yield [23]. For example, the rice wst1 mutant (a white-striped leaf mutant), characterized by chloroplast abnormalities, displayed not only lower pigment content but also significant reductions in agronomic traits including the number of effective panicles, plant height, seed setting rate, and thousand-grain weight [24]. Similarly, EMS-induced wheat mutants exhibited comprehensive declines in photosynthetic parameters and yield components, with tiller number and yield per plant decreasing by up to 61% [25].

Leaf color mutants serve as invaluable genetic resources for investigating genes regulating chlorophyll biosynthesis and chloroplast development [26–28]. The rapid development of functional genomics and high-throughput sequencing technologies has significantly advanced our understanding of the molecular mechanisms underlying leaf color variation. Among these technologies, RNA-seq has emerged as a particularly powerful tool due to its cost-effectiveness and rapid turnaround time, enabling efficient screening of differentially expressed genes (DEGs) and associated metabolic pathways in leaf color mutants [29]. Transcriptomic analyses have consistently demonstrated that leaf color mutants exhibit differential expression of genes involved in chlorophyll metabolism, photosynthesis, and ROS detoxification. For instance, Ahmad et al. [30] reported comprehensive transcriptome alterations in these pathways across various rice leaf color mutants. Zhang et al. [31] observed reduced photosynthetic pigment content and lower net photosynthetic rates in the NAU31 mutant during flowering stage, with corresponding downregulation of photosynthesis-related DEGs. Conversely, the rice ygl7 mutant, while showing reduced pigment content, displayed enhanced photoelectron utilization and photosynthetic parameters, likely due to upregulation of key photosynthetic genes [32].

RNA-seq technology has also enabled precise genotype-phenotype correlations. For example, CHLD mutations in mung bean result in chlorophyll deficiency accompanied by chloroplast dysfunction and ultrastructural defects [33]. Similarly, the rice pgl10 mutant, which lacks functional protochlorophyllide reductase, exhibits both reduced pigment content and abnormal thylakoid ultrastructure, along with significant alterations in chlorophyll metabolism-related gene expression [34, 35].

Leaf color mutations are generally regarded as agriculturally undesirable due to their negative impacts on photosynthesis and yield-related traits [36, 37]. However, their distinct phenotypic expression at early developmental stages makes them valuable genetic markers in plant breeding programs. The visible differences between mutants and wild-type plants enable efficient identification and elimination of pseudohybrids during seedling selection [38, 39]. For instance, the maize ZmSig2A mutant with valine deletion displays reversible leaf yellowing, providing an effective visual marker for hybrid purity assessment [40]. Similarly, targeted knockdown of phytoene desaturase (PDS) induces leaf bleaching, serving as a reliable phenotypic indicator for verifying successful gene editing [41]. Beyond their utility in breeding, leaf color mutants offer unique opportunities to investigate plant-pathogen interactions, particularly for pathogens that rely on host photosynthetic activity [42]. Previous studies indicate that leaf color mutations can influence plant responses to abiotic stress. Research on leaf color mutants facilitates the identification of stress-related genes, enabling the development of stress-resistant crop varieties [43]. Furthermore, light-colored leaf mutants may exhibit reduced heat absorption, enhancing their adaptation to high-temperature environments and mitigating light inhibition through altered light absorption properties [44]. Understanding the adaptive mechanisms of leaf color variation could inform optimal crop arrangement strategies in response to climate change.

Oat (Avena sativa L.), an annual crop in the Poaceae family, serves as an important dual-purpose plant for both human consumption and animal feed. Its global cultivation is attributed to exceptional broad adaptability and superior nutritional quality. Wild-type oat varieties typically display lanceolate leaves with color variations from dark to light green depending on genotype. Leaf color is an important phenotypic characteristic of oat growth, development, and physiological metabolism, directly influencing its photosynthetic efficiency, stress tolerance, yield, and quality. In forage oats, leaf color significantly correlates with hay quality [45, 46]. Consequently, studying oat leaf color mutants contributes to both theoretical understanding (photosynthesis, stress response, and metabolic regulation) and practical applications (molecular breeding and specialty variety development). These mutants have multidisciplinary applications spanning molecular biology, ecological agriculture, and high-value product development. Chemical mutagenesis rapidly generates mutants with novel traits, serving as an efficient method for oat germplasm innovation and resource enrichment [47]. The induced leaf color mutants are directly applicable to diverse studies, particularly in oat pigment metabolism, photosynthesis mechanisms, stress resistance, and yield-quality relationships [48]. However, chemical mutagenesis studies in oats remain limited, with particularly scarce reports on leaf color mutants.

In this study, we characterized a novel zebra-striped mutant generated through sodium azide (SA) mutagenesis, which exhibits a stable white-striped leaf phenotype. This mutant provides an excellent model system for investigating striped leaf formation, a phenomenon whose molecular basis remains poorly understood. Our comprehensive investigation compared wild-type and mutant plants across multiple levels: (1) morphological and agronomic traits, (2) photosynthetic performance and pigment composition, and (3) genome-wide transcriptomic profiles with emphasis on chlorophyll biosynthesis pathways. Through this integrated physiological and molecular approach, we reveal how leaf color mutations impact photosynthetic efficiency and plant development in oats. These findings not only advance our understanding of photosynthetic regulation in cereals but also provide crucial genetic resources for identifying functional genes controlling leaf coloration in oats.

Materials and methods

Plant materials

The study employed two experimental materials: the wild-type oat cultivar Everleaf (WT) and its derived zebra-striped mutant (MT). Everleaf, serving as the control genotype, is characterized by dwarf growth habit, high leaf biomass production, and dark green lanceolate leaves, the seeds were provided by College of Pratacultural Science at Gansu Agricultural University (in 2019). The zebra-striped mutant was generated through chemical mutagenesis of Everleaf using sodium nitrite (SA). In 2019, we treated Everleaf (600 seeds) with 15 mM SA (Shanghai Yuanju Biotechnology Co., Ltd.) for 2 h to induce mutagenesis, then sowed the seeds to generate the M₁ population. In 2020, M₂ plants were cultivated and phenotyped, revealing a stable white-stripe mutant from this mutagenesis. Compared with the wild type, the mutant leaves exhibited irregular white stripes. From 2021 to 2023, the mutant was propagated over three generations (M₃-M₅), confirming stable inheritance of the white-stripe phenotype. Each generation was planted with mutant and wild-type plants in adjacent rows (30 cm row spacing, 10 cm plant spacing, and 4–5 cm sowing depth). All experiments were performed at the Forage Crop Experiment Station, Gansu Agricultural University (Lanzhou, China). All analyses used M5 generation zebra-striped mutant plants to maintain phenotypic consistency.

Field experiment

Experimental site and environmental conditions

The field experiment was conducted at the Pasture Experiment Station of Gansu Agricultural University (36°03’N, 103°40’E), located in the northwestern region of Lanzhou at the western margin of the Loess Plateau. The site has an elevation of 1,473 m above sea level and features a temperate semi-arid climate, characterized by mean annual temperature of 11 °C, 172 days frost-free period, and 350–400 mm annual precipitation: (70% occurring June-September). Pre-planting soil analysis (0–20 cm depth) revealed the following properties: 8.80 g·kg^-1 organic matter, 42.88 mg·kg^-1 available N, 35.72 mg·kg^-1 available P, 177.19 mg·kg^-1 available K and a pH of 7.71.

Field experimental design

In 2024, field phenotypic comparison trials of WT and MT will be conducted. The experiment employed a randomized complete block design with three replications. Key cultivation practices included planting method of precision spot-sowing, with 30 cm row spacing × 10 cm plant spacing and 4–5 cm sowing depth. A paired-row design was implemented, with WT and MT plants cultivated in adjacent rows to ensure uniform environmental conditions. Border rows were established to minimize edge effects. Irrigation was applied at critical growth stages (tillering, jointing, flowering, grain filling) and weed was controlled by manual weeding during tillering stage.

Morphological assessments were conducted at 10-day intervals from seedling emergence until maturity. Thirty plants of each of the two materials were randomly selected to determine their phenotypic and agronomic traits. Phenotypic traits, including plant height, culm thickness, flag leaf length, flag leaf width, penultimate leaf length and penultimate leaf width, were measured at the grain-filling stage, while the number of tillers was recorded at tillering stage. At harvest, plants from each treatment were individually collected, dried, and threshed. Subsequently, main panicle seed number, main panicle seed weight and thousand-grain weight were determined (Specific measurement methods: Table S1).

Controlled environment experiment

A complementary pot study was conducted in growth chambers at Gansu Agricultural University with the following parameters: 25/18°C (day/night) temperature, 65–75% relative humidity, 500–700 µmol m^-2 s^-1 photosynthetic photon flux density. A 2:1 mixture of topsoil (0–30 cm depth) and substrate was prepared in polyethylene pots (30 × 25 cm), with each pot containing 8 kg of the mixture and 1.5 L basal water.

Uniform seeds of both wild-type (WT) and mutant (MT) lines were sterilized with 75% ethanol for 30 s followed by 2% NaClO for 10 min, and thoroughly rinsed with sterile distilled water. Twenty seeds were planted per pot at a depth of 5 cm, seedlings were thinned to maintain 10 uniform plants per pot based on consistent growth vigor at the three-leaf growth stage. Three independent biological replicates were established for each genotype; each biological replicate consisted of 5 pots arranged in a completely randomized design within the growth chamber. During the jointing stage, the first fully expanded leaves from 5 representative plants per genotype were collected for chlorophyll content measurement and photosynthetic characterization.

Measurement of Chl content

Chlorophyll determination followed the methods of Arnon [49] and Shi et al. [50]. The leaves to be tested were cut and mixed, and 0.5 g of each sample was weighed into 15 mL centrifuge tubes, and 10 mL of 95% ethanol was added. The extract was then protected from light for 24 h. The OD values at 664, 647, and 470 nm were measured using a UV spectrophotometer, with 95% ethanol as a blank control. The concentrations of chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Ca + b), and carotenoids (Car) were then calculated. The formulas are as follows:

Where: Ca and Cb represent the concentrations of chlorophyll a and b, respectively, while Car refers to the total concentration of carotenoids. After determining the pigment concentrations using the above formula, the content of each pigment per unit of fresh tissue weight was calculated as follows:

Measurement of photosynthetic properties and chlorophyll fluorescence parameters

Measurements were taken once at the tip, middle, and base of the leaves to be tested, and the average values were used to compare photosynthetic and fluorescence parameters between the two materials. The measurement method are as follows:

Stomatal conductance (Gs), net photosynthetic rate (Pn), intercellular CO₂ concentration (Ci), and transpiration rate (Tr) of different oat materials were measured using a LI-6400 portable photosynthesizer (LI-COR, USA). Photosynthetic parameters of oats were measured under a red and blue light source with an intensity of 1600 µmol/(m²·s) from 9:00 AM to 12:00 PM and from 2:00 PM to 5:00 PM on a sunny day. The ratio of net photosynthesis rate to transpiration rate was used to calculate water use efficiency.

Chlorophyll fluorescence parameters were determined using a Mini-IMAGING-PAM modulated fluorescence imaging system (Heinz Walz GmbH, Effeltrich, Germany). Measurements were performed by placing the first leaf of WT and MT in a dark environment for 30 min (three biological replicates per genotype). Following dark adaptation, minimum fluorescence (Fo) was measured under weak actinic light (≤ 0.1 µmol m^-2 s^-1), followed by a 2800 µmol m^-2 s^-1 saturation pulse to determine maximum fluorescence (Fm). Under ambient light, steady-state fluorescence (Ft) was recorded before applying a saturation pulse to determine light-adapted maximum fluorescence (Fm’). Read the electron transfer rate (ETR) at the end of the measurement.

Key chlorophyll fluorescence parameters were calculated as: Potential quantum efficiency of PSⅡ: Fv/Fo = (Fm-Fo)/Fo; Maximum quantum efficiency of PSⅡ: Fv/Fm = (Fm-Fo)/Fm; Maximum photochemical quantum yield of PSII: ΦPSII = (Fm’-Ft)/Fm’; Photochemical quenching: qP = (Fm’-Ft)/(Fm’-Fo); Non-photochemical quenching: NPQ = (Fm-Fm’)/(Fm-Fo).

RNA extraction, library construction, and RNA-seq

At jointing stage, the apical first leaves were collected from both WT and MT plants. Three biological replicates per genotype (designated WT-1 to WT-3 and MT-1 to MT-3) were used for cDNA library construction. Total RNA was extracted using the Biomarker Plant Total RNA Isolation Kit (Biomarker, Beijing). RNA purity and concentration were assessed using a NanoDrop 2000 spectrophotometer (Implen, Westlake Village, CA, USA), while integrity was verified with an Agient2100/LabChip GX (Agilent Technologies, Santa Clara, CA, USA).

Qualified RNA samples were processed for library preparation. Poly(A) + mRNA was enriched using Oligo (dT) magnetic beads and fragmented. Using mRNA as template, first- and second-strand cDNA were synthesized, purified, and processed through end repair, A-tailing, and adapter ligation. Size selection was performed using AMPure XP beads, followed by PCR amplification to generate final libraries. Library quality was assessed by Q-PCR (required effective concentration > 2 nM). Qualified libraries were sequenced (PE150) on an Illumina HiSeq™ 2500 platform.

Comparison with reference genomes

Transcriptome sequencing and analysis were conducted by Biomarker Biotechnology Co., Ltd. (Beijing, China). High-quality clean reads were obtained by filtering raw data using SOAPnuke software to remove adapter-containing and low-quality Reads. The resulting clean Reads were then aligned to the reference genome using HISAT2 (version 2.2.1.0) [51] for accurate mapping and localization of reads (https://wheat.pw.usda.gov/GG3/graingenes_downloads/oat-ot3098-pepsico).

Gene expression analysis

Differential gene expression analysis was conducted using DESeq2 (v1.30.1) with FPKM normalization via RSEM (v1.3.3) [52]. Differentially expressed genes (DEGs) were identified using thresholds of |log2FC| ≥ 1 (fold change ≥ 2) and FDR-adjusted p-value < 0.01. DEGs were functionally annotated using multiple databases, followed by enrichment analysis of Gene Ontology (GO) terms and KEGG pathways.

qRT-PCR validation

Total RNA was extracted from leaves using RNA simple total RNA kit (Tiangen Biotech (Beijing) Co., Beijing, China). Reverse transcription using PrimeScript™ RT kit (Takara Biomedical Technology Co., Beijing, China). qRT-PCR was performed using the SuperReal PreMix Plus (SYBR Green) kit with a reaction system of 20 ul. Gene-specific primers were designed using NCBI Primer-BLAST and synthesized by Sangon Biotech (Shanghai) Co., Ltd. The sequences of 20 primer pairs used for qRT-PCR are listed in Table S2. Actin gene was used as internal reference. Relative expression levels were calculated as 2^−ΔΔCT [53].

Statistical analysis

All of the data were collected using Microsoft Excel and analyzed by ANOVA. The mean separation was performed using the Student’s test with a significance level of P < 0.05. Analyses were conducted using SPSS v26.0 for Windows (SPSS Inc., USA). The related figures were drawn using GraphPad Prism v10.0 and Origin Pro v9.8 software.

Results

Phenotype, chlorophyll content, and photosynthetic characteristics

Leaf color variation in the MT line became apparent after the seedling stage. Compared to the WT, the mutant exhibited distinct white stripes randomly distributed across the leaves, extending to the sheaths and stalks. This phenotypic trait remained stable throughout development. Despite the altered leaf pigmentation, the mutant showed no significant differences in plant height, stem thickness, or leaf dimensions compared to the WT, leading to its designation as a white-striped leaf mutant (Fig. 1A-E).

Fig. 1.

Differences in phenotypes, agronomic traits and photosynthetic fluorescence parameters between wild type and zebra-stripe mutant. Phenotypic differences between wild-type (left) and zebra-stripe mutant (right) at tillering stage (A); Close-up view of the zebra-stripe mutant (B); Close-up image of the phenotype of the zebra stripe mutant at booting stage (C and E); Phenotype of zebra stripe mutant at booting stage (D); Differences in pigment content between wild-type and zebra-stripe mutant (F); Differences in photosynthetic characteristics between wild-type and zebra-stripe mutant (G), Net photosynthetic rate (Pn: µmol CO₂·m⁻²·s⁻¹); Transpiration rate (Tr: mmol H₂O·m⁻²·s⁻¹); Water use efficiency (WUE: µmol CO₂·mmol⁻¹ H₂O); Stomatal conductance (Gs: mol·m⁻²·s⁻¹); Intercellular CO₂ concentration (Ci: ppm); Differences in chlorophyll fluorescence parameters between wild-type and zebra-striped mutant (H), Electron transfer rate (ETR); Maximum quantum efficiency of PSⅡ (Fv/Fm); Potential quantum efficiency of PSⅡ (Fv/Fo); Maximum photochemical quantum yield of PSII (ФPSⅡ); Non-photochemical quenching (NPQ); Photochemical quenching (qP); Initial Fluorescence (Fo); Maximum Fluorescence (Fm); Phenotypic and agronomic traits differences between wild type (WT) and zebra-stripe mutant (MT) (I), Tillers number (TN), Plant Height (PH: cm), Culm Thickness (CT: mm), Flag-Leaf Length (FLL: cm), FLW: Flag-Leaf Width (FLW: cm), Penultimate leaf length (PLL: cm), Penultimate leaf width (PLW: cm), Main Panicle Seed Number (MPSN), Main Panicle Seed Weight (MPSW: g), Thousand-grain weight (TGW: g). *: P < 0.05, **: P < 0.01, ***: P < 0.001, ns: not significant, the same below

Pigment analysis revealed significant changes in chlorophyll and carotenoid levels. The mutant displayed reduced concentrations of all pigments except Chl b (Fig. 1F). The most pronounced decrease was observed in Car (43.80% reduction), followed by Chl a (28.83% reduction, WT: 0.610 mg/g), while Chl b increased slightly (3.56%, WT: 0.497 mg/g). Consequently, the total chlorophyll content in the mutant was 13.07% lower than in the WT (1.148 mg/g).

Chlorophyll content plays a critical role in determining photosynthetic efficiency. In the MT, photosynthetic performance was significantly impaired compared to the WT. Except for WUE, all measured photosynthetic parameters showed highly significant differences (P < 0.0001) between the WT and mutant (Fig. 1G). The most pronounced reduction was observed in Tr, which decreased by 70.09% (WT: 1.451 mmol H₂O·m^-2·s^-1), followed by a 68.23% (WT: 9.851 µmol CO₂·m^-2·s^-1) decline in Pn. Despite the substantial reductions in Pn and Tr, WUE increased by 6.67% (WT: 7.324 µmol CO₂·mmol^-1 H₂O) in the mutant. Additionally, Gs and Ci were significantly lower in the mutant, declining by 66.85% and 12.61%, respectively.

Chlorophyll fluorescence parameters were analyzed to assess alterations in light absorption and energy transfer within the photosynthetic apparatus. Compared to the WT, the MT exhibited significantly lower values for both Fo and Fm, with reductions of 29.31% and 46.85%, respectively. These results indicate a decreased number of active PSII reaction centers in the mutant. The Fv/Fm was 13.24% lower in the mutant, while the Fv/Fo decreased by 33.98%. Additionally, the ΦPSII showed a significant reduction (16.57% lower) in the mutant. Similar trends were observed for qP, NPQ, and ETR, all of which were significantly lower in the mutant compared to the WT (Fig. 1H).

Comparative phenotypic analysis between the WT and MT lines revealed minimal differences in vegetative growth parameters (Fig. 1I). While tiller number and penultimate leaf width showed non-significant increases in the MT line, all measured yield-related traits exhibited reductions. The most substantial decreases were observed in reproductive characteristics: main panicle seed weight showed a 26.59% reduction, followed by a 16.95% decrease in main panicle seed number. Morphological analysis indicated subtle alterations in leaf architecture. The MT line displayed modest, non-significant reductions in flag leaf dimensions (both length and width) and penultimate leaf length compared to WT. Most notably, the mutant exhibited distinct leaf coloration differences, consistent with its white-striped phenotype.

Identification of DEGs

To elucidate the molecular basis of the striped-leaf phenotype, we performed RNA-seq analysis on both WT and MT plants using three biological replicates per genotype. Quality assessment revealed strong negative correlations between groups, with between-group variation significantly greater than within-group variation (Figs. S1, S2). Principal component analysis (PCA) demonstrated clear segregation between WT and MT transcriptomic profiles, confirming the robustness of our experimental design. Comparative transcriptome analysis identified 1,958 differentially expressed genes (DEGs), comprising 571 up-regulated and 1,387 down-regulated genes in MT relative to WT (Fig. 2A, B). Hierarchical clustering of all DEGs revealed distinct expression patterns between genotypes, with the majority of genes showing downregulation in MT plants (Fig. 2C). This global transcriptomic reprogramming suggests substantial alterations in multiple biological processes in the zebra-striped mutant.

Fig. 2.

Transcriptome analysis of DEGs in WT and MT. Comparison of DEGs in WT and MT (A). Volcano plot showing the DEGs between two different libraries (B). The threshold q < 0.05 was used to determine the significance of DEGs. Red and green dots represent up- and downregulated genes, respectively, and gray dots indicate transcripts that did not significantly change in the WT library compared to MT. Hierarchical clustering of all of the DEGs was based on the log10RPKM values (C). The color spectrum from blue to red represents gene expression intensity, ranging from low to high, respectively

Functional enrichment analysis of DEGs

Gene Ontology (GO) and KEGG pathway analyses were performed to characterize the 1,958 DEGs identified between wild-type and mutant plants. The DEGs were categorized into three primary GO domains: biological process (BP), cellular component (CC), and molecular function (MF). In the BP category, we observed significant enrichment for processes including chlorophyll biosynthesis (GO:0015995), light response (GO:0009416), and glycolysis (GO:0006096). The CC analysis revealed strong enrichment for chloroplast-related terms (GO:0009507), particularly chloroplast stroma (GO:0009570) and photosystem I components (GO:0009522). MF analysis identified key functions such as magnesium ion binding (GO:0000287), carbohydrate binding (GO:0030246), and protochlorophyllide reductase activity (GO:0016630) (Fig. 3A).

Fig. 3.

GO and KEGG pathway enrichment analysis of DEGs between WT and MT. GO enrichment analysis shows the 30 most enriched GO second-level classification term (A). KEGG enrichment analysis shows the 20 most enriched KEGG terms (B). The 10 most highly enriched GO terms in biological process (C). The 10 most highly enriched GO terms in cellular fractions (D). The 10 most highly enriched GO terms in molecular function (E). Left ring: Displays the IDs of differentially expressed genes. Left ring color: The color depth represents the log2FC value of differentially expressed genes. Right ring: Represents different GO terms, with each color corresponding to a specific GO term. Chord lines: Connects the differentially expressed genes enriched in each GO term, showing the relationship between differentially expressed genes and GO terms

Notably, hierarchical analysis of second-level GO terms showed that downregulated DEGs were predominantly associated with chloroplast organization, light intensity response, and chloroplast localization in the BP category (Fig. 3C). Similarly, in the CC category, downregulated genes were enriched for chloroplast structure and photosystem components, while MF analysis revealed downregulation of magnesium chelatase and carotene 7,8-desaturase activities (Fig. 3D, E). These findings suggest comprehensive disruption of chloroplast-related processes in the zebra-striped mutant.

KEGG pathway analysis revealed that the DEGs were significantly enriched in five major functional categories: (1) cellular processes, (2) environmental information processing, (3) genetic information processing, (4) metabolism, and (5) organismal systems. Among these, metabolic pathways showed the most pronounced alterations, with particularly strong enrichment in phenylpropanoid biosynthesis (60 DEGs), starch and sucrose metabolism (41 DEGs) and carbon metabolism (37 DEGs). Notably, we also identified significant enrichment in photosynthesis-related pathways, including carbon fixation in photosynthetic organisms, carotenoid biosynthesis, porphyrin and chlorophyll metabolism (Fig. 3B, S3). These results demonstrate that the zebra-leaf mutation induces comprehensive metabolic reprogramming in oat leaves. The observed pathway alterations suggest that the mutation affects not only pigment biosynthesis (chlorophyll and carotenoids) but also fundamental photosynthetic processes, particularly carbon fixation and carbohydrate metabolism. The coordinated downregulation of these critical metabolic and photosynthetic pathways likely contributes to the observed phenotypic differences in leaf coloration and growth characteristics between WT and MT plants.

Identification of DEGs related to chlorophyll and carotenoid metabolism

To elucidate the molecular basis of the white-striped leaf phenotype, we conducted targeted analysis of DEGs involved in two critical pigment metabolic pathways: porphyrin and chlorophyll metabolism (ko00860) and carotenoid biosynthesis (ko00906). In the porphyrin and chlorophyll metabolism pathway, we identified five significantly down-regulated genes (p < 0.05, fold change ˃2) (Fig. 4A), including two magnesium-chelatase subunit D (CHLD) genes (Pepsico1_Contig14783.path1, Pepsico1_Contig17327.path2), and three protochlorophyllide oxidoreductase (POR) genes (Pepsico1_Contig6816.path1, Pepsico1_Contig6921.path1, Pepsico1_Contig7188.path2). The coordinated downregulation of these key genes suggests substantial impairment in magnesium ion incorporation into protoporphyrin IX (mediated by CHLD) and protochlorophyllide-to-chlorophyllide conversion (catalyzed by POR).

Fig. 4.

Differences between WT and MT in chlorophyll and carotenoid biosynthetic pathways. Chlorophyll and carotenoid biosynthesis pathway (A). Red boxes and numbers indicate the number of upregulated genes, green boxes and numbers indicate the number of downregulated genes, and blue boxes indicate the presence of both up and down regulated genes. Glu: L-Glutanyl-tRNA; ALA: 5-Aminolevulinic acid; PBG: Porphobilinogen; Proto IX: Protoporphyrin IX; CHLD: magnesium chelatase subunit D; CHLI: magnesium chelatase subunit I; CHLH: magnesium chelatase subunit H; Mg-Proto IX: Mg- Protoporphyrin IX; POR: Protochlorophyllide reductase; Chl a: Chlorophyll a; Chl b: Chlorophyll b; ZDS: zeta-carotene desaturase; BCH: β-carotene-hydroxylase. Expression profile clustering of the chlorophyll and carotenoid biosynthesis pathways. (B). Expression ratios are based on log2 fragments per kilobase of transcript per million mapped reads (FPKM) values, where each vertical column represents a sample, and each horizontal row represents a single gene

For carotenoid metabolism (Fig. 4B), we observed significant downregulation of β-carotene desaturase (ZDS; Pepsico2_Contig6418.path1), upregulation of β-carotene hydroxylase (Pepsico2_Contig14786.path1). These expression changes likely shift carotenoid biosynthesis toward the production of oxygenated derivatives, particularly increased conversion of β-carotene to xanthophylls (lutein and zeaxanthin), and potential reduction in upstream carotenoid precursors. Our findings demonstrate that the zebra-leaf mutation disrupts multiple steps in both chlorophyll and carotenoid biosynthesis pathways. The altered expression patterns of these key metabolic genes provide a molecular explanation for the observed pigment deficiencies and leaf variegation phenotype in the mutant plants.

Identification of DEGs related to photosynthesis

Analysis of DEGs in photosynthetic pathways (ko00195, ko00196) revealed significant disruptions in multiple components of the photosynthetic apparatus. Two ferredoxin genes (NewGene_3218, NewGene_6740) showed significant downregulation, these genes encode crucial electron carriers in the photosynthetic electron transport chain. All four identified photosynthesis-antenna protein genes (Pepsico1_Contig2468.path1, Pepsico1_Contig7357.path1/.path2, Pepsico1_Contig7720.path1) were downregulated. Contrastingly, one light-harvesting complex II chlorophyll a/b binding protein 6 gene (Pepsico1_Contig8258.path1) was upregulated (Fig. 5A, B). For carbon fixation, key enzymes in the Calvin cycle were downregulated, including phosphoenolpyruvate carboxylase (NewGene_30222) and triosephosphate isomerase (NewGene_19014) (Fig. S4; Fig. 5B). These transcriptional changes collectively suggest impaired electron transport capacity, altered light-harvesting efficiency, and reduced carbon fixation potential. The coordinated downregulation of these critical photosynthetic components likely contributes to decreased photosynthetic efficiency, compromised energy conversion processes, and reduced carbon assimilation capacity in the white-striped mutant plants. The observed upregulation of specific light-harvesting components may represent a compensatory mechanism in response to the overall photosynthetic impairment.

Fig. 5.

DEGs involved in photosynthesis - antenna proteins and carbon fixation in photosynthetic organisms’ pathways. Photosynthesis - antenna proteins pathway (A). Red boxes indicate the number of upregulated genes, green boxes indicate the number of downregulated genes. Expression profile clustering of the photosynthesis - antenna proteins and carbon fixation in photosynthetic organisms’ pathways. B Expression ratios are based on log2 fragments per kilobase of transcript per million mapped reads (FPKM) values, where each vertical column represents a sample, and each horizontal row represents a single gene

Identification of DEGs related to starch and sucrose metabolism

Our transcriptomic analysis revealed significant alterations in starch and sucrose metabolism (ko00500), with 41 DEGs identified in the mutant compared to wild-type plants (Fig. 6). The expression profile showed a predominant downregulation pattern, with only five genes upregulated and 36 genes downregulated in MT. Downregulation of sucrose synthase (NewGene_9299) and glucose-1-phosphate adenylyltransferase (Pepsico1_Contig10494.path2) impaired the conversion of sucrose to starch, potentially affecting carbon storage capacity. All 26 genes encoding carbohydrate-active enzymes were downregulated, including glucan endo-1,3-β-glucosidase (GN1/2/3) - specific hydrolases for 1,3-beta-glucan, β-glucosidase (bglX) - converts oligosaccharides to glucose monomers, and endoglucanase (EG) - cleaves cellulose into cello-oligosaccharides. This comprehensive downregulation suggests reduced hydrolytic capacity for complex carbohydrates, impaired fructose-to-glucose conversion and disrupted sucrose metabolism. Downregulation of alpha-amylase (AMY/amyA/malS; NewGene_36048) inhibits starch-to-glucose conversion, potentially limiting available energy substrates. The observed transcriptional changes demonstrate a systemic downregulation of carbohydrate metabolic pathways in the zebra-striped mutant. These alterations likely result from reduced photosynthetic output due to impaired chlorophyll synthesis, compromised carbon fixation capacity and altered energy allocation strategies. The coordinated suppression of these metabolic enzymes suggests a fundamental rewiring of carbon metabolism in the mutant, which may contribute to the observed growth alterations and provide insights into the metabolic consequences of leaf variegation.

Fig. 6.

Differences between WT and MT in starch and sucrose metabolism pathways. UDP-Glc: UDP-glucose; ADP-Glc: ADP-glucose; G6P: D-Glucose-6-phosphate; INV/sacA: beta-fructofuranosidase; SUS: sucrose synthase; TPS: trehalose 6-phosphate synthase/phosphatase; TREH/treA/treF: alpha, alpha-trehalase; GN1/2/3: glucan endo-1,3-beta-glucosidase 1/2/3; bglX: beta-glucosidase; EG: endoglucanase; GlgC: glucose-1-phosphate adenylyltransferase; GBE1/glgB: 1,4-alpha-glucan branching enzyme/Starch branching enzyme; AMY/amyA/malS: alpha-amylase; β – Amy: beta-amylase. Red boxes and numbers indicate the number of upregulated genes, green boxes and numbers indicate the number of downregulated genes, and blue boxes indicate the presence of both up and down regulated genes

Validation of transcriptome data by qRT-PCR analysis

To confirm the reliability of our RNA sequencing results, we performed quantitative real-time PCR (qRT-PCR) analysis on 20 candidate DEGs representing key pathways involved in chlorophyll and carotenoid biosynthesis, photosynthetic electron transport and leaf pigmentation regulation. The selected genes included key enzymes in pigment metabolism (CHLD, POR, ZDS), photosystem components (light-harvesting complex proteins) and carbohydrate metabolic enzymes. The qRT-PCR results demonstrated strong concordance with the RNA-seq data (Fig. 7), with consistent expression patterns observed for all validated genes.

Fig. 7.

qRT-PCR of 20 DEGs in WT and MT leaves. Error bars indicate means ± SD based on three independent experiments. Significant differences were determined using the Student’s t test in WT compared with MT

Discussion

The zebra-leaf phenotype is closely linked to chlorophyll and carotenoid metabolism

Leaf color mutants are valuable resources for studying photosynthesis, chloroplast development, and chlorophyll biosynthesis. SA is a highly effective azide-based mutagen that induces A/T → G/C base substitutions, leading to point mutations [54]. Its mutagenic efficiency is pH-dependent, with optimal activity observed at pH = 3 [55]. In this study, we used the oat zebra-striped mutant, a white-striped leaf phenotype derived from the wild-type Everleaf through SA-induced mutagenesis. The mutant exhibited random white stripes starting at the seedling stage, which persisted throughout the reproductive phase. From the M₂ to M₅ generations, the mutant maintained genetic stability and displayed no other obvious phenotypic differences compared to the wild type. These characteristics make the zebra-striped mutant a suitable model for investigating leaf color formation and photosynthesis-related processes in oats.

Chloroplast development and photosynthetic pigments are critical determinants of leaf coloration in higher plants. During the vegetative stage, the predominance of chlorophyll masks other pigments, resulting in green leaves. Studies have shown that chlorophyll-deficient mutants often exhibit altered leaf phenotypes. For instance, Zhang et al. [56] reported significantly lower Chl a and Chl b levels in the rice white-stripe mutant wsp1 compared to the wild type throughout development. Similarly, Feng et al. [57] identified a zebra-striped mutant (z15) from EMS-treated rice, displaying markedly reduced chlorophyll and carotenoid contents.

In this study, we analyzed chlorophyll content in WT and mutant oat leaves. While Chl b increased slightly (3.56%) in MT, Chl a and total chlorophyll were significantly lower than in WT, suggesting that the decline in Chl a primarily drove the reduction in total chlorophyll. According to the classification by Falbel and Staehelin [58], leaf color mutants can be grouped into four types: Chl a-deficient, Chl b-deficient, total chlorophyll-deficient, and total chlorophyll-increasing. Our zebra-striped mutant falls into the Chl a-deficient category.

The Chl a/b ratio reflects photosynthetic efficiency. In MT, this ratio was significantly lower than in WT, consistent with findings by Shi et al. [50] in a space-induced wheat albino mutant (mta), where the Chl a/b ratio decreased by 1.47%. The observed reduction in Chl a content was the primary cause of the diminished Chl a/b ratio in our study. Such a decline suggests either impaired Chl a synthesis or accelerated degradation, while Chl b synthesis remains stable, ultimately compromising photosynthetic capacity. To compensate, plants may adjust chlorophyll composition to sustain photosynthesis [59].

Chlorophyll biosynthesis is a complex process that occurs in chloroplast membranes and requires coordinated regulation of multiple enzymes and genes [60]. Transcriptome analysis in this study revealed significant differences between WT and MT plants, identifying 1,958 DEGs, comprising 571 up-regulated and 1,378 down-regulated genes. Functional enrichment analysis (GO and KEGG) showed that these DEGs were primarily associated with chlorophyll metabolism, photosynthesis, and carbohydrate metabolic pathways. These findings indicate substantial alterations in chlorophyll biosynthesis and photosynthetic processes in the MT compared to WT. The downregulation of genes related to these pathways likely contributes to the observed white-striped phenotype, which is consistent with previous findings by Shim et al. [61].

Our investigation focused on porphyrin and chlorophyll metabolism, the central pathways for chlorophyll biosynthesis. In MT plants, the downregulation of two CHLD genes resulted in Mg²⁺ deficiency compared to WT. Magnesium chelatase activity, which is essential for chlorophyll synthesis, requires the coordinated function of three subunits (CHLI, CHLD, and CHLH) [62, 63]. Our findings demonstrate that CHLD downregulation impaired substrate recognition and binding, consequently blocking the conversion of protoporphyrin IX to Mg-protoporphyrin IX and limiting chlorophyll synthesis rates in MT [64].

POR plays a pivotal role in chlorophyll biosynthesis by catalyzing the conversion of protochlorophyllide to chlorophyllide. This enzyme is critical for leaf coloration, as evidenced by POR mutations causing yellow/white phenotypes in rice [65] and reduced POR transcriptional activity in Arabidopsis leaf color mutants [66]. The three POR isoforms (PORA, PORB, and PORC) exhibit distinct expression patterns, with PORA being particularly important during early photomorphogenesis [67]. In MT plants, all three PORA genes were downregulated, significantly inhibiting chlorophyll biosynthesis. These results align with [68], who identified POR downregulation as the primary cause of reduced chlorophyll accumulation in rice mutants. Interestingly, we observed a slight increase in Chl b levels in MT compared to WT, which contrasts with previous reports. While transcriptomic analysis revealed two upregulated POR genes, their potential association with elevated Chl b levels requires further experimental validation.

Alterations in CHLD expression impact both chlorophyll and carotenoid biosynthesis pathways [69]. Previous studies have demonstrated varied effects on carotenoid profiles in different mutants, rice ygl7 mutants containing a nonsynonymous SNP in OsCHLD exhibited significantly reduced carotenoid content [32], while OsCHLI shift mutants showed decreased total carotenoid and lutein levels but elevated zeaxanthin accumulation [61]. In our study, CHLD downregulation not only impaired chlorophyll synthesis but also reduced total carotenoid content by 43.8% in MT compared to WT. Interestingly, while Shim et al. [61] reported increased zeaxanthin accumulation through downregulation of zeaxanthin epoxidase (OsZEP1), our transcriptome data revealed an alternative regulatory mechanism. We observed upregulated β-carotene hydroxylase (BCH) genes that enhanced α-carotene metabolism in MT. This finding aligns with Wu et al. [70], who documented a 40-fold increase in zeaxanthin content in BCH-upregulated wheat mutants. Such carotenoid profile modifications likely represent adaptive responses to impaired photosynthesis, as plants often adjust carotenoid composition to optimize light energy utilization [71, 72].

In our MT plants, the chlorophyll content of the white stripe mutant decreased significantly, leading to impaired photosynthesis and forcing MT to activate its defense mode. By upregulating BCH-related genes, it promoted the conversion of α-carotene and β-carotene into zeaxanthin and lutein, thereby enhancing its own photoprotection mechanism, maintaining the stability of the photosystem, and ensuring normal photosynthesis [73, 74]. At the same time, the upregulation of BCH-related genes can also promote the activity of the lutein cycle (zeaxanthin ⇌ epoxy zeaxanthin ⇌ zeaxanthin), thereby dissipating excess light energy that cannot be utilized, stabilizing the structure of the thylakoid membrane, preventing the disintegration of PS II, and strengthening the photoprotective function of white stripe leaves [75, 76]. However, the regulatory role of BCH shows species-specific variation, as evidenced by Berman et al. [77] who observed increased β-carotene accumulation in BCH-upregulated maize endosperm. These contrasting findings highlight the diverse metabolic functions of BCH across plant species.

The zebra-leaf phenotype is associated with photosynthetic gene expression and impaired photosynthetic performance

Alterations in photosynthetic pigments directly affect plant photosynthetic capacity, with chlorophyll-deficient mutants typically showing reduced photosynthetic efficiency [78]. Our comparative analysis revealed significant reductions in key photosynthetic parameters in MT in net photosynthetic rate (Pn, 68.23%), transpiration rate (Tr, 70.09%), stomatal conductance (Gs, 66.85%), and intercellular CO₂ concentration (Ci, 12.61%), compared to WT plants. These findings align with previous reports in EMS-induced wheat mutants showing 88–89% reductions in Pn, Gs, and Tr [79], as well as observations in other cereal leaf color mutants [16, 80].

Notably, we observed enhanced water use efficiency in MT plants, likely attributable to the substantial reduction in stomatal conductance. As stomata serve as primary channels for gas exchange, decreased stomatal conductance reduces both transpiration and stomatal respiration, thereby improving water use efficiency [81]. Chlorophyll fluorescence analysis further demonstrated impaired photosynthetic function in MT, with all measured parameters (Fo, Fm, NPQ) being significantly lower than in WT. Similar reductions in Fo, Fm, and NPQ were reported in rice leaf color mutants [82], though contrasting results were observed in wheat albino mutants showing increased NPQ [83].

The 17.45% decrease in NPQ in our MT plants suggests compromised photoprotective capacity. This reduction may result from structural damage to the photosynthetic apparatus, decreased PSII reaction center activity, or altered pigment organization impairing proper energy dissipation [84–86]. These impairments in light energy allocation likely led to photooxidative damage, further exacerbating the photosynthetic deficiencies observed in the zebra-striped mutant.

The photosynthetic electron transfer rate (ETR) serves as a crucial indicator of leaf photosynthetic capacity [87, 88]. In our study, the ETR of the MT exhibited a 35.55% reduction compared to WT plants. Transcriptomic analysis revealed the downregulation of two ferredoxin-encoding genes, which play a pivotal role in photosynthetic electron transport by mediating electron transfer from Photosystem I (PSI) to NADP⁺ [89, 90]. This downregulation likely contributes to the impaired electron transfer efficiency observed in MT plants. Similar ETR reductions have been reported in chlorophyll-deficient mutants of maize and wheat [70, 91].

Furthermore, our analysis identified significant downregulation of four genes encoding light-harvesting complex II chlorophyll a/b binding proteins (LHCB1) in the photosynthesis-antenna proteins pathway. Since LHCB proteins are essential for both light energy capture and the structural stability of Photosystem II (PSII) [92, 93], their reduced expression in MT plants likely compromises light-harvesting efficiency, energy allocation between photosystems, and SII complex integrity. These findings collectively demonstrate that the reduced photosynthetic efficiency in MT plants stems from coordinated downregulation of key genes involved in both photosynthetic electron transport (ferredoxin) and light-harvesting processes (LHCB1). The observed alterations in electron transfer and light energy capture provide a mechanistic explanation for the impaired photosynthetic performance in the zebra-striped mutant.

Photosynthesis converts atmospheric CO₂ into soluble carbohydrates that drive plant growth [94]. Our study identified significant downregulation of two key carbon fixation enzymes: triose phosphate isomerase (TPI) and phosphoenolpyruvate carboxylase (PEPC). TPI maintains Calvin cycle intermediate balance, while PEPC enhances CO₂ concentration in C₄/CAM plants, improving RuBisCO efficiency and reducing photorespiration. In MT plants, these downregulations might cause DHAP accumulation and reduced CO₂ concentration in bundle sheath cells, collectively impairing Calvin, C₄, and CAM cycle function. This metabolic disruption likely contributes to MT’s reduced photosynthetic efficiency, consistent with reports that ZmTPI4 mutations impair photosynthesis and carbon metabolism in maize [95].

The photosynthetic-yield relationship was evident in MT’s agronomic performance. Key yield components – MPSN and MPSW - decreased by 26.59% and 16.95% respectively, mirroring observations in chlorophyll-deficient mutants of rice [96, 97], wheat [31], and maize [98]. We attribute this to a cascade effect: reduced chlorophyll content → impaired photosynthesis → limited assimilate production → compromised nutrient translocation to reproductive organs → abnormal spikelet development [99–101]. These results demonstrate how photosynthetic defects propagate from biochemical pathways to whole-plant physiology, ultimately determining crop productivity.

Leaf color mutations significantly reduce photosynthetic pigment content and impair photosynthetic capacity. Since photosynthesis is tightly coupled with carbon allocation, chlorophyll deficiency often leads to dysregulated starch and sucrose metabolism [102]. In our study, the mutant exhibited significantly lower chlorophyll content compared to wild-type plants. Transcriptomic analysis identified 41 differentially expressed genes in starch and sucrose metabolic pathways, with 35 showing downregulation in MT. This widespread downregulation impaired the conversion of starch and sucrose to downstream products (fructose and maltose), consistent with findings in other chlorophyll-deficient mutants [91].

The observed metabolic disruptions may be attributed to multiple factors: (1) Impaired chloroplast function due to Mg²⁺ deficiency, which is known to reduce both chlorophyll content and starch/sucrose anabolism [103]; (2) Inhibition of sucrose catabolic enzymes, as Mg²⁺ serves as an essential cofactor for the conversion of sucrose to UDP-glucose [104]; and (3) Significant downregulation of CHLD, a key gene in chlorophyll biosynthesis that may indirectly affect carbohydrate metabolism. These metabolic alterations likely contribute to the reduced photosynthetic performance observed in MT plants.

As easily scorable morphological markers, leaf color variants represent valuable genetic resources for crop improvement. Our comprehensive analysis of the oat zebra-striped mutant - integrating physiological, biochemical, agronomic, and transcriptomic approaches - has revealed crucial differences in gene expression and metabolic pathways between MT and WT plants. These findings not only advance our understanding of photosynthetic pigment biosynthesis in oats but also provide practical tools for breeding programs aimed at enhancing photosynthetic efficiency. The identified genetic markers and metabolic insights will facilitate the development of oat varieties with improved photosynthetic performance and productivity. However, a limitation of this study is the lack of direct evidence establishing gene-phenotype regulatory relationships. Leaf color significantly impacts oat agronomic traits and economic value through its regulation of photosynthetic efficiency, stress responses, and metabolic flux allocation. Understanding the genetic and physiological mechanisms underlying oat leaf color variation could identify molecular breeding targets and facilitate development of climate-resilient, high-yielding, and high-quality oat varieties. Future studies should integrate multi-omics approaches to comprehensively analyze the leaf color-gene regulatory network. Mutant alleles could be functionally validated through genetic complementation assays, sequencing, and other molecular approaches.

Conclusion

Compared to wild-type oats, the zebra-striped mutant exhibits significant changes in leaf color, with the appearance of white stripes on the leaves leading to a marked decrease in the content of pigments such as chlorophyll and carotenoids. This results in severe impairment of photosynthesis, weakened agronomic traits, and a significant reduction in grain yield. Transcriptome analysis revealed that DEGs associated with leaf color variation were downregulated in the zebra-striped mutant, leading to impaired chlorophyll and carotenoid biosynthesis, photosynthesis, and photosynthetic carbon fixation pathways. These transcriptional alterations likely underlie the distinctive white-striped phenotype in oats. These findings provide fundamental insights into oat pigment biosynthesis and photosynthesis mechanisms, while offering valuable genetic resources for oat improvement.

Author contributions

Doudou Lin: conceptualization, investigation, writing and original draft. Jikuan Chai and Liang Zeng: investigation and formal analysis. Kuiju Niu: guidance on writing. Xin Liu and Yanming Ma: data collection statistics. Guiqin Zhao: funding acquisition and manuscript modifications.

Funding

This research was supported by the Chief Scientist Program in Gansu Province, National Center of Pratacultural Technology Innovation (under preparation) and The Excellent Doctoral Student Program. (nos. 23ZDKA013, CCPTZX2023B05 and 24JRRA680).

Data availability

All data sheets and images to process data are available upon request to the corresponding author. The datasets generated and/or analysed during the current study are available in the NCBI SRA repository, [ACCESSION NUMBER TO DATASETS: PRJNA1268920].

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.