ARTICLE   Open Access    

Genetic and epigenetic variations underlying flavonoid divergence in Beihua and Sijihua honeysuckles

  • # Authors contributed equally: Xianyun Yu, Hang Yu

More Information
  • Received: 30 July 2024
    Revised: 02 September 2024
    Accepted: 24 September 2024
    Published online: 11 October 2024
    Epigenetics Insights  17 Article number: e002 (2024)  |  Cite this article
  • Flavonoids are important antibacterial and antiviral active substances which are the most crucial medicinal components of honeysuckle. However, the content of medicinally active substances in different honeysuckle cultivars is significantly different. Genetic variations and epigenetics play essential roles in plant evolution and trait improvement. Here, we performed multi-omics sequencing of two honeysuckle cultivars (Beihua and Sijihua) at different stages (WB and GF). The results revealed 9,909,981 SNPs in the genomes of the two cultivars, and 12,688 high-impact SNPs were found to regulate genes involved in important biological pathways, such as plant stress resistance. Furthermore, it was found that the majority of differentially methylated cytosines (DMCs, 81%) between Beihua and Sijihua were associated with SNPs. SNP-related DMCs were associated with 76% of the genes, among which 3,325 DEGs (e.g., LjPAL, LjCHI, and LjFLS) were significantly enriched in the flavonoid biosynthesis pathway. The presence of a large number of SNP-related DMCs in the flanking and gene regions of these genes may have led to the overexpression of the genes in Beihua, which increased the accumulation of flavonoids in Beihua. In summary, the present study provides theoretical and technical support for improving the genetic and epigenetic traits of honeysuckle.
  • Atractylodes macrocephala Koidz. (common names 'Baizhu' in Chinese and 'Byakujutsu' in Japanese) is a diploid (2n = 2x = 24) and out-crossing perennial herb in the Compositae family, and has a long history of cultivation in temperate and subtropical areas of East Asia as it is widely used in traditional herbal remedies with multiple pharmacological activities[13]. The 'Pharmacopoeia of the People's Republic of China' states that 'Baizhu' is the dry rhizome of A. macrocephala Koidz. (Atractylodis Macrocephalae Rhizoma, AMR). However, in Japanese traditional medicine 'Baizhu' can be referred to both: A. japonica or A. macrocephala[4].

    A. macrocephala is naturally endemic to China and cultivated in more than 200 towns in China, belonging to Zhejiang, Hunan, Jiangxi, Anhui, Fujian, Sichuan, Hubei, Hebei, Henan, Jiangsu, Guizhou, Shanxi, and Shaanxi Provinces[3]. A. macrocephala grows to a height of 20–60 cm (Fig. 1). The leaves are green, papery, hairless, and generally foliole with 3–5 laminae with cylindric glabrous stems and branches. The flowers grow and aggregate into a capitulum at the apex of the stem. The corollas are purplish-red, and the florets are 1.7 cm long. The achenes, densely covered with white, straight hairs, are obconic and measure 7.5 mm long. The rhizomes used for medicinal purposes are irregular masses or irregularly curving cylinders about 3–13 cm long and 1.5–7 cm in diameter with an outwardly pale greyish yellow to pale yellowish color or a sparse greyish brown color. The periderm-covered rhizomes are externally greyish brown, often with nodose protuberances and coarse wrinkles. The cross-sections are white with fine dots of light yellowish-brown to brown secretion. Rhizomes are collected from plants that are > 2 years old during the spring. The fibrils are removed, dried, and used for medicinal purposes[5, 6].

    Figure 1.  Plant morphology of A. macrocephala.

    The medicinal properties of AMRs are used for spleen deficiency, phlegm drinking, dizziness, palpitation, edema, spontaneous sweating, benefit Qi, and fetal restlessness[7]. The AMR contains various functional components, among which high polysaccharide content, with a yield close to 30%[8]. Therefore, the polysaccharides of A. macrocephala Koidz. rhizome (AMRP) are essential in assessing the quality control and bioactivity of A. macrocephala. Volatile oil accounts for about 1.4% of AMR, with atractylon and atractylodin as the main components[9]. Atractylon can be converted to atractylenolide I (AT-I), atractylenolide II (AT-II), and atractylenolide III (AT-III) under ambient conditions. AT-III can be dehydrated to AT-II under heating conditions[10, 11]. AMRs, including esters, sesqui-, and triterpenes, have a wide range of biological activities, such as improving immune activity, intestinal digestion, neuroprotective activity, immune anti-inflammatory, and anti-tumor.

    In recent years, research on the pharmacological aspects of AMR has continued to increase. Still, the discovery of the main active components in AMR is in its infancy. The PAO-ZHI processing of AMR is a critical step for AMR to exert its functional effects, but also, in this case, further work is required. Studies on the biosynthesis of bioactive compounds and different types of transcriptomes advanced current knowledge of A. macrocephala, but, as mentioned, required more systematic work. Ulteriorly, an outlook on the future research directions of A. macrocephala was provided based on the advanced technologies currently applied in A. macrocephala (Fig. 2).

    Figure 2.  Current progress of A. macrocephala.

    A. macrocephala is distributed among mountainous regions more than 800 m above sea level along the middle and lower reaches of the Yangtze River (China)[5]. Due to over-exploitation and habitat destruction, natural populations are rare, threatened, and extinct in many locations[1,12]. In contrast to its native range, A. macrocephala is widely cultivated throughout China, in a total area of 2,000–2,500 ha, with a yield of 7,000 t of rhizomes annually[13]. A. macrocephala is mainly produced in Zhejiang, Anhui, and Hebei (China)[14]. Since ancient times, Zhejiang has been the famous producing area and was later introduced to Jiangxi, Hunan, Hebei, and other places[15]. Wild A. macrocephala is currently present in at least 14 provinces in China. It is mainly distributed over three mountain ranges, including the Tianmu and Dapan mountains in Zhejiang Province and the Mufu mountains along the border of Hunan and Jiangxi Provinces. A. macrocephala grows in a forest, or grassy areas on mountain or hill slopes and valleys at an altitude of 600–2,800 m. A. macrocephala grows rapidly at a temperature of 22–28 °C, and favors conditions with total precipitation of 300–400 mm evenly distributed among the growing season[16]. Chen et al. first used alternating trilinear decomposition (ATLD) to characterize the three-dimensional fluorescence spectrum of A. macrocephala[17]. Then they combined the three-dimensional fluorescence spectrum with partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor method (kNN) to trace the origin of Atractylodes samples. The results showed that the classification models established by PLS-DA and kNN could effectively distinguish the samples from three major Atractylodes producing areas (Anhui, Hunan, and Zhejiang), and the classification accuracy rate (CCR) of Zhejiang atractylodes was up to 80%, and 90%, respectively[17]. Zhang et al. compared the characteristics, volatile oil content, and chemical components of attested materials from six producing areas of Zhejiang, Anhui, Hubei, Hunan, Hebei, and Henan. Differences in the shape, size, and surface characteristics were reported, with the content of volatile oil ranging from 0.58% to 1.22%, from high to low, Hunan (1.22%) > Zhejiang (1.20%) > Anhui (1.02%) > Hubei (0.94%) > Henan (0.86%) > Hebei (0.58%)[18]. This study showed that the volatile oil content of A. macrocephala in Hunan, Anhui, and Hubei is not much different from that of Zhejiang, which is around 1%. A. macrocephala is a local herb in Zhejiang, with standardized cultivation techniques, with production used to reach 80%–90% of the country. However, in recent years, the rapid development of Zhejiang's real estate economy has reduced the area planted with Zhejiang A. macrocephala, resulting in a sudden decrease in production. Therefore, neighboring regions, such as Anhui and Hunan, vigorously cultivate A. macrocephala, and the yield and quality of A. macrocephala can be comparable to those of Zhejiang. The results were consistent with the data reports[18]. Guo et al. analyzed the differentially expressed genes of Atractylodes transcripts from different regions by the Illumina HiSeq sequencing platform. It was found that 2,333, 1,846, and 1,239 DEGs were screened from Hubei and Hebei, Anhui and Hubei, and Anhui and Hebei Atrexia, respectively, among which 1,424, 1,091, and 731 DEGs were annotated in the GO database. There were 432, 321, and 208 DEGs annotated in the KEGG database. These DEGs were mainly related to metabolic processes and metabolic pathways of secondary metabolites. The highest expression levels of these genes were found in Hubei, indicating higher terpenoid production in Hubei[19]. Other compounds were differentially accumulated in Atractylodes. Chlorogenic acid from Hebei was 0.22%, significantly higher than that from Zhejiang and Anhui[20]. Moreover, the content of neochlorogenic acid and chlorogenic acid decreased after processing, with the highest effect reported in Zhejiang, with the average transfer rate of neochlorogenic acid and chlorogenic acid reaching 55.68% and 55.05%[20]. All these changes would bring great help in distinguishing the origins of A. macrocephala.

    Medicinal AMR can be divided into raw AMR and cooked AMR. The processing method is PAO-ZHI; the most traditional method is wheat bran frying. The literature compared two different treatment methods, crude A. macrocephala (CA) and bran-processed A. macrocephala, and found that the pharmacological effects of AMR changed after frying with wheat bran, mainly in the anti-tumor, antiviral and anti-inflammatory effects[21]. The anti-inflammatory effect was enhanced, while the anti-tumor and antiviral effects were somewhat weakened, which may be related to the composition changes of the compounds after frying. The study of the content of AT-I, II, and III, and atractyloside A, in rat serum provided helpful information on the mechanism of wheat bran processing[22]. In addition to frying wheat bran, Sun et al. used sulfur fumigation to treat AMR[23]. They found that the concentration of different compounds changed, producing up to 15 kinds of terpenoids. Changes in pharmacological effects were related to treatment and the type of illumination[24,25]. Also, artificial light can improve the various biological functions. A. macrocephala grew better under microwave electrodeless light, with a chlorophyll content of 57.07 ± 0.65 soil and plant analyzer develotrnent (SPAD)[24]. The antioxidant activity of AMR extract treated with light-emitting diode (LED)-red light was the highest (95.3 ± 1.1%) compared with other treatments[24]. The total phenol and flavonoid contents of AMR extract treated with LED-green light were the highest at 24.93 ± 0.3 mg gallic acid equivalents (GAE)/g and 11.2 ± 0.3 mg quercetin equivalents (QE)/g compared with other treatments[24, 25]. Polysaccharides from Chrysanthemun indicum L.[26] and Sclerotium rolfsiisacc[27] can improve AMR's biomass and bioactive substances by stimulating plant defense and thus affect their efficacy. In summary, there are compositional differences between A. macrocephala from different origins. Besides, different treatments, including processing mode, light irradiation, and immune induction factors, which can affect AMR's biological activity, provide some reference for the cultivation and processing of A. macrocephala (Fig. 3).

    Figure 3.  Origin, distribution and processing of A. macrocephala.

    The AMR has been reported to be rich in polysaccharides, sesquiterpenoids (atractylenolides), volatile compounds, and polyacetylenes[3]. These compounds have contributed to various biological activities in AMR, including immunomodulatory effects, improving gastrointestinal function, anti-tumor activity, neuroprotective activity, and anti-inflammatory.

    AMRP has received increasing attention as the main active component in AMR because of its rich and diverse biological activities. In the last five years, nine AMRP have been isolated from AMR. RAMP2 had been isolated from AMR, with a molecular weight of 4.354 × 103 Da. It was composed of mannose, galacturonic acid, glucose, galactose, and arabinose, with the main linkages of →3-β-glcp-(1→, →3,6-β-glcp-(1→, →6-β-glcp-(1→, T-β-glcp-(1→, →4-α-galpA-(1→, →4-α-galpA-6-OMe-(1→, →5-α-araf-(1→, →4,6-β-manp-(1→ and →4-β-galp-(1→[28]. Three water-soluble polysaccharides AMAP-1, AMAP-2, and AMAP-3 were isolated with a molecular weight of 13.8 × 104 Da, 16.2 × 104 Da, and 8.5 × 104 Da, respectively. Three polysaccharides were deduced to be natural pectin-type polysaccharides, where the homogalacturonan (HG) region consists of α-(1→4)-linked GalpA residues and the ramified region consists of alternating α-(1→4)-linked GalpA residues and α-(1→2)-linked Rhap residues. Besides, three polysaccharides were composed of different ratios of HG and rhamnogalacturonan type I (RG-I) regions[29]. Furthermore, RAMPtp has been extracted from AMR with a molecular weight of 1.867 × 103 Da. It consists of glucose, mannose, rhamnose, arabinose, and galactose with 60.67%, 14.99%, 10.61%, 8.83%, and 4.90%, connected by 1,3-linked β-D Galp and 1,6-linked β-D Galp residues[30]. Additionally, PAMK was characterized by a molecular weight of 4.1 kDa, consisting of galactose, arabinose, and glucose in a molar ratio of 1:1.5:5, with an alpha structure and containing 96.47% polysaccharide and small amounts of protein, nucleic acid, and uric acid[31]. Another PAMK extracted from AMR had a molecular weight of 2.816 × 103 Da and consisted of glucose and mannose in molar ratios of 0.582 to 0.418[32]. Guo et al. isolated PAMK with a molecular weight of 4.748 × 103 g/mol from AMR, consisting of glucose, galactose, arabinose, fructose, and mannose in proportions of 67.01%, 12.32%, 9.89%, 1.18%, and 0.91%, respectively[33]. In addition, AMP1-1 is a neutral polysaccharide fragment with a molecular weight of 1.433 kDa isolated from AMR. It consists of glucose and fructose, and the structure was identified as inulin-type fructose α-D-Glcp-1→(2-β-D-Fruf-1)7[34]. These reports indicated that, in general, polysaccharides are extracted by water decoction, ultrasonic-assisted extraction, enzyme hydrolysis method, and microwave-assisted extraction. The separation and purification are column chromatography, stepwise ethanol precipitation, and ultrafiltration. Their physicochemical properties and structural characterization are generally achieved by determining the molecular weight, determining the monosaccharide composition, analyzing the secondary structure, and glycosidic bond configuration of polysaccharides with Fourier transform infrared (FT-IR) and nuclear magnetic resonance (NMR). The advanced structures of polysaccharides can be identified by high-performance size exclusion chromatography-multiangle laser light scattering (HPSEC-MALLS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM) techniques (Table 1). AMRP has various physiological functions, including immunomodulatory effects, improving gastrointestinal function, and anti-tumor activity. The related biological activities, animal models, monitoring indicators, and results are summarized in Table 1.

    Table 1.  Components and bioactivity of polysaccharides from Atractylodes macrocephala Koidz. Rhizome.
    Pharmacological activitiesDetailed functionPolysaccharides informationModelDoseTest indexResultsRef.
    Immunomodulatory effectsRestore immune
    function
    /Chicken models
    (HS-induced)
    200 mg/kgOxidative index;
    Activities of mitochondrial complexes and ATPases;
    Ultrastructure in chicken spleens;
    Expression levels of cytokines, Mitochondrial dynamics- and apoptosis-related genes
    Alleviated
    the expression of
    IL-1 ↑,TNF-α ↑, IL-2 ↓, IFN- γ ↓; mitochondrial dynamics- and anti-apoptosis-related genes ↓; pro-apoptosis-related genes ↑;
    the activities of mitochondrial complexes and ATPases ↓ caused by HS
    [35]
    Regulate the immune function/Chicken models
    (HS-induced)
    200 mg/kgiNOS–NO activities;
    ER stress-related genes;
    Apoptosis-related genes;
    Apoptosis levels
    Alleviated NO content ↑; activity of iNOS ↑ in the chicken spleen; GRP78, GRP94, ATF4, ATF6, IRE ↑; caspase3 ↑; Bcl-2 ↓ caused by HS[36]
    Relieve immunosuppressionCommercial AMR powder (purity 70%)Geese models
    (CTX-induced)
    400 mg/kgSpleen development;
    Percentages of leukocytes in peripheral blood
    Alleviated the spleen damage;
    T and B cell proliferation ↓; imbalance of leukocytes; disturbances of humoral; cellular immunity caused by CTX
    [37]
    Active the lymphocytesCommercial AMR powder (purity 95%)Geese models
    (CTX-induced)
    400 mg/kgThymus morphology;
    The level of serum GMC-SF, IL-1b, IL-3, IL-5;
    mRNA expression of CD25, novel_mir2, CTLA4 and CD28 signal pathway
    Maintain normal cell morphology of thymus;
    Alleviated GMC-SF ↓, IL-1b ↓, IL-5↓, IL-6↓, TGF-b↓; IL-4 ↑, IL-10 ↑; novel_mir2 ↓, CD25↓, CD28↓ in thymus and lymphocytes caused by CTX
    [38]
    Alleviate immunosuppressionCommercial AMR powder (purity 70%)Geese models
    (CTX-induced)
    400 mg/kgThymus development;
    T cell proliferation rate;
    The level of CD28, CD96, MHC-II;
    IL-2 levels in serum;
    differentially expressed miRNAs
    Alleviated thymus damage;
    T lymphocyte proliferation rate ↓; T cell activation ↓; IL-2 levels ↓ caused by CTX;
    Promoted novel_mir2 ↑; CTLA4 ↓; TCR-NFAT signaling pathway
    [39]
    Alleviates T cell activation declineCommercial AMR powder (purity 95%)BALB/c female mice (CTX-induced)200 mg/kgSpleen index;
    Morphology, death, cytokine concentration of splenocytes;
    Th1/Th2 ratio, activating factors of lymphocytes;
    T cell activating factors;
    mRNA expression level in CD28 signal pathway
    Improved the spleen index;
    Alleviated abnormal splenocytes morphology and death; Balance Th1/Th2 ratio; IL-2 ↑, IL-6 ↑, TNF-α ↑, IFN-γ ↑; mRNA levels of CD28, PLCγ-1, IP3R, NFAT, AP-1 ↑
    [40]
    Immunoregulation and ImmunopotentiationCommercial AMR powder (purity 80%)BMDCs (LPS-induced);
    Female BALB/c mice (ovalbumin as a model antigen)
    /Surface molecule expression of BMDCs;
    Cytokines secreted by dendritic cell supernatants;
    OVA-specific antibodies in serum;
    Cytokines in serum;
    Lymphocyte immunophenotype
    Expression of CD80 and CD86 ↑; IL-1β ↑, IL-12 ↑, TNF-α↑ and IFN-γ ↑; OVA-specific antibodies in serum ↑; Secretion of cytokines ↑; Proliferation rate of spleen lymphocytes ↑; Activation of CD3+CD4+ and CD3+CD8+ lymphocytes[46]
    Increase immune-response capacity of the spleen in miceCommercial AMR powder (purity 70%)BALB/c female mice100, 200, 400 mg/kgSpleen index;
    Concentrations of cytokines;
    mRNA and protein expression levels in TLR4 signaling
    In the medium-PAMK group:
    IL-2, IL-4, IFN-c, TNF-a ↑; mRNA and protein expression of TLR4, MyD88, TRAF6, TRAF3, NF-κB in the spleen ↑
    [41]
    Immunological activityCommercial AMR powder (purity 80%)Murine splenic lymphocytes (LPS or PHA-induced)13, 26, 52, 104, 208 μg/mLT lymphocyte surface markersLymphocyte proliferation ↑;
    Ratio of CD4+/CD8+ T cells ↑
    [47]
    Immunomodulatory activityTotal carbohydrates content 95.66 %Mouse splenocytes
    (Con A or LPS-induced)
    25, 50, 100 μg/mLSplenocyte proliferation;
    NK cytotoxicity;
    Productions of NO and cytokines;
    Transcription factor activity;
    Signal pathways and receptor
    Promoted splenocyte proliferation; Cells enter S and G2/M phases; Ratios of T/B cells ↑; NK cytotoxicity ↑; Transcriptional activities of NFAT ↑; NF-κB, AP-1 ↑; NO, IgG, IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-6, IL-10, IL-12p40, IL-12p70, IL-13, IFN-γ, TNF-α, G-CSF, GM-CSF, KC, MIP-1α, MIP-1β, RANTES, Eotaxin ↑[42]
    Promote the proliferation of thymic epithelial cellsContents of fucrhaara, galactose, glucose, fructose,
    and xylitol: 0.98%, 0.40%, 88.67%, 4.47%, and 5.47%
    MTEC1 cells50 μg/mLCell viability and proliferation;
    lncRNAs, miRNAs, and mRNAs expression profiles in MTEC1 cells
    The differential genes were 225 lncRNAs, 29 miRNAs, and 800 mRNAs; Genes enriched in cell cycle, cell division, NF-κB signaling, apoptotic process, and MAPK signaling pathway[44]
    Immunomodulatory activityMW: 4.354 × 103 Da;
    Composed of mannose, galacturonic acid, glucose, galactose and arabinose;
    The main linkages are →3-β-glcp-(1→, →3,6-β-glcp-(1→, →6-β-glcp-(1→, T-β-glcp-(1→,
    →4-α-galpA-(1→, →4-α-galpA-6-OMe-(1→, →5-α-araf-(1→, →4,6-β-manp-(1→ and →4-β-galp-(1→
    CD4+ T cell50, 100, 200 μg/mLMolecular weight;
    Monosaccharide composition;
    Secondary structure;
    Surface topography;
    Effect on Treg cells
    Treg cells percentage ↑; mRNA expressions of Foxp3, IL-10 and IL-2 ↑; STAT5 phosphorylation levels ↑; IL-2/STAT5 pathway[28]
    Immunostimulatory activityMW of AMAP-1, AMAP-2, and AMAP-3 were 13.8×104 Da, 16.2×104 Da and 8.5×104 Da;
    HG region consists of α-(1→4)-linked GalpA residues
    RAW264.7 cells (LPS-induced)80, 40, 200 μg/mLMolecular weight;
    Total carbohydrate;
    Uronic acid contents;
    Secondary structure;
    Monosaccharide composition;
    Immunostimulatory activity
    RG-I-rich AMAP-1 and AMAP-2 improved the release of NO[29]
    Immunomodulatory effectMW: 1.867×103 Da;
    Contents of glucose, mannose, rhamnose,
    arabinose and galactose: 60.67%, 14.99%, 10.61%, 8.83% and 4.90%
    SMLN lymphocytes25
    μg/ml
    Molecular weight;
    Monosaccharide composition;
    Ultrastructure;
    Intracellular Ca2+concentration;
    Target genes;
    Cell cycle distribution
    [Ca2+]i ↑; More cells in S and G2/M phases; IFN-γ ↑, IL-17A ↑; mRNA expressions of IL-4 ↓[30]
    Macrophage activationTotal carbohydrates content 95.66 %RAW264.7 macrophages (LPS-induced)25, 50, 100 μg/mLPinocytic activity;
    Phagocytic uptake;
    Phenotypic characterization;
    Cytokine production;
    Bioinformatics analysis;
    Transcription factor inhibition
    IL-6, IL-10 and TNF-α ↑; CCL2 and CCL5 ↑; Pinocytic and phagocytic activity ↑; CD40, CD80, CD86, MHC-I, MHC-II ↑; NF-κB and Jak-STAT pathway[43]
    Immunomodulatory effectTotal carbohydrates content 95.66 %SMLN lymphocytes25, 50, 100 μg/mLCytokine production;
    CD4+ and CD8+ lymphocytes;
    Target genes;
    Bioinformatics analysis;
    T and B lymphocyte proliferation;
    Receptor binding and blocking
    IFN-γ, IL-1α, IL-21, IFN-α, CCL4, CXCL9, CXCL10 ↑; CD4+ and CD8+subpopulations proportions ↑;
    c-JUN, NFAT4, STAT1, STAT3 ↑;
    67 differentially expressed miRNAs (55 ↑ and
    12 ↓), associated with immune system pathways; Affect T and B lymphocytes
    [45]
    Improving gastrointestinal functionRelieve enteritis and improve intestinal
    flora disorder
    Commercial AMR powder (purity 70%);
    Contents of fucrhaara, galactose, glucose, xylitol, and fructose: 0.98%, 0.40%, 88.67%, 4.47%, and 5.47%
    Goslings (LPS-induced)400 mg/kgSerum CRP, IL-1β, IL-6, and TNF-α levels;
    Positive rate of IgA;
    TLR4, occludin, ZO-1, cytokines, and immunoglobulin mRNA expression;
    Intestinal flora of gosling excrement
    Relieved IL-1β, IL-6, TNF-α levels in serum ↑; the number of IgA-secreting cells ↑; TLR4 ↑; tight junction occludin and ZO-1 ↓; IL-1β mRNA expression in the small intestine ↑; Romboutsia ↓ caused by LPS[48]
    Ameliorate ulcerative colitisMW: 2.391 × 104 Da;
    Composed of mannose, glucuronic acid, glucose and arabinose in a molar ratio of 12.05:6.02:72.29:9.64
    Male C57BL/6J mice (DDS-induced)10, 20, 40 mg/kg bwHistopathological evaluation;
    Inflammatory mediator;
    Composition of gut microbiota;
    Feces and plasma for global metabolites profiling
    Butyricicoccus, Lactobacillus ↑;
    Actinobacteria, Akkermansia, Anaeroplasma, Bifidobacterium, Erysipelatoclostridium, Faecalibaculum, Parasutterella,
    Parvibacter, Tenericutes, Verrucomicrobia ↓;
    Changed 23 metabolites in fecal content; 21 metabolites in plasma content
    [49]
    Attenuate ulcerative colitis/Male SD rats (TNBS-induced);
    Co-culture BMSCs and IEC-6 cells
    540 mg/kg
    (for rats);
    400 μg/mL (for cell)
    Histopathological analysis;
    Cell migration;
    Levels of cytokines
    Potentiated BMSCs’ effect on preventing colitis and homing the injured tissue, regulated cytokines;
    BMSCs and AMP promoted the migration of IEC
    [52]
    Against intestinal mucosal injuryMW: 3.714 × 103 Da;
    Composed of glucose, arabinose, galactose, galacturonic acid, rhamnose
    and mannose with molar ratios of 59.09:23.22:9.32:4.70:2.07:1.59
    Male C57BL/6 mice (DDS-induced)100 mg/kgIntestinal morphology;
    IL-6, TNF-α and IL-1β in serum;
    mRNA expression;
    Intestinal microbiota
    Alleviated body weight ↓; colon length ↓; colonic damage caused by DSS;
    Over-expression of TNF-α, IL-1β, IL-6 ↓; Infiltration of neutrophils in colon ↓; Mucin 2 ↑;
    Tight junction protein Claudin-1 ↑;
    Harmful bacteria content ↓;
    Beneficial bacteria content ↑
    [50]
    Against intestinal injuryTotal carbohydrates 95.66 %IECs (DDS-induced)5, 25, 50 μg/mLCell proliferation and apoptosis;
    Expression levels of intercellular TJ proteins;
    lncRNA screening
    Proliferation and survival of IECs ↑;
    Novel lncRNA ITSN1-OT1 ↑;
    Blocked the nuclear import of phosphorylated STAT2
    [51]
    Anti-tumor activityInduce apoptosis in transplanted H22 cells in miceMW: 4.1× 103 Da;
    Neutral heteropolysaccharide composed of galactose, arabinose, and glucose with α-configuration (molar ratio, 1:1.5:5)
    Female Kunming mice100 and 200 mg/kg (for rats)Secondary structure;
    Molecular weight;
    Molecular weight;
    Thymus index and Spleen index;
    Lymphocyte Subpopulation in peripheral blood;
    Cell cycle distribution
    In tumor-bearing mice CD3+, CD4+, CD8+ ↓;
    B cells ↑
    [31]
    Regulate the innate immunity of colorectal cancer cellsCommercial AMR powder (purity 70%)C57BL/6J mice (MC38 cells xenograft model)500 mg/kgExpression of pro-inflammatory cytokines and secretionIL-6, IFN-λ, TNF-α, NO ↑ through MyD88/TLR4-dependent signaling pathway;
    Survival duration of mice with tumors ↑;
    Prevent tumorigenesis in mice
    [54]
    Induce apoptosis of Eca-109 cellsMW: 2.1× 103 Da;
    Neutral hetero polysaccharide composed
    of arabinose and glucose (molar ratio, 1:4.57) with pyranose rings and α-type and β-type glycosidic linkages
    Eca-109 cells0.25, 0.5, 1, 1.5, 2.00 mg/mLCell morphology;
    Cell cycle arrest;
    Induction of apoptosis
    Accelerate the apoptosis of Eca109 cells[53]
    '/' denotes no useful information found in the study.
     | Show Table
    DownLoad: CSV

    To study the immunomodulatory activity of AMRP, the biological models generally adopted are chicken, goose, mouse, and human cell lines. Experiments based on the chicken model have generally applied 200 mg/kg doses. It was reported that AMRP protected the chicken spleen against heat stress (HS) by alleviating the chicken spleen immune dysfunction caused by HS, reducing oxidative stress, enhancing mitochondrial function, and inhibiting cell apoptosis[35]. Selenium and AMRP could improve the abnormal oxidation and apoptosis levels and endoplasmic reticulum damage caused by HS, and could act synergistically in the chicken spleen to regulate biomarker levels[36]. It indicated that AMRP and the combination of selenium and AMRP could be applied as chicken feed supplementation to alleviate the damage of HS and improve chicken immunity.

    The general application dose in the goose model is also 200 mg/kg, and the main injury inducer is cyclophosphamide (CTX). AMRP alleviated CTX-induced immune damage in geese and provided stable humoral immune protection[37]. Little is known about the role of AMRP in enhancing immunity in geese through the miRNA pathway. It was reported that AMRP alleviated CTX-induced decrease in T lymphocyte activation levels through the novel _mir2/CTLA4/CD28/AP-1 signaling pathway[38]. It was also reported that AMRP might be achieved by upregulating the TCR-NFAT pathway through novel_mir2 targeting of CTLA4, thereby attenuating the immune damage induced by CTX[39]. This indicated that AMRP could also be used as goose feed supplementation to improve the goose's autoimmunity.

    The typical injury inducer for mouse models is CTX, and the effects on mouse spleen tissue are mainly observed. BALB/c female mice were CTX-induced damage. However, AMRP increased cytokine levels and attenuated the CTX-induced decrease in lymphocyte activation levels through the CD28/IP3R/PLCγ-1/AP-1/NFAT signaling pathway[40]. It has also been shown that AMRP may enhance the immune response in the mouse spleen through the TLR4-MyD88-NF-κB signaling pathway[41].

    Various cellular models have been used to study the immune activity of AMRP, and most of these studies have explored the immune activity with mouse splenocytes and lymphocytes. Besides, the commonly used damage-inducing agents are LPS, phytohemagglutinin (PHA), and concanavalin A (Con A).

    In one study, the immunoreactivity of AMRP was studied in cultured mouse splenocytes. LPS and Con A served as controls. Specific inhibitors against mitogen-activated protein kinases (MAPKs) and NF-κB significantly inhibited AMRP-induced IL-6 production. The results suggested that AMRP-induced splenocyte activation may be achieved through TLR4-independent MAPKs and NF-κB signaling pathways[42]. Besides, AMRP isolated from AMR acting on LPS-induced RAW264.7 macrophages revealed that NF-κB and Jak-STAT signaling pathways play a crucial role in regulating immune response and immune function[43]. RAMP2 increased the phosphorylation level of STAT5 in Treg cells, indicating that RAMP2 could increase the number of Treg cells through the IL-2/STAT5 signaling pathway[28]. Furthermore, the relationship between structure and immune activity was investigated. Polysaccharides rich in RG-I structure and high molecular weight improved NO release from RAW264.7 cells. Conversly, polysaccharides rich in HG structure and low molecular weight did not have this ability, indicating that the immunoreactivity of the polysaccharide may be related to the side chain of RG-I region[29]. Moreover, the effect of AMRP on the expression profile of lncRNAs, miRNAs, and mRNAs in MTEC1 cells has also been investigated. The differentially expressed genes include lncRNAs, Neat1, and Limd1. The involved signaling pathways include cell cycle, mitosis, apoptotic process, and MAPK[44].

    Xu et al. found that AMRP affects supramammary lymph node (SMLN) lymphocytes prepared from healthy Holstein cows. Sixty-seven differentially expressed miRNAs were identified based on microRNA sequencing and were associated with immune system pathways such as PI3K-Akt, MAPKs, Jak-STAT, and calcium signaling pathways. AMRP exerted immunostimulatory effects on T and B lymphocytes by binding to T cell receptor (TCR) and membrane Ig alone, thereby mobilizing immune regulatory mechanisms within the bovine mammary gland[45].

    AMRP can also be made into nanostructured lipid carriers (NLC). Nanoparticles as drug carriers can improve the action of drugs in vivo. NLC, as a nanoparticle, has the advantages of low toxicity and good targeting[46]. The optimization of the AMRP-NLC preparation process has been reported. The optimum technologic parameters were: the mass ratio of stearic acid to caprylic/capric triglyceride was 2:1. The mass ratio of poloxamer 188 to soy lecithin was 2:1. The sonication time was 12 min. The final encapsulation rate could reach 76.85%[47]. Furthermore, AMRP-NLC interfered with the maturation and differentiation of bone marrow-derived dendritic cells (BMDCs). Besides, AMRP-NLC, as an adjuvant of ovalbumin (OVA), could affect ova-immunized mice with enhanced immune effects[46].

    AMRP also has the effect of alleviating intestinal damage. They are summarized in Table 1. The common damage-inducing agents are lipopolysaccharide (LPS), dextran sulfate sodium (DDS), and trinitrobenzene sulfonic acid (TNBS). A model of LPS-induced enteritis in goslings was constructed to observe the effect of AMRP on alleviating small intestinal damage. Gosling excrement was analyzed by 16S rDNA sequencing to illuminate the impact of AMRP on the intestinal flora. Results indicated that AMRP could maintain the relative stability of cytokine levels and immunoglobulin content and improve intestinal flora disorder[48]. Feng et al. used DDS-induced ulcerative colitis (UC) in mice and explored the alleviating effects of AMRP on UC with 16S rDNA sequencing technology and plasma metabolomics. The results showed that AMRP restored the DDS-induced disruption of intestinal flora composition, regulated the production of metabolites such as short-chain fatty acids and cadaveric amines, and regulated the metabolism of amino acids and bile acids by the host and intestinal flora[49]. A similar study has reported that AMRP has a protective effect on the damage of the intestinal mucosal barrier in mice caused by DSS. It was found that AMRP increased the expression of Mucin 2 and the tight junction protein Claudin-1. In addition, AMRP decreased the proportion of harmful bacteria and increased the potentially beneficial bacteria content in the intestine[50]. The protective effect of AMRP on DSS-induced damage to intestinal epithelial cells (IECs) has also been investigated. The results showed that AMRP promoted the proliferation and survival of IECs.

    In addition, AMRP induced a novel lncRNA ITSN1-OT1, which blocked the nuclear import of phosphorylated STAT2 and inhibited the DSS-induced reduced expression and structural disruption of tight junction proteins[51]. AMRP can also act in combination with cells to protect the intestinal tract. The ulcerative colitis model in Male Sprague-Dawley (SD) rats was established using TNBS, and BMSCs were isolated. IEC-6 and BMSCs were co-cultured and treated by AMRP. The results showed that AMRP enhanced the prevention of TNBS-induced colitis in BMSCs, promoted the migration of IEC, and affected the expression of various cytokines[52]. These reports indicated that the 16S rDNA sequencing technique could become a standard method to examine the improvement of gastrointestinal function by AMRP.

    AMRP has anti-tumor activity and other biological activities. AMRP can induce apoptosis in Hepatoma-22 (H22) and Eca-109 cells and modulate the innate immunity of MC38 cells. For instance, the anti-tumor effects of AMRP were investigated by constructing a tumor-bearing mouse model of H22 tumor cells. AMRP blocked the S-phase of H22 tumor cells and induced an immune response, inhibiting cell proliferation[31]. In addition, AMRP can inhibit cell proliferation through the mitochondrial pathway and by blocking the S-phase of Eca-109 tumor cells[53]. AMRP affects MC38 tumor cells, and the anti-tumor effect of AMRP was investigated with Toll-like receptor 4 (TLR4) KO C57BL/6 mice and the construction of the MC38 tumor cell xenograft model. AMRP significantly inhibited the development of MC38 cells in mice and prolonged the survival of tumor-bearing mice. AMRP activity was diminished in TLR4 KO mice. Combined with the immunoblotting assay results, it was shown that TLR4 regulated the MyD88-dependent signaling pathway, which has a critical effect on the anti-tumor effect of AMRP[54].

    AMR contains a large number of bioactive compounds. Among them, small molecule compounds include esters, sesquiterpenes, and other compounds. These small molecule compounds have significant pharmacological activities, including anti-tumor, neuroprotective, immunomodulatory, and anti-inflammatory. In the last five years, small molecule compounds have been increasingly identified (Fig. 4), with atractylenolides as the main component of AMR extracts[11]. Atractylenolides are a small group of sesquiterpenoids. Atractylenolides include AT-I, AT-II, and AT-III, lactones isolated from AMR.

    Figure 4.  Structure of small molecule compounds with bioactivities from AMR. Atractylenolide I (1); Atractylenolide II (2); Atractylenolide III (3); 3β-acetoxyl atractylenolide I (4); 4R,5R,8S,9S-diepoxylatractylenolide II (5); 8S,9S-epoxyla-tractylenolide II (6); Atractylmacrols A (7); Atractylmacrols B (8); Atractylmacrols C (9); Atractylmacrols D (10); Atractylmacrols E (11); 2-[(2E)-3,7-dimethyl-2,6-octadienyl]-6-methyl-2,5-cyclohexadiene-1,4-dione (12); 8-epiasterolid (13); (3S,4E,6E,12E)-1-acetoxy-tetradeca-4,6,12-triene-8,10-diyne-3,14-diol (14); (4E,6E,12E)-tetradeca-4,6,12-triene-8,10-diyne-13,14-triol (15); 1-acetoxy-tetradeca-6E,12E-diene-8, 10-diyne-3-ol (16); 1,3-diacetoxy-tetradeca-6E, 12E-diene-8,10-diyne (17); Biatractylenolide II (18); Biepiasterolid (19); Biatractylolide (20).

    The anti-tumor activity was mainly manifested by AT-I and AT-II, especially AT-I (Table 2). Anti-tumor activity has been studied primarily in vivo and in vitro. However, there is a lack of research on the anti-tumor activity of atractylenolide in human clinical trials. The concentration of atractylenolide applied on cell lines was < 400 μM, or < 200 mg/kg on tumor-bearing mice.

    Table 2.  Anti-tumor activity of atractylenolides.
    TypesSubstancesModelIndexDoseSignal pathwayResultsRef.
    Human colorectal cancerAT-IIIHCT-116 cell;
    HCT-116 tumor xenografts bearing in nude mice
    Cell viability;
    Cell apoptotic;
    mRNAs and protein
    expressions of Bax, Bcl-2, caspase-9 and caspase-3
    25, 50, 100, 200 μM (for cell);
    50, 100,
    200 mg/kg (for rats)
    Bax/Bcl-2 signaling pathwayPromoting the expression of proapoptotic related gene/proteins; Inhibiting the expression of antiapoptotic related gene/protein; Bax↑; Caspase-3↓; p53↓; Bcl-2↓[55]
    Human gastric carcinomaAT-IIHGC-27 and AGS cell
    Cell viability;
    Morphological changes;
    Flow cytometry;
    Wound healing;
    Cell proliferation, apoptosis, and motility
    50, 100, 200, 400 μMAkt/ERK signaling pathwayCell proliferation, motility↓; Cell apoptosis↑; Bax↑;
    Bcl-2↓; p-Akt↓; p-ERK↓
    [56]
    Mammary
    tumorigenesis
    AT-IIMCF 10A cell;
    Female SD rats (NMU-induced)
    Nrf2 expression and nuclear accumulation;
    Cytoprotective effects;
    Tumor progression;
    mRNA and protein levels of Nrf2;
    Downstream detoxifying enzymes
    20, 50, 100 μM (for cell);
    100 and 200 mg/kg (for rats)
    JNK/ERK-Nrf2-ARE signaling pathway;
    Nrf2-ARE signaling pathway
    Nrf2 expressing↑; Nuclear translocation↑; Downstream detoxifying enzymes↓; 17β-Estradiol↓; Induced malignant transformation[57]
    Human colon adenocarcinomaAT-IHT-29 cellCell viability;
    TUNEL and Annexin V-FITC/PI double stain;
    Detection of initiator and
    executioner caspases level
    10, 20, 40, 80, 100 μMMitochondria-dependent pathwayPro-survival Bcl-2↓; Bax↑; Bak↑; Bad↑; Bim↑; Bid↑; Puma↑[58]
    Sensitize triple-negative
    TNBC cells to paclitaxel
    AT-IMDA-MB-231 cell;
    HS578T cell;
    Balb/c mice (MDA-MB-231 cells-implanted)
    Cell viability
    Transwell migration
    CTGF expression
    25, 50, 100 μM (for cell);
    50 mg/kg (for rats)
    /Expression and secretion of CTGF↓; CAF markers↓; Blocking CTGF expression and fibroblast activation[59]
    Human ovarian cancerAT-IA2780 cellCell cycle;
    Cell apoptosis;
    Cyclin B1 and CDK1 level
    12.5, 25, 50, 100 and 200 μMPI3K/Akt/mTOR
    signaling pathway
    Cyclin B1, CDK1↓; Bax↑;
    Caspase-9↓; Cleaved caspase-3↓; Cytochrome c↑; AIF↑; Bcl-2↓; Phosphorylation level of PI3K, Akt, mTOR↓
    [60]
    Impaired metastatic properties transfer of CSCsAT-ILoVo-CSCs; HT29-CSCsCell migration
    and invasion;
    miR-200c expression;
    Cell apoptosis
    200 μMPI3K/Akt/mTOR signaling pathwaySuppressing miR-200c activity; Disrupting EV uptake by non-CSCs[61]
    Colorectal cancerAT-IHCT116 cell;
    SW480 cell;
    male BALB/c nude mice (HCT116-implanted)
    Cell viability;
    Cell apoptosis;
    Glucose uptake;
    Lactate Production;
    STAT3 expression;
    Immunohistological analysis
    25, 50, 100, 150, 200 μM (for cell);
    50 mg/kg (for rats)
    JAK2/STAT3 signalingCaspase-3↑; PARP-1↓;
    Bax↑; Bcl-2↓; Rate-limiting glycolytic
    enzyme HK2↓; STAT3 phosphorylation↓
    [62]
    Human lung cancerAT-INSCLC cells (A549 and H1299);
    female nude mice (A549-Luc cells- implanted)
    Cell viability;
    Cell cycle;
    Phosphorylation and protein expression of
    ERK1/2, Stat3,
    PDK1, transcription factor SP1;
    mRNA levels of PDK1 gene
    12.5, 25, 50, 100, 150 μM (for cell);
    25 and 75 mg/kg (for rats)
    /ERK1/2↑; Stat3↓; SP1↓;
    PDK1↓
    [63]
    '/' denotes no useful information found in the study.
     | Show Table
    DownLoad: CSV

    AT-III affects human colorectal cancer. AT-II affects human gastric carcinoma and mammary tumorigenesis. AT-I affects human colon adenocarcinoma, human ovarian cancer, metastatic properties transfer of Cancer stem cells (CSCs), colorectal cancer, and human lung cancer, and enhances the sensitivity of triple-negative breast cancer cells to paclitaxel. Current techniques have made it possible to study the effects of atractylenolide on tumors at the signaling pathway level (Table 2). For instance, AT-III significantly inhibited the growth of HCT-116 cells and induced apoptosis by regulating the Bax/Bcl-2 apoptotic signaling pathway. In the HCT116 xenograft mice model, AT-III could inhibit tumor growth and regulate the expression of related proteins or genes. It indicated that AT-III could potentially treat human colorectal cancer[55]. AT-II significantly inhibited the proliferation and motility of HGC-27 and AGS cells and induced apoptosis by regulating the Akt/ERK signaling pathway. It suggested that AT-II can potentially treat gastric cancer[56]. However, in this study, the anti-tumor effects of AT-II in vivo were not examined. AT-II regulated intracellular-related enzyme expression in MCF 10A cells through the JNK/ERK-Nrf2-ARE signaling pathway. AT-II reduced inflammation and oxidative stress in rat mammary tissue through the Nrf2-ARE signaling pathway. AT-II inhibited tumor growth in the N-Nitroso-N-methyl urea (NMU)-induced mammary tumor mice model, indicating that AT-II can potentially prevent breast cancer[57]. AT-I induced apoptosis in HT-29 cells by activating anti-survival Bcl-2 family proteins and participating in a mitochondria-dependent pathway[58]. It indicated that AT-I is a potential drug effective against HT-29 cells. However, the study was only conducted in vitro; additional in vivo experimental data are needed. AT-I can enhance the sensitivity of triple-negative breast cancer (TNBC) cells to paclitaxel. MDA-MB-231 and HS578T cell co-culture systems were constructed, respectively. AT-I was found to impede TNBC cell migration. It also enhanced the sensitivity of TNBC cells to paclitaxel by inhibiting the conversion of fibroblasts into cancer-associated fibroblasts (CAFs) by breast cancer cells. In the MDA-MB-231 xenograft mice model, AT-I was found to enhance the effect of paclitaxel on tumors and inhibit the metastasis of tumors to the lung and liver[59]. AT-I inhibited the growth of A2780 cells through PI3K/Akt/mTOR signaling pathway, promoting apoptosis and blocking the cell cycle at G2/M phase change, suggesting a potential therapeutic agent for ovarian cancer[60]. However, related studies require in vivo validation trials. CSCs are an important factor in tumorigenesis. CSCs isolated from colorectal cancer (CRC) cells can metastasize to non-CSCs via miR-200c encapsulated in extracellular vesicles (EVs).

    In contrast, AT-I could inhibit the activity and transfer of miR-200c. Meanwhile, interfere with the uptake of EVs by non-CSCs. This finding contributes to developing new microRNA-based natural compounds against cancer[61]. AT-I has the function of treating colorectal cancer. HCT116 and SW480 cells were selected for in vitro experiments, and AT-I was found to regulate STAT3 phosphorylation negatively. The HCT116 xenograft mice model was constructed, and AT-I was found to inhibit the growth of HCT116. AT-I induced apoptosis in CRC cells, inhibited glycolysis, and blocked the JAK2/STAT3 signaling pathway, thus exerting anti-tumor activity[62]. The in vitro experiments were performed with A549 and H1299 cell lines. The in vivo experiments were performed to construct the A549-Luc xenograft mice model. The results showed that AT-I inhibited lung cancer cell growth by activating ERK1/2. AT-I inhibited SP1 protein expression and phosphorylation of Stat3, decreasing PDK1 gene expression. The study showed that AT-I could inhibit lung cancer cell growth and targeting PDK1 is a new direction for lung cancer treatment[63]. The research on the anti-tumor of atractylenolide is relatively complete, and there are various signaling pathways related to its anti-tumor activity. Based on the above information, the anti-tumor mechanism of atractylenolide in the past five years was schemed (Fig. 5).

    Figure 5.  Schematic diagram for the anti-tumor mechanism of atractylenolides.

    In recent years, few studies have been conducted on the neuroprotective activity of esters or sesquiterpenoids from AMR. The neuroprotective effects of AT-III have been studied systematically. Biatractylolide has also been considered to have a better neuroprotective effect. New compounds continue to be identified, and their potential neuroprotective effects should be further explored. The related biological activities, animal models, monitoring indicators, and results are summarized in Table 3. Neuroprotective effects include the prevention and treatment of various diseases, such as Parkinson's, Alzheimer's, anti-depressant anxiety, cerebral ischemic injury, neuroinflammation, and hippocampal neuronal damage. In vivo and in vitro will shed light on the potential effect of sesquiterpenoids from AMR and other medicinal plants.

    Table 3.  Neuroprotective effects of esters and sesquiterpenoids.
    ActivitiesSubstancesModelIndexDoseSignal pathwayResultsRef.
    Establish a PD modelAT-II; AT-I;
    Biepiasterolid;
    Isoatractylenolide I;
    AT-III; 3β-acetoxyl atractylenolide I;
    (4E,6E,12E)- tetradeca-4,6,12-triene-8,10-diyne-13,14-triol;
    (3S,4E,6E,12E)-1-acetoxy-tetradeca-4,6,12-triene-8,10-diyne-3,14-diol
    SH-SY5Y cell (MPP+-induced)Cell viability10, 1, 0.1 μM/All compounds have inhibitory activity on MPP+-
    induced SH-SY5Y cell
    [64]
    /4R,5R,8S,9S-diepoxylatractylenolide II;
    8S,9S-epoxyla-tractylenolide II
    BV-2 microglia cells (LPS-induced)Cell viability;
    NO synthase
    inhibitor;
    IL-6 levels
    6.25, 12.5, 25, 50, 100 μMNF-κB signaling
    pathway
    NO inhibition with IC50 values
    of 15.8, and 17.8 μМ, respectively;
    IL-6 ↓
    [65]
    Protecting Alzheimer’s diseaseBiatractylolidePC12 cell (Aβ25-35-induced);
    Healthy male Wistar rats (Aβ25-35-induced)
    Cell viability;
    Morris water maze model;
    TNF-α, IL-6, and IL-1β
    20, 40, 80 μM (for cells);
    0.1, 0.3, 0.9 mg/kg (for rats)
    NF-κB signaling
    pathway
    Reduce apoptosis; Prevent cognitive decline; Reduce the activation of NF-κB signal pathway[66]
    /BiatractylolidePC12 and SH-SY5Y cell (glutamate-induced)Cell viability;
    Cell apoptosis;
    LDA;
    Protein expression
    10, 15, 20 μMPI3K-Akt-GSK3β-Dependent
    Pathways
    GSK3β protein expression ↓;
    p-Akt protein expression ↑
    [67]
    Parkinson's DiseaseAT-IBV-2 cells (LPS-induced);
    Male C57BL6/J mice (MPTP-intoxicated)
    mRNA and protein levels;
    Immunocytochemistry; Immunohistochemistry;
    25, 50, 100 μM (for cells);
    3, 10, 30 mg/kg/mL (for rats)
    /NF-κB ↓; HO-1 ↑; MnSOD ↑; TH-immunoreactive neurons ↑; Microglial activation ↓[68]
    Anti depressant like effectAT-IMale ICR mice (CUMS induced depressive like behaviors)Hippocampal neurotransmitter levels;
    Hippocampal pro inflammatory cytokine levels;
    NLRP3 inflammasome in the hippocampi
    5, 10, 20 mg/kg/Serotonin ↓;
    Norepinephrine ↓; NLRP3 inflammasome ↓; (IL)-1β ↓
    [69]
    Alzheimer's diseaseBiatractylenolide II/AChE inhibitory activities;
    Molecular docking
    //Biatractylenolide II can interact with PAS and CAS of AChE[70]
    Cerebral ischemic injury and
    neuroinflammation
    AT-IIIMale C57BL/6J mice (MCAO- induced);
    Primary microglia (OGDR
    stimulation)
    Brain infarct size;
    Cerebral blood flow;
    Brain edema;
    Neurological deficits;
    Protein expressions of proinflammatory;
    Anti-inflammatory
    cytokines
    0.01, 0.1, 1, 10, 100 μM (for cells);
    0.1–10 mg/kg
    (for rats)
    JAK2/STAT3/Drp1-dependent mitochondrial fissionBrain infarct size ↓;
    Restored CBF;
    ameliorated brain edema; Improved neurological deficits;
    IL-1β ↓; TNF-α ↓; IL-6 ↓;
    Drp1 phosphorylation ↓
    [71]
    Reduces depressive- and anxiogenic-like behaviorsAT-IIIMale SD rats (LPS-induced and CUMS rat model)Forced swimming test;
    Open field test;
    Sucrose preference test;
    Novelty-suppressed feeding test;
    Proinflammatory cytokines levels
    3, 10, 30 mg/kg/30 mg/kg AT-III produced an anxiolytic-like effect; Prevented depressive- and anxiety-like behaviors; Proinflammatory cytokines levels ↓[72]
    Alleviates
    injury in rat
    hippocampal neurons
    AT-IIIMale SD rats (isoflurane-induced)Apoptosis and autophagy in the hippocampal neurons;
    Inflammatory factors;
    Levels of p-PI3K,
    p-Akt, p-mTOR
    1.2, 2.4, 4.8 mg/kgPI3K/Akt/mTOR signaling pathwayTNF-α ↓; IL-1β ↓; IL-6 ↓; p-PI3K ↑; p-Akt ↑; p-mTOR ↑[73]
    ''/' denotes no useful information found in the study.
     | Show Table
    DownLoad: CSV

    Zhang et al. identified eight compounds from AMR, two newly identified, including 3β-acetoxyl atractylenolide I and (3S,4E,6E,12E)-1-acetoxy-tetradecane-4,6,12-triene-8,10-diyne-3,14-diol. 1-Methyl-4-phenylpyridinium (MPP+) could be used to construct a model of Parkinson's disease. A model of MPP+-induced damage in SH-SY5Y cells was constructed. All eight compounds showed inhibitory effects on MPP+-induced damage[64]. Si et al. newly identified eight additional sesquiterpenoids from AMR. A model of LPS-induced BV-2 cell injury was constructed. 4R, 5R, 8S, 9S-diepoxylatractylenolide II and 8S, 9S-epoxylatractylenolide II had significant anti-neuroinflammatory effects. Besides, the anti-inflammatory effect of 4R, 5R, 8S, 9S-diepoxylatractylenolide II might be related to the NF-κB signaling pathway[65]. Biatractylolide has a preventive effect against Alzheimer's disease. In vitro experiments were conducted by constructing an Aβ25-35-induced PC12 cell injury model. In vivo experiments were conducted by constructing an Aβ25-35-induced mice injury model to examine rats' spatial learning and memory abilities. Biatractylolide reduced hippocampal apoptosis, alleviated Aβ25-35-induced neurological injury, and reduced the activation of the NF-κB signaling pathway. Thus, it can potentially treat Aβ-related lesions in the central nervous system[66]. It has also been shown that biatractylolide has neuroprotective effects via the PI3K-Akt-GSK3β-dependent pathway to alleviate glutamate-induced damage in PC12 and SH-SY5Y cells[67]. The attenuating inflammatory effects of AT-I were examined by constructing in vivo and in vitro Parkinson's disease models. Furthermore, AT-I alleviated LPS-induced BV-2 cell injury by reducing the nuclear translocation of NF-κB. AT-I restored 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced behavioral impairment in C57BL6/J mice, protecting dopaminergic neurons[68]. AT-I also has anti-depressant effects. Chronic unpredictable mild stress (CUMS) induced depressive behavior in institute of cancer research (ICR) mice, and AT-I achieved anti-depressant function by inhibiting the activation of NLRP3 inflammatory vesicles, thereby reducing IL-1β content levels[69]. Biatractylenolide II is a newly identified sesquiterpene compound with the potential for treating Alzheimer's disease. The AChE inhibitory activity of biatractylenolide II was measured, and molecular simulations were also performed. It was found to interact with the peripheral anion site and active catalytic site of AChE[70]. AT-III has a broader neuroprotective function. The middle cerebral artery (MCAO) mouse model and oxygen-glucose deprivation-reoxygenation (OGDR) microglia model were constructed. AT-III was found to ameliorate brain edema and neurological deficits in MCAO mice. In addition, AT-III suppressed neuroinflammation and reduced ischemia-related complications through JAK2/ STAT3-dependent mitochondrial fission in microglia[71]. In order to investigate the anti-depressant and anti-anxiolytic effects of AT-III, the LPS-induced depression model and CUMS model were constructed. Combined with the sucrose preference test (SPT), novelty-suppressed feeding test (NSFT), and forced swimming test (FST) to demonstrate that AT-III has anti-depressant and anti-anxiolytic functions by inhibiting hippocampal neuronal inflammation[72]. In addition, AT-III also has the effect of attenuating hippocampal neuronal injury in rats. An isoflurane-induced SD rats injury model was constructed. AT-III alleviated apoptosis, autophagy, and inflammation in hippocampal neurons suggesting that AT-III can play a role in anesthesia-induced neurological injury[73]. However, AT-III attenuates anesthetic-induced neurotoxicity is not known.

    Immunomodulatory and anti-inflammatory activities are studied in vivo and in vitro. The construction of an inflammatory cell model in vitro generally uses RAW 264.7 macrophages. Different cells, such as BV2 microglia, MG6 cells, and IEC-6 cells, can also be used. Active compounds' immune and anti-inflammatory activity is generally examined using LPS-induced cell and mouse models. For enteritis, injury induction is performed using TNBS and DSS. Several studies have shown that AT-III has immunomodulatory and anti-inflammatory activities. Other sesquiterpene compounds also exhibit certain activities. The related biological activities, animal models, monitoring indicators, and results are summarized in Table 4. For example, five new sesquiterpene compounds, atractylmacrols A-E, were isolated from AMR. The anti-inflammatory effect of the compounds was examined with LPS-induced RAW264.7 macrophage damage, and atractylmacrols A-E were found to inhibit NO production[74]. Three compounds, 2-[(2E)-3,7-dimethyl-2,6-octadienyl]-6-methyl-2, 5-cyclohexadiene-1, 4-dione (1); 1-acetoxy-tetradeca-6E,12E-diene-8, 10-diyne-3-ol (2); 1,3-diacetoxy-tetradeca-6E, 12E-diene-8,10-diyne (3) were isolated from AMR. All three compounds could inhibit the transcriptional activity and nuclear translocation of NF-κB. The most active compound was compound 1, which reduced pro-inflammatory cytokines and inhibited MAPK phosphorylation[75]. Twenty-two compounds were identified from AMR. LPS-induced RAW 264.7 macrophages and BV2 cell injury models were constructed, respectively. Among them, three compounds, AT-I, AT-II, and 8-epiasterolid showed significant damage protection in both cell models and inhibited LPS-induced cell injury by inactivating the NF-κB signaling pathway[76]. To construct a TNBS-induced mouse colitis model, AT-III regulated oxidative stress through FPR1 and Nrf2 signaling pathways, alleviated the upregulation of FPR1 and Nrf2 proteins, and reduced the abundance of Lactobacilli in injured mice[77]. AT-III also has anti-inflammatory effects in peripheral organs. A model of LPS-injured MG6 cells was constructed. AT-III alleviated LPS injury by significantly reducing the mRNA expression of TLR4 and inhibiting the p38 MAPK and JNK pathways[78]. It indicated that AT-III has the potential as a therapeutic agent for encephalitis. The neuroprotective and anti-inflammatory effects of AT-III were investigated in a model of LPS-induced BV2 cell injury and a spinal cord injury (SCI) mouse model. AT-III alleviated the injury in SCI rats, promoted the conversion of M1 to M2, and attenuated the activation of microglia/macrophages, probably through NF-κB, JNK MAPK, p38 MAPK, and Akt signaling pathways[79]. AT-III has a protective effect against UC. DSS-induced mouse model and LPS-induced IEC-6 cell injury model were constructed. AT-III alleviated DSS and LPS-induced mitochondrial dysfunction by activating the AMPK/SIRT1/PGC-1α signaling pathway[80].

    Table 4.  Immunomodulatory and anti-inflammatory activities of esters and sesquiterpenoids.
    ActivitiesSubstanceModelIndexDoseSignal pathwayResultRef.
    Against LPS-induced NO productionAtractylmacrols A-ERAW264.7 macrophages (LPS-induced)Isolation;
    Structural identification;
    Inhibition activity of
    NO production
    25 μM/Have effects on LPS-induced NO production[74]
    Anti-inflammatory2-[(2E)-3,7-dimethyl-2,6-octadienyl]-6-methyl-2,5-cyclohexadiene-1,
    4-dione;
    1-acetoxy-tetradeca-6E,12E-diene-8, 10-diyne-3-ol;
    1,3-diacetoxy-tetradeca-6E, 12E-diene-8,
    10-diyne
    RAW 264.7
    macrophages (LPS-induced)
    Level of NO and PGE2;
    Level of iNOS, COX-2;
    Levels of pro-inflammatory cytokines;
    Phosphorylation of MAPK(p38, JNK, and ERK1/2)
    2 and 10 μMNF-κB signaling pathwayIL-1β ↓; IL-6 ↓; TNF-α ↓;
    p38 ↓; JNK ↓; ERK1/2 ↓
    [75]
    Anti-inflammatoryAT-I; AT-II;
    8-epiasterolid
    RAW264.7 macrophages;
    BV2 microglial cells (LPS-
    induced)
    Structure identification;
    NO, PGE2 production;
    Protein expression of iNOS, COX-2, and cytokines
    40 and 80 μMNF-κB signaling pathway.NO ↓; PGE2 ↓; iNOS ↓;
    COX-2 ↓; IL-1β ↓; IL-6 ↓; TNF-α ↓
    [76]
    Intestinal inflammationAT-IIIMale C57BL/6 mice (TNBS-induced)Levels of myeloperoxidase;
    Inflammatory factors;
    Levels of the prooxidant markers, reactive oxygen species, and malondialdehyde;
    Antioxidant-related enzymes;
    Intestinal flora
    5, 10, 20 mg/kgFPR1 and Nrf2 pathwaysDisease activity index score ↓; Myeloperoxidase ↓; Inflammatory factors interleukin-1β ↓; Tumor necrosis factor-α ↓; Antioxidant enzymes catalase ↓; Superoxide dismutase ↓; Glutathione peroxidase ↓; FPR1 and Nrf2 ↑; Lactobacilli ↓[77]
    Anti-inflammatoryAT-IIIMG6 cells (LPS-
    induced)
    mRNA and protein levels of TLR4,
    TNF-α, IL-1β, IL-6, iNOS, COX-2;
    Phosphorylation of p38 MAPK and JNK
    100 μMp38 MAPK and JNK signaling pathwaysTNF-α ↓; IL-1β ↓; IL-6 ↓;
    iNOS ↓; COX-2 ↓
    [78]
    Ameliorates spinal cord injuryAT-IIIBV2 microglial (LPS-
    induced);
    Female SD rats (Infinite Horizon impactor)
    Spinal cord lesion area;
    Myelin integrity;
    Surviving neurons;
    Locomotor function;
    Microglia/macrophages;
    Inflammatory factors
    1, 10, 100 μM (for cell);
    5 mg/kg (for rats)
    NF-κB,
    JNK MAPK, p38 MAPK, and Akt pathways
    Active microglia/macrophages;
    Inflammatory mediators ↓
    [79]
    Ulcerative colitisAT-IIIIEC-6 (LPS-induced);
    C57BL/6J male mice (DSS-induced)
    MDA,GSH content;
    SOD activity;
    Intestinal permeability;
    Mitochondrial membrane potential;
    Complex I and complex IV activity
    40 and 80 μM (for cell);
    5 and 10 mg/kg (for rats)
    AMPK/
    SIRT1/PGC-1α signaling pathway
    Disease activity index ↓;
    p-AMPK ↑; SIRT1 ↑;
    PGC-1α ↑;
    Acetylated PGC-1α ↑
    [80]
    '/' denotes no useful information found in the study.
     | Show Table
    DownLoad: CSV

    The biosynthetic pathways for bioactive compounds of A. macrocephala are shown in Fig. 6. The biosynthetic pathways of all terpenes include the mevalonate (MVA) pathway in the cytosol and the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway in the plastid[81]. The cytosolic MVA pathway is started with the primary metabolite acetyl-CoA and supplies isopentenyl (IPP), and dimethylallyl diphosphate (DMAPP) catalyzed by six enzymatic steps, including acetoacetyl-CoA thiolase (AACT), hydroxymethylglutaryl-CoA synthase (HMGS), hydroxymethylglutaryl-CoA reductase (HMGR), mevalonate kinase (MVK), phosphomevalonate kinase (PMK) and mevalonate 5-phosphate decarboxylase (MVD)[82]. IPP and DMAPP can be reversibly isomerized by isopentenyl diphosphate isomerase (IDI)[83]. In the MEP pathway, D-glyceraldehyde-3-phosphate (GAP) and pyruvate are transformed into IPP and DMAPP over seven enzymatic steps, including 1-deoxy-d-xylulose 5-phosphate synthase (DXS), 1-deoxy-d-xylulose 5-phosphate reductoisomerase (DXR), 2C-methyl-d-erythritol 4-phosphate cytidyltransferase (MECT), 4-(cytidine 5′-diphospho)-2C-methyl-d-erythritol kinase (CMK), 2C-methyl-d-erythritol-2,4-cyclodiphosphate synthase (MECP), 4-hydroxy-3-methylbut-2-enyl diphosphate synthase (HDS) and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR) were involved in the whole process[84]. The common precursor of sesquiterpenes is farnesyl diphosphate (FPP) synthesized from IPP and DMAPP under the catalysis of farnesyl diphosphate synthase (FPPS)[85]. Various sesquiterpene synthases, such as β-farnesene synthase (β-FS), germacrene A synthase (GAS), β-caryophyllene synthase (QHS), convert the universal precursor FPP into more than 300 different sesquiterpene skeletons in different species[8689]. Unfortunately, in A. macrocephala, only the functions of AmFPPS in the sesquiterpenoid biosynthetic pathway have been validated in vitro[90]. Identifying sesquiterpene biosynthesis in A. macrocephala is difficult due to the lack of: isotope-labeled biosynthetic pathways, constructed genetic transformation system, and high-quality genome.

    Figure 6.  Biosynthetic pathways for bioactive compounds of A. macrocephala.

    With the gradual application of transcriptome sequencing technology in the study of some non-model plants, the study of A. macrocephala has entered the stage of advanced genetics and genomics. Yang et al. determined the sesquiterpene content in the volatile oil of AMR by gas chromatography and mass spectrometry (GC-MS) in A. macrocephala. Mixed samples of leaves, stems, rhizomes, and flowers of A. macrocephala were sequenced by Illumina high throughput sequencing technology[91]. Similarly, compounds' relative content in five A. macrocephala tissue was quantitatively detected by ultra-performance liquid chromatography-tandem mass spectrometry. Sesquiterpenoids accumulations in rhizomes and roots were reported[90]. Seventy-three terpenoid skeleton synthetases and 14 transcription factors highly expressed in rhizomes were identified by transcriptome analysis. At the same time, the function of AmFPPS related to the terpenoid synthesis pathway in A. macrocephala was verified in vitro[90]. In addition to the study of the different tissue parts of A. macrocephala, the different origin of A. macrocephala is also worthy of attention. The AMR from different producing areas was sequenced by transcriptome. Seasonal effects in A. macrocephala were also studied. Interestingly, compared with one-year growth AMR, the decrease of terpenes and polyketone metabolites in three-year growth AMR was correlated with the decreased expression of terpene synthesis genes[92]. Infestation of Sclerotium rolfsii sacc (S. rolfsii) is one of the main threats encountered in producing A. macrocephala[93]. To explore the expression changes of A. macrocephala-related genes after chrysanthemum indicum polysaccharide (CIP) induction, especially those related to defense, the samples before and after treatment were sequenced. The expression levels of defense-related genes, such as polyphenol oxidase (PPO) and phenylalanine ammonia-lyase (PAL) genes, were upregulated in A. macrocephala after CIP treatment[94].

    Traditional Chinese Medicine (TCM), specifically herbal medicine, possesses intricate chemical compositions due to both primary and secondary metabolites that exhibit a broad spectrum of properties, such as acidity-base, polarity, molecular mass, and content. The diverse nature of these components poses significant challenges when conducting quality investigations of TCM[95]. Recent advancements in analytical technologies have contributed significantly to the profiling and characterizing of various natural compounds present in TCM and its compound formulae. Novel separation and identification techniques have gained prominence in this regard. The aerial part of A. macrocephala (APA) has been studied for its anti-inflammatory and antioxidant properties. The active constituents have been analyzed using high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS). The results indicated that APA extracts and all sub-fractions contain a rich source of phenolics and flavonoids. The APA extracts and sub-fractions (particularly ACE 10-containing constituents) exhibited significant anti-inflammatory and antioxidant activity[96]. In another study, a four-dimensional separation approach was employed using offline two-dimensional liquid chromatography ion mobility time-of-flight mass spectrometry (2D-LC/IM-TOF-MS) in combination with database-driven computational peak annotation. A total of 251 components were identified or tentatively characterized from A. macrocephala, including 115 sesquiterpenoids, 90 polyacetylenes, 11 flavonoids, nine benzoquinones, 12 coumarins, and 14 other compounds. This methodology significantly improved in identifying minor plant components compared to conventional LC/MS approaches[97]. Activity-guided separation was employed to identify antioxidant response element (ARE)-inducing constituents from the rhizomes of dried A. macrocephala. The combination of centrifugal partition chromatography (CPC) and an ARE luciferase reporter assay performed the separation. The study's results indicate that CPC is a potent tool for bioactivity-guided purification from natural products[98]. In addition, 1H NMR-based metabolic profiling and genetic assessment help identify members of the Atractylodes genus[99]. Moreover, there were many volatile chemical compositions in A. macrocephala. The fatty acyl composition of seeds from A. macrocephala was determined by GC-MS of fatty acid methyl esters and 3-pyridylcarbinol esters[100]. Fifteen compounds were identified in the essential oil extracted from the wild rhizome of Qimen A. macrocephala. The major components identified through gas chromatography-mass spectrometry (GC-MS) analysis were atractylone (39.22%) and β-eudesmol (27.70%). Moreover, gas purge microsolvent extraction (GP-MSE) combined with GC-MS can effectively characterize three species belonging to the Atractylodes family (A. macrocephala, A. japonica, and A. lancea)[101].

    So far, the research on A. macrocephala has focused on pharmacological aspects, with less scientific attention to biogeography, PAO-ZHI processing, biosynthesis pathways for bioactive compounds, and technology application. The different origins lead to specific differences in appearance, volatile oil content, volatile oil composition, and relative percentage content of A. macrocephala. However, A. macrocephala resources lack a systematic monitoring system regarding origin traceability and quality control, and there is no standardized process for origin differentiation. Besides, the PAO-ZHI processing of A. macrocephala is designed to reduce toxicity and increase effectiveness. The active components will have different changes before and after processing. But current research has not been able to decipher the mechanism by which the processing produces its effects. Adaptation of in vivo and in vitro can facilitate understanding the biological activity. The choice of the models and doses is particularly important. The recent studies that identified AMR bioactivities provided new evidence but are somewhat scattered. For example, in different studies, the same biological activity corresponds to different signaling pathways, but the relationship between the signaling pathways has not been determined. Therefore, a more systematic study of the various activities of AMR is one of the directions for future pharmacological activity research of A. macrocephala. In addition, whether there are synergistic effects among the active components in AMR also deserves further study, but they are also more exhaustive. As for the biosynthesis of bioactive compounds in A. macrocephala, the lack of isotopic markers, mature genetic transformation systems, and high-quality genomic prediction of biosynthetic pathways challenge the progress in sesquiterpene characterization. In recent years, the transcriptomes of different types of A. macrocephala have provided a theoretical basis and research foundation for further exploration of functional genes and molecular regulatory mechanisms but still lack systematicity. Ulteriorly, applying new technologies will gradually unlock the mystery of A. macrocephala.

    This work was supported by the Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties (2021C02074), National Young Qihuang Scholars Training Program, National 'Ten-thousand Talents Program' for Leading Talents of Science and Technology Innovation in China, National Natural Science Foundation of China (81522049), Zhejiang Provincial Program for the Cultivation of High level Innovative Health Talents, Zhejiang Provincial Ten Thousands Program for Leading Talents of Science and Technology Innovation (2018R52050), Research Projects of Zhejiang Chinese Medical University (2021JKZDZC06, 2022JKZKTS18). We appreciate the great help/technical support/experimental support from the Public Platform of Pharmaceutical/Medical Research Center, Academy of Chinese Medical Science, Zhejiang Chinese Medical University.

  • The authors declare that they have no conflict of interest.

  • Supplemental Table S1 Summary of Sijihua and Beihua RNA-seq reads mapping.
    Supplemental Table S2 Summary of whole genome resequencing mapping.
    Supplemental Table S3 Classification of SNPs based on genome location.
    Supplemental Table S4 Number of genes affected by SNP mutation.
    Supplemental Table S5 Summary of Sijihua and Beihua BS-seq reads mapping.
    Supplemental Table S6 Mining and analyzing flavonoid pathway genes of honeysuckle.
    Supplemental Fig. S1 The significance analysis between Beihua WB and GF, as well as Sijihua WB and GF.
    Supplemental Fig. S2 DNA methylation landscape of Beihua and Sijihua.
    Supplemental Fig. S3 The correlation between SNPs and DNA methylation.
    Supplemental Fig. S4 DNA methylation patterns of protein-coding genes and TEs in Sijihua and Beihua in the WB.
    Supplemental Fig. S5 DNA methylation patterns of protein-coding genes and TEs in Sijihua and Beihua in the GF.
    Supplemental Fig. S6 SNP lead to changes in the type and level of DNA methylation and characterization of the diversity of DMC sites.
    Supplemental Fig. S7 CG≥TG-type mutations in relation to CG methylation and gene expression during the GF stage.
    Supplemental Fig. S8 GO enrichment analysis of CG≥TG-related genes.
    Supplemental Fig. S9 The pattern of differentially expressed genes related to SNP-associated DMCs.
    Supplemental Fig. S10 The comparison of DNA methylation levels of 35 key genes of the flavonoid pathway in two stages of Sijihua and Beihua.
    Supplemental Fig. S11 Heatmaps showing the expression levels of 35 key genes, the number of SNPs in the gene body of the genes, the number of SNP-associated DMCs, and the number of non-SNP-associated DMCs in Beihua and Sijihua during the WB and GF stages (* represents genes that showed significant differences in both stages).
    Supplemental Fig. S12 Genome browser showing DNA methylation and expression levels of LjFLS (EVM0027194) and representative screenshots of two types of DMCs in the promoter region (Chr01:50581502-50581602).
  • [1]

    Saito K, Yonekura-Sakakibara K, Nakabayashi R, Higashi Y, Yamazaki M, et al. 2013. The flavonoid biosynthetic pathway in Arabidopsis: structural and genetic diversity. Plant Physiology and Biochemmistry 72:21−34

    doi: 10.1016/j.plaphy.2013.02.001

    CrossRef   Google Scholar

    [2]

    Andersen JR, Zein I, Wenzel G, Darnhofer B, Eder J, et al. 2008. Characterization of phenylpropanoid pathway genes within European maize (Zea mays L.) inbreds. BMC Plant Biology 8:2

    doi: 10.1186/1471-2229-8-2

    CrossRef   Google Scholar

    [3]

    Hoang VL, Innes DJ, Shaw PN, Monteith GR, Gidley MJ, et al. 2015. Sequence diversity and differential expression of major phenylpropanoid-flavonoid biosynthetic genes among three mango varieties. BMC Genomics 16:561

    doi: 10.1186/s12864-015-1784-x

    CrossRef   Google Scholar

    [4]

    Kato M, Miura A, Bender J, Jacobsen SE, Kakutani T. 2003. Role of CG and non-CG methylation in immobilization of transposons in Arabidopsis. Current Biology 13:421−26

    doi: 10.1016/S0960-9822(03)00106-4

    CrossRef   Google Scholar

    [5]

    Adato A, Mandel T, Mintz-Oron S, Venger I, Levy D, et al. 2009. Fruit-surface flavonoid accumulation in tomato is controlled by a SlMYB12-regulated transcriptional network. PLoS Genetics 5:e1000777

    doi: 10.1371/journal.pgen.1000777

    CrossRef   Google Scholar

    [6]

    Huang J, Zhang C, Zhao X, Fei Z, Wan K, et al. 2016. The Jujube Genome Provides Insights into Genome Evolution and the Domestication of Sweetness/Acidity Taste in Fruit Trees. PLoS Genetics 12:e1006433

    doi: 10.1371/journal.pgen.1006433

    CrossRef   Google Scholar

    [7]

    Huang X, Kurata N, Wei X, Wang ZX, Wang A, et al. 2012. A map of rice genome variation reveals the origin of cultivated rice. Nature 490:497−501

    doi: 10.1038/nature11532

    CrossRef   Google Scholar

    [8]

    Hufford MB, Xu X, van Heerwaarden J, Pyhäjärvi T, Chia JM, et al. 2012. Comparative population genomics of maize domestication and improvement. Nature Genetics 44:808−11

    doi: 10.1038/ng.2309

    CrossRef   Google Scholar

    [9]

    Lei Y, Yang L, Duan S, Ning S, Li D, et al. 2022. Whole-genome resequencing reveals the origin of tea in Lincang. Frontiers in Plant Science 13:984422

    doi: 10.3389/fpls.2022.984422

    CrossRef   Google Scholar

    [10]

    Ren G, Zhang X, Li Y, Ridout K, Serrano-Serrano ML, et al. 2021. Large-scale whole-genome resequencing unravels the domestication history of Cannabis sativa. Science Advances 7:eabg2286

    doi: 10.1126/sciadv.abg2286

    CrossRef   Google Scholar

    [11]

    Zhao H, Sun S, Ding Y, Wang Y, Yue X, et al. 2021. Analysis of 427 genomes reveals moso bamboo population structure and genetic basis of property traits. Nature Communications 12:5466

    doi: 10.1038/s41467-021-25795-x

    CrossRef   Google Scholar

    [12]

    Kaeppler SM, Kaeppler HF, Rhee Y. 2000. Epigenetic aspects of somaclonal variation in plants. Plant Molecular Biology 43:179−88

    doi: 10.1023/A:1006423110134

    CrossRef   Google Scholar

    [13]

    Iwasaki M, Paszkowski J. 2014. Epigenetic memory in plants. The EMBO Journal 33:1987−98

    doi: 10.15252/embj.201488883

    CrossRef   Google Scholar

    [14]

    Jones MJ, Goodman SJ, Kobor MS. 2015. DNA methylation and healthy human aging. Aging Cell 14:924−32

    doi: 10.1111/acel.12349

    CrossRef   Google Scholar

    [15]

    Kulis M, Esteller M. 2010. DNA methylation and cancer. Advances in Genetics 70:27−56

    doi: 10.1016/B978-0-12-380866-0.60002-2

    CrossRef   Google Scholar

    [16]

    Weber M, Schübeler D. 2007. Genomic patterns of DNA methylation: targets and function of an epigenetic mark. Current Opinion in Cell Biology 19:273−80

    doi: 10.1016/j.ceb.2007.04.011

    CrossRef   Google Scholar

    [17]

    Lin L, Wang S, Zhang J, Song X, Zhang D, et al. 2022. Integrative analysis of transcriptome and metabolome reveals the effect of DNA methylation of chalcone isomerase gene in promoter region on Lithocarpus polystachyus Rehd flavonoids. Synthetic and Systems Biotechnology 7:928−40

    doi: 10.1016/j.synbio.2022.05.003

    CrossRef   Google Scholar

    [18]

    Strygina K, Khlestkina E. 2022. Flavonoid biosynthesis genes in Triticum aestivum L.: methylation patterns in cis-regulatory regions of the duplicated CHI and F3H genes. Biomolecules 12:689

    doi: 10.3390/biom12050689

    CrossRef   Google Scholar

    [19]

    Jia H, Jia H, Lu S, Zhang Z, Su Z, et al. 2022. DNA and histone methylation regulates different types of fruit ripening by transcriptome and proteome analyses. Journal of Agricultural and Food Chemistry 70:3541−56

    doi: 10.1021/acs.jafc.1c06391

    CrossRef   Google Scholar

    [20]

    An YQC, Goettel W, Han Q, Bartels A, Liu Z, et al. 2017. Dynamic changes of genome-wide DNA methylation during soybean seed development. Scientific Reports 7:12263

    doi: 10.1038/s41598-017-12510-4

    CrossRef   Google Scholar

    [21]

    Huang H, Liu R, Niu Q, Tang K, Zhang B, et al. 2019. Global increase in DNA methylation during orange fruit development and ripening. Proceedings of the National Academy of Sciences of the United States of America 116:1430−36

    doi: 10.1073/pnas.1815441116

    CrossRef   Google Scholar

    [22]

    Povilus RA, Friedman WE. 2022. Transcriptomes across fertilization and seed development in the water lily Nymphaea thermarum (Nymphaeales): evidence for epigenetic patterning during reproduction. Plant Reproduction 35:161−78

    doi: 10.1007/s00497-022-00438-3

    CrossRef   Google Scholar

    [23]

    Ossowski S, Schneeberger K, Lucas-Lledó JI, Warthmann N, Clark RM, et al. 2010. The rate and molecular spectrum of spontaneous mutations in Arabidopsis thaliana. Science 327:92−94

    doi: 10.1126/science.1180677

    CrossRef   Google Scholar

    [24]

    Kiefer C, Willing EM, Jiao WB, Sun H, Piednoël M, et al. 2019. Interspecies association mapping links reduced CG to TG substitution rates to the loss of gene-body methylation. Nature Plants 5:846−55

    doi: 10.1038/s41477-019-0486-9

    CrossRef   Google Scholar

    [25]

    Xue Y, Shi Y, Qi Y, Yu H, Zou C, et al. 2022. Epigenetic and Genetic Contribution for Expression Bias of Homologous Alleles in Polyploid Sugarcane. Agronomy 12:2852

    doi: 10.3390/agronomy12112852

    CrossRef   Google Scholar

    [26]

    Zhong Z, Feng S, Mansfeld BN, Ke Y, Qi W, et al. 2023. Haplotype-resolved DNA methylome of African cassava genome. Plant Biotech nology Journal 21:247−49

    doi: 10.1111/pbi.13955

    CrossRef   Google Scholar

    [27]

    Becker C, Hagmann J, Müller J, Koenig D, Stegle O, et al. 2011. Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature 480:245−49

    doi: 10.1038/nature10555

    CrossRef   Google Scholar

    [28]

    He L, Xu X, Li Y, Li C, Zhu Y, et al. 2013. Transcriptome analysis of buds and leaves using 454 pyrosequencing to discover genes associated with the biosynthesis of active ingredients in Lonicera japonica Thunb. PLoS One 8:e62922

    doi: 10.1371/journal.pone.0062922

    CrossRef   Google Scholar

    [29]

    Liu T, Yang J, Liu S, Zhao Y, Zhou J, et al. 2020. Regulation of chlorogenic acid, flavonoid, and iridoid biosynthesis by histone H3K4 and H3K9 methylation in Lonicera japonica. Molecular Biology Reports 47:9301−11

    doi: 10.1007/s11033-020-05990-7

    CrossRef   Google Scholar

    [30]

    Huang W, Xiong L, Zhang L, Zhang F, Han X, et al. 2022. Study on content variation of flavonoids in different germplasm during development of Lonicerae Japonicae Flos. Chinese Traditional and Herbal Drugs 53:3156−64

    doi: 10.7501/j.issn.0253-2670.2022.10.026

    CrossRef   Google Scholar

    [31]

    Yu H, Cui N, Guo K, Xu W, Wang H. 2023. Epigenetic changes in the regulation of carotenoid metabolism during honeysuckle flower development. Horticultural Plant Journal 9:577−88

    doi: 10.1016/j.hpj.2022.11.003

    CrossRef   Google Scholar

    [32]

    Yu H, Guo K, Lai K, Shah MA, Xu Z, et al. 2022. Chromosome-scale genome assembly of an important medicinal plant honeysuckle. Scientific Data 9:226

    doi: 10.1038/s41597-022-01385-4

    CrossRef   Google Scholar

    [33]

    Xanthopoulou A, Manioudaki M, Bazakos C, Kissoudis C, Farsakoglou AM, et al. 2020. Whole genome re-sequencing of sweet cherry (Prunus avium L.) yields insights into genomic diversity of a fruit species. Horticulture Research 7:60

    doi: 10.1038/s41438-020-0281-9

    CrossRef   Google Scholar

    [34]

    Xu Q, Wu L, Luo Z, Zhang M, Lai J, et al. 2022. DNA demethylation affects imprinted gene expression in maize endosperm. Genome Biology 23:77

    doi: 10.1186/s13059-022-02641-x

    CrossRef   Google Scholar

    [35]

    Wang ZH, Zhang D, Bai Y, Zhang YH, Liu Y, et al. 2013. Genomewide variation in an introgression line of rice-Zizania revealed by whole-genome re-sequencing. PLoS One 8:e74479

    doi: 10.1371/journal.pone.0074479

    CrossRef   Google Scholar

    [36]

    Wang H, Beyene G, Zhai J, Feng S, Fahlgren N, et al. 2015. CG gene body DNA methylation changes and evolution of duplicated genes in cassava. Proceedings of the National Academy of Sciences of the United States of America 112:13729−34

    doi: 10.1073/pnas.1519067112

    CrossRef   Google Scholar

    [37]

    Gent JI, Ellis NA, Guo L, Harkess AE, Yao Y, et al. 2013. CHH islands: de novo DNA methylation in near-gene chromatin regulation in maize. Genome Research 23:628−37

    doi: 10.1101/gr.146985.112

    CrossRef   Google Scholar

    [38]

    Schmitz RJ, He Y, Valdés-López O, Khan SM, Joshi T, et al. 2013. Epigenome-wide inheritance of cytosine methylation variants in a recombinant inbred population. Genome Research 23:1663−74

    doi: 10.1101/gr.152538.112

    CrossRef   Google Scholar

    [39]

    Schmitz RJ, Schultz MD, Lewsey MG, O'Malley RC, Urich MA, et al. 2011. Transgenerational epigenetic instability is a source of novel methylation variants. Science 334:369−73

    doi: 10.1126/science.1212959

    CrossRef   Google Scholar

    [40]

    Selvaraj S, Krishnaswamy S, Devashya V, Sethuraman S, Krishnan UM. 2014. Flavonoid–metal ion complexes: a novel class of therapeutic agents. Medicinal Research Reviews 34:677−702

    doi: 10.1002/med.21301

    CrossRef   Google Scholar

    [41]

    Zhong S, Fei Z, Chen YR, Zheng Y, Huang M, et al. 2013. Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening. Nature Biotechnology 31:154−59

    doi: 10.1038/nbt.2462

    CrossRef   Google Scholar

    [42]

    Zheng X, Wang T, Cheng T, Zhao L, Zheng X, et al. 2022. Genomic variation reveals demographic history and biological adaptation of the ancient relictual, lotus (Nelumbo Adans). Horticulture Research 9:uhac029

    doi: 10.1093/hr/uhac029

    CrossRef   Google Scholar

    [43]

    Zilberman D, Coleman-Derr D, Ballinger T, Henikoff S. 2008. Histone H2A.Z and DNA methylation are mutually antagonistic chromatin marks. Nature 456:125−29

    doi: 10.1038/nature07324

    CrossRef   Google Scholar

    [44]

    Bewick AJ, Ji L, Niederhuth CE, Willing EM, Hofmeister BT, et al. 2016. On the origin and evolutionary consequences of gene body DNA methylation. Proceedings of the National Academy of Sciences of the United States of America 113:9111−16

    doi: 10.1073/pnas.1604666113

    CrossRef   Google Scholar

    [45]

    Kim KD, El Baidouri M, Abernathy B, Iwata-Otsubo A, Chavarro C, et al. 2015. A Comparative Epigenomic Analysis of Polyploidy-Derived Genes in Soybean and Common Bean. Plant Physiology 168:1433−47

    doi: 10.1104/pp.15.00408

    CrossRef   Google Scholar

    [46]

    Wang ZL, Wang S, Kuang Y, Hu ZM, Qiao X, Ye M. 2018. A comprehensive review on phytochemistry, pharmacology, and flavonoid biosynthesis of Scutellaria baicalensis. Pharmaceutical Biology 56:465−84

    doi: 10.1080/13880209.2018.1492620

    CrossRef   Google Scholar

    [47]

    Ji H, Shin Y, Lee C, Oh H, Yoon IS, et al. 2021. Genomic Variation in Korean japonica Rice Varieties. Genes 12:1749

    doi: 10.3390/genes12111749

    CrossRef   Google Scholar

    [48]

    Li R, Maioli A, Lanteri S, Moglia A, Bai Y, et al. 2023. Genomic analysis highlights putative defective susceptibility genes in tomato germplasm. Plants 12:2289

    doi: 10.3390/plants12122289

    CrossRef   Google Scholar

    [49]

    Skarzyńska A, Pawełkowicz M, Pląder W. 2021. Influence of transgenesis on genome variability in cucumber lines with a thaumatin II gene. Physiology and Molecular Biology of Plants 27:985−96

    doi: 10.1007/s12298-021-00990-8

    CrossRef   Google Scholar

    [50]

    Cui Y, Ge Q, Zhao P, Chen W, Sang X, et al. 2021. Rapid mining of candidate genes for verticillium wilt resistance in cotton based on BSA-Seq analysis. Frontiers in Plant Science 12:703011

    doi: 10.3389/fpls.2021.703011

    CrossRef   Google Scholar

    [51]

    Mas-Gómez J, Cantín CM, Moreno MÁ, Martínez-García PJ. 2022. Genetic diversity and genome-wide association study of morphological and quality traits in peach using two Spanish peach germplasm collections. Frontiers in Plant Science 13:854770

    doi: 10.3389/fpls.2022.854770

    CrossRef   Google Scholar

    [52]

    Eichten SR, Stuart T, Srivastava A, Lister R, Borevitz JO. 2016. DNA methylation profiles of diverse Brachypodium distachyon align with underlying genetic diversity. Genome Research 26:1520−31

    doi: 10.1101/gr.205468.116

    CrossRef   Google Scholar

    [53]

    Hu W, Ji C, Shi H, Liang Z, Ding Z, et al. 2021. Allele-defined genome reveals biallelic differentiation during cassava evolution. Molecular Plant 14:851−54

    doi: 10.1016/j.molp.2021.04.009

    CrossRef   Google Scholar

    [54]

    Yin YC, Zhang XD, Gao ZQ, Hu T, Liu Y. 2019. The research progress of chalcone isomerase (CHI) in plants. Molecular Biotechnology 61:32−52

    doi: 10.1007/s12033-018-0130-3

    CrossRef   Google Scholar

    [55]

    Jiang W, Yin Q, Wu R, Zheng G, Liu J, et al. 2015. Role of a chalcone isomerase-like protein in flavonoid biosynthesis in Arabidopsis thaliana. Journal of Experimental Botany 66:7165−79

    doi: 10.1093/jxb/erv413

    CrossRef   Google Scholar

    [56]

    Wang M, Zhang Y, Zhu C, Yao X, Zheng Z, et al. 2021. EkFLS overexpression promotes flavonoid accumulation and abiotic stress tolerance in plant. Plant Physiology 172:1966−82

    doi: 10.1111/ppl.13407

    CrossRef   Google Scholar

    [57]

    Jia D, Li Z, Dang Q, Shang L, Shen J, et al. 2020. Anthocyanin biosynthesis and methylation of the MdMYB10 promoter are associated with the red blushed-skin mutant in the red striped-skin "Changfu 2" apple. Journal of Agricultural and Food Chemistry 68:4292−304

    doi: 10.1021/acs.jafc.9b07098

    CrossRef   Google Scholar

    [58]

    Muir SR, Collins GJ, Robinson S, Hughes S, Bovy A, et al. 2001. Overexpression of petunia chalcone isomerase in tomato results in fruit containing increased levels of flavonols. Nature Biotechnology 19:470−74

    doi: 10.1038/88150

    CrossRef   Google Scholar

    [59]

    Yuan Y, Zuo J, Zhang H, Li R, Yu M, et al. 2022. Integration of Transcriptome and Metabolome Provides New Insights to Flavonoids Biosynthesis in Dendrobium huoshanense. Frontiers in Plant Science 13:850090

    doi: 10.3389/fpls.2022.850090

    CrossRef   Google Scholar

    [60]

    Schilbert HM, Schöne M, Baier T, Busche M, Viehöver P, et al. 2021. Characterization of the Brassica napus Flavonol Synthase Gene Family Reveals Bifunctional Flavonol Synthases. Frontiers in Plant Science 12:733762

    doi: 10.3389/fpls.2021.733762

    CrossRef   Google Scholar

  • Cite this article

    Yu X, Yu H, Lu Y, Zhang C, Wang H. 2024. Genetic and epigenetic variations underlying flavonoid divergence in Beihua and Sijihua honeysuckles. Epigenetics Insights 17: e002 doi: 10.48130/epi-0024-0002
    Yu X, Yu H, Lu Y, Zhang C, Wang H. 2024. Genetic and epigenetic variations underlying flavonoid divergence in Beihua and Sijihua honeysuckles. Epigenetics Insights 17: e002 doi: 10.48130/epi-0024-0002

Figures(5)

Article Metrics

Article views(1158) PDF downloads(206)

Other Articles By Authors

ARTICLE   Open Access    

Genetic and epigenetic variations underlying flavonoid divergence in Beihua and Sijihua honeysuckles

Epigenetics Insights  17 Article number: e002  (2024)  |  Cite this article

Abstract: Flavonoids are important antibacterial and antiviral active substances which are the most crucial medicinal components of honeysuckle. However, the content of medicinally active substances in different honeysuckle cultivars is significantly different. Genetic variations and epigenetics play essential roles in plant evolution and trait improvement. Here, we performed multi-omics sequencing of two honeysuckle cultivars (Beihua and Sijihua) at different stages (WB and GF). The results revealed 9,909,981 SNPs in the genomes of the two cultivars, and 12,688 high-impact SNPs were found to regulate genes involved in important biological pathways, such as plant stress resistance. Furthermore, it was found that the majority of differentially methylated cytosines (DMCs, 81%) between Beihua and Sijihua were associated with SNPs. SNP-related DMCs were associated with 76% of the genes, among which 3,325 DEGs (e.g., LjPAL, LjCHI, and LjFLS) were significantly enriched in the flavonoid biosynthesis pathway. The presence of a large number of SNP-related DMCs in the flanking and gene regions of these genes may have led to the overexpression of the genes in Beihua, which increased the accumulation of flavonoids in Beihua. In summary, the present study provides theoretical and technical support for improving the genetic and epigenetic traits of honeysuckle.

    • Genetic variations and epigenetics play important roles in regulating flavonoid accumulation in plants. Genetic variation refers to DNA sequence differences present in the genomes of different individuals, impacting coding sequences, promoter regions, and regulatory elements. These differences can directly or indirectly influence gene expression levels and regulatory patterns, thereby impacting the plant's phenotype. The most common genetic variation is single nucleotide polymorphism (SNP), resulting from changes in a single nucleotide in the DNA sequence. They can be classified as deletion, insertion, or substitution mutations. Research has indicated that SNPs in coding regions can alter amino acid sequences, impacting protein function. Numerous studies on the diversity of gene sequences related to flavonoid biosynthesis have been conducted in plants such as Arabidopsis thaliana[1], maize[2], mango[3], barley[4], and tomato[5]. The results revealed a significant correlation between SNP content in genes associated with flavonoid biosynthesis and the accumulation of flavonoid compounds. Furthermore, genetic variations lead to phenotypic differences among cultivars of important crops like jujube[6], camellia[7], hemp[8], bamboo[9], rice[10], and maize[11]. Plant phenotypic changes are influenced not only by genetic variations but also by epigenetic factors[12]. Epigenetics refers to heritable changes in gene expression patterns without altering the DNA sequence, encompassing DNA methylation, histone modifications, and chromatin remodeling[13]. DNA methylation is a conserved epigenetic marker across eukaryotes, and it is involved in many important biological processes, such as human aging[14], cancer development[15], genome integrity[16], gene imprinting, and plant stress responses. Studies have shown that DNA methylation impacts the accumulation of flavonoid compounds in plants. For instance, in Lithocarpus polystachyus Rehd[17], Triticum aestivum L.[18] and tomatoes[19], DNA methylation occurring in promoter and coding regions regulates gene expression associated with flavonoid biosynthesis, impacting flavonoid compound accumulation. DNA methylation plays a pivotal role in crucial biological processes in seeds[20], embryos[21], and flowers[22]. Consequently, both genetic and epigenetic variations significantly contribute to flavonoid compound accumulation in plants.

      However, sequence variations and epigenetic modifications are not independent phenomena, they interact intricately and culminate in phenotypic transformation. Methylated cytosines are more prone to induce a higher mutation rate at the sequence level, leading to changes in methylation levels and consequently influencing gene expression. Previous studies have shown that in Arabidopsis[23], lotus[24], and sugarcane[25], methylated cytosines are more susceptible to mutation compared to unmethylated cytosines, resulting in CG=>TG type variations and causing changes in methylation that impact plant gene expression. However, SNPs can also lead to changes in the sequence type or methylation levels of DNA. When cytosine is mutated to other types of bases (A, T, and G), the methylation pattern or level can also change, resulting in a difference in methylation levels at these sites[26]. Similarly, when other bases (A, T, and G) mutate to cytosine, the methylation type and methylation level were acquired, resulting in differences between the two sites. In addition, non-cytosine mutations also result in changes in methylation patterns and methylation levels, for example, CHG=>CHH and CHH=>CG. Studies in Arabidopsis[27] and cassava[26] have revealed that SNPs can change both methylation type and level. This indicated a close relationship between genetic variations and epigenetic during plant growth and development. However, current research on the co-regulatory mechanisms of genetic and epigenetic variations in plant phenotypes remains relatively limited.

      Honeysuckle (Lonicera japonica Thunb.), belongs to the genus Lonicera of the family Caprifoliaceae is named for its flower development process (Fig. 1a), in which the color changes from silver-white to golden-yellow. Honeysuckle is rich in medicinally active compounds, including luteoloside, chlorogenic acid, flavonoids, and sesquiterpenes. Pharmacopeia records indicate that the flowering stage, known as the white bud (WB) stage, has the highest content of medicinally active compounds[28]. Among these, flavonoids play a crucial role in antiviral activity[29] and have been used to treat various viruses. Sijihua and Beihua No. 1 (Beihua) are the two most common honeysuckle cultivars in China and show significant phenotypic differences. Sijihua has a short WB stage, lasting only 2−3 d, whereas Beihua can have a WB stage lasting up to 20 d, allowing for a longer storage period of high-content medicinal active compounds. Additionally, the flowers exhibit conspicuous color variations at distinct developmental stages. Research has demonstrated that Beihua consistently displays higher levels of total flavonoids and other essential medicinal compounds compared to Sijihua[30]. A recent study revealed that DNA methylation in Sijihua affects the expression of carotenoid-related genes, leading to variations in flower color during different stages of development[31]. Honeysuckle, a crucial antiviral medicinal plant source, exhibits significant phenotypic differences among cultivars, including variations in metabolite accumulation and other traits. Growth and development, particularly flowering, are regulated by DNA methylation. This makes the honeysuckle an ideal material for studying the co-regulatory mechanisms of genetic variations and epigenetics in flavonoid compound synthesis.

      The release of the honeysuckle genome has laid the foundation for studying the genomic structure of different cultivars[32]. Whole-genome resequencing technology to sequence honeysuckle will provide a comprehensive and high-resolution view of genetic variations among different cultivars. We aimed to elucidate the molecular mechanisms underlying phenotypic differences and accumulation of active compounds in different honeysuckle cultivars from the perspectives of genetic and epigenetic variations. In this study, multi-omics approaches were employed, including whole-genome resequencing, whole-genome bisulfite sequencing, and transcriptome sequencing. This comprehensive investigation focused on Beihua No. 1 (Beihua) and Sijihua during two key stages (WB and GF), characterized by differences in phenotypes and metabolite accumulation. Using whole-genome resequencing, a large number of genetic variations were identified a pseudo-reference genome was constructed for Beihua, single-base resolution DNA methylation profiles were compared between Beihua and Sijihua at different flowering stages, the characteristics of DNA methylation changes examined in different contexts. The relationship between SNPs and differentially methylated cytosines (DMCs) in honeysuckle genomes during two developmental stages were explored. Furthermore, the impact of SNPs on key enzyme-encoding genes involved in the flavonoid biosynthesis pathwaywere analyzed, thereby revealing crucial factors contributing to the differences in flavonoid biosynthesis and accumulation between the two cultivars. This study reveals the molecular mechanisms by which genetic variations and epigenetic co-regulation to phenotypic differences and variations in metabolite accumulation in the two honeysuckle cultivars. This study provides new insights into honeysuckle breeding and cultivation techniques and serves as a reference for exploring regulatory mechanisms of growth and development through the application of genetic variations and epigenetics in other species.

    • In this study, flower tissues in different stages of honeysuckle cultivar 'Beihua No.1' (abbreviated as 'Beihua' throughout the entire article) were collected, including juvenile bud (JB), green bud (GB), white bud (WB), silver flower (SF), and golden flower (GF), from Zhongke Honeysuckle Planting Cooperative in Pingyi County, Shandong, China (35°31'02'' N, 117°36'55'' E). Whole-genome bisulfite sequencing (WGBS) and transcriptome sequencing were performed using flower tissues from two stages of the honeysuckle cultivar Beihua, namely, the WB and GF stages. Young leaf tissue from Beihua was used for the resequencing.

      Three biological WGBS libraries were sequenced using the Hiseq X10 sequencer (Illumina, San Diego, CA, USA) as paired-end 150-bp reads. Three biological libraries were generated using the VHTS Universal V6 RNA-seq Library Prep Kit following the manufacturer's instructions (Illumina). All libraries were sequenced using the Novaseq 6000 platform (Illumina, San Diego, CA, USA), and 150 bp paired-end reads were generated.

      The reference genome for the cultivar 'Sijihua' of honeysuckle was obtained from the NCBI database under accession number PRJNA794868. Additionally, transcriptome data for the bud stage (WB: SRX14408207 − SRX14408209) and the flowering stage (GF: SRX144082013 − SRX144082015) were downloaded from the same database. Whole-genome bisulfite sequencing (WGBS) data for the WB stage (SRR18684863 − SRR18684865) and GF stage (SRR18684866 − SRR18684868) were also retrieved from NCBI. It is worth noting that the collection location and environmental conditions for the cultivation of the Sijihua cultivar of honeysuckle are identical to those of Beihua (Honeysuckle Planting Cooperative in Pingyi County, Shandong, China (35°31'02'' N, 117°36'55'' E)[31].

    • For resequencing library construction, DNA isolation was fragmented using Bioruptor (ThermoFisher Scientific, Waltham, MA, USA), resulting in libraries with approximately 300 bp fragment sizes. Quality control of the libraries was performed using the Qubit dsDNA HS Assay Kit (ThermoFisher Scientific) and Agilent 2100 Bioanalyzer System (Agilent Technologies, Santa Clara, CA, USA). High-quality DNA libraries were then sequenced on the BGISEQ-500 platform, generating reads of 150 bp in length.

      WGBS libraries were constructed and prepared using the TruSeq DNA L T kit (Illumina) as described previously. Three biological WGBS libraries were sequenced on Illumina Hiseq X10 sequencers as paired-end 150-bp reads.

      Total RNA was extracted from the honeysuckle cultivar 'Beihua' petals at the WB and GF stages using the cetyltrimethylammonium bromide (CTAB) method. Three biological libraries were prepared using the VHTS Universal V6 RNA-seq Library Prep Kit following the manufacturer's instructions from Illumina. Subsequently, all libraries were sequenced using the Novaseq 6000 platform from Illumina, located in San Diego, CA, USA, producing 150 bp paired-end reads.

    • We used FASQC (www.bioinformatics.babraham.ac.uk/projects/fastqc) for quality control of the generated FASTQ files, after which low-quality reads were filtered out, including those with more than 10% N content and more than 50% of low-quality bases (< 10). The net data obtained after filtering were compared to the Sijihua reference genome using BWA software (v0.7.12). Duplicates generated by PCR amplification were labeled and removed using the Picard package (https://sourceforge.net/projects/picard/). The 'HaplotypeCaller' function of GATK4 (version 4.1.4.1, https://hub.docker.com/r/broadinstitute/gatk/) was employed to generate GVCF files. Raw variant calling sets underwent hard filtering with parameters 'QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0'. The VCF file generated by hard filtering included the chromosome number of SNPs, SNP positions, reference bases, mutant bases, etc. Next, bedtools maskfasta was used to align the SNP information of the VCF file in the reference genome of Sijihua and finally constructed the pseudo-genome of Beihua. The downstream analysis script add_ka_ks.pl from MCScanX was used for calculating Ka/Ks (non-synonymous/synonymous) values for each gene pair.

      SNP annotation was performed using SNPEff software (https://sourceforge.net/projects/snpeff/) based on the constructed Beihua pseudogenome, SNPs were classified into intergenic regions, upstream, downstream, Splice-site-donor, Splice-site-acceptor, UTR-5-prime, UTR-3-prime, and SNPs encoding exons were further divided into synonymous and nonsynonymous mutations.

    • Raw bisulfite sequencing reads of Beihua was trimmed by Trimmomatic v0.39 to obtain clean data and aligned to the replaced Beihua pseudogenome by BSMAP v2.90. A 4% mismatch rate was allowed per 150 bp of read length and only uniquely mapped reads were retained for subsequent analysis. Next, reads aligned to unmethylated lambda DNA were used to estimate the conversion rate. Whether cytosine is methylated or not was identified mainly based on the conversion rate and binomial distribution. The methylation level of each cytosine was calculated using the methration.py script of the BSMAP software. The formula for calculating the level of cytosine methylation is #C/(#C+#T), with #C representing the methylated cytosine and #T being the unmethylated cytosine. Similarly, whole genome bisulfite sequencing data of Sijihua were calculated by the same method. DMCs between the two cultivars of Beihua and Sijihua were identified using methylkit default parameters. In addition, the absolute methylation levels of white and golden flowers of both cultivars, under the same contexts of CG, CHG, and CHH, were to vary more than 40%, 20%, and 10%, respectively. In this paper, both the loss of CG, CHG, and CHH (CG-loss, CHG-loss, and CHH-loss) and the gain of CG, CHG, and CHH (CG-gain, CHG-gain, and CHH-gain) were defined as DMCs, and this type of DMCs is relative to the two extremes of variation from the presence to the absence and from the absence to the presence of the two cultivars.

    • For each biological replicate of the Beihua and Sijihua transcriptome data, we used Trimmomatic v0.39 for quality control and aligned to the Beihua pseudogenome and Sijihua reference genome using HISAT2 default parameters, and we kept only uniquely mapped reads to estimate expression values (Supplemental Table S1). The expression of each gene we quantified using stringTie v2.1.7. Next, DESeq2 v1.32.0 was used to identify differentially expressed genes in white buds and golden flowers of both Beihua and Sijihua cultivars and differentially expressed genes were required to satisfy Log2|FC| > 2 and FDR < 0.05.

    • GOATOOLS was used for gene ontology (GO) enrichment of overlapping genes of differentially expressed genes (DEGs) in white buds and golden flowers of both Beihua and Sijihua cultivars. Only GO terms with P-values less than 0.05 were retained for analysis. Heatmap, and GO enrichment plot of DEGs were made using R software version 3.5.

    • To reveal the genomic variations between Sijihua and Beihua, Beihua was resequenced (Fig. 1a), generating 3.7 Gb of high-quality clean data. Reads were mapped to Sijihua's reference genome using BWA software. Over 91% of read pairs successfully aligned at a coverage depth of 30 × (Supplemental Table S2), facilitating subsequent genetic variation analysis. 9,909,981 SNPs were identified in the Sijihua genome. The majority of these SNPs were situated in intergenic regions (72.42%), followed by intronic regions (8.4%), upstream (8.35%), and downstream (7.34%) regions (Fig. 1b, Supplemental Table S3). SNPs were classified into four groups based on their impact on genes (https://pcingola.github.io/SnpEff/se_inputoutput/). 12,688 high-impact SNPs (0.13%) resulted in stop-gained, stop-loss, splice-site acceptor, splice-site donor, and start-lost mutations. 102,188 low-impact SNPs (1.03%) resulted in synonymous coding, start gained, synonymous stop, or non-synonymous start mutations. Additionally, 158,107 moderate-impact SNPs (1.6%) caused non-synonymous mutations in the coding region. The remaining 9,636,998 SNPs (97.25%) had a modifying impact on intergenic, intronic, upstream, downstream, UTR-3-prime, and UTR-5-prime regions. A total of 38,808 genes were affected by four SNP categories. These included 7,555 genes associated with high-impact SNPs, 24,967 genes associated with moderate-impact SNPs, 23,256 genes associated with low-impact SNPs, and 38,787 genes associated with modifier-type SNPs (Fig. 1c, Supplemental Table S4). High-impact SNPs may be involved in the expression of important genes with key functions[33]. Therefore, a Gene Ontology (GO) functional enrichment analysis of genes associated with high-impact SNPs were performed. Regardless of the white bud (WB) or golden flower (GF) stage, genes related to high-impact SNPs were significantly enriched in response to stimuli, response to viruses, gene silencing regulation, and RNA interference regulation (Fig. 1d).

      Previous studies have reported the construction of pseudo-reference genomes with incomplete genome assembly by using genetic variation substitution in phylogenetic closed cultivars, such as to obtain pseudo-reference genomes for maize Mo17 and rice Matsumae, researchers substituted SNP information for maize B73[34], and rice RZ35[35]. Since the genomes of Sijihua and Beihua are very similar (SNP ratio of 1.12%), a pseudo-reference genome was created for Beihua by replacing the SNP sites with the reference genome of Sijihua.

      Figure 1. 

      Identification and classification of SNPs. (a) Different development stages of Beihua and Sijihua. Five stages are juvenile bud (JB), green bud (GB), white bud (WB), silver flower (SF), and golden flower (GF), respectively. (b) Classification of SNPs based on genome location. (c) Number of genes affected by SNPs (SNPs were divided into four impact types: high, moderate, low, and modifier). (d) GO enrichment analysis of DEGs related to high-impact SNPs.

    • To investigate DNA methylation distinctions between the two honeysuckle cultivars, DNA methylation was sequenced at two stages of floral development (WB and GF) in Beihua using the WGBS technique (Supplemental Table S5). Compared with DNA methylation in Sijihua[31], no significant differences in global CG DNA methylation levels were found between the two cultivars at different developmental stages (T-test, WB: p-value = 0.17, GF: p-value = 0.62). we observed Beihua had significantly higher global DNA methylation levels than Sijihua in the CHG and CHH sequence contexts (T-test, p-value < 0.05). This phenomenon was consistently observed in both WB and GF stages (Fig. 2a). The methylation of WB and GF in Beihua and Sijihua were also analyzed. It was found that in Beihua, only the CHH methylation level showed significant differences, while in Sijihua, methylation levels in all three contexts exhibited significant differences (Supplemental Fig. S1). Furthermore, the average DNA methylation levels in 500 Kb windows across the genome were calculated and observed varying degrees of bias in all three sequence contexts. Specifically, the CG methylation distribution deviated to the right (Supplemental Fig. S2a & e), indicating that CG methylation levels were lower in Beihua than in Sijihua. The differential distribution of CHG and CHH methylation showed a significant left deviation (Supplemental Fig. S2b, f, c, & h), indicating that the methylation levels of CHG and CHH were significantly higher in Beihua than in Sijihua. These results were consistent with global DNA methylation levels (Fig. 2a).

      The number of methylated cytosines were further compared between Sijihua and Beihua, revealing that Sijihua had 138,186,587 methylated cytosines, whereas Beihua had 113,582,475. During the WB stage, Sijihua had a higher number of methylated cytosines in the CG (27,577,348), CHG (21,837,724), and CHH (88,771,515) contexts compared to Beihua, which had CG (22,491,550), CHG (17,537,206), and CHH (17,537,206) methylated cytosines. A similar trend was observed during the golden flower stage, indicating that Beihua's DNA structure is less susceptible to methylation than Sijihua (Fig. 2b). When calculating the methylation levels of each cytosine, both CG and CHG methylation in the two honeysuckle cultivars showed frequent occurrences of two extremes (0 and 1), representing the completely methylated and unmethylated states, respectively. Both showed a bimodal distribution pattern. In Sijihua, 72.9% and 36.6% of the CG and CHG methylation sites, respectively, occurred in a completely methylated state, whereas in Beihua, 69% and 33.7% of the CG and CHG methylation sites, respectively, occurred in a completely methylated state. The proportion of completely methylated CG and CHG in Sijihua was higher than that in Beihua, this trend remained consistent at the GF stage (Fig. 2c & d). Furthermore, we found that the higher percentage of completely methylated CG in Sijihua, approximately 80.7% (18,525,847 / 22,956,440) of CG methylation, could be maintained from WB to GF. In contrast, in Beihua, only 76.6% (13,962,522 / 18,227,835) of CG methylation was maintained from WB to GF. This suggests that during the process of maintaining these methylation types, CG methylation in Sijihua may be more stable and faithfully replicated during DNA replication than that in Beihua[36].

      To investigate methylation distribution in two honeysuckle genomes, gene density, transposable elements (TE) density, SNP density, and average methylation level were calculated in the three methylation contexts of Sijihua and Beihua (Fig. 2e). The TE-enriched regions of both Sijihua and Beihua showed higher DNA methylation levels and lower gene density at both WB and GF stages (Fig. 2e, Supplemental Fig. S2d). This phenomenon in many flowering plants, such as Arabidopsis[37], soybean[38], maize[39], rapeseed[40], and tomato[41]. SNPs showed an uneven distribution across the genome, with a high content occurring in regions with elevated CG and CHG methylation levels, while being scarce in CHH-methylated regions (Fig. 2e). To explore the relationship between DNA methylation and SNP density, the correlation between SNP density and methylation in three sequence contexts were analyzed. Significantly, we observed that SNP density was positively correlated with CG and CHG methylation levels, and negatively correlated with CHH methylation (Supplemental Fig. S3). This suggests that highly methylated cytosines may influence the stability of DNA sequences, especially the CG and CHG methylation.

      To examine the methylation patterns in the coding regions of the two honeysuckles, the methylation levels within the gene coding regions and their 2 Kb flanking regions for both Sijihua and Beihua were calculated. CG methylation levels in the coding regions were higher in Sijihua than in Beihua, whereas CHG and CHH methylation levels were lower in Sijihua than in Beihua. This pattern was consistent across both WB and GF (Supplemental Figs S4ac, S5a-c). Based on previous reports indicating the abundance of transposable element insertions in the coding regions of honeysuckles[31], the genes were classified into two types: TE-related genes and TE-unrelated genes. Subsequently, the DNA methylation patterns of these two gene types were calculated. The methylation pattern of the coding regions of TE-related genes was the same as the genome-wide methylation pattern in both honeysuckle cultivars during the two stages, whereas TE-unrelated genes showed significantly reduced methylation levels. This suggests that TE insertions play a crucial role in maintaining the DNA methylation levels in both Sijihua and Beihua (Supplemental Figs. S4df, S5df). In addition, the DNA methylation of the TE region and the 2 Kb flanking region were calculated. The CG methylation levels of TE in Beihua was higher than that in Sijihua at two stages. However, the CHG methylation level in the TE regions did not differ significantly between these two cultivars at the WB stage, but showed marked differences at the GF stage, indicating dynamic methylation levels of TEs during honeysuckle flower development. Surprisingly, CHH methylation levels of TE regions in Beihua were significantly higher than in Sijihua at both stages (Supplemental Figs S4gi, S5gi). Overall, the methylation levels of Sijihua and Beihua differed significantly and varied with developmental stages.

      Figure 2. 

      DNA methylation landscape of Beihua and Sijihua. (a) Comparison of whole-genome DNA methylation levels in Beihua and Sijihua during the WB and the GF stages. Three biological replicates were calculated as dots over the bar graph (T-test, * p-value < 0.05, NS represents non-significant). (b) The relative proportions of methyl-cytosine in the contexts of CG, CHG, and CHH in Beihua and Sijihua during the WB and GF stages. (c)−(d) Distribution of methyl-cytosine methylation levels in all three sequence contexts in Beihua and Sijihua during the WB(C) and GF(D) stages were compared. (e) Circos plot showing gene density, TE density, SNP density, and DNA methylation levels for all three contexts of the WB stage.

    • By comparing the DNA methylation patterns between Sijihua and Beihua, significant differences in methylation levels of all three sequence contexts were observed. To gain a deeper understanding of the specific sites displaying differential methylation, the study focused on comparing methylation levels between Sijihua and Beihua. During the WB stage, the analysis identified 1,602,266 (19%) DMCs, including 641,008 CG DMCs, 589,493 CHG DMCs, and 371,765 CHH DMCs. Notably, hyper-methylation (Sijihua > Beihua) was more prevalent than hypomethylation in three methylation contexts. This trend persisted in the GF stage (Fig. 3a). However, it is important to consider that genetic variation (SNPs) can also contribute to the variation in DNA methylation sites by influencing the sequence context or altering methylation levels[26]. To further understand how genetic variations (SNPs) impact DNA methylation sites in honeysuckles, 6,679,481 (81%) SNP-associated DMCs were identified at both WB and GF stages. It was observed that 2,539,019 DMCs resulted from cytosine loss (36%), 1,808,116 DMCs were attributed to cytosine gain (26%), and 2,332,346 DMCs were ascribed to other types of mutations (38%). For example, an illustrative snapshot from the Genome Browser displayed various SNP-associated DMCs, including CHH-gain, CG-gain, CHG-loss, CG-loss, and CHH-loss (Fig. 3b). Genetic variations can lead to changes in both methylation type and level. Moreover, it was found that the number of SNP-associated DMCs was much greater than the number of non-SNP-associated DMCs (DMCs caused by different methylation levels in the same context) when comparing differentially methylated cytosines within the two honeysuckles (Fig. 3a). These findings underscore the potential substantial contribution of genetic variations in driving disparities in DNA methylation levels between the two honeysuckles.

      Regardless of the number of cytosine methylation type mutations or non-cytosine mutations, it was found that the CHH type was the most affected in terms of cytosine loss, gain, and other types of mutations, followed by the CHG and CG types (Fig. 3c & d). Among other types of mutations, the significance of guanine (G) loss and gain was particularly evident, playing a pivotal role in the dynamic transition between distinct methylation types. Transitions from CG to CHH and from CHG to CHH are the most prevalent, with a predominant proportion of guanine (G) mutations mutating to adenine (A) or thymine (T). Following this trend, mutations from CHH to CG and CHH to CHG emerged as the next most frequent occurrences, primarily involving the conversion of adenine (A) to guanine (G) (Fig. 3e). It is worth noting that these mutations not only led to changes in methylation type but also influenced the methylation levels themselves.

      Specially, when CG sites underwent mutations, transforming into CHG or CHH sites, they exhibited a higher likelihood of becoming hypomethylated. Conversely, CHH sites transitioning to CHG or CG sites were more prone to hypermethylation (Supplemental Fig. S6a). Extending the examination to the diversity of the 500 bp flanking sequences surrounding the SNP-associated DMC sites, it was observed that downstream nucleotide diversity was higher than that upstream of the DMC sites. Additionally, nucleotide diversity at the DMC sites markedly exceeded that within the adjacent flanking sequences. Intriguingly, this phenomenon was not observed at non-DMC sites. The higher nucleotide diversity at DMC sites underscores their susceptibility to frequent natural selection, implying the potential for intricate interplay between SNPs and DNA methylation dynamics (Supplemental Fig. S6b).

      Figure 3. 

      Identification of differentially methylated cytosines in the honeysuckle genome. (a) The proportion of two DMC types in the honeysuckle genome, including non-SNP-associated DMCs and SNP-associated DMCs. (b) Representative screenshots of DMCs. Methylation site losses, gains, and mC context changes were indicated on the underside of the track (red: Sijihua; blue: Beihua; Light blue shading represents non-SNP-associated DMCs; Light green represents SNP-associated DMCs). (c) The proportion of methylation types of the SNP-associated DMCs. (d) Sankey diagram showing the number of changes in DNA methylation types in three contexts between Sijihua and Beihua. (e) Sunburst chart showing the number of methylation type changes caused by nucleotide mutation.

    • To further investigate the effect of cytosine loss on DNA methylation levels, the base substitution rates were calculated for homologous genes between Beihua and Sijihua. The rate of C=>T base substitutions was higher than that of other mutation types (Fig. 4a), consistent with studies in Arabidopsis[23] and lotus[42]. Within the honeysuckle genome, methylated cytosines exhibited a greater propensity to mutate into C=>T compared to unmethylated cytosines (1,155,283 for mC=>T, 636,736 for non-mC=>T, binomial test, p-value = 1.727e-10), resulting in a higher frequency of CG to TG mutations[24]. However, CG=>TG mutations were not evenly distributed throughout the genome. The majority of these mutations occurred in intergenic regions (272,395), followed by introns (19,132) and exons (44,634) (Fig. 4b).

      To validate whether the mutation from CG methylation type to TG in honeysuckle impacted gene methylation levels, we assessed CG methylation levels within the gene body and the 2 Kb flanking regions of both Sijihua and Beihua genes. The analysis revealed significant differences in the gene body regions of both Sijihua and Beihua at both stages (Mann-Whitney test, p-value < 0.001) (Fig. 4c, Supplemental Fig. S7a). When genes unaffected by CG=>TG mutations were considered, as expected, no significant differences (Mann-Whitney test, p-value > 0.05) were observed in the gene-body regions at two stages of Sijihua and Beihua (Fig. 4d, Supplemental Fig. S7b). Subsequently, the study was focused specifically on genes with CG=>TG mutations and calculated the CG methylation levels in their gene body and 2 Kb flanking regions[43]. Intriguingly, significant differences in the gene body methylation levels between Sijihua and Beihua were observed (Mann-Whitney test, p-value < 0.001) (Fig. 4e, Supplemental Fig. S7c). This indicates that genetic variations leading to CG=>TG mutations directly affect CG methylation levels in the gene body. Furthermore, it was observed that genes affected by CG=>TG mutations not only showed reduced CG methylation levels in the gene body but also displayed a significant reduction in gene expression levels in Beihua and Sijihua (Fig. 4fh, Supplemental Fig. S7df). This observation is consistent with previous findings in other plants such as Arabidopsis[44], and Brassica napus[45], where higher CG methylation within the gene body promotes gene transcription.

      In summary, CG=>TG mutations not only altered the methylation levels, but also affected gene expression levels. To understand the functions of these genes affected by CG=>TG mutations, GO enrichment analysis was performed and identified their predominant enrichment in transcriptional regulation, stress response, flavonoid biosynthesis pathways, responses to viruses, biosynthesis of flavonoids, flavonols, and flavonoids (Supplemental Fig. S8). Collectively, these findings underscore the critical role played by the interactions between epigenetic and genetic variations in regulating gene functions in honeysuckle.

      Figure 4. 

      Relationship of CG=>TG type mutations with CG methylation and gene expression. (a) The substitution rate of nucleotides in homologous genes between Beihua and Sijihua. (b) The distribution of CG=>TG mutation sites on the honeysuckle genome. Comparison of CG methylation levels in the body region and the flanking 2 Kb region of (c) all genes, (d) CG=>TG unrelated genes, and (e) CG=>TG-related genes during the WB stage in Sijihua and Beihua. The expression levels of (f) all genes, (g) CG=>TG unrelated genes, and (h) CG=>TG-related genes during the WB stage in Sijihua and Beihua were compared (Mann-Whitney test, *** p-value < 0.001, NS represents non-significant).

    • To investigate deeper into the impact of SNP-associated DMCs, genes were identified that had overlaps with SNP-associated DMCs within their gene body and 2 Kb flanking regions, designating them as genes related to SNP-associated DMCs. It was found that SNP-associated DMCs were linked to 76% (29,899/39,320) of the genes within the honeysuckle genome. Among these genes, 5,324 and 7,067 genes showed differential expression (DEGs, log2|FC| > 2, FDR < 0.05) in the WB and GF stages, respectively, with an overlap of 3,325 genes displaying differential expression in both stages (Fig. 5a). Notably, the majority of genes (3,158/3,325) that exhibited differential expression at both stages showed consistent expression trends. Specifically, in both the WB and GF stages, the expression levels of these genes in Beihua were either higher than those in Sijihua (C3) or lower than those in Sijihua (C4) (Supplemental Fig. S9a). Interestingly, GO enrichment analysis of these genes with consistent expression trends revealed significant enrichment in processes related to flavonoid biosynthesis, flavonol biosynthesis, cellular glucan metabolism, stress response pathways, and floral whorl development (Supplemental Fig. S9b). Overall, genes affected by both SNPs and DMCs play important biological functions in various critical pathways, indicating their substantial regulatory significance.

      The above analysis has highlighted those genes affected by both SNPs and DMCs were significantly enriched in the flavonoid biosynthesis pathway. Flavonoids represent a vital group of secondary metabolites in plants renowned for their natural medicinal properties. They play crucial roles in plant growth, development, and defense against biotic and abiotic stresses. The synthesis of flavonoids originates from phenylalanine through the phenylpropanoid pathway, involving several key enzymes (Fig. 5d)[46]. In the investigation of the effects of SNPs and DMCs on flavonoid biosynthesis in honeysuckle, 35 homologous genes encoding key enzymes in the flavonoid biosynthesis pathway in the honeysuckle genome were identified (Fig. 5d, Supplemental Table S6). Despite the presence of a high number of SNPs in both the gene body and promoter regions of these 35 homologous genes, their Ka/Ks ratios are considerably lower than 1. This suggests that these genes have undergone purifying selection during evolution and possess relatively conserved structures and sequences. Consequently, genetic variation may not be the primary driving factor behind the differences in flavonoid biosynthesis between the two honeysuckles (Fig. 5b & e).

      Furthermore, by comparing the methylation levels of these 35 genes in Sijihua and Beihua at different stages, significant differences were observed in both the gene body and promoter regions. Additionally, some genes showed significant changes in expression. For example, LjPAL (EVM0007831), LjCHS (EVM0026111), LjCHI (EVM0013981), and LjFLS (EVM0027194) showed significantly higher expression levels in Beihua than in Sijihua at both the WB and GF stages (Fig. 5c, Supplemental Fig. S10). Interestingly, it was found that both the gene body and promoter regions of these 35 genes contained SNP-associated DMCs, as well as non-SNP-associated DMCs. These DMCs appear to play crucial roles in the regulation of gene expression (Supplemental Fig. S11). For example, a genome browser showed that LjCHI (EVM0013981) and LjFLS (EVM0027194) have a large number of SNP-associated DMCs and non-SNP-associated DMCs in the promoter region. Additionally, the expression levels of these genes were significantly higher in Beihua than in Sijihua (Fig. 5f & g, Supplemental Fig. S12). Therefore, the promoter regions of these key enzyme genes contain numerous nucleotide mutations that lead to changes in promoter region methylation, directly affecting gene expression. This may be a crucial factor contributing to the higher flavonoid levels in Beihua compared to Sijihua.

      Figure 5. 

      The relationship between genes related to SNP-associated DMCs and the flavonoid pathway. (a) Venn diagram showing the number of overlapping differentially expressed genes (DEGs) associated with SNP-associated DMCs between WB and GF. (b) Boxplot showing Ka/Ks ratios of 35 key genes and genomes in the flavonoid pathway of honeysuckle (Mann-Whitney test. *** p-value < 0.001). (c) Boxplot showing the methylation levels of CG, CHG, and CHH in the promoter regions of 35 key genes in the flavonoid pathway during the WB in Beihua and Sijihua (Mann-Whitney test. *** p-value < 0.001). (d) The pathway of flavonoid synthesis in plants. (e) Heatmaps showing the expression levels of 35 key genes of the flavonoid pathway, the number of SNPs in the upstream region of the genes, the number of SNP-associated with DMCs, and the number of non-SNP-associated with DMCs for Beihua and Sijihua at the WB and GF (* represents DEGs in the WB and GF stages). (f) Genome browser showing DNA methylation level and expression level of LjCHI (EVM0013981). (g) Genome browser showing representative screenshots of two types of DMCs in the promoter region (Chr01: 82302800 − 82302800) of LjCHI (EVM0013981).

    • In this study, whole-genome resequencing, whole-genome bisulfite sequencing, and transcriptome sequencing techniques were employed to sequence different honeysuckle cultivars. From both the genetic and epigenetic perspectives, the factors underlying the phenotypic differences observed between the two honeysuckles were investigated. Previous studies have revealed that high-impact SNPs in the genomes of various plants, such as rice[47], tomato[48], cucumber[49], cotton[50], and peach[51], have significant effects on important agronomic traits. These high-impact SNPs have been observed to play crucial roles in plant resistance. For example, in tomato, resistance genes carrying high-impact SNPs exhibit reduced susceptibility to Oidium neolycopersici infection, resulting in enhanced resistance. In cotton, the GhDRP gene with high-impact SNPs showed decreased expression upon infection with V. dahliae, resulting in leaf yellowing, shedding, and severe cell damage. This indicates a robust lignification response to counteract pathogen attacks. The present study identified a substantial number of genes in the honeysuckle genome affected by high-impact SNPs, significantly enriched in resistance pathways, such as response to fungi, defense response to virus, and response to mechanical stimulus. These findings provide a solid foundation for improving the desirable traits of honeysuckle cultivars.

      Subsequently, the genome-wide single-base resolution DNA methylomes between Beihua and Sijihua were compared. Notably, significant differences in DNA methylation were observed between these two honeysuckles in the three contexts (CG, CHG, and CHH). Some studies have suggested that changes in DNA methylation are influenced by genetic variations, subsequently regulating alterations in gene expression. In the present study, a correlation between SNP density and different methylation types was observed. Specifically, SNP density showed a significant positive correlation with CG and CHG methylation, but a negative correlation with CHH methylation. Previous reports have also indicated a positive correlation between differential methylation regions and the density of SNP variations, suggesting that genetic variations in proximity may influence some of the methylation differences observed between different cultivars[52]. Furthermore, a higher density of SNPs were also observed at sites with differentially methylated cytosines, a phenomenon previously observed in the cassava genome[26].

      In the genome of honeysuckle, there is a pronounced preference for nucleotide mutations (C=>T), primarily driven by cytosine methylation. While DNA methylation plays a critical role in regulating gene expression, it can also have detrimental effects because methylated cytosines are more susceptible to deamination, leading to the conversion of cytosine to thymine. Similar phenomena have been observed in the Arabidopsis[23], canola[24], and sugarcane[25]. The interplay between genetic variation and DNA methylation has a regulatory effect on gene expression[53]. The results revealed that genes related to Beihua affected by CG=>TG had significantly lower methylation levels in the gene body compared to Sijihua, and a significant difference in the gene expression between Beihua and Sijihua.

      Numerous studies have indicated that phenylalanine ammonia-lyase (PAL)[54] and chalcone isomerase (CHI)[55] served as the first and second rate-limiting enzymes in flavonoid synthesis, respectively. Overexpression of these enzymes leads to a significant accumulation of flavonoids. Additionally, the overexpression of flavanol synthase (FLS) results in a substantial accumulation of flavonoids[56]. Therefore, the promoter regions of these key enzyme genes contain numerous nucleotide mutations that lead to changes in promoter region methylation, directly affecting gene expression. This may be a crucial factor contributing to the higher flavonoid levels in Beihua compared to Sijihua. These findings align with prior research on the apple genome[57]. In summary, the interplay between SNP and DMCs in the biosynthesis of flavonoids in honeysuckle regulates the expression changes of genes related to key enzymes, thereby influencing the accumulation of flavonoids.

      A large number of DMCs in the genomes of two honeysuckles were identified, with most of them being SNP-associated DMCs. These DMCs were associated with approximately 80% of the genes and were significantly enriched in several vital pathways, such as the flavonoid biosynthesis pathway, flavonol biosynthesis pathway, anticancer pathway, and cellular glucan metabolic process. In particular, SNP-associated DMCs were found in genes related to key enzymes in the flavonoid synthesis pathway. Some of these genes directly influenced the accumulation of flavonoids and exhibited significant differential expression, including LjPAL (EVM0007831), LjCHI (EVM0013981), and LjFLS (EVM0027194). Previous studies have shown that the expression levels of LjPAL, LjCHI, and LjFLS are positively correlated with flavonoid accumulation in other plants, such as Arabidopsis[55], tomato[58], Dendrobium officinale[59], and Brassica napus[60]. In summary, the interplay between genetic variation and epigenetic regulation exerts a substantial influence on gene expression, leading to noticeable phenotypic differences between the two honeysuckles. These findings provide novel insights into breeding and cultivation techniques for honeysuckles.

    • In this study, 9,909,981 SNPs were identified in Sijihua, including 12,688 high-impact SNPs that were significantly enriched in the stress resistance pathways. By comparing the DNA methylation patterns of Beihua and Sijihua, significant differences in DNA methylation levels were observed between the two honeysuckles. Thus, a substantial number of DMCs were identified between these two honeysuckles, with 81% of which were SNP-associated DMCs. Furthermore, methylated cytosines are more prone to mutation, resulting in CG=>TG, and altered DNA methylation, further regulating gene expression. SNP-associated DMCs are linked to 76% of protein-coding genes, with 3,325 genes exhibiting differential expression in both stages (WB and GF), and significantly enriched in the biosynthetic pathway of flavonoids. In the flavonoid pathway, important genes affecting flavonoid accumulation such as LjPAL, LjCHI, and LjFLS showed significant differences. The flanking 2 Kb region and body region of these genes produced a large number of SNP-associated DMCs, which is likely to be the reason that SNP-associated DMCs are regulating the overexpression of genes in Beihua, resulting in an increase in the accumulation of flavonoids in Beihua.

    • The authors confirm contribution to the paper as follows: study conception and design: Wang H; re-sequencing performing and DNA methylation analysis: Yu X; transcriptome performing and biological pathway analysis: Yu X, Yu H; tissues collection and analysis: Lu Y, Zhang C; draft manuscript preparation: Yu X, Wang H. All authors reviewed the results and approved the final version of the manuscript.

    • The raw reads generated in this study have been deposited in the CNCB sequence read archive (SRA) with the accession number PRJCA018541. The reference genome, WGBS, and RNA-seq public data of Sijihua were downloaded from the NCBI database under the project numbers: PRJNA794868, PRJNA824715, and PRJNA813701, respectively.

      • This work was supported by the National Natural Science Foundation of China (32160142), Guangxi Natural Science Foundation (2023GXNSFDA026034), Sugarcane Research Foundation of Guangxi University (2022GZA002), and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources (SKLCUSA-b202302) to Haifeng Wang.

      • The authors declare that they have no conflict of interest.

      • # Authors contributed equally: Xianyun Yu, Hang Yu

      • Supplemental Table S1
      • Supplemental Table S1 Summary of Sijihua and Beihua RNA-seq reads mapping.
      • Supplemental Table S2 Summary of whole genome resequencing mapping.
      • Supplemental Table S3 Classification of SNPs based on genome location.
      • Supplemental Table S4 Number of genes affected by SNP mutation.
      • Supplemental Table S5 Summary of Sijihua and Beihua BS-seq reads mapping.
      • Supplemental Table S6 Mining and analyzing flavonoid pathway genes of honeysuckle.
      • Supplemental Fig. S1 The significance analysis between Beihua WB and GF, as well as Sijihua WB and GF.
      • Supplemental Fig. S2 DNA methylation landscape of Beihua and Sijihua.
      • Supplemental Fig. S3 The correlation between SNPs and DNA methylation.
      • Supplemental Fig. S4 DNA methylation patterns of protein-coding genes and TEs in Sijihua and Beihua in the WB.
      • Supplemental Fig. S5 DNA methylation patterns of protein-coding genes and TEs in Sijihua and Beihua in the GF.
      • Supplemental Fig. S6 SNP lead to changes in the type and level of DNA methylation and characterization of the diversity of DMC sites.
      • Supplemental Fig. S7 CG≥TG-type mutations in relation to CG methylation and gene expression during the GF stage.
      • Supplemental Fig. S8 GO enrichment analysis of CG≥TG-related genes.
      • Supplemental Fig. S9 The pattern of differentially expressed genes related to SNP-associated DMCs.
      • Supplemental Fig. S10 The comparison of DNA methylation levels of 35 key genes of the flavonoid pathway in two stages of Sijihua and Beihua.
      • Supplemental Fig. S11 Heatmaps showing the expression levels of 35 key genes, the number of SNPs in the gene body of the genes, the number of SNP-associated DMCs, and the number of non-SNP-associated DMCs in Beihua and Sijihua during the WB and GF stages (* represents genes that showed significant differences in both stages).
      • Supplemental Fig. S12 Genome browser showing DNA methylation and expression levels of LjFLS (EVM0027194) and representative screenshots of two types of DMCs in the promoter region (Chr01:50581502-50581602).
      • © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (5)  References (60)
  • About this article
    Cite this article
    Yu X, Yu H, Lu Y, Zhang C, Wang H. 2024. Genetic and epigenetic variations underlying flavonoid divergence in Beihua and Sijihua honeysuckles. Epigenetics Insights 17: e002 doi: 10.48130/epi-0024-0002
    Yu X, Yu H, Lu Y, Zhang C, Wang H. 2024. Genetic and epigenetic variations underlying flavonoid divergence in Beihua and Sijihua honeysuckles. Epigenetics Insights 17: e002 doi: 10.48130/epi-0024-0002

Catalog

  • About this article

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return