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Genetic resources resistant to black spot (Alternaria alternate) identified from Chrysanthemum-related genera and potential underlying mechanisms

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An Author Correction to this article was published on 18 February 2025, http://doi/10.48130/opr-0025-0008.

  • Chrysanthemum black spot disease caused by Alternaria alternate infestation is a widespread and extremely destructive foliar disease of chrysanthemums. We compared the resistance of 14 chrysanthemum relatives to chrysanthemum black spot disease, and identified the main indicators for the evaluation and screening of chrysanthemum disease resistance, which is of great significance in laying the foundation for a larger-scale screening of chrysanthemum relatives for disease resistance and the breeding of new disease-resistant cultivars. After artificial inoculation and identification, two disease-resistant germplasm resources, 11 moderately resistant materials, and one sensitive material were obtained. In both resistant and susceptible species, we found that the trichome density and leaf wax content of the resistant material were significantly higher than that of the sensitive material, while the stomata size was smaller than that of the sensitive material. In addition, we found that the leaf extract of the disease-resistant germplasm effectively inhibited the growth rate of A. alternate mycelium on the plate, and GC-MS components found that the leaves of resistant germplasm contained more volatile antifungal organic compounds, of which the abundant falcarinol and Germacrene D might play an important role in resistance to chrysanthemum black spot disease. In summary, epidermal trichome density, wax content and terpene substance content are three important reference indicators for disease resistance evaluation of related genera of chrysanthemum. The identified resistant germplasm can also be used as parents for future cross-breeding or as rootstocks.
  • Starting in the early 2000s, China has experienced rapid growth as an emerging wine market. It has now established itself as the world's second-largest grape-growing country in terms of vineyard surface area. Furthermore, China has also secured its position as the sixth-biggest wine producer globally and the fifth-most significant wine consumer in terms of volume[1]. The Ningxia Hui autonomous region, known for its reputation as the highest quality wine-producing area in China, is considered one of the country's most promising wine regions. The region's arid or semiarid climate, combined with ample sunlight and warmth, thanks to the Yellow River, provides ideal conditions for grape cultivation. Wineries in the Ningxia Hui autonomous region are renowned as the foremost representatives of elite Chinese wineries. All wines produced in this region originate from grapes grown in their vineyards, adhering to strict quality requirements, and have gained a well-deserved international reputation for excellence. Notably, in 2011, Helan Mountain's East Foothill in the Ningxia Hui Autonomous Region received protected geographic indication status in China. Subsequently, in 2012, it became the first provincial wine region in China to be accepted as an official observer by the International Organisation of Vine and Wine (OIV)[2]. The wine produced in the Helan Mountain East Region of Ningxia, China, is one of the first Agricultural and Food Geographical Indications. Starting in 2020, this wine will be protected in the European Union[3].

    Marselan, a hybrid variety of Cabernet Sauvignon and Grenache was introduced to China in 2001 by the French National Institute for Agricultural Research (INRA). Over the last 15 years, Marselan has spread widely across China, in contrast to its lesser cultivation in France. The wines produced from Marselan grapes possess a strong and elegant structure, making them highly suitable for the preferences of Chinese consumers. As a result, many wineries in the Ningxia Hui Autonomous Region have made Marselan wines their main product[4]. Wine is a complex beverage that is influenced by various natural and anthropogenic factors throughout the wine-making process. These factors include soil, climate, agrochemicals, and human intervention. While there is an abundance of research available on wine production, limited research has been conducted specifically on local wines in the Eastern Foot of Helan Mountain. This research gap is of significant importance for the management and quality improvement of Chinese local wines.

    Ion mobility spectrometry (IMS) is a rapid analytical technique used to detect trace gases and characterize chemical ionic substances. It achieves this through the gas-phase separation of ionized molecules under an electric field at ambient pressure. In recent years, IMS has gained increasing popularity in the field of food-omics due to its numerous advantages. These advantages include ultra-high analytical speed, simplicity, easy operation, time efficiency, relatively low cost, and the absence of sample preparation steps. As a result, IMS is now being applied more frequently in various areas of food analysis, such as food composition and nutrition, food authentication, detection of food adulteration, food process control, and chemical food safety[5,6]. The orthogonal hyphenation of gas chromatography (GC) and IMS has greatly improved the resolution of complex food matrices when using GC-IMS, particularly in the analysis of wines[7].

    The objective of this study was to investigate the changes in the physicochemical properties of Marselan wine during the winemaking process, with a focus on the total phenolic and flavonoids content, antioxidant activity, and volatile profile using the GC-IMS method. The findings of this research are anticipated to make a valuable contribution to the theoretical framework for evaluating the authenticity and characterizing Ningxia Marselan wine. Moreover, it is expected that these results will aid in the formulation of regulations and legislation pertaining to Ningxia Marselan wine in China.

    All the grapes used to produce Marselan wines, grow in the Xiban vineyard (106.31463° E and 38.509541° N) situated in Helan Mountain's East Foothill of Ningxia Hui Autonomous Region in China.

    Folin-Ciocalteau reagent, (±)-6-Hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), 2,20-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), 2,4,6-tris (2-pyridyl)-s-triazine (TPTZ), anhydrous methanol, sodium nitrite, and sodium carbonate anhydrous were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Reference standards of (+)-catechin, gallic acid, and the internal standard (IS) 4-methyl-2-pentanol were supplied by Shanghai Yuanye Bio-Technology Co., Ltd (Shanghai, China). The purity of the above references was higher than 98%. Ultrapure water (18.2 MΩ cm) was prepared by a Milli-Q system (Millipore, Bedford, MA, USA).

    Stage 1−Juice processing: Grapes at the fully mature stage are harvested and crushed, and potassium metabisulfite (5 mg/L of SO2) was evenly spread during the crushing process. The obtained must is transferred into stainless steel tanks. Stage 2−Alcoholic fermentation: Propagated Saccharomyces cerevisiae ES488 (Enartis, Italy) are added to the fresh must, and alcoholic fermentation takes place, after the process is finished, it is kept in the tanks for 7 d for traditional maceration to improve color properties and phenolics content. Stage 3−Malolactic fermentation: When the pomace is fully concentrated at the bottom of the tanks, the wine is transferred to another tank for separation from these residues. Oenococcus oeni VP41 (Lallemand Inc., France) is inoculated and malic acid begins to convert into lactic acid. Stage 4−Wine stabilization: After malolactic fermentation, potassium metabisulfite is re-added (35 mg/L of SO2), and then transferred to oak barrels for stabilization, this process usually takes 6-24 months. A total of four batches of samples during the production process of Marselan wine were collected in this study.

    Total polyphenols were determined on 0.5 mL diluted wine sample using the Folin-Ciocalteu method[8], using gallic acid as a reference compound, and expressed as milligrams of gallic acid equivalents per liter of wine. The total flavonoid content was measured on 0.05 mL of wine sample by a colorimetric method previously described[9]. Results are calculated from the calibration curve obtained with catechin, as milligrams of catechin equivalents per liter of wine.

    The antioxidative activity was determined using the ABTS·+ assay[10]. Briefly, the ABTS·+ radical was prepared from a mixture of 88 μL of potassium persulfate (140 mmol/L) with 5 mL of the ABTS·+ solution (7 mmol/L). The reaction was kept at room temperature under the absence of light for 16 h. Sixty μL samples were mixed with 3 mL of ABTS·+ solution with measured absorption of 0.700 ± 0.200 at 734 nm. After 6 min reaction, the absorbance of samples were measured with a spectrophotometer at 734 nm. Each sample was tested in triplicate. The data were expressed as mmol Trolox equivalent of antioxidative capacity per liter of the wine sample (mmol TE/L). Calibration curves, in the range 64.16−1,020.20 μmol TE/L, showed good linearity (R2 ≥ 0.99).

    The FRAP assay was conducted according to a previous study[11]. The FRAP reagent was freshly prepared and mixed with 10 mM/L TPTZ solution prepared in 20 mM/L FeCl3·6H2O solution, 40 mM/L HCl, and 300 mM/L acetate buffer (pH 3.6) (1:1:10; v:v:v). Ten ml of diluted sample was mixed with 1.8 ml of FRAP reagent and incubated at 37 °C for 30 min. The absorbance was determined at 593 nm and the results were reported as mM Fe (II) equivalent per liter of the wine sample. The samples were analyzed and calculated by a calibration curve of ferrous sulphate (0.15−2.00 mM/mL) for quantification.

    The volatile compounds were analyzed on a GC-IMS instrument (FlavourSpec, GAS, Dortmund, Germany) equipped with an autosampler (Hanon Auto SPE 100, Shandong, China) for headspace analysis. One mL of each wine was sampled in 20 mL headspace vials (CNW Technologies, Germany) with 20 μL of 4-methyl-2-pentanol (20 mg/L) ppm as internal standard, incubated at 60 °C and continuously shaken at 500 rpm for 10 min. One hundred μL of headspace sample was automatically loaded into the injector in splitless mode through a syringe heated to 65 °C. The analytes were separated on a MxtWAX capillary column (30 m × 0.53 mm, 1.0 μm) from Restek (Bellefonte, Pennsylvania, USA) at a constant temperature of 60 °C and then ionized in the IMS instrument (FlavourSpec®, Gesellschaft für Analytische Sensorsysteme mbH, Dortmund, Germany) at 45 °C. High purity nitrogen gas (99.999%) was used as the carrier gas at 150 mL/min, and drift gas at 2 ml/min for 0−2.0 min, then increased to 100 mL/min from 2.0 to 20 min, and kept at 100 mL/min for 10 min. Ketones C4−C9 (Sigma Aldrich, St. Louis, MO, USA) were used as an external standard to determine the retention index (RI) of volatile compounds. Analyte identification was performed using a Laboratory Analytical Viewer (LAV) 2.2.1 (GAS, Dortmund, Germany) by comparing RI and the drift time of the standard in the GC-IMS Library.

    All samples were prepared in duplicate and tested at least six times, and the results were expressed as mean ± standard error (n = 4) and the level of statistical significance (p < 0.05) was analyzed by using Tukey's range test using SPSS 18.0 software (SPSS Inc., IL, USA). The principal component analysis (PCA) was performed using the LAV software in-built 'Dynamic PCA' plug-in to model patterns of aroma volatiles. Orthogonal partial least-square discriminant analysis (OPLS-DA) in SIMCA-P 14.1 software (Umetrics, Umeă, Sweden) was used to analyze the different volatile organic compounds in the different fermentation stages.

    The results of the changes in the antioxidant activity of Marselan wines during the entire brewing process are listed in Table 1. It can be seen that the contents of flavonoids and polyphenols showed an increasing trend during the brewing process of Marselan wine, which range from 315.71−1,498 mg CE/L and 1,083.93−3,370.92 mg GAE/L, respectively. It was observed that the content increased rapidly in the alcoholic fermentation stage, but slowly in the subsequent fermentation stage. This indicated that the formation of flavonoid and phenolic substances in wine mainly concentrated in the alcoholic fermentation stage, which is consistent with previous reports. This is mainly because during the alcoholic fermentation of grapes, impregnation occurred to extract these compounds[12]. The antioxidant activities of Marselan wine samples at different fermentation stages were detected by FRAP and ABTS methods[11]. The results showed that the ferric reduction capacity and ABST·+ free radical scavenging capacity of the fermented Marselan wines were 2.4 and 1.5 times higher than the sample from the juice processing stage, respectively, indicating that the fermented Marselan wine had higher antioxidant activity. A large number of previous studies have suggested that there is a close correlation between antioxidant activity and the content of polyphenols and flavonoids[1315]. Previous studies have reported that Marselan wine has the highest total phenol and anthocyanin content compared to the wine of Tannat, Cabernet Sauvignon, Merlot, Cabernet Franc, and Syrah[13]. Polyphenols and flavonoids play an important role in improving human immunity. Therefore, Marselan wines are popular because of their high phenolic and flavonoid content and high antioxidant capacity.

    Table 1.  GC-IMS integration parameters of volatile compounds in Marselan wine at different fermentation stages.
    No. Compounds Formula RI* Rt
    [sec]**
    Dt
    [RIPrel]***
    Identification
    approach
    Concentration (μg/mL) (n = 4)
    Stage 1 Stage 2 Stage 3 Stage 4
    Aldehydes
    5 Furfural C5H4O2 1513.1 941.943 1.08702 RI, DT, IS 89.10 ± 4.05c 69.98 ± 3.22c 352.16 ± 39.06b 706.30 ± 58.22a
    6 Furfural dimer C5H4O2 1516.6 948.77 1.33299 RI, DT, IS 22.08 ± 0.69b 18.68 ± 2.59c 23.73 ± 2.69b 53.39 ± 9.42a
    12 (E)-2-hexenal C6H10O 1223.1 426.758 1.18076 RI, DT, IS 158.17 ± 7.26a 47.57 ± 2.51b 39.00 ± 2.06c 43.52 ± 4.63bc
    17 (E)-2-pentenal C5H8O 1129.2 333.392 1.1074 RI, DT, IS 23.00 ± 4.56a 16.42 ± 1.69c 18.82 ± 0.27b 18.81 ± 0.55b
    19 Heptanal C7H14O 1194.2 390.299 1.33002 RI, DT, IS 17.28 ± 2.25a 10.22 ± 0.59c 14.50 ± 8.84b 9.11 ± 1.06c
    22 Hexanal C6H12O 1094.6 304.324 1.25538 RI, DT, IS 803.11 ± 7.47c 1631.34 ± 19.63a 1511.11 ± 26.91b 1526.53 ± 8.12b
    23 Hexanal dimer C6H12O 1093.9 303.915 1.56442 RI, DT, IS 588.85 ± 7.96a 93.75 ± 4.67b 92.93 ± 3.13b 95.49 ± 2.50b
    29 3-Methylbutanal C5H10O 914.1 226.776 1.40351 RI, DT, IS 227.86 ± 6.39a 33.32 ± 2.59b 22.36 ± 1.18c 21.94 ± 1.73c
    33 Dimethyl sulfide C2H6S 797.1 193.431 0.95905 RI, DT, IS 120.07 ± 4.40c 87.a02 ± 3.82d 246.81 ± 5.62b 257.18 ± 3.04a
    49 2-Methylpropanal C4H8O 828.3 202.324 1.28294 RI, DT, IS 150.49 ± 7.13a 27.08 ± 1.48b 19.36 ± 1.10c 19.69 ± 0.92c
    Ketones
    45 3-Hydroxy-2-butanone C4H8O2 1293.5 515.501 1.20934 RI, DT, IS 33.20 ± 3.83c 97.93 ± 8.72b 163.20 ± 21.62a 143.51 ± 21.48a
    46 Acetone C3H6O 836.4 204.638 1.11191 RI, DT, IS 185.75 ± 8.16c 320.43 ± 12.32b 430.74 ± 3.98a 446.58 ± 10.41a
    Organic acid
    3 Acetic acid C2H4O2 1527.2 969.252 1.05013 RI, DT, IS 674.66 ± 46.30d 3602.39 ± 30.87c 4536.02 ± 138.86a 4092.30 ± 40.33b
    4 Acetic acid dimer C2H4O2 1527.2 969.252 1.15554 RI, DT, IS 45.25 ± 3.89c 312.16 ± 19.39b 625.79 ± 78.12a 538.35 ± 56.38a
    Alcohols
    8 1-Hexanol C6H14O 1365.1 653.825 1.32772 RI, DT, IS 1647.65 ± 28.94a 886.33 ± 32.96b 740.73 ± 44.25c 730.80 ± 21.58c
    9 1-Hexanol dimer C6H14O 1365.8 655.191 1.64044 RI, DT, IS 378.42 ± 20.44a 332.65 ± 25.76a 215.78 ± 21.04b 200.14 ± 28.34b
    13 3-Methyl-1-butanol C5H12O 1213.3 414.364 1.24294 RI, DT, IS 691.86 ± 9.95c 870.41 ± 22.63b 912.80 ± 23.94a 939.49 ± 12.44a
    14 3-Methyl-1-butanol dimer C5H12O 1213.3 414.364 1.49166 RI, DT, IS 439.90 ± 29.40c 8572.27 ± 60.56b 9083.14 ± 193.19a 9152.25 ± 137.80a
    15 1-Butanol C4H10O 1147.2 348.949 1.18073 RI, DT, IS 157.33 ± 9.44b 198.92 ± 3.92a 152.78 ± 10.85b 156.02 ± 9.80b
    16 1-Butanol dimer C4H10O 1146.8 348.54 1.38109 RI, DT, IS 24.14 ± 2.15c 274.75 ± 12.60a 183.02 ± 17.72b 176.80 ± 19.80b
    24 1-Propanol C3H8O 1040.9 274.803 1.11042 RI, DT, IS 173.73 ± 4.75a 55.84 ± 2.16c 80.80 ± 4.99b 83.57 ± 2.34b
    25 1-Propanol dimer C3H8O 1040.4 274.554 1.24784 RI, DT, IS 58.20 ± 1.30b 541.37 ± 11.94a 541.33 ± 15.57a 538.84 ± 9.74a
    28 Ethanol C2H6O 930.6 231.504 1.11901 RI, DT, IS 5337.84 ± 84.16c 11324.05 ± 66.18a 9910.20 ± 100.76b 9936.10 ± 101.24b
    34 Methanol CH4O 903.6 223.79 0.98374 RI, DT, IS 662.08 ± 13.87a 76.94 ± 2.15b 61.92 ± 1.96c 62.89 ± 0.81c
    37 2-Methyl-1-propanol C4H10O 1098.5 306.889 1.35839 RI, DT, IS 306.91 ± 4.09c 3478.35 ± 25.95a 3308.79 ± 61.75b 3313.85 ± 60.88b
    48 1-Pentanol C5H12O 1257.6 470.317 1.25222 RI, DT, IS 26.13 ± 2.52c 116.50 ± 3.71ab 112.37 ± 6.26b 124.17 ± 7.04a
    Esters
    1 Methyl salicylate C8H8O3 1859.6 1616.201 1.20489 RI, DT, IS 615.00 ± 66.68a 485.08 ± 31.30b 470.14 ± 23.02b 429.12 ± 33.74b
    7 Butyl hexanoate C10H20O2 1403.0 727.561 1.47354 RI, DT, IS 95.83 ± 17.04a 62.87 ± 3.62a 92.59 ± 11.88b 82.13 ± 3.61c
    10 Hexyl acetate C8H16O2 1298.6 524.366 1.40405 RI, DT, IS 44.72 ± 8.21a 33.18 ± 2.17d 41.50 ± 4.38c 40.89 ± 4.33b
    11 Propyl hexanoate C9H18O2 1280.9 499.577 1.39274 RI, DT, IS 34.65 ± 3.90d 70.43 ± 5.95a 43.97 ± 4.39b 40.12 ± 4.05c
    18 Ethyl hexanoate C8H16O2 1237.4 444.749 1.80014 RI, DT, IS 55.55 ± 5.62c 1606.16 ± 25.63a 787.24 ± 16.95b 788.91 ± 28.50b
    20 Isoamyl acetate C7H14O2 1127.8 332.164 1.30514 RI, DT, IS 164.22 ± 1.00d 243.69 ± 8.37c 343.51 ± 13.98b 365.46 ± 1.60a
    21 Isoamyl acetate dimer C7H14O2 1126.8 331.345 1.75038 RI, DT, IS 53.61 ± 4.79d 4072.20 ± 11.94a 2416.70 ± 49.84b 2360.46 ± 43.29c
    26 Isobutyl acetate C6H12O2 1020.5 263.605 1.23281 RI, DT, IS 101.65 ± 1.81a 15.52 ± 0.67c 44.87 ± 3.21b 45.96 ± 1.41b
    27 Isobutyl acetate dimer C6H12O2 1019.6 263.107 1.61607 RI, DT, IS 34.60 ± 1.05d 540.84 ± 5.64a 265.54 ± 8.31c 287.06 ± 3.66b
    30 Ethyl acetate dimer C4H8O2 885.2 218.564 1.33587 RI, DT, IS 1020.75 ± 6.86d 5432.71 ± 6.55a 5052.99 ± 9.65b 5084.47 ± 7.30c
    31 Ethyl acetate C4H8O2 878.3 216.574 1.09754 RI, DT, IS 215.65 ± 3.58a 38.29 ± 2.37c 71.59 ± 2.99b 69.32 ± 2.85b
    32 Ethyl formate C3H6O2 838.1 205.127 1.19738 RI, DT, IS 175.48 ± 3.79d 1603.20 ± 13.72a 1472.10 ± 5.95c 1509.08 ± 13.26b
    35 Ethyl octanoate C10H20O2 1467.0 852.127 1.47312 RI, DT, IS 198.86 ± 36.71b 1853.06 ± 17.60a 1555.51 ± 24.21a 1478.05 ± 33.63a
    36 Ethyl octanoate dimer C10H20O2 1467.0 852.127 2.03169 RI, DT, IS 135.50 ± 13.02d 503.63 ± 15.86a 342.89 ± 11.62b 297.28 ± 14.40c
    38 Ethyl butanoate C6H12O2 1042.1 275.479 1.5664 RI, DT, IS 21.29 ± 2.68c 1384.67 ± 8.97a 1236.52 ± 20.21b 1228.09 ± 5.09b
    39 Ethyl 3-methylbutanoate C7H14O2 1066.3 288.754 1.26081 RI, DT, IS 9.70 ± 1.85d 200.29 ± 4.21a 146.87 ± 8.70b 127.13 ± 12.54c
    40 Propyl acetate C5H10O2 984.7 246.908 1.48651 RI, DT, IS 4.57 ± 1.07c 128.63 ± 4.28a 87.75 ± 3.26b 88.49 ± 1.99b
    41 Ethyl propanoate C5H10O2 962.1 240.47 1.46051 RI, DT, IS 10.11 ± 0.34d 107.08 ± 3.50a 149.60 ± 5.39c 167.15 ± 12.90b
    42 Ethyl isobutyrate C6H12O2 971.7 243.229 1.56687 RI, DT, IS 18.29 ± 2.61d 55.22 ± 1.07c 98.81 ± 4.67b 104.71 ± 4.73a
    43 Ethyl lactate C5H10O3 1352.2 628.782 1.14736 RI, DT, IS 31.81 ± 2.91c 158.03 ± 2.80b 548.14 ± 74.21a 527.01 ± 39.06a
    44 Ethyl lactate dimer C5H10O3 1351.9 628.056 1.53618 RI, DT, IS 44.55 ± 2.03c 47.56 ± 4.02c 412.23 ± 50.96a 185.87 ± 31.25b
    47 Ethyl heptanoate C9H18O2 1339.7 604.482 1.40822 RI, DT, IS 39.55 ± 6.37a 38.52 ± 2.47a 28.44 ± 1.52c 30.77 ± 2.79b
    Unknown
    1 RI, DT, IS 15.53 ± 0.18 35.69 ± 0.80 12.70 ± 0.80 10.57 ± 0.86
    2 RI, DT, IS 36.71 ± 1.51 120.41 ± 3.44 198.12 ± 6.01 201.19 ± 3.70
    3 RI, DT, IS 44.35 ± 0.88 514.12 ± 4.28 224.78 ± 6.56 228.32 ± 4.62
    4 RI, DT, IS 857.64 ± 8.63 33.22 ± 1.99 35.05 ± 5.99 35.17 ± 3.97
    * Represents the retention index calculated using n-ketones C4−C9 as external standard on MAX-WAX column. ** Represents the retention time in the capillary GC column. *** Represents the migration time in the drift tube.
     | Show Table
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    This study adopted the GC-IMS method to test the volatile organic compounds (VOCs) in the samples from the different fermentation stages of Marselan wine. Figure 1 shows the gas phase ion migration spectrum obtained, in which the ordinate represents the retention time of the gas chromatographic peaks and the abscissa represents the ion migration time (normalized)[16]. The entire spectrum represents the aroma fingerprints of Marselan wine at different fermentation stages, with each signal point on the right of the relative reactant ion peak (RIP) representing a volatile organic compound detected from the sample[17]. Here, the sample in stage 1 (juice processing) was used as a reference and the characteristic peaks in the spectrum of samples in other fermentation stages were compared and analyzed after deducting the reference. The colors of the same component with the same concentration cancel each other to form a white background. In the topographic map of other fermentation stages, darker indicates higher concentration compared to the white background. In the 2D spectra of different fermentation stages, the position and number of peaks indicated that peak intensities are basically the same, and there is no obvious difference. However, it is known that fermentation is an extremely complex chemical process, and the content and types of volatile organic compounds change with the extension of fermentation time, so other detection and characterization methods are needed to make the distinction.

    Figure 1.  2D-topographic plots of volatile organic compounds in Marselan wine at different fermentation stages.

    To visually display the dynamic changes of various substances in the fermentation process of Marselan wine, peaks with obvious differences were extracted to form the characteristic fingerprints for comparison (Fig. 2). Each row represents all signal peaks selected from samples at the same stage, and each column means the signal peaks of the same volatile compound in samples from different fermentation stages. Figure 2 shows the volatile organic compounds (VOCs) information for each sample and the differences between samples, where the numbers represent the undetermined substances in the migration spectrum library. The changes of volatile substances in the process of Marselan winemaking is observed by the fingerprint. As shown in Fig. 2 and Table 2, a total of 40 volatile chemical components were detected by qualitative analysis according to their retention time and ion migration time in the HS-GC-IMS spectrum, including 17 esters, eight alcohols, eight aldehydes, two ketones, one organic acid, and four unanalyzed flavor substances. The 12 volatile organic compounds presented dimer due to ionization of the protonated neutral components before entering the drift tube[18]. As can be seen from Table 2, the VOCs in the winemaking process of Marselan wine are mainly composed of esters, alcohols, and aldehydes, which play an important role in the construction of aroma characteristics.

    Figure 2.  Fingerprints of volatile organic compounds in Marselan wine at different fermentation stages.
    Table 2.  Antioxidant activity, total polyphenols, and flavonoids content of Marselan wine at different fermentation stages.
    Winemaking stage TFC (mg CE/L) TPC (mg GAE/L) FRAP (mM FeSO4/mL) ABTs (mM Trolox/L)
    Stage 1 315.71 ± 0.00d 1,083.93 ± 7.79d 34.82c 38.92 ± 2.12c
    Stage 2 1,490.00 ± 7.51c 3,225.51 ± 53.27c 77.32b 52.17 ± 0.95b
    Stage 3 1,510.00 ± 8.88a 3,307.143 ± 41.76b 77.56b 53.04 ± 0.76b
    Stage 4 1,498.57 ± 6.34b 3,370.92 ± 38.29a 85.07a 57.46 ± 2.55a
    Means in the same column with different letters are significantly different (p < 0.05).
     | Show Table
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    Esters are produced by the reaction of acids and alcohols in wine, mainly due to the activity of yeast during fermentation[19], and are the main components of fruit juices and wines that produce fruit flavors[20,21]. In this study, it was found that they were the largest detected volatile compound group in Marselan wine samples, which is consistent with previous reports[22]. It can be observed from Table 2 that the contents of most esters increased gradually with the extension of fermentation time, and they mainly began to accumulate in large quantities during the stage of alcohol fermentation. The contents of ethyl hexanoate (fruity), isoamyl acetate (banana, pear), ethyl octanoate (fruity, pineapple, apple, brandy), ethyl acetate (fruity), ethyl formate (spicy, pineapple), and ethyl butanoate (sweet, pineapple, banana, apple) significantly increased at the stage of alcoholic fermentation and maintained a high level in the subsequent fermentation stage (accounting for 86% of the total detected esters). These esters can endow a typical fruity aroma of Marselan wine, and played a positive role in the aroma profiles of Marselan wine. Among them, the content of ethyl acetate is the highest, which is 5,153.79 μg/mL in the final fermentation stage, accounting for 33.6% of the total ester. However, the content of ethyl acetate was relatively high before fermentation, which may be from the metabolic activity of autochthonous microorganisms present in the raw materials. Isobutyl acetate, ethyl 3-methyl butanoate, propyl acetate, ethyl propanoate, ethyl isobutyrate, and ethyl lactate were identified and quantified in all fermentation samples. The total contents of these esters in stage 1 and 4 were 255.28 and 1,533.38 μg/mL, respectively, indicating that they may also have a potential effect on the aroma quality of Marselan wine. The results indicate that esters are an important factor in the formation of flavor during the brewing process of Marselan wine.

    Alcohols were the second important aromatic compound in Marselan wine, which were mainly synthesized by glucose and amino acid decomposition during alcoholic fermentation[23,24]. According to Table 2, eight alcohols including methanol, ethanol, propanol, butanol, hexanol, amyl alcohol, 3-methyl-1-butanol, and 2-methyl-1-propanol were detected in the four brewing stages of Marselan wine. The contents of ethanol (slightly sweet), 3-methyl-1-butanol (apple, brandy, spicy), and 2-methyl-1-propanol (whiskey) increased gradually during the fermentation process. The sum of these alcohols account for 91%−92% of the total alcohol content, which is the highest content of three alcohols in Marselan wine, and may be contributing to the aromatic and clean-tasting wines. On the contrary, the contents of 1-hexanol and methanol decreased gradually in the process of fermentation. Notably, the content of these rapidly decreased at the stage of alcoholic fermentation, from 2,026.07 to 1,218.98 μg/mL and 662.08 to 76.94 μg/mL, respectively, which may be ascribed to volatiles changed from alcohols to esters throughout fermentation. The reduction of the concentration of some alcohols also alleviates the strong odor during wine fermentation, which plays an important role in the improvement of aroma characteristics.

    Acids are mainly produced by yeast and lactic acid bacteria metabolism at the fermentation stage and are considered to be an important part of the aroma of wine[22]. Only one type of acid (acetic acid) was detected in this experiment, which was less than previously reported, which may be related to different brewing processes. Acetic acid content is an important factor in the balance of aroma and taste of wine. Low contents of volatile acids can provide a mild acidic smell in wine, which is widely considered to be ideal for producing high-quality wines. However, levels above 700 μg/mL can produce a pungent odor and weaken the wine's distinctive flavor[25]. The content of acetic acid increased first and then decreased during the whole fermentation process. The content of acetic acid increased rapidly in the second stage, from 719.91 to 3,914.55 μg/mL reached a peak in the third stage (5,161.81 μg/mL), and decreased to 4,630.65 μg/mL in the last stage of fermentation. Excessive acetic acid in Marselan wine may have a negative impact on its aroma quality.

    It was also found that the composition and content of aldehydes produced mainly through the catabolism of amino acids or decarboxylation of ketoacid were constantly changing during the fermentation of Marselan wines. Eight aldehydes, including furfural, hexanal, heptanal, 2-methylpropanal, 3-methylbutanal, dimethyl sulfide, (E)-2-hexenal, and (E)-2-pentenal were identified in all stage samples. Among them, furfural (caramel bread flavor) and hexanal (grass flavor) are the main aldehydes in Marselan wine, and the content increases slightly with the winemaking process. While other aldehydes such as (E)-2-hexenal (green and fruity), 3-methylbutanol (fresh and malt), and 2-methylpropanal (fresh and malt) were decomposed during brewing, reducing the total content from 536.52 to 85.15 μg/mL, which might potently affect the final flavor of the wine. Only two ketones, acetone, and 3-hydroxy-2-butanone, were detected in the wine samples, and their contents had no significant difference in the fermentation process, which might not affect the flavor of the wine.

    To more intuitively analyze the differences of volatile organic compounds in different brewing stages of Marselan wine samples, principal component analysis was performed[2628]. As presented in Fig. 3, the points corresponding to one sample group were clustered closely on the score plot, while samples at different fermentation stages were well separated in the plot. PC1 (79%) and PC2 (18%) together explain 97% of the total variance between Marselan wine samples, indicating significant changes in volatile compounds during the brewing process. As can be seen from the results in Fig. 3, samples of stages 1, 2, and 3 can be distinguished directly by PCA, suggesting that there are significant differences in aroma components in these three fermentation stages. Nevertheless, the separation of stage 3 and stage 4 samples is not very obvious and both presented in the same quadrant, which means that their volatile characteristics were highly similar, indicating that the volatile components of Marselan wine are formed in stage 3 during fermentation (Fig. S1). The above results prove that the unique aroma fingerprints of the samples from the distinct brewing stages of Marselan wine were successfully constructed using the HS-GC-IMS method.

    Figure 3.  PCA based on the signal intensity obtained with different fermentation stages of Marselan wine.

    Based on the results of the PCA, OPLS-DA was used to eliminate the influence of uncontrollable variables on the data through permutation test, and to quantify the differences between samples caused by characteristic flavors[28]. Figure 4 revealed that the point of flavor substances were colored according to their density and the samples obtained at different fermentation stages of wine have obvious regional characteristics and good spatial distribution. In addition, the reliability of the OPLS-DA model was verified by the permutation method of 'Y-scrambling'' validation. In this method, the values of the Y variable were randomly arranged 200 times to re-establish and analyze the OPLS-DA model. In general, the values of R2 (y) and Q2 were analyzed to assess the predictability and applicability of the model. The results of the reconstructed model illustrate that the slopes of R2 and Q2 regression lines were both greater than 0, and the intercept of the Q2 regression line was −0.535 which is less than 0 (Fig. 5). These results indicate that the OPLS-DA model is reliable and there is no fitting phenomenon, and this model can be used to distinguish the four brewing stages of Marselan wine.

    Figure 4.  Scores plot of OPLS-DA model of volatile components in Marselan wine at different fermentation stages.
    Figure 5.  Permutation test of OPLS-DA model of volatile components in Marselan wine at different fermentation stages (n = 200).

    VIP is the weight value of OPLS-DA model variables, which was used to measure the influence intensity and explanatory ability of accumulation difference of each component on classification and discrimination of each group of samples. In previous studies, VIP > 1 is usually used as a screening criterion for differential volatile substances[2830]. In this study, a total of 22 volatile substances had VIP values above 1, indicating that these volatiles could function as indicators of Marselan wine maturity during fermentation (see Fig. 6). These volatile compounds included furfural, ethyl lactate, heptanal, dimethyl sulfide, 1-propanol, ethyl isobutyrate, propyl acetate, isobutyl acetate, ethanol, ethyl hexanoate, acetic acid, methanol, ethyl formate, ethyl 3-methylbutanoate, ethyl acetate, hexanal, isoamyl acetate, 2-methylpropanal, 2-methyl-1-propanol, and three unknown compounds.

    Figure 6.  VIP plot of OPLS-DA model of volatile components in Marselan wine at different fermentation stages.

    This study focuses on the change of volatile flavor compounds and antioxidant activity in Marselan wine during different brewing stages. A total of 40 volatile aroma compounds were identified and collected at different stages of Marselan winemaking. The contents of volatile aroma substances varied greatly at different stages, among which alcohols and esters were the main odors in the fermentation stage. The proportion of furfural was small, but it has a big influence on the wine flavor, which can be used as one of the standards to measure wine flavor. Flavonoids and phenols were not only factors of flavor formation, but also important factors to improve the antioxidant capacity of Marselan wine. In this study, the aroma of Marselan wines in different fermentation stages was analyzed, and its unique aroma fingerprint was established, which can provide accurate and scientific judgment for the control of the fermentation process endpoint, and has certain guiding significance for improving the quality of Marselan wines (Table S1). In addition, this work will provide a new approach for the production management of Ningxia's special wine as well as the development of the native Chinese wine industry.

  • The authors confirm contribution to the paper as follows: study conception and design: Gong X, Fang L; data collection: Fang L, Li Y; analysis and interpretation of results: Qi N, Chen T; draft manuscript preparation: Fang L. All authors reviewed the results and approved the final version of the manuscript.

  • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

  • This work were supported by the project of Hainan Province Science and Technology Special Fund (ZDYF2023XDNY031) and the Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences in China (Grant No. 1630122022003).

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

  • Supplemental Table S1 List of plant material.
    Supplemental Table S2 Composition of volatile organic compounds in leaves.
    Supplemental Fig. S1 The relationship of different genus.
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  • Cite this article

    Zhan Q, Li W, Liu Y, Zhao S, Chen S, et al. 2024. Genetic resources resistant to black spot (Alternaria alternate) identified from Chrysanthemum-related genera and potential underlying mechanisms. Ornamental Plant Research 4: e001 doi: 10.48130/opr-0023-0023
    Zhan Q, Li W, Liu Y, Zhao S, Chen S, et al. 2024. Genetic resources resistant to black spot (Alternaria alternate) identified from Chrysanthemum-related genera and potential underlying mechanisms. Ornamental Plant Research 4: e001 doi: 10.48130/opr-0023-0023

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Genetic resources resistant to black spot (Alternaria alternate) identified from Chrysanthemum-related genera and potential underlying mechanisms

Ornamental Plant Research  4 Article number: e001  (2024)  |  Cite this article

An Author Correction to this article was published on 18 February 2025, http://doi/10.48130/opr-0025-0008.

Abstract: Chrysanthemum black spot disease caused by Alternaria alternate infestation is a widespread and extremely destructive foliar disease of chrysanthemums. We compared the resistance of 14 chrysanthemum relatives to chrysanthemum black spot disease, and identified the main indicators for the evaluation and screening of chrysanthemum disease resistance, which is of great significance in laying the foundation for a larger-scale screening of chrysanthemum relatives for disease resistance and the breeding of new disease-resistant cultivars. After artificial inoculation and identification, two disease-resistant germplasm resources, 11 moderately resistant materials, and one sensitive material were obtained. In both resistant and susceptible species, we found that the trichome density and leaf wax content of the resistant material were significantly higher than that of the sensitive material, while the stomata size was smaller than that of the sensitive material. In addition, we found that the leaf extract of the disease-resistant germplasm effectively inhibited the growth rate of A. alternate mycelium on the plate, and GC-MS components found that the leaves of resistant germplasm contained more volatile antifungal organic compounds, of which the abundant falcarinol and Germacrene D might play an important role in resistance to chrysanthemum black spot disease. In summary, epidermal trichome density, wax content and terpene substance content are three important reference indicators for disease resistance evaluation of related genera of chrysanthemum. The identified resistant germplasm can also be used as parents for future cross-breeding or as rootstocks.

    • Chrysanthemum is one of the most widely cultivated flowers worldwide due to its outstanding ornamental, medicinal, and beverage value. However, in chrysanthemum planting areas, it is easy to be infected by bacteria and fungi, which induces various diseases, among which black spot disease caused by Alternaria alternate is one of the main diseases of chrysanthemum. Chrysanthemum black spot disease (hereafter referred to as CBS) causes black spots on the leaves, stunted plants, reduced flower production and quality, and in severe cases the whole plant withers and dies, so this causes huge economic losses to chrysanthemum growers and companies. As far as we know, pesticide residues and environmental degradation can result from applying pesticides as a traditional method of disease management. Screening and cultivating resistant germplasm of chrysanthemums is a friendly way to control CBS. Resistant germplasm can be used as parents of chrysanthemum hybrid breeding and rootstock of chrysanthemum asexual propagation. Previous studies have shown that chrysanthemum-related genera (abbreviated as CRG below) usually hold excellent resistance, including tolerance to salt[1, 2], drought[3, 4], waterlogging[5], heat[6], heavy metals[7], insect resistance[8], and fungal suppression[9, 10], among other things. The lack of excellent germplasm resources for disease tolerance continues to be a constraint for disease resistance breeding, and disease resistance screening in CRG is still insufficient.

      As we know, the main defense mechanisms of plants against diseases are physical and chemical defenses, among which physical defense mechanisms include trichomes, stomata, and a waxy layer. Trichomes were special structural appendages developed from plant epidermal cells, which produced a role in plant defense against biotic and abiotic stresses and were closely related to plant disease resistance[11]. Stomata were the main channels of contact between plants and the outside world, and their density, size, and aperture also affect the successful invasion of pathogenic bacteria and are closely related to plant disease resistance[12, 13]. For example, research revealed that trichome density and stomata density were possible contributors to willow rust resistance[14]. Wax was a class of secondary metabolites that evolved during the long-term ecological adaptation of plants and was widely involved in plant responses to biotic stress and abiotic physiological processes[1517]. The epicuticular wax layer functions as a physical barrier against abiotic stresses and biotic stresses caused by pathogens or pests[18].

      Plant secondary metabolites were the chemical defense factors of plants against pathogen infection, and there were many types of them. There were three major classes of plant antimicrobial secondary metabolites: terpenoids, phenolic, and nitrogenous compounds[19]. Secondary metabolites could perform as biochemical fortresses to ward off pathogen invasion in plant disease resistance, in addition to functioning as signaling substances in the signal transduction of plant disease resistance[2022]. A previous study indicated that terpenoids were the main compounds in chrysanthemum leaves[23].

      Previous research has examined the connection between leaf physical defense structures and resistance to black spot in 17 chrysanthemum cultivars[24]. Another study found that volatile terpenoids released from cultivated chrysanthemum leaves increased after being infested by pathogenic fungi[25]. However, there has been inadequate exploration of germplasm with superior resistance to black spot in CRG. Moreover, the relationship between their physical and chemical defense mechanisms and disease resistance remains unclear.

      We employed two artificial inoculation techniques to assess the resistance of 14 CRG against black spot disease and investigate the resistance mechanisms. Our study showed that in vitro leaf inoculation is a dependable and effective method for quickly evaluating the resistance of chrysanthemum plants against black spot disease. Additionally, we observed distinct differences in leaf structure between tolerant germplasm and susceptible germplasm. Further analysis of volatile substances in the leaves demonstrated that disease-resistant germplasm exhibited stronger antifungal properties, which were characterized using GC-MS (Gaschromatography-mass spectrometry) analysis to identify the composition of these volatile substances. These results contribute valuable germplasm resources for the future breeding of disease-resistant chrysanthemums.

    • The experimental plant materials were obtained from the 'China Chrysanthemum Germplasm Resource Conservation Center' of Nanjing Agricultural University, and the species names and classifications are shown in Supplemental Table S1 and Supplemental Fig. S1. The rooted seedlings were cultured in a 3:1 vermiculite mixture. Seedlings were grown in long-day conditions (16 h light, 25 °C, relative humidity 75%)[26, 27]. Plants with uniform growth were randomly divided into two groups (n = 15): the control group and the treatment group. The strain (A. alternata) used in the study was isolated in our laboratory from leaves with typical symptoms of CBS. The leaf inoculation was carried out as previously described[27]. The fungus used for inoculation was grown in potato dextrose broth (PDB) medium at 28 °C shaking at 200 rpm for 24 h. Then 2 mL of mycelial fluid was inoculated on the back of the third fully expanded leaf of the plant, one leaf per plant and two loci. The inoculated leaves were covered with a ziplock bag, and the control group was inoculated with PDB. After inoculation was completed, the plants were placed in dark conditions at a temperature of 28 °C and 80% humidity for 48 h.

      At the same time, we used the method of inoculation with detached leaves. The simplified detached leaf inoculation assay was the same as previous. We chose the third fully expanded leaf of a fresh and healthy plant, rinsed with sterile water and wrapped with moistened skim cotton on the petiole area, then the leaf was placed in a clean Petri dish and inoculated as described previously. After the inoculation was completed, the plants were sealed with plastic wrap and cultured in the dark for 7 d.

    • The severity of disease was divided into 0~3 levels: leaf health, no disease for level 0, lesion area accounted for less than 25% of the leaf for level 1, lesion area accounted for 25% to 50% of the leaf for level 2, lesion area accounted for more than 50% of the leaf for level 3. Counted the number of incidences at each level and calculated the incidence and disease index (DSI) for each species.

      Incidence(%)=ThenumberofdiseasedplantsTotalnumberofplantstreated×100
      DSI=(Diseasegrade×Numberofinfectedplants)Thehighestdiseasegrade×Thetotalnumberofinoculatedplants

      Disease resistance evaluation levels were classified according to DSI, with DSI = 0 for immunity (I), 0 < DSI ≤ 30 for resistance (R), 30 < DSI ≤ 50 for moderate resistance (MR), and DSI > 50 for susceptibility resistance (S).

    • To investigate the variation in physical defenses between resistant and sensitive materials, we examined the trichomes and stomata present on the lower epidermis of leaves. We selected the second fully grown fresh leaf below the upper portion of the plant and cut it at the same spot on both sides of the main vein, resulting in an area of approximately 3 mm2. Next, we treated the leaf with a 2.5% glutaraldehyde fixative to preserve it, and stored it in a refrigerator at 4 °C after leaving it at room temperature for 2 h. The samples were then observed and photographed using scanning electron microscopy (SU8100, 3.0 kV, SEM). We analyzed three leaves for each material, with six fields of view examined for each leaf.

    • We investigated the role of leaf wax by measuring the quantity of wax in the leaves. We determined the wax content (mg/g) in the fresh leaves by accurately weighing each plant's fresh leaves. Then, we cut the leaves and soaked them in 10 mL of chloroform for 2 min. The resulting solution was filtered into a beaker with a known weight. After the chloroform evaporated, we reweighed the leaves and subtracted the weight of the beaker to calculate the wax content (mg/g) in the fresh leaves. We repeated this process 20 times for each material and recorded the average value.

    • The second or third fully expanded fresh leaf below the tip of the plant was taken, and five plants were mixed and sampled three times, for a total of 15 plants. Each 0.2 g of the freshly ground sample was added with 1 mL of ethyl acetate solution, vortexed and mixed, and then shaken in a shaker at 28 °C and 200 rpm/h for 1 h. The upper clear liquid was selected as the material to be used[28].

    • At a temperature of approximately 50 °C, 500 μL of the extract was placed in an unconsolidated Potato Dextrose Agar (PDA) medium. The concentration of the preparation was extracted : PDA = 1:200. After mixing, the extract was poured into a sterile Petri dish for solidification. Next, the fungal blocks were picked and placed in the middle of the treated PDA medium, and the test was conducted with PDA medium with ethyl acetate (control 2) and blank treatment (control 1) as the control, with 15 sample sizes set for each treatment. The mycelial extension diameter (cm) was counted after 4 d and photographed and recorded.

      Inhibitionrate(%)=(Colonydiameterofcontrol2Treatedcolonydiameterofextract)Colonydiameterofcontrol2×100%
    • Rapid injection of 500 μL of ethyl acetate (containing 0.002% ethyl nonanoate as an internal standard) was performed into a 250 mg vial of Germacrene D. The vial was quickly wrapped with a sealing film and shaken to mix well, and the mother liquor was prepared for use. Take 20 μL of the mother liquor into a brown bottle containing 200 μL of ethyl acetate, mix well and seal it as the reagent to be used.

      After placing the bacterial plots in the centre of the plate, 200 μL of the prepared Germacrene D reagent was sucked up and applied onto the PDA plate with the help of an applicator stick, avoiding the fungus plots when applying the reagent; the PDA plate coated with ethyl acetate was used as the control; five replicates were set for each treatment. The prepared plates were incubated in the dark at 28 °C in a light incubator, and the mycelial growth diameter was measured after 7 d.

    • The sample preparation and extraction of leaf metabolites were the same. For each sample, 0.2 g of fresh leaf sample was mixed with 1 mL of ethyl acetate solution containing 0.002% nonyl acetate as an internal standard. The analysis was performed using a GC-MS system equipped with an HP-5 capillary column (30 m × 0.25 mm × 0.25 μm, Agilent Technologies, USA) and a 7000 D mass spectrometer (Agilent Technologies, USA). The carrier gas for gas chromatography was high-purity helium (He2, 99.999%), with a flow rate of 1 mL/min. The injection was performed using a 40:1 split injection with an injection temperature of 250 °C. Both liquid extraction and solid-phase microextraction (SPME) were used without splitting. The temperature gradient was set at a rate of 20 °C/min, starting from 40 °C and ramping up to 260 °C, followed by a 5-min hold at 260 °C. The cycle time optimization was performed using rapid cooling. The ionization mode of the mass spectrometer was electron ionization (EI), with an ionization voltage of 70 eV. The ion source temperature was set at 230 °C, and the ion source excitation energy was 70 eV. The solvent delay was 3 min. The GC-MS interface temperature was set at 260 °C, and the mass spectrometry analysis was performed in full-scan mode, with a mass scanning range of 20 to 500 atomic mass units (amu). The total time required for a single sample analysis was 40 min. The instrument was equipped with an automatic sample injector, and the injection volume was 100 μL.

    • The area of each lesion was measured using Image J, and data analysis was performed with SPSS 26 software. These data were integrated and visualized using the R programming language and GraphPad Prism 8.0. The qualitative analysis of volatile organic compounds (VOCs) was identified by comparing the retention times of substances in the NIST (National Institute of Standards and Technology) mass spectrometry database and the mass spectra of the standards, the quantification was based on the peak area of the mass spectra.

    • For disease assays, simplified detached leaf inoculation assay and whole plant inoculation assay were performed. We divided the 14 germplasms into resistant (R), moderately resistant (MR), and sensitive (S) according to the disease index. The results of identification using in vitro leaf inoculation were listed in Table 1. The statistical results after 7 d of inoculation showed that C. japonese was a resistant material (DSI = 24). Eleven germplasms, including C. ornatum and A. vulgaris, were identified as MR. Meanwhile, A. vulgaris Variegate, and A. pacificum had disease indices of 55 and 57, respectively, and were both identified as S. With the prolongation of the inoculation time, the area of the lesion continued to expand, and the lesion spreading speed of susceptible germplasms were much faster than that of resistant germplasms (Fig. 1a).

      Table 1.  Evaluation of disease resistance after inoculation of isolated leaves of CRG.

      NameIncidence rate (%)Percentage of spot area (%)Disease index (DSI)Resistance type
      C. japonese735.924R
      C. ornatum1006.733MR
      A. vulgaris1006.233MR
      A. leucophylla1007.533MR
      A. parviflora10016.233MR
      A. rubripes10015.133MR
      A. annua10016.033MR
      A. sieversiana10018.433MR
      A. indices10024.133MR
      A. viridisquama10019.333MR
      A. yunnanensis10026.633MR
      A. japonica10022.336MR
      A. vulgaris Variegate10030.755S
      A. pacificum10052.857S

      Figure 1. 

      Differences in disease phenotype of different plants after inoculation. From left to right: the disease degree of leaves deepens. n = 15. (a) Disease symptoms on 4 and 7 d after inoculation of isolated leaves. (b) Disease symptoms of whole plants at post 2 d inoculation. Scale bar = 1 cm.

      The results of identification using whole plant inoculation are shown in Table 2. We performed two independent replicates, at the same time, and the correlation coefficient was 0.974** (** means p < 0.01) which suggested good reproducibility. Typical disease symptoms appeared 2 d after plant inoculation (Fig. 1b). Based on the results of the DSI division, two germplasms with 'disease resistance' grade were obtained as C. japonese and A. parviflora. Meanwhile, 11 'moderately resistant' germplasms including A. japonica and A. vulgaris, etc. A. pacificum were still susceptible. A. pacificum had the largest average spot area percentage among the test materials, with a mean value of 42.7%, followed by A. vulgaris Variegate, with an average spot area percentage of 20.5%. The top three with a smaller proportion of lesion area were A. japonica, A. parviflora, and C. japonese, which were 4.2%, 5.7%, and 6.6%, respectively. Although A. japonica has the smallest mean lesion area percentage, it is not the most resistant.

      Table 2.  Evaluation of disease resistance after inoculation of whole plants of CRG.

      NameEXP 1EXP 2Average percentage
      of spot area (%)
      Average DSIResistance type
      Incidence rate (%)DSIIncidence rate (%)DSI
      C. japonese832892306.629R
      A. parviflora1003283285.730R
      A. japonica9231100334.232MR
      A. vulgaris10033100336.933MR
      A. leucophylla100331003315.433MR
      A. rubripes100331003310.433MR
      A. yunnanensis100331003312.133MR
      A. indices100331003319.233MR
      A. viridisquama100331003320.133MR
      A. sieversiana100331003318.033MR
      A. annua100361003618.836MR
      C. ornatum100401003816.139MR
      A. vulgaris Variegate100421004420.543MR
      A. pacificum100621005742.760S

      Comparing the results above of the isolated leaf identification and plant inoculation identification, it can be seen that the agreement of the two results was very good, with a Pearson correlation coefficient for the ratios of 0.872** (** means that p < 0.01, data not shown). Combining the two methods, C. japonese and A. parviflora were identified as R, 11 germplasms such as A. japonica, C. ornatum, A. vulgaris, A. vulgaris Variegate as MR, A. pacificum as S.

    • The results of resistance identification prompted us to explore the defense mechanism of plant disease resistance. Therefore, we selected three typical species for further analysis of leaf lower epidermis structure, namely stress resistant (C. japonese, abbreviated as R1 below, and A. parviflora, abbreviated as R2 below), and sensitive (A. pacificum, abbreviated as S below).

      The morphology of the lower epidermal trichome under the leaves of the three species were found to be quite different through leaf SEM (Fig. 2). R1 trichomes were long and fine 'T'-shaped (Fig. 2a), while S was short and broad 'T'-shaped (Fig. 2c). The trichomes of R2 were mostly 'V'-shaped (Fig. 2b). To determine if the observed CRG resistance phenotype was associated with trichome density, we quantified trichomes on the leaves of R and S. The comparison of trichome density showed that the density of trichome under the leaves of S was 6.2/mm2, while the density of trichome under R1 was as high as 29.21/mm2, 4.71 times higher than that of S, and the density of trichome on the leaves of the plants was negatively correlated with the DSI with a correlation coefficient of −0.998* (Fig. 3f). In short, the higher the density of plant trichomes, the greater the resistance to CBS.

      Figure 2. 

      Scanning electron microscopy (SEM) images of the lower epidermis of leaves from disease-resistant and susceptible materials. From left to right, the parts that were observed under the scanning electron microscope (red box part) showed the distribution and shape of trichome and stomata respectively. The red arrows indicate epidermal hairs, and the yellow arrows point to the stomata.

      Figure 3. 

      Correlation analysis of leaf wax content and lower epidermal structure with disease index. (a) Trichome densities of leaf abaxial surfaces, n = 30. (b) Stomata length and width of leaf abaxial surfaces, n = 30. (c) Stomata aperture, n = 30. (d) Stomata densities of leaf abaxial surfaces, n = 30. (e) Wax content of leaves, n = 20. (f) Visualization of the correlation analysis, Pearson correlation coefficient. All bar charts show mean ± SD. Red and green represent positive and negative correlations, respectively, and color intensity reflects the magnitude of the correlation. DI refers to disease index; TD refers to trichome density; SL refers to stomata length; SW refers to stomata width; SA refers to stomata aperture; SD refers to stomata density; WC refers to wax content.

      Upon further analysis of the stomata, there were marked differences in the stomatal aperture, size and density of the three plants (Fig. 3). To quantify the degree of stomatal closure, we expressed it in terms of stomatal aperture, which was calculated as the ratio of stomatal width to length. The stomata of S were mostly open, while the stomata of R1 were largely closed (Fig. 2, Fig. 3d). Additionally, the stomatal length and width of S were significantly greater than R1 and R2 (Fig. 3c). In addition, we found differences in stomatal density across species (Fig. 3b), but no clear correlation with plant resistance.

      The next question we wished to address was to understand whether plant wax content was related to resistance. Wax content was significantly different in species with different resistance levels (Fig. 3e). R1 had the highest wax content at 28.6 mg/g, whereas S leaves had the lowest at 9.4 mg/g, resulting in a 3.04-fold difference between the two.

    • To further explore the chemical defense mechanism of resistant and sensitive materials, the antifungal activity of plant leaf extracts was determined by plate inhibition test. The results of the experiments were calculated after 4 d of treatment. We found that A. alternata grew significantly more on PDA without leaf extract (Fig. 4). Overall, the fungal inhibition effect of the resistant material was better than that of the susceptible material, although the inhibition rate did not have a regular correlation with the DSI. Unexpectedly, the inhibitory effect of R2 leaf extracts was significantly higher than that of R1.

      Figure 4. 

      Antifungal activity of leaf extracts against A. alternate. (a) The morphology of the colony on the PDA medium after the leaves extracts were co-cultured with the A. alternate for 4 d. (b) Antifungal rate statistics.

      Encouraged by the divergence in vitro antifungal effect, the key antifungal substances were explored. Therefore, GC-MS was used to analyze the composition of VOCs. The retention times for each compound separated by GC-MS were showed in Supplemental Table S2, heat map of the VOCs via GC-MS are shown in Fig. 5. Among the three species, 36 kinds of terpenes were detected, the main components were monoterpenes and sesquiterpenes (Fig. 5b). In terms of the content of VOCs in leaves, the highest concentration was found in sesquiterpenes. Furthermore, unlike the other two species, R2 exhibited a very low content of monoterpenes, while other organic volatiles were relatively high; with falcarinol being the main component (Fig. 5c).

      Figure 5. 

      VOCs components identification of leaf extracts. (a) Heat map with major VOCs (above 1% of total VOCs present in chromatograms) emitted by three species of CRG. Colors reflect the VOC’s relative content, n = 3. (b) Venn diagram of the proportion of different classes of terpenes. (c) Statistics of different types of VOCs content in the leaves and the proportion of total VOCs content in the leaves.

      The study found that disease-resistant material had significantly higher terpenoid content than disease-susceptible material (Fig. 6a). Beta-Ylangene, beta-Copaene, Germacrene D, gamma-muurolene and neophytadiene were present in all three materials with relatively high content. Additionally, analysis demonstrated a positive correlation between the content of these substances and plant disease resistance (Fig. 6g). Interestingly, our experimental materials contained abundant amounts of falcarinol and Germacrene D, which had been identified as antifungal substances[29, 30].

      Figure 6. 

      The contents of volatiles in leaves of different germplasm were significantly different and correlated with plant disease index. (a) Comparison of total volatiles content in leaves. (b)−(f) Comparison of the contents of main volatiles in leaves of different species. (g) Visualization of the correlation analysis, Pearson correlation coefficient. Red and blue represent positive and negative correlations, respectively, and color intensity reflects the magnitude of the correlation. DI refers to disease index, TV refers to total VOCs, BC refers to beta-Copaene, BY refers to beta-Ylangene, GD refers to Germacrene D, GM refers to gamma-muurolene, NE refers to neophytadiene.

      Considering that the content of Germacrene D was significantly positively correlated with plant resistance, and the relative content of Germacrene D was abundant, we further analyzed the antifungal activity of Germacrene D, and found that it can significantly inhibit the mycelia growth of A. alternate (Fig. 7). Therefore, we hypothesise that the strength of plant disease resistance is influenced by the terpene content in the leaves, and that an abundance of terpenes contributes to the ability of the plant to fight off invading pathogens.

      Figure 7. 

      Inhibition of hyphae growth of A. alternate by Germacrene D reagent. (a) The morphology of the colony on the PDA medium after 7 d. (b)Statistical difference of colony growth diameter between control and treatment.

    • Excavation of high-quality germplasm resources of wild relatives is an important way to breed chrysanthemums for disease resistance. It was found that the progeny of crosses between cultivated chrysanthemums and Artemisia spp. had better black spot resistance than their parents[31, 32]. In addition, grafting through superior germplasm as rootstocks has become a common and effective method of improving disease resistance in crops[33]. The resistant germplasm screened by this experiment can be used as hybrid parent or grafting stock for resistance improvement of the chrysanthemum in the future. However, when encountering large quantities of germplasm resources, the problem of accurately and efficiently screening germplasm resources without destroying them is a problem that remains to be overcome. Detached leaf inoculation assays were used to determine plant germplasm resistance to diseases, such as soybean germplasm for resistance to Phagophore pachyrhizi[34], tomato germplasm resistance to late blight[35], apple genotypes resistance to Alternaria blotch[36] and oat (Avena sterilis) resistance to crown rust[37]. Among the whole-plant and exfoliated leaf screening techniques for the identification of anthracnose resistance in strawberry plants, scholars noted that the study was used to develop an exfoliated leaf curation method for strawberries that can reliably and rapidly determine the degree of resistance of strawberry germplasm to anthracnose[38]. We discovered that two inoculation methods showed largely consistent results and that they both reflected the differences in disease resistance between the different materials. This means that the isolated leaf method can be prioritized for primary screening when screening germplasm resources in large quantities in the future, which will effectively reduce the workload. Furthermore, screening for CBS resistance using isolated chrysanthemum leaves is an alternative to inoculating whole plants and may eliminate damage to the desired germplasm.

    • The trichome of plants not only increases the thickness of the epidermis but also behaves as a physical barrier against external invasion. The results of this study showed that the species with the highest trichome height were also the most resistant, in agreement with Patil et al. findings[39]. At the same time, R had the highest density of trichomes, and the large number of trichomes enriched on the surface of the plant leaves did not facilitate the invasion of the pathogens and thus reduced the disease of the plant, which has also been investigated in other species[40]. For example, highly resistant grapes had more trichomes and thicker cuticles on the leaves than susceptible germplasm[41], and in a study of resistance to the fungus Didymella bryoniae in Cucurbitaceae, it was found that the higher the density of leaf trichomes, the smaller the average leaf necrosis area[42]. However, based on phylogenetic statistics, the severity of Asteraceae powdery mildews is not related to trichome density[43]. In summary, the relationship between trichomes and plant resistance might be related to plant species, pathogen species, and mode of invasion.

      It is worth noting that the stomatal length, width, and aperture of susceptible materials are much larger than those of resistant varieties. Stomata are natural openings through which many pathogenic fungi enter plants and the outside world, and their closure is the pivotal line of defense against plant pathogens[44, 45]. These findings suggest that the larger size of stomata and bigger stomata aperture are more conducive to pathogen invasion. Although previous studies have shown that stomatal density is related to plant disease resistance[46], this study did not find a clear regularity between resistance and stomatal density, consistent with Yang et al.[47].

      Waxes played a critical role in resisting infestation by bacterial and fungal pathogens[48]. Tian et al. showed that the wax layer was a powerful physical structural barrier for plants to resist and delay invasion by pathogenic fungi and that the wax content and trichome density of bitter melon leaves can be used as reference indicators for the identification of resistance to powdery mildew in bitter melon[49]. Equally, in this experiment, the wax content of R1 was found to be 3.04 times higher than that of S. That means the wax content of the resistant material was higher than that of the disease susceptible material. Combining the leaf trichome and wax content , it could be assumed that the physical defense of the leaf played an outstanding role in the resistance of C. japonese to pathogenic infestation.

    • Plants respond to pathogenic infestation by releasing high amounts of VOCs, which can either serve as a direct defense against pathogens or as a signal for an antimicrobial response. According to several studies, there was a link between plant VOCs[50] release and resistant plant strains. Grapevine genotypes were resistant to grapevine downy mildew release more monoterpenes and sesquiterpenes than sensitive genotypes[51]. Additionally, we discovered that the composition and the number of VOCs from plant leaves varied significantly across the range of resistance. Furthermore, different species in vitro antifungal activity of A. alternata varies, which may be connected to the part of key antifungal substances.

      Moreover, another important finding was that the R2 has a significant inhibitory effect compared to others. It is noteworthy that the relative contents of Germacrene D and falcarinol in the leaf VOCs of R2 were high. It had been found that big root geranium was associated with the defense mechanism of cashew (Anacardium occidentale) leaves in response to invasion by the black mold fungus Pilgeriella anacardii Arx & Müller[52]. Germacrene D was a signal molecule that inhibits the spread of Phytophthora from necrotic parts of poplar bark to healthy living tissue[53]. In strawberry fruit, methyl jasmonate (MeJA) improved resistance to grey mold infection by inducing FaTPS1 expression and rapidly increasing terpene content, particularly Germacrene D[54]. In addition, Pinus nigra volatile oil rich in terpenoids (Germacrene D-4-ol) and structurally similar terpenoids (Germacrene D) had inhibitory effects against Aspergillus niger and Bacillus subtilis[55]. Falcarinol was thought to act as a plant chemical defense agent to thwart infection by devastating pathogens[56]. Falcarinol was also beneficial to human health because of its outstanding pharmacological effects[5759]. In addition, falcarinol has excellent antioxidant activity and antibacterial activity[60]. Based on the evidence from this work, it would be seen that Germacrene D and falcarinol, which had antifungal activity in R2, might play a direct defense role in response to A. alternata mycelial invasion.

      Over a long period of evolution, plants have developed complex disease-resistance mechanisms to resist pathogenic fungi. The organic extracts of S leaves were relatively effective in this study, but S showed weak resistance to the disease when identified by artificial inoculation. This might be due to deficiencies in physical defense, sparse epidermal hairs, larger stomata, and few wax contents, which facilitate the invasion of mycelium. On the contrary, the leaf surface structure of disease-resistant materials and its antifungal volatile metabolites play a key role in resisting pathogen infection.

    • In this study, we compared the resistance of 14 species of Chrysanthemum-related genera to Chrysanthemum black spot disease and found that the inoculation of detached leaves can be used as a favorable adjunct to the primary resistance screening of germplasm resources. At the same time, we analyzed the physical and chemical defense mechanisms of disease-resistant and susceptible species. The superior tolerance of C. japonese was likely related to its physical defense, a combination of its trichome layer, its closure of stomata, and its abundant wax content, which reduced the invasion of pathogens. In contrast, the tolerance of A. parviflora was due to the prominent role of its chemical defense, its high relative content of VOCs substances in the leaves, and its significant fungi inhibitory effect, of which two substances, Germacrene D and falcarinol, might be the crucial inhibitory substances. The density of the leaf trichome and the wax content can be used as reference indicators for the identification of resistance to CBS in CRG. The resistance of CRG to the CBS can be partly explained by differences in physical and chemical defenses. The evaluation of the disease tolerance of the CRG further enriched the disease tolerance germplasm resource bank of the chrysanthemum and clarified the different physical and chemical responses of three chrysanthemum-related genera with great differences in disease tolerance, and it has certain reference significance for the research of chrysanthemum disease resistance mechanism.

    • The authors confirm contribution to the paper as follows: study conception and design: Guan Z, Liu Y; data collection: Zhan Q, Li W; analysis and interpretation of results: Zhao S, Chen S , Fang W, Chen F; draft manuscript preparation: Zhan Q. All authors reviewed the results and approved the final version of the manuscript.

    • All data generated or analyzed during this study are included in this published article and its supplementary information files.

      • This work was supported by grants from the National Natural Science Foundation of China (32171854) , the Jiangsu seed industry revitalization project [JBGS (2021) 094] and the Jiangsu Forestry Science and Technology Innovation and Promotion Project [LYKJ(2021)13].

      • The authors declare that they have no conflict of interest. Sumei Chen is the Editorial Board member of Ornamental Plant Research who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial Board member and the research groups.

      • Copyright: © 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/.
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    Zhan Q, Li W, Liu Y, Zhao S, Chen S, et al. 2024. Genetic resources resistant to black spot (Alternaria alternate) identified from Chrysanthemum-related genera and potential underlying mechanisms. Ornamental Plant Research 4: e001 doi: 10.48130/opr-0023-0023
    Zhan Q, Li W, Liu Y, Zhao S, Chen S, et al. 2024. Genetic resources resistant to black spot (Alternaria alternate) identified from Chrysanthemum-related genera and potential underlying mechanisms. Ornamental Plant Research 4: e001 doi: 10.48130/opr-0023-0023

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