Identification and functional validation of hub genes responding to waterlogging stress during the filling period in quinoa (Chenopodium Quinoa Willd.)

Abstract

Background

Quinoa is a new type of grain crop that has become popular in recent years. It is resistant to drought, and saline-alkali conditions. However, global warming has led to increased rainfall, making it susceptible to waterlogging stress during the growing season. The filling period is a critical period for plant growth and development, but there have been few reports on quinoa response under stress condition during the filling period in recent years.

Methods

Based on transcriptome data, combined with morphological and physiological indicators and significant metabolites under waterlogging stress, WGCNA (Weighted gene co-expression network analysis) was used to construct a co-expression network associated with it, mine relevant hub genes, and perform overexpression and functional verification of one of the genes.

Results

We used the WGCNA method to identify 11 hub genes that respond to waterlogging stress, six of which are transcription factors encoded by the AP2/ERF, NAC, and C3H gene families. One of the hub genes, LOC110688032, was overexpressed in Arabidopsis, and its T2 generation seedlings were phenotyped and functionally validated. It was found that overexpression of the LOC110688032 gene in Arabidopsis enhanced the tolerance to waterlogging of the transgenic.

Conclusions

The results of this study provide valuable insights for quinoa resistance research and lay a solid foundation for the future development of superior quinoa varieties with good tolerance to waterlogging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-026-08132-6.

Introduction

Quinoa (Chenopodium quinoa Willd.) belongs to the Amaranthaceae quinoa subfamily quinoa genus of dicotyledonous plants, native to the Andean region, so far more than 7,000 years of history of cultivation, locally known as the “mother of all grains” [1]. Quinoa is rich in nutritional value, rich in protein, fat, vitamins, minerals, etc., also rich in phenols, flavonoids, amino acids and other plant active substances, antibacterial, antioxidant, antidiabetic and anti-inflammatory physiological functions, long-term consumption helps to prevent disease and maintain good health [2, 3]. According to the Food and Agriculture Organization of the United Nations (FAO), quinoa is the only food that can satisfy the basic nutritional needs of the human body as a single plant, and is therefore known as “the most suitable whole food for human beings” [4, 5].

Waterlogging stress is one of the common abiotic stresses [6]. Prolonged waterlogging stress causes changes in the antioxidant enzyme systems of plants, and low oxygen conditions stimulate and exacerbate the production of reactive oxygen species (ROS) and some non-enzymatic antioxidants (including flavonoids) [7]. Hypoxic conditions block normal plant metabolism, and membrane lipid peroxidation accumulates more malondialdehyde (MDA), which damages the cells [8]. Plants undergo a series of changes in their morphology and physiology and antioxidant enzyme systems under waterlogging stress, and in order to adapt to the adverse environment, plants adopt certain regulatory mechanisms to cope with the adverse effects of waterlogging stress on them. For example, ROS and hydrogen peroxide (H₂O₂) can act as signaling molecules that enhance ethanol fermentation capacity under stress, thereby improving plant survival rates and adaptability; secondly, plants adapt to waterlogging stress by regulating morphological structure, energy metabolism, endogenous hormone biosynthesis, and signal transduction processes [9].

Weighted gene co-expression network analysis (WGCNA) is a tool for discovering patterns of correlation among genes and identifying relationships between co-expression modules and traits [10]. In recent years, there have been a number of reports using the WGCNA approach to explore plants and abiotic stresses, as reflected in quinoa. For example, Zhang et al. identified 12 core genes for quinoa seedling resistance to low temperature stress using the WGCNA method [11]. Wang et al. mined 10 core genes for quinoa seedling resistance to waterlogging using WGCNA [12]. Related studies have been conducted on other plants in terms of flooding stress. Shang et al. identified four specific modules closely related to red clover waterlogging stress by WGCNA and functionally annotated the 14 hub genes obtained [13]. Deng et al. identified many key genes that may be involved in responding to the lotus waterlogging response by differentially expressed gene (DEG) analysis and WGCNA analysis [14]. These are all reports of using WGCNA methods to study plants responding to abiotic stress at the seedling stage, while there are few reports of using WGCNA methods to study at the filling period in relation to abiotic stress as of now, so there is a need to urgently study the response mechanism of quinoa under waterlogging stress at the filling period to enrich the molecular study of quinoa responding to waterlogging stress.

The filling period is a critical period for determining the yield and quality of quinoa, but few studies have been reported on the response of quinoa to waterlogging stress during the filling period. Waterlogging stress seriously affects the quality of seeds, and the longer the duration of stress, the smaller the reservoir capacity of seeds, the slower the rate of filling, the shorter the filling period, the lower the grain weight, and the poorer the quality [15, 16]. Moreover, in the context of global warming, rainfall or heavy rainfall is expected to be more frequent, especially when the planting season falls during the rainy season, and waterlogging disasters will be frequent. Therefore, there is an urgent need to study the waterlogging tolerance of plants and their mechanisms to sustain agricultural production and promote effective adaptation of agriculture to climate change [9]. Therefore, this study used the high-yielding quinoa variety Red Quinoa (Dianli-1844), independently bred by Yunnan Agricultural University, as the experimental material, and conducted waterlogging treatment and recovery treatment during the filling period. To elucidate the regulatory mechanisms underlying the response of quinoa to waterlogging stress during the filling period, this study integrated morphological traits, physiological indices, and significantly altered metabolites, and employed WGCNA to construct a co-expression gene network associated with these factors, thereby identifying the hub genes of quinoa in coping with waterlogging stress at the filling period. Subsequently, one of these core genes was overexpressed in the model plant Arabidopsis thaliana, followed by subcellular localization and functional validation assays. This research is intended to provide insights for improving waterlogging tolerance in quinoa and developing waterlogging-tolerant quinoa varieties by dissecting the regulatory mechanisms of quinoa’s response to waterlogging stress during the filling period.

Materials and methods

Experimental materials and design

A high-generation of red quinoa (Dianli-1844), independently selected and bred by Yunnan Agricultural University (YAU), was planted in the Modern Agricultural Education and Research Base of YAU (Xundian County, Kunming, China) as the test material. The test was started by selecting uniform and consistent seeds and evenly sowing them in pots (50 cm× 50 cm× 50 cm), which were managed according to conventional cultivation techniques (average temperature: 23 ℃, sunshine duration: about 12 h, sowing depth: 2 ~ 3 cm) in the early stage of the experiment. At the filling period (15 days after spike flowering), plants with consistent growth and flowering were selected for waterlogging treatment (the waterlogging liquid level was about 10 cm above the soil). The non-waterlogged treatment with normal water and fertilizer management, served as the control. Sampling in the course of the experiment a total of three times, the first sampling in the flooded 7 days, found that the treatment group leaves obviously yellow when the treatment group and the control group were sampled; the second sampling in the flooded 14 days, found that the treatment group leaves yellowing intensified so that the treatment group and the control group were sampled separately; at the same time, another flooded 14 days of the treatment group of the water was poured out, and the recovery of the 14 days of the treatment and the control group were sampled separately for the third time. All sampling sites were seeds and leaves in the middle of the spike, and the samples were stored in liquid nitrogen at -80 ℃ immediately after sampling. The samples (seeds) were then sent to Wuhan Metware Biotechnology Co., Ltd (www.metware) for metabolomic and transcriptomic analyses, with three biological replicates and a total of 18 samples. We designated the 7 day controls and treatments were numbered as C7 and T7, respectively; 14 day controls and treatments were numbered as C14 and T14, respectively; and 28 day controls and treatments were numbered as C28 and RE, respectively (Table 1).

Table 1.

Sample information

Sampling period	Material Name	Sampling time	Control group	Treatment group
The filling period	Dianli-1844 (red quinoa)	7 days	C7	T7
		14 days	C14	T14
		28 days	C28	RE

C7 represents normal growth for 7 days. C14 represents normal growth for 14 days. C28 represents normal growth for 28 days. T7 represents waterlogging for 7 days. T14 represents waterlogging for 14 days. RE represents 14 days of waterlogging followed by 14 days of recovery

Determination of morphological and physiological and biochemical indicators

Leaves and seeds of quinoa were sampled separately during the filling period with three biological replicates. Morphological indicators to be measured were plant height, leaf area, fresh weight (1000 seeds weight) and dry weight (1000 seeds weight), measured as follows:

1. Plant height (cm): plants representative of the overall height of the treatments were selected from each treatment, and three plants were selected for measurement, with plant height being the distance from the base to the top of the uppermost unfolded leaf;

2. Leaf area (cm2): at the same time as the plant height measurement, the leaves were taken from the same part of the quinoa plant in the middle and upper position, and measured by using a TPYX-A leaf morphometer (TPYX-A, Zhejiang, China, https://www.tpyn.net), and the average value was taken from the multiple measurements;

3. Determination of fresh weight (g): 1000 seeds of fresh quinoa were taken from the middle of the spike during the irrigation period and different days, and their fresh weight was accurately weighed.

4. Determination of dry weight (g): After measuring the fresh weight, 1000 seeds of each treatment were oven-dried at 110°C for 30 min and baked at 80°C until constant weight. The paper bags and seeds were removed, cooled to room temperature, weighed for dry weight, and counted as thousand seeds.

The physiological indicators to be measured are Soluble Sugar (SS), Soluble Protein (SP), Proline (Pro), MDA, Superoxide dismutase (SOD, EC: 1.15. 1.1), Peroxidase (POD, EC 1.11.1.7), Catalase (CAT, EC 1.11.1.6). The kits used for the determination of physiological indicators were produced from Nanjing Jiancheng Bioengineering Research Institute Co. Ltd (http://www.njjcbio.com), and the determination of their contents was performed in accordance with the instructions of the kits.

Measurement and analysis of the transcriptome and metabolome

In this study, RNA extraction was performed using the method of Zeng et al. [17], and RNA integrity and DNA contamination were assessed by agarose gel electrophoresis. RNA concentration was quantified using a Qubit 2.0 fluorometer, and integrity was detected by an Agilent 2100 bioanalyzer. Qualified samples were subjected to library construction [18], and the library insert size was detected by Agilent 2100, and after meeting the expectations, the effective concentration of the library (> 2nM) was accurately determined by RT-qPCR and analyzed for quality control [19]. Differentially expressed genes were screened using the criteria |log2Fold Change| ≥ 1 and FDR < 0.05. Gene comparisons were performed using fatuts v1.6.2, with gene expression levels quantified using FPKM (fragments per thousand base pairs mapped per million) [20]. Differential expression analysis was accomplished by DESeq2 v1.22.1, and the Benjamini-Hochberg method was used to correct the P-value to obtain the False Discovery Rate (FDR). Differential gene function annotation was systematically analyzed by several databases, such as Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Karyotic Orthologous Groups (KOG), Pfam, Swiss-Prot, TrEMBL, and NR databases.

Metabolites were extracted with reference to the method of Wang et al. [21] and quantitatively analyzed using the multiple reaction monitoring (MRM) mode of triple quadrupole mass spectrometry to obtain the metabolite spectral data, peak area integrations, and calibration results [22]. The screening criteria for significantly different metabolites were variable importance projection (VIP) > 1 and fold change ≥ 2 or ≤ 0.5 [23]. Subsequently, metabolites were annotated by the KEGG compound database and mapped to KEGG pathway database for pathway analysis [24]. Finally, metabolite set enrichment analysis (MSEA) was utilized to assess the regulation of key metabolic pathways and p-values were calculated by hypergeometric tests to determine statistical significance.

Weighted gene co-expression network analysis

Construction of WGCNA co-expression network

Genes with FPKM values below five were filtered out, and the remaining genes underwent WGCNA analysis. The WGCNA package (version 1.6.1) within the R software (version 4.2.1) was used to analyze and construct the gene co-expression network [25]. Afterwards, the weight values were calculated using pickSoftThreashold in the WGCNA package, and in order to make the network conform to the scale-free network distribution, we selected power as 24 and utilized the default settings of the blockwiseModules function to construct the scale-free network [26]. Associate morphological physiological data and metabolic data with the filtered 11,675 (Table S1) genes to better analyze the relationships between modules and traits. Specifically, these genes were associated with morphological data, physiological data, and flavonoid metabolite data, respectively, and the selected Power values were all 24. In addition, the physiological data were correlated with the flavonoid metabolite data and all metabolite data, and the selected Power values were 19 and 4, respectively.

Screening of specificity modules and GO and KEGG feature enrichment analysis

The specific modules associated with waterlogging stress were screened by calculating the correlation coefficients r and p values between the eigenvectors Module Eigengene (ME) of each module and different traits. In this study, modules with |r| > 0.6 and p < 0.01 were selected as specific modules, and the obtained specific modules could be analyzed for GO and KEGG enrichment in the next step [27]. Multiple hypothesis testing methods were utilized for correction, and a corrected p < 0.05 was considered to be significantly enriched.

Screening of hub genes in important modules

The top 20 candidate hub genes were preliminarily screened by calculating the KME (module characteristic gene connectivity) values of the genes within the module. Based on the gene connectivity theory, the strength of the regulatory effects of genes within the module is positively correlated with their connectivity, and genes with high connectivity are more likely to exert pivotal regulatory functions [28]. To further validate the candidate genes, the CytoNCA plug-in in Cytoscape 3.9.1 software [29] platform was used to calculate the BC value (Betweenness), identify the hub genes by topological characterization, and construct the gene interaction network.

Identification and analysis of transcription factors (TFs)

ITAK (IAITAM, Canton, Ohio, USA) software was used for systematic identification and functional annotation of TFs families [30, 31]. First, the protein sequences of the core genes were obtained from the National Center for Biotechnology Information (NCBI) database, and TF family prediction was performed based on the PlantTFDB database [32] to better understand the function of each gene. The distribution patterns of their conserved motifs were predicted using the MEME (Multiple Em for Motif Elicitation) tool [33].

Real-time quantitative PCR (RT-qPCR) analysis

To validate the reliability of gene expression, we randomly selected six core genes for RT-qPCR analysis, performing three biological replicates and three technical replicates for each sample. Primers for the selected core genes were designed using BeaconDesign 7.9 software, with the TUB-6 gene chosen as the internal reference gene [12]. RT-qPCR was then performed using PerfectStart SYBR qPCR Supermix (TransGen Biotech, Beijing, China) according to the reaction configuration. The RT-qPCR reaction volume was 20 µL (TransGen Biotech, Beijing, China). Thermal cycling conditions were set as follows: 94 °C (30 s), 94 °C (5 s), 60 °C (30 s), repeated for 40 cycles (Table S2). Normalization for each sample was performed using the 2^−ΔΔCT method [34].

Functional validation study of the quinoa LOC110688032 gene

Experimental materials

The transgenic recipient material was wild-type (WT) A. thaliana Columbia, and the subcellular localization material was Nicotiana benthamiana. The strains and vectors used in the experiment were Escherichia coli DH5α competent cells, Agrobacterium GV3101 competent cells, and the subcellular localization vector pBWA(V)HS-GFP. The reagents, equipment, and instruments used in the experiment are shown in Table S3.

Construction of overexpression vectors and subcellular localization

Based on the sequence conservation, homology analysis and the matching degree of known stress-tolerant functional genes, LOC110688032 with high homology to the reported key genes of plant stress tolerance was preferentially selected for functional verification. We constructed an overexpression vector for the identified core gene LOC110688032 (AP2/ERF family). The constructed subcellular localization GFP fusion vector was used to transiently transform tobacco leaves via Agrobacterium mediated transformation. Empty pBWA(V)HS-GFP was used as a control. The transformed tobacco plants were cultured for 2 days, and GFP fluorescence signals were observed using a laser confocal microscope (Nikon, C2-ER) and photographed for the record.

Cultivation and transformation of genetically modified A. thaliana

The plant resistance of the overexpression vector is kanamycin sulfate (Kan). Arabidopsis is screened using MS medium supplemented with Kan. Before planting Arabidopsis, the seeds are stored at 4 °C. Disinfect with 95% ethanol for 10 min and 75% ethanol for 10 min, rinse with sterile water 2–3 times (1 min per rinse), and evenly spread on the selection medium (kana: 60 mg/L). Incubate at 4°C for 2–3 days, then transfer the plates to 23–25°C and incubate under a 16 h light/8 h dark cycle for 10–14 days.

After 20 days of growth, positive seedlings are detected, Arabidopsis genomic DNA is extracted, and PCR testing is performed to identify positive plants. After harvesting T1-generation positive seedlings, perform drying and dehulling treatment. Treat T1 Arabidopsis seeds using the aforementioned method, then transplant the selected surviving seedlings into nutrient soil and incubate at 23°C under 16 h light/8 h dark conditions for an additional 20 days. Extract Arabidopsis genomic DNA again, perform PCR detection, and continue to identify positive plants until harvest to obtain T2-generation positive seeds.

Phenotypic identification and physiological parameter measurement of transgenic Arabidopsis T2 generation after treatment

This study yielded five independent T2 transgenic lines, from which lines exhibiting stable expression and consistent phenotypes were selected for subsequent experiments. Subsequently, 10-day-old seedlings were transplanted into square pots (five plants per pot, three replicates), and flooding treatment was applied one week after flowering. For statistical analysis, data were analyzed using one-way ANOVA combined with Duncan’s multiple range test (P < 0.05). After waterlogging treatment of T2 transgenic Arabidopsis plants, their morphology was observed, and physiological indicators such as chlorophyll, MDA, SOD, POD, and CAT were measured in the aboveground parts. Among them, chlorophyll content was measured using a portable chlorophyll meter (TYS-B, Zhejiang, China) on Arabidopsis leaves and expressed as SPAD values. MDA content, SOD, POD, and CAT activity were measured using kits from Puyin Bioengineering Co., Ltd. (spectrophotometric method), following the instructions provided in the kit manual (https://www.pytbio.com/bk_27368260.html).

Data processing and analysis

Data analysis was performed using Microsoft Excel 2021, statistical significance analysis was conducted using IBM SPSS 23, and graphs were created using GraphPad Prism v 9.0. Different letters indicate significant differences (P < 0.05).

Results

Morphological changes in quinoa under waterlogging stress during the filling period

To understand the morphological changes of quinoa under waterlogging stress, plant height, leaf area, fresh weight and dry weight were measured, and the results showed that under waterlogging stress, T7 had the lowest plant height, leaf area, fresh weight and dry weight, while C28 had the highest of all the four morphological indexes (Fig. 1). This indicates that waterlogging causes morphological changes in quinoa and limits its normal growth and development. In terms of fresh weight, the difference between the C28 vs. RE treatment group and the control group was significant. Overall, from the three groups of C7 vs. T7, C14 vs. T14, and C28 vs. RE, all the morphological indexes of the treatment group were reduced compared with the control group, but the gap between the recovery and the control group was narrowed. This indicated that waterlogging stress limited the normal growth and development of quinoa, so that the morphological indexes of quinoa were affected to different degrees, but timely recovery could minimize the losses.

Fig. 1.

Morphological changes in quinoa during the filling period under waterlogging stress. A Plant height; B Leaf area; C Dry weight; D Fresh weight. Different letters (a-e) indicate significant differences (P < 0.05). Statistical analysis was performed using one-way ANOVA followed by Duncan’s multiple range test, with all data presented as mean ± SD (standard deviation) from n = 3 biological replicates

Physiological changes in quinoa seeds under waterlogging stress during the filling period

In order to understand the physiological changes of quinoa seeds under waterlogging stress, several physiological indicators of SS, SP, Pro, MDA, SOD, POD, and CAT were measured, and the results showed that under waterlogging stress, among the various physiological indicators, the contents of SS and SP were the highest in RE, and the lowest in C7; the contents of Pro and MDA were the highest in T14, and the lowest in C7; the contents of SOD, POD, and CAT were the highest in C14 and T7 the lowest. In terms of significance, in terms of SS, SP, MDA, and POD, C7 vs. T7, C14 vs. T14, and C28 vs. RE differed significantly between treatments and controls (Fig. 2A, B, D, and F); in terms of Pro and SOD, C7 vs. T7 and C14 vs. T14 differed significantly between treatments and controls (Fig. 2C, E). Overall, SS, SP, Pro, and MDA were elevated in quinoa seeds after waterlogging (Fig. 2A, D), and after recovery SS and SP were elevated, while Pro and MDA were reduced. SOD, POD, and CAT enzyme activities decreased after waterlogging (Fig. 2E, G), but POD rose after recovery (Fig. 2F), and SOD and CAT (Fig. 2E, G) contents decreased. This indicated that all physiological indices were affected to varying degrees after waterlogging, but timely drainage minimized the effects.

Fig. 2.

Physiological changes in quinoa during the filling period under waterlogging stress. A SS; B Soluble protein; C Pro; D MDA; E SOD activity; F POD activity; G CAT activity. Different letters (a-e) indicate significant differences (P < 0.05). Statistical analysis was performed using one-way ANOVA followed by Duncan’s multiple range test, with all data presented as mean ± SD (standard deviation) from n = 3 biological replicates

Correlation analysis between morphology and physiology

Morphological and physiological indexes under waterlogging stress were correlated (Fig. 3), MDA and Pro were mostly negatively correlated with each other, and the rest of the indexes were mostly positively correlated with each other. The positive correlation between plant height and fresh weight was significant and the highest among the morphological indexes, while the positive correlation between SS and SP, MDA and Pro was significant and the highest among the physiological indexes. All of the above showed that there was a good correlation between the indicators of quinoa plants under waterlogging stress, which was more favorable for our next analysis.

Fig. 3.

Heatmap of correlation between morphological and physiological indicators. Note: Pearson’s correlation is indicated in the figure; red denotes positive correlation, blue denotes negative correlation, and the darker the color the more significant

Analysis of flavonoid metabolites in quinoa under waterlogging stress during the filling period

Through analysis of metabolites under waterlogging stress, we found that flavonoid metabolites were the most abundant under waterlogging stress. A total of 438 flavonoid compounds were detected (Table S4), categorized into seven groups: chalcones (3.65%), flavanols (5.02%), flavanones (10.27%), flavanonols (2.28%), flavones (34.25%), flavanols (36.53%), isoflavones (1.83%), and other flavonoids (6.16%) (Fig. 4A). Among these, flavanols and flavones were the most abundant, while isoflavones and other flavonoids were the least abundant. Furthermore, a cluster heatmap analysis of flavonoid compounds in the 18 samples revealed that flavonoid compounds accumulated in each group, with particularly significant changes observed in C7 vs. T7, C14 vs. T14, and C28 vs. RE (Fig. 4B), where the flavonoid metabolites in the treatment groups exhibited significantly higher expression levels compared to the control groups, suggesting that flavonoid metabolites are important metabolic responses to waterlogging stress.

Fig. 4.

A Pie chart showing the proportion of flavonoid metabolites in the secondary classification; B Heatmap showing the clustering of flavonoid metabolites. Note: Red indicates high expression levels, green indicates low expression levels

Construction of weighted gene co-expression network

The results of the cluster analysis of the samples showed no outlier samples (Fig. 5A). In order to select the appropriate soft threshold of the weighting coefficient, we calculated it using the pickSoftThreshold function, and finally selected Power as 24 to construct the co-expression network (Fig. 5B), which satisfies the R² close to 0.8. Subsequently, we used the dynamic cutting method to divide the network modules and finally obtained six modules (Fig. 5C). Among them, the turquoise module has the largest proportion; the blue module is the second largest. The robustness of these WGCNA modules was evaluated using its specific metrics, including scale-free topology fitting R², module preservation statistic Zsummary, and module overlap consistency.

Fig. 5.

A Sample clustering diagram. The horizontal coordinate represents sample clustering, one column represents one sample; B Topology of quinoa seed network with different soft threshold powers. x-axis denotes the weight parameter β. y-axis in the left panel denotes the square of the correlation coefficient between log(k) and log(p(k)) in the corresponding network. The y-axis in the right graph represents the average of the hierarchical clustering dendrogram based on topological overlap matrix (TOM) of all genes in the corresponding gene module. C Gene clustering tree and division of modules

Identification of specificity modules

Morphological data and physiological data as traits were analyzed as correlations with filtered genes and correlation heatmaps were drawn, respectively. In the gene morphology association heatmap (Fig. 6A), six co-expression modules of genes associated with flooding stress were identified. Turquuoise module showed high positive correlation with all morphological indicators, with the highest correlation coefficient with fresh weight (r = 0.69, p = 0.0016); Blue module was negatively correlated with all morphological indicators, with the fresh weight The Blue module was negatively correlated with all morphological indicators, with the highest correlation coefficient with fresh weight (r = -0.73, p = 0.00064). In the gene physiological association heatmap (Fig. 6B), six co-expression modules of genes associated with flooding stress were identified, and the Turquuoise module was positively correlated with most of the physiological indexes, with the most significant correlation and the highest correlation was with SP (r = 0.91, p = 0.00000019). The Blue module was mostly negatively correlated with physiological indexes, and the highest negative correlation with SP (r = -0.69, p = 0.00000015). Negatively correlated with physiological indicators, with the highest negative correlation with SP (r = -0.69, p = 0.0015).

Fig. 6.

Weighted gene co-expression network diagram. A Gene co-expression network module and morphology correlation heatmap; B Gene co-expression network module and physiology correlation heatmap; C Gene co-expression network and flavonoid metabolite correlation heatmap; D Physiology and flavonoid metabolite correlation heatmap; E Physiology and metabolite correlation heatmap. Note: Y-axis represents the module name and X-axis represents the trait name. The numbers represent the correlation coefficients and corresponding P values between traits and modules. Blue indicates negative correlation; red indicates positive correlation; the darker the color, the stronger the correlation

Significant accumulation of flavonoids in differential metabolites in treatment groups. Therefore, the differential metabolite flavonoids were subjected to association analysis with genes, and six modules were obtained (Fig. 6C), and most of the flavonoid metabolites in the Blue module showed significant negative correlations with genes (P < 0.001), with Lajp003377 (flavonoids) showing the most significant positive correlation (P < 0.001) and the largest with the Blue module (r = 0.92, p = 0.000000055). We also correlated the differential metabolite flavonoids with physiological indicators and obtained three modules (Fig. 6D), among which SP showed the highest correlation with the Turquuoise module (r = -0.76, p = 0.00026), indicating that the accumulation of metabolites in the Turquuoise module was related to the changes in SP content. In summary, we concluded that the Blue module and Turquoise module are relevant and specific modules in response to waterlogging stress, and therefore they were subsequently analyzed for further mining of candidate genes.

In order to explore the association between metabolites and physiological data under waterlogging stress, we correlated differential metabolites with physiological indicators and obtained a total of 12 modules (Fig. 6E), among which the correlation of MDA and Pro with the Pink module was the most significant, suggesting that the accumulation of metabolites in the Pink module was related to MDA and Pro. Given the relatively high relevance of the Pink module, its metabolites were analyzed (Table S5), and it was found that flavonoids, phenolic acids, alkaloids, lipids, amino acids, and organic acids were more abundant among these metabolites, suggesting that these substances play important roles in coping with waterlogging stress.

GO and KEGG enrichment analysis of key modules

GO and KEGG analyses of genes in two specific modules, Blue and Turquoise, were performed to understand gene function and metabolic classification under waterlogging stress. The GO pathway is generally divided into three categories: biological process (BP), cellular component (CC) and molecular function (MF) (Fig. 7A and B). The results showed that the genes in the Blue module were mainly enriched in small molecule catabolic process (GO: 0044282) and purine-containing compound metabolic process (GO: 0072521) in BP. CC was mainly enriched in the peroxisome (GO: 0005777) and microbody (GO: 0042579). MF was mainly enriched in vitamin binding (GO: 0019842) and oxidoreductase activity, acting on CH-OH group of donors (GO: 0016614) (Fig. 7C). The genes in the Turquoise module were mainly enriched in RNA splicing, via transesterification reactions (GO: 0000375) and RNA splicing, via transesterification reactions with bulged adenosine as nucleophile (GO: 0000377) in BP. CC was mainly enriched in ribosomal subunit (GO: 0044391) and Cellular component (GO: 0022626). MF was mainly enriched in rRNA binding (GO: 0019843) and translation regulator activity (GO: 0045182) (Fig. 7D). The above shows that quinoa may respond to waterlogging stress by changing enzyme activity, signal transduction pathway and various energy metabolism processes.

Fig. 7.

GO and KEGG enrichment analysis of two specific module candidate genes. Blue module: A GO annotation, C GO enrichment, E KEGG enrichment; Turquoise module: B GO annotation, D GO enrichment, F KEGG enrichment. Note: The x-axis of GO annotation shows the GO terms for BP, CC and MF. y-axis shows the number of genes associated with the GO terms. x-axis of GO enrichment shows the percentage of genes in the GO terms, and y-axis shows the GO-enriched terms. x-axis of KEGG enrichment shows the percentage of genes in the KEGG terms, and y-axis shows the KEGG-enriched terms

KEGG enrichment analysis of genes in the Blue and Turquoise specific modules revealed that genes in both modules were involved in metabolic pathways (ko01100), biosynthesis of secondary metabolites (ko01110), and carbon metabolism (ko01200). In addition, the genes in the Blue module were also involved in plant hormone signal transduction (ko04075), biosynthesis of amino acids (ko01230) (Fig. 7E); the genes in the Turquoise module were also involved in ribosome (ko03010), spliceosome (ko03040), biosynthesis of cofactors (ko01240) (Fig. 7F). Taken together, the GO and KEGG results suggest that the blue and turquoise modules harbor important genes that may respond to waterlogging stress by regulating pathways such as phytohormone signaling, energy metabolism, and amino acid biosynthesis.

Screening of hub genes

Based on the above results, we utilized the blue and turquoise modules to construct gene interaction networks to find regulatory core genes. The top 20 genes with KME values were selected as initial candidate genes in each of the two modules (Table S6), and then the BC values of the candidate genes were calculated to filter out the key genes, and finally 11 key genes were identified in the two modules (Fig. 8). There are five hub genes in the Blue module: LOC110692291, LOC110703195, LOC110711500, LOC110693569, LOC110684437; and six hub genes in the Turquoise module: LOC110736252, LOC110691720, LOC110708629, LOC110688032, LOC110706855, and LOC110730331.

Fig. 8.

A Scatterplot of blue module members. B Scatterplot of turquoise module members. Note: The larger the dot, the more important the gene, and the red color represents the hub gene

Identification of transcription factors in hub genes

To understand the expression of TFs under waterlogging stress, we found that the TFs under waterlogging stress mainly belonged to the gene families of FAR1, bHLH, B3, MYB, C2H2, AP2/ERF, NAC, WRKY, C3H, and bZIP (Table S7). Therefore, we performed TF identification on the 11 hub genes and identified a total of six TFs (Table 2), the blue module has two TFs and the turquoise module has four TFs.

Table 2.

Functional annotation of hub genes in the specificity module related to Quinoa response to inundation stress

Module	Module Candidate Hub Genes	Transcription Factor Family	Gene Function
Blue	gene-LOC110692291	-	Isoamylase 1, chloroplastic-like
	gene-LOC110703195	-	4-alpha-glucanotransferase, chloroplastic/amyloplastic-like
	gene-LOC110711500	-	Selenium-binding protein 1-like
	gene-LOC110693569	NAC	NAC transcription factor 25-like
	gene-LOC110684437	AP2/ERF-ERF	Ethylene-responsive transcription factor RAP2-12-like
Turquoise	gene-LOC110736252	-	Enables TBP-class protein binding
	gene-LOC110691720	-	ABC transporter F family member 1
	gene-LOC110708629	C3H	zinc finger CCCH domain-containing protein 2-like
	gene-LOC110688032	AP2/ERF-ERF	Ethylene-responsive transcription factor RAP2-12-like
	gene-LOC110706855	NAC	NAC transcription factor 25-like
	gene-LOC110730331	AP2/ERF-ERF	Ethylene-responsive transcription factor ERF104-like

Interestingly, the AP2/ERF TF family (LOC110684437, LOC110730331, LOC110688032) appeared in the hub genes of both modules, with previous studies in rice Sub1A-1 [35] and OsEREBP1 [36], A. thaliana AtERF71/HRE2 [35], maize ZmEREB180 [37], and barley HvERF2.11 [38] reported AP2/ERF TF family in response to waterlogging stress characterization, so we further identified these genes by motifs (Fig. 9). The results showed that a total of three motifs were identified from the eight AP2/ERF-encoded genes, LOC110684437, and LOC110688032 shared three motifs with them, and LOC110730331 shared two motifs with them. Additionally, gene function annotation revealed that two of the three AP2/ERF TFs are ethylene-responsive TFs (Table 2). Therefore, we hypothesized that the AP2/ERF family is more closely related in quinoa response to flooding stress.

Fig. 9.

A Sequence distribution analysis of 8 AP2-ERF family genes. The P-value indicates the significance of motif enrichment in this gene. B Conserved domain sequence logo diagram. From top to bottom, these represent the consensus sequences corresponding to motif 1, motif 2, and motif 3

RT-qPCR validation

To determine the authenticity and reliability of the transcriptome data expression levels, we randomly selected six genes from the identified core genes for RT-qPCR analysis (Fig. 10, Table S8). The results showed that the expression trend of RT-qPCR was consistent with the expression pattern of RNA-seq data, indicating that our transcriptome data were reliable.

Fig. 10.

Verification of gene expression levels of six hub genes by RT-qPCR

Subcellular localization and analysis of genetic transformation experiments in Arabidopsis

Subcellular localization and PCR identification of positive strains

We selected LOC110688032 from the AP2-ERF family of core genes for overexpression vector construction and performed subcellular localization and functional validation. Using Agrobacterium mediated transformation, the overexpression vector was transformed into tobacco leaves. Subcellular localization results indicated that LOC110688032 in tobacco leaves is primarily localized to the nuclear region, with minimal expression in the cytoplasm (Fig. 11A).

Fig. 11.

A Subcellular localization map of LOC110688032. Note: Fluorescence channel, chloroplast channel, brightfield, and overlay image from left to right for empty vector control (35 S::GFP) and target gene protein (35 S::LOC110688032-GFP), with scale bars of 20 μm. B PCR identification results for transgenic Arabidopsis positive lines. Note: Leftmost marker is positive control, rightmost marker is negative control, second from right is water control. C Expression analysis of WT Arabidopsis and transgenic Arabidopsis. Statistical analysis was performed using one-way ANOVA followed by Duncan’s multiple range test, with all data presented as mean ± SD (standard deviation) from n = 3 biological replicates

Transgenic Arabidopsis plants were obtained through Agrobacterium mediated transformation, and their DNA and RNA were extracted for PCR and qPCR identification. The results showed that most transgenic strains amplified clear bands and expressed higher levels than WT Arabidopsis (Fig. 11B, C). This indicates that LOC110688032 has been integrated into the Arabidopsis genome.

Phenotypic analysis of transgenic Arabidopsis under waterlogging stress

By observing the phenotypes of WT and transgenic Arabidopsis under waterlogging treatment, we investigated whether LOC110688032 could enhance the waterlogging tolerance of Arabidopsis. As shown in the figure (Fig. 12), after 3 days of waterlogging treatment, the leaves of both transgenic and WT Arabidopsis turned pale yellow. Notably, the leaves of WT Arabidopsis exhibited more severe yellowing and wilting compared to the two transgenic Arabidopsis lines, with impaired growth; in contrast, the transgenic Arabidopsis leaves showed milder yellowing, and most plants continued to elongate and flower. After 5 days of recovery culture, the growth of the transgenic strain was better than that of the WT, while the WT Arabidopsis wilted severely and the yellowing of the leaves worsened. In summary, waterlogging stress affects the growth and development of plants, and transgenic Arabidopsis has better tolerance.

Fig. 12.

Phenotype map of transgenic Arabidopsis under waterlogging stress. Note: 0 days: before waterlogging treatment; T3: 3 days after waterlogging treatment; R5: 5 days after waterlogging treatment and recovery cultivation. WT is WT Arabidopsis, and LOC110688032 is transgenic Arabidopsis

Physiological analysis of transgenic Arabidopsis under waterlogging stress

As shown in the figure (Fig. 13A), there was no significant difference in chlorophyll content (SPAD) between WT and transgenic Arabidopsis at 0 days before waterlogging. However, after 3 days of flooding treatment, both WT and transgenic Arabidopsis exhibited a decreasing trend in chlorophyll content (SPAD), but the decrease in WT Arabidopsis was significantly greater than that in transgenic Arabidopsis. After 5 days of recovery cultivation, both transgenic and WT Arabidopsis showed an increase in chlorophyll content, with transgenic Arabidopsis exhibiting significantly higher value than WT Arabidopsis. This shows that transgenic Arabidopsis has better tolerance to waterlogging and can alleviate the damage caused by waterlogging stress to plants. However, as shown in the figure (Fig. 13B). The MDA changes in transgenic and WT Arabidopsis before and after waterlogging was opposite to that of chlorophyll, which also indicates that transgenic Arabidopsis has better tolerance to waterlogging and suffers less damage.

Fig. 13.

Physiological effects of waterlogging stress on WT Arabidopsis and transgenic Arabidopsis. A MDA; B Chlorophyll content (SPAD); C SOD activity; D POD activity; E CAT activity. Note: 0 days: before waterlogging treatment; T3: 3 days after waterlogging treatment; R5: 5 days after waterlogging treatment and recovery cultivation. WT is wild-type Arabidopsis, and LOC110688032 is transgenic Arabidopsis. Note: The lowercase letters in the figure indicate significant differences (P < 0.05) between different Arabidopsis plants at the same time point. Statistical analysis was performed using one-way ANOVA followed by Duncan’s multiple range test, with all data presented as mean ± SD (standard deviation) from n = 3 biological replicates

As shown in the figure (Fig. 13C, E), there were no significant differences in the activity of the three enzymes between WT and transgenic Arabidopsis at 0 days before waterlogging. However, after 3 days of waterlogging treatment, the SOD, POD, and CAT activities of transgenic Arabidopsis significantly increased and were significantly higher than those of WT Arabidopsis. After 5 days of recovery culture, the activities of the three enzymes in transgenic Arabidopsis were also significantly higher than those in WT Arabidopsis. This indicates that transgenic Arabidopsis has better tolerance to waterlogging and may alleviate the damage caused by waterlogging stress to plants by reducing the oxidative stress of ROS on plants.

Discussion

Effects of waterlogging stress on the morphology and physiology of quinoa during the filling period

Waterlogging stress is the main factor that causes yield reduction in quinoa, and the stress affects not only plant morphology, but also plant physiology and metabolism. Firstly, in terms of morphology, it was found that waterlogging reduced leaf area (69%) and plant height (30%) of all sorghum cultivars [39], which is consistent with the results of this experimental study. In addition, maize is intolerant to waterlogging, and waterlogging inhibits maize growth, resulting in a decrease in plant height, ear height, dry weight, leaf area index, and seed characteristics [6], which is consistent with the results of this study. We hypothesized that the damage to plants is greater when they are subjected to stress during the filling period, such as in wheat, which is more critical during the flowering and filling period, leading to substantial yield losses [40]; waterlogging stress slows down peanut seed filling, affects seed development, and leads to a decrease in dry weight of the seed [41]. In soybeans, seed growth dynamics changed during the filling period when subjected to heat and water stresses [42], and these studies well illustrate the importance of the filling period for the seed, and that exposure to stresses affects seed development.

Effects of waterlogging stress on osmoregulatory substances and antioxidant enzyme systems in quinoa during the filling period

Prolonged oxygen deprivation in the plant root system under waterlogging stress can lead to changes in plant physiology and metabolism, affecting plant growth [43]. Osmoregulation protects against waterlogging stress and ensures normal plant growth by reducing cellular water potential [44]. Xiong et al. [45] found that the enzyme activities and SS and SP of maize seedlings changed to different degrees after waterlogging compared with the control, and POD, MDA, and SS showed an increasing trend, which was consistent with the results of the present study, and the POD, MDA, and SS of waterlogging-treated groups in this experiment were significantly higher than those of the control group. The MDA content was reduced after recovery in this experiment, which indicated that timely drainage was beneficial to reduce the damage. In addition, Barickman et al. [46] found that the Pro content in cucumber increased under waterlogging stress and there were differences in the content of SS, which was basically consistent with the results of this study, and we speculate that quinoa can respond to waterlogging stress by regulating the content of osmotic substances after waterlogging to resist the stress.

Plants can rely on antioxidant enzyme systems to reduce the extent of oxidative damage under waterlogging stress [47]. In addition, Ateeq et al. found [48] that the activities of SOD, POD and CAT were significantly reduced under waterlogging stress, in which the enzyme activities of the waterlogging-treated group were significantly lower than those of the control group, which suggests that waterlogging stress destroys antioxidant enzyme systems in quinoa. Previous studies have shown that for plants first flooded for a period of time and then recovered, according to the degree of tolerance found that some varieties can completely return to normal growth other physiological and biochemical aspects are unaffected, while some varieties even after the recovery of normal growth conditions, have caused irreversible damage to them [49, 50]. These evidence indicate that the recovery ability of different plants is different, and timely drainage is useful for restoring the growth ability of plants, and can stop the continuous damage caused by waterlogging on plants in time to minimize the loss. Therefore, when selecting and breeding flood-tolerant varieties, we should prioritize those with high recovery ability.

Changes of metabolites and metabolic pathways of quinoa in response to waterlogging stress during the filling period

Metabolites serve as a bridge between gene expression and phenotypic changes, through which a better understanding of plant response mechanisms and physiological states to the environment can be achieved [51]. In this study, flavonoids, phenolic acids, alkaloids, lipids, amino acids, and organic acids were significantly enriched under flooding stress. Whereas, phenolic acids were found to increase the antioxidant capacity of rice seedlings thus responding to flooding stress [52]; flooding stress significantly increased the total flavonoid content of chrysanthemum and affected secondary metabolic biosynthesis [53]. Shang et al. found that the differential accumulation of flavonoids, amino acids and other metabolites under waterlogging stress plays an important role in duckweed response to waterlogging stress and the study of waterlogging-tolerant varieties [54]. Above multiple studies we can also see that waterlogging stress affects the accumulation of metabolites.

We speculate that flavonoids play an important role in waterlogging stress. Many TFs including MYB, ERF, WRKY, and bHLH have been reported to affect flavonoid synthesis by regulating the expression of these target genes [55]. Many of the genes in our module belong to these TFs. We speculate that transcription factors may mediate flavonoid synthesis. Research indicates that AP2/ERF TFs participate in regulating flavonoid biosynthesis in plants [56]. waterlogging stress can influence flavonoid accumulation by modulating the expression of key enzymes in the flavonoid synthesis pathway. For instance, Li et al. demonstrated that under waterlogging stress, the flavonoid biosynthetic pathway in quinoa grains avoids excessive product accumulation by inhibiting enzyme activities, such as those of chalcone isomerase (CHI) and flavanol synthase (FLS) [57]. Jiang et al. demonstrated that under flooding stress, certain genes regulate flavonoid accumulation by modulating the activity of enzymes such as flavanone 3-hydroxylase (F3H), CHI, and chalcone synthase (CHS), thereby enhancing quinoa’s resistance to waterlogging stress. Multiple TFs, structural genes, and key metabolites synergistically regulate flavonoid biosynthesis to mitigate oxidative damage caused by flooding [58]. Wang et al. demonstrated that quinoa significantly enhances flavonoid synthesis under waterlogging stress through the coordinated regulation of key enzymes (CHS, F3H, FLS) and transcription factors (MYB and NF-YC) [59].

TFs of quinoa in response to waterlogging stress during the filling period

Many studies have shown a close relationship between TFs and waterlogging stress, e.g., Zhao et al. identified several differentially expressed TFs in chrysanthemum under waterlogging and reoxygenation conditions, most of which belonged to the AP2/ERF, bHLH and MYB families [60]. In addition, WRKY [61], ARF [62], MYB [63] and bZIP [64], BHLH [65], NAC [66], TFs were also found to be associated with waterlogging stress. Our TFs under waterlogging stress were mainly concentrated in the gene families of AP2/ERF, bHLH, WRKY, NAC, MYB, C2H2, C3H, and bZIP, which were basically consistent with the previous studies on the TFs involved in the response to waterlogging stress. All these findings fully demonstrate the strong correlation between waterlogging and various TFs in quinoa. In summary, the hub genes we identified play key roles in response to waterlogging stress.

Functional analysis of the LOC110688032 gene

Previous study has reported that after 10 days of waterlogging stress, transgenic and WT plants showed significant differences in phenotype and physiology, with the overexpressed kiwifruit strain performing well, while the WT plants showed wilting leaves [67]. In this study, the overexpression plants and WT plants showed significant differences before and after flooding. The degree of yellowing of overexpression plants after flooding was lighter, showing better waterlogging resistance. Leaves also suffer oxidative damage under waterlogging stress. The accumulation of MDA can induce lipid peroxidation, thereby damaging cell membranes [68]. By measuring the MDA content in plant before and after flooding, we found that the increase of MDA content in transgenic Arabidopsis was significantly lower than that in WT Arabidopsis after waterlogging treatment and recovery. This indicates that the transgenic lines are less affected by waterlogging and have a certain ability to tolerate waterlogging. Waterlogging stress inhibits chlorophyll synthesis in plant leaves, but plants with strong tolerance to waterlogging can control the extent of chlorophyll degradation to some extent. This study found that before waterlogging stress, there was little difference in chlorophyll content between transgenic Arabidopsis and WT Arabidopsis, but after 3 days of waterlogging stress and 5 days of recovery, the chlorophyll content of transgenic Arabidopsis was higher than that of WT Arabidopsis. This finding is consistent with the results of Liu et al. [7], indicating that genetically modified Arabidopsis has better physiological conditions and that LOC110688032 can improve quinoa’s tolerance to waterlogging stress.

Excessive ROS accumulation directly damages cells and causes lipid peroxidation. Plants rely on antioxidant systems to reduce ROS-induced oxidative damage to cells [69]. The higher the antioxidant enzyme activity, the stronger the ability to scavenge ROS, indicating that increased antioxidant enzyme activity helps enhance the plant’s tolerance to waterlogging. The results of this study showed that transgenic Arabidopsis showed higher SOD, CAT and POD enzyme activities than WT under waterlogging stress, indicating that the decrease of ROS in transgenic Arabidopsis may be due to the enhancement of ROS metabolism by these enzymes. This shows that transgenic Arabidopsis has better tolerance to waterlogging and may alleviate the damage caused by waterlogging stress to plants by reducing the oxidative stress of ROS in plants. This finding is consistent with the results of Luan et al. [70]. The enhanced antioxidant enzyme activity, reduced membrane damage, and improved ROS scavenging ability constitute an important physiological mechanism driven by overexpression of LOC110688032 in plants under waterlogging stress.

AP2/ERF TFs are induced by mechanical injury, hypoxia, and other abiotic stresses. Heterologous expression of these factors in different plant species enhances plant resistance to abiotic stresses [71]. As an important family of TFs in plants, it plays an indispensable role in waterlogging stress and signal transduction pathways, and has been found to be involved in waterlogging stress in many species, including Arabidopsis, rice, cherry, and cucumber [72]. In this study, overexpression of LOC110688032 in quinoa enhanced its tolerance to flooding. Previous study has shown that overexpression of AVERF75 in kiwifruit [73] limited the excessive accumulation of ROS, significantly enhancing the flood tolerance of transgenic tobacco and kiwifruit. In studies on the flood tolerance of chrysanthemums [74], overexpression of CmERF5 and CmRAP2.3 reduced ROS levels and improved the flooding tolerance of chrysanthemums. Additionally, studies have found that RAP2.6 L overexpression delays waterlogging-induced premature senescence by increasing stomatal closure rather than antioxidant enzyme activity [18], and overexpression of genes such as ZmERF055 and PhERF2 in related studies also significantly increases plant water tolerance [75, 76].

Conclusion

This study subjected quinoa to waterlogging treatment and recovery treatment during the filling period, revealing that quinoa adapts to waterlogging stress by regulating changes in morpho physiology, antioxidant enzyme systems, and osmoregulatory substances. Using WGCNA analysis, two key stress-responsive modules, Blue and Turquoise, were identified. Eleven central genes responding to waterlogging stress were determined, including six TFs encoded by the AP2/ERF, NAC, and C3H gene families. These core genes likely play crucial roles in the waterlogging stress response during filling period. Furthermore, overexpression of the AP2/ERF family member LOC110688032 enhanced Arabidopsis tolerance to waterlogging stress. These findings expand and enrich the molecular understanding of quinoa’s response to waterlogging stress, providing crucial insights for improving quinoa’s waterlogging tolerance and developing waterlogging-resistant cultivars.

Abbreviations

WGCNA

Weighted gene co-expression network analysis

FAO

The Food and Agriculture Organization of the United Nations

ROS

Reactive oxygen species

MDA

Malondialdehyde

DEG

Differentially expressed gene

SS

Soluble Sugar

SP

Soluble Protein

Pro

Proline

SOD

Superoxide dismutase

POD

Peroxidase

CAT

Catalase

FPKM

Fragments Per Kilobase of exon model per Million mapped fragments

FDR

False Discovery Rate

KEGG

Kyoto Encyclopedia of Genes and Genomes

GO

Gene Ontology

KOG

Karyotic Orthologous Groups

MRM

Multiple reaction monitoring

VIP

Variable Importance in Projection

ME

Module Eigengene

RT-qPCR

Real-time fluorescence quantitative PCR

Kan

Kanamycin sulfate

BP

Biological process

CC

Cellular component

MF

Molecular function

Authors’ contributions

TYB: Data curation, Formal Analysis, Methodology, Conception, Writing-original draft, Writing-review & editing. HCJ: Methodology, Conception, Investigation, Writing-review & editing. FGJ: Methodology, Supervision, Validation. QXW: Resources, Methodology, Validation. XHL: Data curation, Formal Analysis, Investigation. PQ: Data curation, Supervision, Project administration, Finding acquisition. All authors contributed to the article and approved the submitted version.

Funding

We gratefully acknowledge the financial support of Yunnan Academician Workstation (202405AF140012) and the “Xingdian Talent” Industry Innovation Talent Program in Yunnan Province (XDYC CYCX-2022-0031).

Data availability

The original data of this study have been deposited on the National Center for Biotechnology Information (NCBI) website under the accession number PRJNA1220019.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.