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2023 Volume 3
Article Contents
Abstract
Introduction
Materials and methods
Plant materials and growth conditions
Experimental treatments and design
Measurements
Soil water content and plant growth and physiological traits
Hyperspectral imaging of phenotypic responses to drought stress
Multispectral imaging of phenotypic responses to drought stress
Chlorophyll fluorescence imaging of physiological response to drought stress
Statistical analysis
Results
Changes in soil water content, leaf relative water content, and visual turf quality during drought stress
Hyperspectral and multispectral imaging of phenotypic responses to drought stress
Chlorophyll fluorescence imaging of photosynthetic responses to drought stress
Correlation among individual imaging indices associated with phenotypic and photosynthetic responses to drought stress
Prediction of turf quality and leaf relative water content using integrated indices from hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence
Discussion
Conclusions
Acknowledgments
Conflict of interest
Supplementary information
Rights and permissions
References
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ARTICLE   Open Access    

D-cysteine desulfhydrase DCD1 participates in tomato resistance against Botrytis cinerea by modulating ROS homeostasis

  • 1.

    School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China

  • 2.

    Winall Hi-Tech Seed Co., Ltd, Hefei 231283, China

  • 3.

    Technical Center of Hefei Customs, Hefei 230022, China

  • 4.

    Technology Center of Hefei Customs, Anhui Province Key Laboratory of Analysis and Detection for Food Safety, Hefei 230022, China

  • # These authors contributed equally: Yuqi Zhao, Kangdi Hu

More Information
  • Tomato is one of the most popular horticultural crops, and many commercial tomato cultivars are particularly susceptible to Botrytis cinerea. Hydrogen sulfide (H2S) is an important gaseous molecule in various plant stress responses. In this study, it was found that endogenous H2S increases in tomato leaves in response to B. cinerea infection, along with a 3.8-fold increase in gene expression of DCD1 which encodes a H2S-generating enzyme D-cysteine desulfhydrase 1 in tomato at 3 DPI. Then we investigated the role of DCD1 in resistance of tomato leaves and fruits to B. cinerea. The mutation of DCD1 by CRIPSR/Cas9 greatly reduced the resistance of tomato leaves and breaker and red fruits to B. cinerea accompanied with increased reactive oxygen species (ROS) especially hydrogen peroxide (H2O2) and malondialdehyde (MDA) content increased by 1.2 and 1.4 times respectively at 5 DPI of leaves. Further investigation showed that DCD1 mutation caused decreased activity of antioxidative enzymes superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT) in both leaves and fruits, in particular, CAT activity in dcd1 mutant was 25.0 % and 41.7 % of that in WT at leaves and red fruits at 5 DPI. DCD1 mutation also caused decreased expression of defense-related genes PAL (encoding phenylalanine ammonia-lyase) and PUB24, and their expression in the dcd1 red fruit is approximately 1.3 and 1.8 times higher than in wild-type red fruit at 5 DPI, respectively. Thus, the work emphasizes the positive role of DCD1 and H2S in plant responses to necrotrophic fungal pathogens. In addition, the work provides strong evidence that fruit at ripened stage is more susceptible to B. cinerea infection compared with green fruit, suggesting that senescence of plant tissues is more favorable to B. cinerea infection.

  • Introduction

    Drought is a major abiotic stress affecting plant growth which becomes even more intensified as water availability for irrigation is limited with current climate changes[1]. Timely detection and identification of drought symptoms are critically important to develop efficient and water-saving irrigation programs and drought-tolerance turfgrasses. However, turfgrass assessments of stress damages have been mainly using the visual rating of turf quality which is subjective in nature and inclined to individual differences in light perception that drives inconsistency in estimating color, texture, and pattern of stress symptoms in grass species[2−4]. Remote sensing with appropriate imaging technology provides an objective, consistent, and rapid method of detecting and monitoring drought stress in large-scale turfgrass areas, which can be useful for developing precision irrigation programs and high-throughput phenotyping of drought-tolerance species and cultivars in breeding selection[5].

    Spectral reflectance and chlorophyll fluorescence imaging are emerging tools for rapid and non-destructive monitoring of drought effects in crops. These tools combine imaging and spectroscopy modalities to rigorously dissect the structural and physiological status of plants[6,7]. Spectral reflectance imaging captures reflected light (one out of three fates of light: reflect, absorb and transmit when striking leaf) at different wavelengths ranging from visible to near-infrared regions to characterize vegetation traits[8,9]. Within spectral reflectance imaging, multispectral imaging on one hand measures reflected light in three to ten different broad spectral bands in individual pixels[10,11]. Hyperspectral imaging on the other hand captures reflected light in narrow and more than 200 contiguous spectral bands. Some absorbed light by leaf is re-radiated back in the form of fluorescence and fluorescence imaging utilizes those lights in red and far-red regions to capture plant physiological status[12]. When drought progresses, plants start to develop various symptoms (physiological modifications) gradually over time[13]. Some of those symptoms include stomata closure, impediment in gas exchange, change in pigment composition and distribution which result in wilting and associated morphological alteration in leaf color (senescence), shape (leaf curling) and overall plant architecture. As different plant components or properties reflect light differently at different wavelengths and patterns of reflectance and fluorescence change along with plant stress and related symptoms development, spectral reflectance and fluorescence imaging provide accurate, reliable and detailed information for crop drought monitoring. Fluorescence imaging primarily based on fluorescing plant components or chlorophyll complex in photosynthetic antenna and reaction centers and therefore it mainly monitors stress development by tracking changes in overall photosynthetic performance or other metabolism that interfere with photosynthetic operation[9,14]. Multispectral imaging, hyperspectral imaging, or chlorophyll fluorescence has been used in different studies for plant responses to drought stress in various plant species[10,15−17]. The comparative approach of multiple imaging technologies could help to find the efficient methods for the evaluation of plant responses and tolerance to drought[18].

    Vegetation indices derived from multispectral or hyperspectral imaging and fluorescence parameters typically are ratio or linear combinations of reflectance and fluorescence emissions from leaves or canopy of plants, respectively[19,20]. Canopy reflectance at different wavelengths and chlorophyll fluorescence varies with canopy color and density and changes with environmental conditions that affect plant growth, including drought stress[14,20,21]. These variations in reflectance and fluorescence are captured by vegetation indices, such as normalized difference vegetation index (NDVI) and fluorescence parameters including the ratio of variable fluorescence to maximum fluorescence (Fv/m) which are commonly used to evaluate environmental impact on plant growth. Other indices reflect physiological health of plants, such as photochemical reflectance index (PRI) has recently been reported to be useful for drought stress assessment in crops[19]. Previous research identified varying sensitivity of PRI and NDVI to detect water stress; for example, Sun et al.[22] found PRI to be a prominent indicator of drought stress whereas Kim et al.[20] discovered NDVI had greater correlation with drought stress development. There are also several conflicting findings on the responsiveness of fluorescence parameters to drought stress. Photochemical efficiency of PSII (Fv/Fm) was found to be greatly related to drought stress by Panigada et al.[23] but Jansen et al.[24] reported Fv/Fm to be relatively insensitive to drought progression. Lu & Zhang[25] identified that coefficient of photochemical quenching (qP) was insensitive to drought stress whereas Moustakas et al.[26] reported that (qP) being the most sensitive indicator of such stress conditions. There is a need for a comprehensive study that examines multiple vegetation indices (both hyperspectral and multispectral indices) and fluorescence parameters, and parallelly assess their sensitivities to reflect plant growth and physiological status during drought stress.

    The objectives of the current study were: (1) to perform comparative analysis of drought responses of vegetation and photosynthetic indices using multispectral, hyperspectral and chlorophyll fluorescence imaging for Kentucky bluegrass (Poa pratensis L.), a cool-season perennial grass species widely used as turfgrass; (2) identify major vegetation and photosynthetic indices from the imaging technologies and correlated to visual turf quality and leaf relative water content from the destructive measurement; and (3) determine the major vegetation and photosynthetic indices that are most responsive or sensitive to the progression of drought stress that may be useful to early detection and monitoring the level of drought stress causing growth and physiological damages in cool-season grass species.

    Materials and methods

    Plant materials and growth conditions

    Sod strips of Kentucky bluegrass cultivar 'Dauntless' were collected from established field plots at the Rutgers Plant Science Research and Extension Farm, Adelphia, NJ, USA. Sods were planted in plastic pots of 18 cm diameter and 20 cm length filled with a mixture of soil (sandy loam, semi-active, mesic Typic Hapludult; pH 6.55; 260 kg·P·ha−1, 300 kg·K·ha−1) and sand in the ratio of 2/1 (v/v). Plants were established for 50-d in a greenhouse with 24/22 °C day/night average temperatures, 12-h average photoperiod and 750 μmol·m−2·s−1 average photosynthetically active radiation (PAR) with natural sunlight and supplemental lightings. Plants were well-watered, trimmed weekly to 100 mm and fertilized weekly with a 24–3.5–10 (N–P–K) fertilizer (Scotts Miracle-Gro) at the rate of 2.6 g·N·m−2 during the establishment period in the greenhouse. Once plants were well-established, they were moved to the controlled environmental growth chamber (GC72, Environmental Growth Chambers, Chagrin Falls, OH, USA). The growth chamber was controlled at 22/18 °C day/night temperature, 60% relative humidity, 12-h photoperiod and 650 μmol·m−2·s−1 PAR at the canopy level. Plants were allowed to acclimate for a week within the growth chamber conditions and then treatments were initiated.

    Experimental treatments and design

    There were two different treatments: well-watered control and drought stress. For the well-watered control, plants were irrigated once every two days with sufficient water until drainage occurred from the pot bottom or when soil water content reached the field capacity. Drought stress was imposed by withholding irrigation from each pot throughout the experiment period. Each treatment had five replicates. The experimental treatments were arranged as a complete randomized design with plants of both treatments randomly placed and relocated in the chamber twice each week to minimize effects of potential microenvironment variations in the growth chamber.

    Measurements

    Soil water content and plant growth and physiological traits

    A time-domain reflectometry system (Model 6050 × 1; Soil Moisture Equipment, Santa Barbara, CA, USA) installed with 20 cm soil moisture probe was used to measure soil volumetric water content. Volumetric water content was measured every two days in each pot to track soil moisture dynamics in control and drought stress treatments. To assess plant responses at different soil moisture levels, turfgrass quality (TQ) and leaf relative water content (RWC) were evaluated. Turfgrass quality was visually rated on a scale of 1-9 depending upon canopy color, uniformity and density[27]. A rating of 1 indicates discolored and completely dead plants, 9 indicates lush green colored healthy plants and 6 indicates the minimum acceptable turfgrass quality. Leaf RWC was measured by soaking 0.2 g fresh leaves in distilled water overnight at 4 °C[28]. Turgid leaves after overnight soaking were oven dried at 70 °C to a constant dry weight. Leaf RWC was calculated as [(fresh weight – dry weight)/ (turgid weight – dry weight)] × 100.

    Hyperspectral imaging of phenotypic responses to drought stress

    Control and drought stress pots were scanned using a close-range benchtop hyperspectral imaging system (Resonon Inc., Bozeman, MT, USA) containing Pika XC2 camera equipped with 23 mm lens. This camera took images in spectral range of 400–1,000 nm with much detailed spectral resolution of 1.9 nm in 447 different spectral channels. The camera provided 1600 spatial pixels and maximum frame rate of 165 frames per second. It had 23.1° field of view and 0.52 milli-radians instantaneous field of view. Resonon hyperspectral imaging systems are line-scan imagers (also referred to as push-broom imagers) that collect spectral data from each pixel on one line at a time. Multiple lines are imaged when an object or pot kept in scanning stage of linear stage assembly underneath the camera is moved by a stage motor. Those line images are assembled to form a complete image. The systems had regulated lights placed above the linear stage assembly to create optimal conditions for performing the scans. Lights were at the same level as the lens on a parallel plane. Distance between lens and the top of grass canopy was maintained at 0.4 m for capturing the best representation of drought progression. All scans were performed using spectronon pro (Resonon Inc., Bozeman, MT, USA) software connected to the camera using a USB cable. Before performing a scan, the lens was appropriately focused, dark current noise was removed and the system was calibrated for reflectance measurement using a white tile provided by the manufacturer. To ensure distortion-free hyperspectral datacube with a unit-aspect-ratio image, benchtop system's swatch settings were adjusted using pixel aspect ratio calibration sheet also provided by the manufacturer. Once the system was ready, controlled- and stressed-pots were scanned individually every two days throughout the experiment. As the lens was focused centrally, obtained images were of the central grass area and were processed using spectronon pro data analysis software. The entire grass image was selected using a selection tool and the spectrum was generated. From each spectrum, vegetation indices were calculated either using built-in plugins or by manually creating algorithms. The list of vegetation indices calculated using image analysis is mentioned in Table 1.

    Table 1.  List of vegetation indices calculated using hyperspectral and multispectral image analysis for drought stress monitoring in Kentucky bluegrass. Name and number in subscript following the letter R in each formula represent the reflectance at individual light and particular wavelength.
    Vegetation indexIndex abbreviation and formula
    Hyperspectral analysisMultispectral analysis
    Structure Independent Pigment IndexSIPI = (R800 – R445) / (R800 + R680)SIPIm = (RNIR840 – RBlue444) / (RNIR840 + RRed668)
    Simple Ratio IndexSRI = R800 / R675SRIm = RNIR840 / RRed668
    Plant Senescence Reflectance IndexPSRI = (R680 –R500) / R750PSRIm = (RRed668 – RBlue475) / RRededge740
    Photochemical Reflectance IndexPRI = (R570 – R531) / (R570 + R531)PRI = (RGreen560 – RGreen531) / (RGreen560 + RGreen531)
    Normalized Difference Vegetation IndexNDVI = (R800 – R680) / (R800 + R680)NDVIm = (RNIR840 – RRed668) / (RNIR840 + RRed668)
    Normalized Difference Red EdgeNDRE = (R750 – R705) / (R750 + R705)NDREm = (RRededge717 – RRed668) / (RRededge717 + RRed668)
     | Show Table
    DownLoad: CSV
    Multispectral imaging of phenotypic responses to drought stress

    Micasense Rededge-MX dual camera system (AgEagle Sensor Systems Inc., Wichita, KS, USA) was used to collect multispectral images of controlled- and drought stressed-pots placed within a light box (1.2 m × 0.6 m × 0.6 m). The multispectral camera system had 1,280 × 960 resolution, 47.2° field of view and 5.4 mm focal length. The camera captured ten different spectral bands simultaneously on a command (Table 2). To allow the multispectral camera system, which was designed for aerial operation, to work in the light box settings, a downwelling light sensor (DLS) module provided by the manufacturer was installed to the camera system. Images were captured manually through WIFI connection from mobile devices or computer to the multispectral camera system. The sensor layout of the dual camera system, while causing negligible error in aerial condition, led to mismatching between spectral bands in a close distance, therefore, spectral bands needed to be overlapped during post-processing. The captured images of individual spectral bands were stored as separate .jpgf image files and then were used to calculate the relevant vegetation indices. Multispectral image analysis was executed using Python (Version 3.10) code by Rublee et al.[29]. Image analysis aligned ten spectral bands using Oriented FAST and Rotated BRIEF algorithm to achieve complete overlap between spectral band images. The reflectance correction panel provided by the manufacturer was used to account for the illumination condition in light box environment and the correction was reflected in pixel value adjustment for each band in python code; vegetation indices based on the aligned images were then calculated using the corresponding formula (Table 1). Images that included background noise were excluded from analysis.

    Table 2.  Spectral band details (center wavelength and band width) for Micasense Rededge-MX dual camera system.
    Band nameCentral wavelength (nm)Band width (nm)
    Blue44444428
    Blue47547532
    Green53153114
    Green56056027
    Red65065016
    Red66866814
    RE70570510
    RE71771712
    RE74074018
    NIR84084257
     | Show Table
    DownLoad: CSV
    Chlorophyll fluorescence imaging of physiological response to drought stress

    Chlorophyll fluorescence images were taken using a pulse amplitude modulated fluorescence imaging system (FC 800-O/1010, Photon System Instruments, Drasov, Czech Republic). A high-speed charge-coupled device (CCD) camera was mounted on a robotic arm placed in the middle of LED light panels. The camera had 720 × 560 pixels spatial resolution, 50 frames per second frame rate and 12-bit depth. Four different LED light panels each of 20 cm × 20 cm size were equipped with 64 orange-red (617 nm) LEDs in three panels and 64 cool-white LEDs (6,500 k) in the rest of one panel. Before making measurements, plants were dark-adapted for 25 min in a dark room to open all PSII reaction centers. The distance between camera and the top of the grass canopy was maintained at 0.3 m while taking images to ensure optimum quality. Images were acquired following the Kautsky effect measured in a pulse amplitude modulated mode[30,31]. Briefly, dark-adapted plants were first exposed to non-actinic measuring light for 5 s to measure minimum fluorescence at the dark-adapted state (Fo). Plants were immediately exposed to 800 ms saturation pulse of 3,350 µmol·m−2·s−1 to measure maximum fluorescence after dark adaptation (Fm). They were kept under dark relaxation for 17 s and then exposed to actinic light 750 µmol·m−2·s−1 for 70 s. Plants were exposed to a series of saturating pulses at 8 s, 18 s, 28 s, 48 s and 68 s during their exposure to actinic light conditions and maximum fluorescence at different light levels and steady state were measured. They were kept under dark relaxation again for 100 s and irradiated with saturating pulses at 28 s, 58 s and 88 s during dark relaxation for measuring maximum fluorescence during the relaxation. Selected durations for each light and dark relaxation state were preset in default quenching-act2 protocol of the fluorescence imaging system. Fluorescence at different light levels and steady states were used to calculate several fluorescence parameters (Table 3).

    Table 3.  Chlorophyll fluorescence parameters calculated from pulse amplitude modulated fluorescence imaging system.
    Chlorophyll fluorescence parameterFormula
    Maximum photochemical efficiency of PSII (Fv / Fm)(Fm-Fo) / Fm
    Photochemical efficiency of open PSII centers
    (F'v / F'm)    		(F'm – F'o) / F'm
    Actual photochemical quantum yield of PSII centers Y(PSII)(F'm – Fs) / F'm
    Photochemical quenching coefficient (Puddle model; qP)(F'm – Fs) / (F'm – F'o)
    Photochemical quenching coefficient (Lake model; qL)qP × F'o / Fs
    Non-photochemical quenching coefficient (qN)(Fm-F'm) / Fm
    Non-photochemical quenching (NPQ)(Fm-F'm) / F'm
    Chlorophyll fluorescence decrease ratio (Rfd)(Fm-Fs) / Fs
     | Show Table
    DownLoad: CSV

    Statistical analysis

    The two-way repeated measure analysis of variance was performed to determine treatment effects and t-test was performed to compare control and drought stress treatments at a given day of measurement. Correlation analysis using all individual observations (five replications for each control and drought stress treatments) was performed to determine the relationship among all measured traits, vegetation indices and fluorescence parameters. Partial least square regression (PLSR) models were developed in SAS JMP (version 13.2; SAS Institute, Cary, NC, USA) for comparing hyperspectral, multispectral and chlorophyll fluorescence imaging in their overall associations with physiological assessments of drought stress. Vegetation indices and fluorescence parameters from individual imaging technologies were predictor variables, and turfgrass quality and leaf relative water content were response variables. A leave one out cross validation approach was used to develop the best performing partial least square model for each imaging technology. A model was first established with all predictor variables and the variable with the lowest importance was removed from the dataset and the model was rebuilt with the remaining variables. The rebuilt model was re-validated using leave one out cross validation and assessed checking root mean PRESS and percent variation explained for cumulative Y values. From each loop of operation, one variable was removed, and a new model was developed. The whole process ended when the last variable was removed and thus no more models could be developed. Finally, a series of models was obtained, and they were compared to identify a model with the highest accuracy for individual imaging technologies. The best performing model from each imaging technology was used to estimate turfgrass quality and leaf relative water content.

    Results

    Changes in soil water content, leaf relative water content, and visual turf quality during drought stress

    The initial soil water content prior to drought stress was maintained at the field capacity of 29% and remained at this level in the well-watered control treatment during the entire experimental period (20 d) (Fig. 1a). SWC in the drought treatment significantly decreased to below the well-watered treatment, beginning at 4 d, and declined to 5.8% by 20 d.

    Figure 1.  Drought stress affected turf quality, leaf relative water content and soil volumetric water content during 20 d of stress period in Kentucky bluegrass. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint

    Leaf RWC was ≥ 93% in all plants prior to drought stress and declined to a significantly lower level than that of the control plants, beginning at 10 d of treatment when SWC declined to 16% (Fig. 1b). TQ began to decrease to a significantly lower level than the that of the well-watered plants at 10 d of drought stress at RWC of 87% and SWC of 16%, and further declined to the minimally acceptable level of 6.0 at 16 d of drought stress when RWC decreased to 66% and SWC dropped to 8% during drought stress (Fig. 1c).

    Hyperspectral and multispectral imaging of phenotypic responses to drought stress

    Most hyperspectral imaging indices, including SIPI (Fig. 2a), SRI (Fig. 2b), PRI (Fig. 2d), NDVI (Fig. 2e) and NDRE (Fig. 2f) exhibited a declining trend during 20-d drought stress while PSRI (Fig. 2C) showed increases during drought stress. The index value of drought-stressed plants became significantly lower than that of the well-watered plants, beginning at 14 d for SIPI and SRI, 16 d for PRI and PSRI, and 18 d for NDVI and NDRE. The multispectral SIPIm and SRIm did not differ between drought-stressed plants from the control plants until 18 d of treatment (Fig. 3a, b) while NDVIm, NDREm , PRIm , and PSRIm values were significantly lower than those of well-watered control plants at 16 d of drought stress (Fig. 3c−f).

    Figure 2.  Vegetation indices generated by hyperspectral sensing and sensitivity of these indices in monitoring drought in Kentucky bluegrass exposed to 20 d of drought stress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint
    Figure 3.  Vegetation indices generated by multispectral image analysis and sensitivity of these indices in monitoring drought in Kentucky bluegrass exposed to 20 d of drought tress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint

    Chlorophyll fluorescence imaging of photosynthetic responses to drought stress

    Chlorophyll fluorescence indices detected drought damages in leaf photosynthesis systems, as shown by declines in different indices during drought stress (Fig. 4). Drought-stressed plants exhibited significant lower chlorophyll fluorescence levels than that of the well-watered plants, beginning at 12 d for NPQ (Fig. 4a), 16 d for Fv/Fm (Fig. 4b), and 18 d for F'V/F'm (Fig. 4c), Y(PSII) (Fig. 4d), qP (Fig. 4e), and qL (Fig. 4f). Separation between drought-stressed and well-watered plants were also evident in index- or parameter- generated images (Fig. 5).

    Figure 4.  Chlorophyll fluorescence parameters measured by pulse amplitude modulated fluorescence imaging system and detection of drought by these parameters in Kentucky bluegrass exposed to 20 d of drought stress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points. NPQ, Non-photochemical quenching; Fv /Fm, Maximum photochemical efficiency of PSII; F'v/F'm, Photochemical efficiency of open PSII centers; Y(PSII), Actual photochemical quantum yield of PSII centers; qP, Photochemical quenching coefficient (Puddle model); qL, Photochemical quenching coefficient (Lake model); qN, Non-photochemical quenching coefficient; Rfd, Chlorophyll fluorescence decrease ratio.
    DownLoad: Full-Size Img PowerPoint
    Figure 5.  Maps generated by the three most drought sensitive indices and parameters [hyperspectral structure independent pigment index (SIPI), multispectral normalized difference vegetation index (NDVIm) and chlorophyll fluorescence NPQ]. These maps clearly separated control and drought stress after 18 d of treatment when majorities of indices and parameters detected drought stress.
    DownLoad: Full-Size Img PowerPoint

    Correlation among individual imaging indices associated with phenotypic and photosynthetic responses to drought stress

    Leaf RWC and TQ had significant correlation with most of indices and parameters calculated using three different imaging sensors (hyperspectral, multispectral and chlorophyll fluorescence) (Table 4). Among the indices, RWC had the strongest correlations with chlorophyll fluorescence NPQ (r = 0.88) and qL (r = 0.89), hyperspectral PRI (r = 0.94), and multispectral PSRIm (−0.92). TQ was most correlated to chlorophyll fluorescence NPQ (r = 0.89), hyperspectral PSRI (r = −0.90), and multispectral PSRIm (r = −0.85).

    Table 4.  Correlations among several physiological traits, vegetation indices and chlorophyll fluorescence parameters.
    RWCTQFV/FmF'v/F'mY(PSII)NPQqNqPqLRfdSIPISRIPSRIPRINDVINDREWBISIPImPSRImPRImNDVImNDREm
    RWC1.00
    TQ0.95*1.00
    FV/Fm0.87*0.85*1.00
    F'v/F'm0.81*0.77*0.95*1.00
    Y(PSII)0.85*0.74*0.80*0.74*1.00
    NPQ0.88*0.89*0.95*0.84*0.75*1.00
    qN0.84*0.83*0.96*0.84*0.77*0.96*1.00
    qP0.82*0.70*0.73*0.66*0.99*0.69*0.72*1.00
    qL0.89*0.81*0.90*0.86*0.97*0.83*0.86*0.95*1.00
    Rfd0.84*0.82*0.89*0.83*0.77*0.92*0.86*0.72*0.83*1.00
    SIPI0.84*0.71*0.63*0.58*0.51*0.57*0.69*0.48*0.60*0.46*1.00
    SRI0.57*0.62*0.44*0.45*0.330.41*0.45*0.300.400.330.83*1.00
    PSRI−0.83*−0.90*−0.90*−0.86*−0.76*−0.83*−0.87*−0.71*−0.86*−0.76*−0.75*−0.57*1.00
    PRI0.94*0.82*0.80*0.76*0.71*0.79*0.71*0.66*0.77*0.78*0.260.17−0.78*1.00
    NDVI0.53*0.65*0.41*0.43*0.41*0.42*0.400.380.43*0.42*0.50*0.42*−0.54*0.311.00
    NDRE0.64*0.73*0.64*0.63*0.45*0.54*0.64*0.400.56*0.44*0.92*0.85*−0.75*0.330.50*1.00
    SIPIm0.52*0.50*0.56*0.58*0.47*0.52*0.49*0.43*0.52*0.51*0.330.28−0.58*0.61*0.270.39−0.281.00
    PSRIm−0.92*−0.85*−0.85*−0.85*−0.83*−0.80*−0.77*−0.79*−0.88*−0.77*−0.40−0.230.77*−0.82*−0.41−0.400.32−0.52*1.00
    PRIm0.20−0.030.06−0.010.280.140.110.310.200.180.050.100.01−0.040.000.090.060.09−0.041.00
    NDVIm0.75*0.74*0.77*0.78*0.67*0.72*0.68*0.62*0.73*0.70*0.43*0.33−0.76*0.81*0.370.47*−0.350.93*−0.76*−0.051.00
    NDREm0.90*0.89*0.89*0.89*0.81*0.83*0.81*0.76*0.88*0.81*0.52*0.41*−0.87*0.87*0.45*0.53*−0.320.62*−0.87*−0.040.85*1.00
    Details for individual abbreviations of vegetation indices and fluorescence parameters used in this table were previously mentioned in Tables 1 & 3. Some other abbreviations are: RWC, leaf relative water content; and TQ, turfgrass quality. Values followed by * indicate significant correlation at p ≤ 0.05. Correlation analysis was performed using all individual data points (five replications for each control and drought stress treatments).
     | Show Table
    DownLoad: CSV

    Correlations among different vegetation indices and parameters were also significant in many cases. Hyperspectral indices such as PSRI and PRI were significantly correlated with all multispectral indices except PRIm. Multispectral NDVIm and NDREm were significantly correlated with all hyperspectral indices. When hyperspectral and multispectral indices were correlated with chlorophyll fluorescence parameters, majorities of these indices significantly associated with fluorescence parameters with exceptions of multispectral PRIm which had weak and positive (r ranges 0.06 to 0.31) associations with fluorescence parameters.

    Prediction of turf quality and leaf relative water content using integrated indices from hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence

    Partial least square regression models were developed by integrating all indices from individual imaging technologies which identified the most reliable imaging systems to detect and monitor plant responses to drought stress. The PLSR model developed using hyperspectral imaging indices had improved predictability (root mean PRESS ≤ 0.38 and percent variation explained ≥ 87) compared to such models developed using other imaging systems and associated indices (Table 5). Comparing multispectral imaging with chlorophyll fluorescence imaging, multispectral imaging had slightly better predictability [root mean PRESS = 0.40 (RWC) and 0.44 (TQ) and percent variation explained = 86 (RWC) and 83 (TQ)] considering similar number of predictor variables used for estimating TQ and RWC in all imaging systems.

    Table 5.  Summary of partial least square model showing predictability of individual models using specific numbers of predictor variables (identified by leave one out cross validation) generated by different sensing technologies. Details of individual abbreviations are mentioned in previous tables. Partial least square was performed using all individual data points (five replications for each control and drought stress treatments).
    Sensing technology used for predictionPredicted
    variable    		No. of predictors usedPredictor variablesRoot mean
    PRESS    		Percent variation explained
    for cumulative Y    		Cumulative Q2
    HyperspectralTQ4PRI, PSRI, NDRE, SIPI0.36870.99
    RWC4PRI, PSRI, NDRE, SIPI0.38890.99
    MultispectralTQ3PSRIm, NDVIm, NDREm0.44850.97
    RWC3PSRIm, NDVIm, NDREm0.40860.97
    Chlorophyll fluorescenceTQ4Fv/Fm, NPQ, qN, qL0.46830.95
    RWC3Fv/Fm, NPQ, qL0.59840.93
     | Show Table
    DownLoad: CSV

    The integrated indices from each of the three imaging systems were highly correlated to TQ, with R2 of 0.90, 0.85, and 0.83 for hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence, respectively (Fig. 6). For RWC, the correlation R2 was 0.88, 0.84, and 0.80, respectively with hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence. The hyperspectral imaging was better be able to predict TQ and RWC compared to other imaging systems (Fig. 6).

    Figure 6.  Comparison of predicted turfgrass quality (TQ) and leaf relative water content (RWC) versus their measured values using partial least square regression model. Turfgrass quality and relative water contents were predicted using various indices generated by hyperspectral, multispectral and chlorophyll fluorescence sensing technologies. The dashed line represents the I:I line. Regression analysis was performed using all individual data points (five replications for each control and drought stress treatments).
    DownLoad: Full-Size Img PowerPoint

    Discussion

    Leaf RWC and TQ are the two most widely used parameters or traits to evaluate turfgrass responses to drought stress[28,32,33]. In this study, RWC detected water deficit in leaves at 10 d of drought stress when SWC declined to 16% and TQ declined to below the minimal acceptable level of 6.0 at 16 d of drought stress when RWC decreased to 66% and SWC dropped to 8% during drought stress. These results suggested that RWC was a sensitive trait to detect water stress in plants, which is in agreement with previous research[34,35]. However, leaf RWC is a destructive measurement and TQ is a subjective estimate. Nondestructive and quantitative detection of stress symptoms in plants through assessing changes in phenotypic and physiological responses of plants to drought stress is critical for developing water-saving irrigation programs and breeding selection traits to increase water use efficiency and improve plant tolerance to drought stress. In this study, some of the phenotypic traits assessed by hyperspectral and multispectral imaging analysis and photosynthetic parameters measured by chlorophyll fluorescence were highly correlated to leaf RWC or visual TQ, as discussed in detail below, which could be used as non-destructive indicators or predictors for the level of drought stress in Kentucky bluegrass and other cool-season turfgrass species.

    The strong correlation of integrated indices from each of the three imaging systems with TQ (R2 of 0.90, 0.85, and 0.83, respectively) and RWC (R2 of 0.88, 0.84, and 0.80, respectively) for hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence suggested that all three non-destructive imaging systems could be used as a non-destructive technique to detect and monitor water stress in Kentucky bluegrass. However, the hyperspectral imaging indices had higher predictability to RWC and visual TQ compared to the indices from multispectral imaging and chlorophyll fluorescence based on the PLSR models. Hyperspectral imaging used in this study captured images in 447 different spectral bands and gathered much more details about individual components of entire vegetation as each component has its own spectral signature. Multispectral imaging captures images with ten spectral bands and chlorophyll fluorescence imaging used only emitted red and far-red lights to snap images. Nevertheless, our results suggested that the PLSR models by integrating all indices from each individual imaging technologies identified the most reliable imaging systems to detect and monitor plant responses to drought stress in this study.

    The indices derived from the three imaging systems varied in their correlation to RWC or TQ in Kentucky bluegrass in this study. Among the indices, RWC had the strongest correlations with chlorophyll fluorescence NPQ (r = 0.88) and qL (r = 0.89), hyperspectral PRI (r = 0.94), and multispectral PSRIm (r = −0.92). TQ was most correlated to chlorophyll fluorescence NPQ (r = 0.89), hyperspectral PSRI (r = −0.90), and multispectral PSRIm (r = −0.85). The indices also varied in their sensitivity to drought stress for Kentucky bluegrass, and therefore they detected drought stress in plants at different times of treatment. The hyperspectral SIPI and SRI were the most responsive to drought stress with significant decline at 14 d followed by PRI and PSRI at 16 d while NDVI and NDRE were slowest showing decline (18 d) in response to drought. Multispectral indices exhibited decline later during drought at 16 d of drought stress for NDVIm, NDREm , PRIm , and PSRIm and 18 d for SIPIm and SRIm. Indices SIPI and SRI are related to leaf carotenoid composition and vegetation density and high spectral resolution of hyperspectral system was able to capture subtle changes in pigment concentration and canopy (slight leaf shrinking and rolling) at early phase of drought progression[36,37]. Index PSRI is indicative of the ratio of bulk carotenoids including α- and β-carotenes to chlorophylls and PRI is sensitive to xanthophyll cycle particularly de-epoxidation of zeaxanthin that releases excess energy as heat in order to photoprotection[38−40]. Activation of photoprotective mechanisms including xanthophyl cycle require a certain level of stress severity depending on type of abiotic stress and plant species[41]. The PSRI calculated using both hyperspectral and multispectral imaging systems exhibited similar trends, and PSRI and PRI from either imaging system detected drought stress after 16 days of treatment applications. In agreement with our results, Das & Seshasai[42] found that PSRI showed similar trends when its value > −0.2 regardless of whether measured using hyperspectral or multispectral imaging. Both PSRI and PRI were also highly correlated to leaf RWC or TQ in Kentucky bluegrass exposed to drought stress in this study, suggesting that these two indices could be useful parameters to detect and monitor plant responses to drought stress.

    Vegetation index of NDVI has been the most widely used vegetation index in several crops such as wheat (Triticum aestivum L.)[43], cool- and warm-season turfgrass species including perennial ryegrass (Lolium perenne L.), tall fescue (Festuca arundinacea Schreb.), seashore paspalum (Paspalum vaginatum Sw.) and hybrid bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt-Davy][2, 44, 45]. For example, Bhandari et al.[43] and Badzmierowski et al.[14] found NDVI was correlated to overall turfgrass quality and chlorophyll content under nitrogen and drought stresses in tall fescue and citrus (Citrus spp.) plants. In this study, NDVI and NDRE were also correlated to leaf RWC and TQ, both NDVI and NDRE calculated from hyperspectral or multispectral imaging were least responsive to drought stress or detected drought stress later than other indices. Hong et al.[46] reported that NDVI being a better indicator than NDRE for early drought stress detection in turfgrasses when these indices were measured by handheld multispectral sensor. Eitel et al.[47] utilized broadband satellite images to estimate NDVI and NDRE and identified NDRE being a better option for early detection of stress condition in woodland area. Either NDVI or NDRE could be used as indices for vegetation density, but not sensitive indicators for plant responses to drought stress or for detecting drought damages in plants.

    Chlorophyll fluorescence reflects the integrity and functionality of photosystems in the light reactions of photosynthesis and serves as a good indicator for photochemical activity and efficiency[48]. The Y(PSII) is an effective quantum yield of photochemical energy conversion and estimates the actual proportion of absorbed light that is used for electron transport[49]. The ratio of F'v/F'm is maximum proportion of absorbed light that can be used for electron transport when all possible PSII reaction centers are open under light adapted state. Parameters qP and qL estimate the fraction of open PSII centers based on 'puddle' and 'lake or connected unit' models of photosynthetic antenna complex, respectively[50]. Rfd is an indicator for photosynthetic quantum conversion associated with functionality of the photosynthetic core unit. Overall, these parameters revolve around the operation status and functioning of PSII reaction centers or the core unit that possesses chlorophyll a-P680 in a matrix of proteins[51]. Parameter NPQ indicates non-photochemical quenching of fluorescence via heat dissipation involving xanthophyll cycle and state transition of photosystems[52]. This parameter is mostly associated with xanthophylls and other pigments in light harvesting antenna complex of photosystems but not with the PSII core unit[53]. Li et al.[9] reported that chlorophyll fluorescence imaging parameters including F'V/F'm have a limitation of late drought detection in plants. Shin et al.[54] reported F'V/F'm, Y(PSII), qP, and qL detected stress effects under severe drought when leaves were completely wilted and fresh weights declined in lettuce (Lactuca sativa L.) seedings. In this study, NPQ and Fv/Fm exhibited significant decline earlier (12−16 d of stress treatment) when drought was in mild to moderate level (> 60% leaf water content) compared to other chlorophyll fluorescence indices. The NPQ was strongly correlated to leaf RWC (r = 0.88) and TQ (r = 0.89) for Kentucky bluegrass exposed to drought stress. These results suggested that NPQ is a sensitive indicator of photosynthetic responses to drought stress and could be a useful parameter for evaluating plant tolerance to drought stress and monitoring drought responses.

    Conclusions

    The comparative analysis of phenotypic and photosynthetic responses to drought stress using three imaging technologies (hyperspectral, multispectral and chlorophyll fluorescence) using the partial least square modeling demonstrated that the integrated vegetation indices from hyperspectral imaging had higher predictability for detecting turfgrass responses to drought stress relative to those from multispectral imaging and chlorophyll fluorescence. Among individual indices, SIPI and SRI from hyperspectral imaging were able to detect drought stress sooner than others while PSRI and PRI from both hyperspectral and multispectral imaging were also highly correlated to leaf RWC or TQ responses to drought stress, suggesting these indices could be useful parameters to detect and monitor drought stress in cool-season turfgrass. While NDVI or NDRE from both hyperspectral and multispectral imaging could be used as indices for vegetation density, but not sensitive indicators for plant responses to drought stress. Among chlorophyll fluorescence indices, NPQ and Fv/Fm were more closely correlated to RWC or TQ while NPQ was most responsive to drought stress, and therefore NPQ could be a useful indicator for detecting and monitoring cool-season turfgrass response to drought stress. The sensitivity and effectiveness of these indices associated with drought responses in this study could be further testified in other cool-season and warm-season turfgrass species under field conditions. As each imaging technology used in this experiment comes with bulky accessories such as LED panels, mounting tower and support system, capturing images within limited space of controlled environmental chambers are challenging. Future research should be in developing multimodal imaging integrating major features of all three technologies and reducing size and space requirement that would deliver improved decision support for drought monitoring and irrigation management in turfgrasses. Development of advanced algorithms that could incorporate broader spectral details or band reflectance for calculating novel vegetation indices are warranted.

    Acknowledgments

    The research presented in this paper was funded by the United State Department of Agriculture - National Institute of Food and Agriculture (2021-51181-35855).

  • The authors declare that they have no conflict of interest. Bingru Huang is the Editorial Board member of Journal Grass 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 her research groups.

  • Supplemental Fig. S1 DCDs from S. lycopersicum, A. thaliana, V. vinifera, P. patens, N. tabacum, O.sativa, T. aestivum, P. bretschneideri, Z. mays, C. annuum, and M. acuminata were analyzed in the phylogenetic tree. The phylogenetic tree was constructed, the Neighbor-Joining was adopted using MEGA 7.0 software.
    Supplemental Table S1 Primers used in the present study.
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  • Cite this article

    Zhao Y, Hu K, Yao G, Wang S, Peng X, et al. 2023. D-cysteine desulfhydrase DCD1 participates in tomato resistance against Botrytis cinerea by modulating ROS homeostasis. Vegetable Research 3:21 doi: 10.48130/VR-2023-0021
    Zhao Y, Hu K, Yao G, Wang S, Peng X, et al. 2023. D-cysteine desulfhydrase DCD1 participates in tomato resistance against Botrytis cinerea by modulating ROS homeostasis. Vegetable Research 3:21 doi: 10.48130/VR-2023-0021
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ARTICLE   Open Access    

D-cysteine desulfhydrase DCD1 participates in tomato resistance against Botrytis cinerea by modulating ROS homeostasis

  • 1.

    School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China

  • 2.

    Winall Hi-Tech Seed Co., Ltd, Hefei 231283, China

  • 3.

    Technical Center of Hefei Customs, Hefei 230022, China

  • 4.

    Technology Center of Hefei Customs, Anhui Province Key Laboratory of Analysis and Detection for Food Safety, Hefei 230022, China

  • Received: 01 February 2023
  • Accepted: 16 May 2023
  • Published online: 03 July 2023
Vegetable Research  3,  Article number: 21  (2023)  |  Cite this article

Abstract: Tomato is one of the most popular horticultural crops, and many commercial tomato cultivars are particularly susceptible to Botrytis cinerea. Hydrogen sulfide (H2S) is an important gaseous molecule in various plant stress responses. In this study, it was found that endogenous H2S increases in tomato leaves in response to B. cinerea infection, along with a 3.8-fold increase in gene expression of DCD1 which encodes a H2S-generating enzyme D-cysteine desulfhydrase 1 in tomato at 3 DPI. Then we investigated the role of DCD1 in resistance of tomato leaves and fruits to B. cinerea. The mutation of DCD1 by CRIPSR/Cas9 greatly reduced the resistance of tomato leaves and breaker and red fruits to B. cinerea accompanied with increased reactive oxygen species (ROS) especially hydrogen peroxide (H2O2) and malondialdehyde (MDA) content increased by 1.2 and 1.4 times respectively at 5 DPI of leaves. Further investigation showed that DCD1 mutation caused decreased activity of antioxidative enzymes superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT) in both leaves and fruits, in particular, CAT activity in dcd1 mutant was 25.0 % and 41.7 % of that in WT at leaves and red fruits at 5 DPI. DCD1 mutation also caused decreased expression of defense-related genes PAL (encoding phenylalanine ammonia-lyase) and PUB24, and their expression in the dcd1 red fruit is approximately 1.3 and 1.8 times higher than in wild-type red fruit at 5 DPI, respectively. Thus, the work emphasizes the positive role of DCD1 and H2S in plant responses to necrotrophic fungal pathogens. In addition, the work provides strong evidence that fruit at ripened stage is more susceptible to B. cinerea infection compared with green fruit, suggesting that senescence of plant tissues is more favorable to B. cinerea infection.

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    Introduction

    • Hydrogen sulfide (H2S) has been identified as a new gasotransmitter after NO and CO, and it plays multiple physiological roles in all living organisms. Accumulating evidence unveiled that H2S participates in seed germination, root morphogenesis, stomatal movement, and photosynthesis[1]. Moreover, H2S serves as a signal to enhance plant acclimation to various abiotic and biotic stresses[2, 3]. H2S could be generated during sulfur assimilation and cysteine decomposition. In sulfate assimilation pathway, H2S is produced mainly through sulfite reductase (SiR), then sulfide is integrated into the first organic sulfur-containing molecule cysteine by O-acetylserine thiol lyase (OAS-TL)[4]. In the other route, H2S is generated from L-cysteine by the catalyzation of L-cysteine desulfhydrase (L-CDes), or D-cysteine by D-cysteine desulfhydrase (D-CDes)[5−7]. Exploration of endogenous H2S-producing enzymes in plants dates back to the 1960s, and after decades of exploration, D-cysteine desulfhydrase was found in Escherichia coli, where it decomposes D‐cysteine into pyruvate, H2S and ammonium[8]. At present, DCD has been found in the study of a variety of plants such as Arabidopsis, Spinach, Chlorella, Zucchini and Tobacco, where it can only use D-Cys as the specific substrate instead of L-Cys[5, 9] A previous study suggested that Cd-induced WRKY13 activates the expression of AtDCD, increasing the production of H2S, thereby improving Arabidopsis tolerance to Cd[10]. And the SlDCD2 mutant exhibited higher ethylene content, enhanced chlorophyll degradation and increased carotenoid accumulation. Additionally, the expression of multiple ripening-related genes, including NYC1, PAO, SGR1, PDS, PSY1, ACO1, ACS2, E4, CEL2 and EXP was enhanced during the dcd2 mutant tomato fruit ripening.[11] In addition, DCD could also improve the ability of Eruca sativa to respond to drought stress[12]. However, whether DCD is involved in biotic stress response in tomato remains to be further studied.

      Over the past decades, the role of various types of sulfur-containing compounds in plant defense and resistance to microbial pathogens has been widely discovered[13], and in addition to the recognized role of glutathione and cysteine, the role of H2S in plant disease resistance has gradually been confirmed. Exogenous H2S protected pear fruit from the infection of the pathogens Aspergillus niger and Penicillium expansum, suggesting that H2S could be developed as an effective fungicide for postharvest storage[14]. H2S fumigation was found to alleviate the decay symptoms of peach fruit inoculated with Pseudomonas and Monilinia fructicola by inhibiting spore germination and hyphal development of Pseudomonas and M. fructicola[15]. Also, H2S application reduced the rotten rate of tomatoes, citrus, apples and kiwifruit inoculated with A. niger and Italian penicillium through disturbance on spore germination and hyphal elongation of the pathogens[16]. H2S donor NaHS significantly inhibited Botryosphaeria Dothidea mycelial growth and enhanced the disease resistance of kiwifruit after harvest[17]. Previous research indicated that the transcription levels of LCD and DCD1 in Arabidopsis increased significantly after 6 h of treatment with Pseudomonas, and the production of endogenous H2S increased by 1.2−1.3 times. Overexpression of AtLCD and AtDCD1 showed increased endogenous H2S production and enhanced resistance to Pst DC3000 increase, while treatment with taurine (H2S scavenger) resulted in decreased resistance to the pathogen, suggesting the potential role of H2S in biotic stress response[18].

      Plants have been endowed with sophisticated barriers to prevent pathogen invasions[19]. Accumulation of reactive oxygen species (ROS) has been observed in a wide range of plant-pathogen interactions[20−22]. For instance, after inoculation with B. cinerea, rapid increase of ROS was found around the penetrated cell wall as well as in the plasma membrane[23]. In the process of maintaining the homeostasis of ROS, the credit of antioxidant enzymes are indispensable[24]. B. cinerea, which is a necrotrophic pathogen, prefer dead cells for nutritional purposes, and thus tissue necrosis caused by ROS during pathogen infection increased plant susceptibility to necrotrophic. Therefore, antioxidant capacity of plants including antioxidative enzymes and molecular antioxidants would be motivated to scavenging excessive ROS. To alleviate necrotrophic pathogens induced ROS stress, increased activity of antioxidant enzymes superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT) were widely observed[15]. Accumulating reports suggested that H2S acts as a signal to alleviate postharvest senescence of multiple fruits and vegetables by maintaining balanced ROS homeostasis through activating antioxidant enzymes. Thus, it is speculating that H2S might attenuate the accumulation of ROS. However, whether and how endogenous H2S interferes with ROS metabolism during tomato infection by B. cinerea is still unknown. In the present study, the gene SlDCD1 encoding D-cysteine desulfhydrase 1 in tomato was mutated by CRISPR/Cas9, and the effect of SlDCD1 mutation on plant susceptibility and ROS metabolism to B. cinerea was evaluated. Besides, the difference in plant response to B. cinerea infection during different fruit ripening stages at green, breaker or red were investigated.

    Materials and methods

      Plant material growth and pathogen inoculation

    • Tomato (S. lycopersicum, Micro Tom) plants were cultured under the following conditions: 16 h day/8 h night cycle, 25 ± 2 °C/20 ± 2 °C day/night temperature, 65 % relative humidity, and 250 μmol·m−2·s−1 light intensity.

      B. cinerea was maintained on potato glucose agar medium in the dark at 25 °C. Conidia of B. cinerea strain were harvested as described by Asselbergh et al.[25]. The conidial suspension was centrifuged for 10 min at 10,000 g. After removal of the supernatant, the conidia were resuspended in inoculation buffer (containing 16.7 mM KH2PO4 and 25 mM glucose) at a concentration of 106·mL−1. Conidia pregerminated for 2 h in the inoculation suspension at 22 °C. Fifty μL conidia suspension was injected into the flesh of tomato fruit at mature green, breaker or red stages, and the leaves were infected by vacuuming (0.8 kg·cm−2, 1 min). Subsequently, fruit and leaves were stored on wet sterile filter papers in petri dishes at 23 °C for 5 d.

      Determination of the amount of H2S release in tomato leaves and gray intensity analysis

    • As mentioned previously[26], the release of H2S in 0.2 g of tomato leaves was determined using lead acetate test strips (cat. number WHA2602501A, Sigma, Darmstadt, Germany). The amount of H2S release is measured according to the color of zinc acetate test strips. Gray intensity analysis of the zinc acetate test strips was performed using ImageJ software.

      Phylogenetic analysis

    • Putative D-cysteine desulfhydrase proteins in S. lycopersicum, A. thaliana, V. vinifera, P. patens, N. tabacum, O.sativa, T. aestivum, P. bretschneideri, Z. mays, C. annuum, and M. acuminata were obtained by the BLASTP tool in the NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi) database with AtDCD1 (NP_001319174.1) as a query. The amino acid sequences of SlDCD1 (NP_001234368) and SlDCD2 (XP_004228490.1) from S. lycopersicum; AtDCD1 and AtDCD2 (NP_001327499.1) from A. thaliana; VvDCD1 (XP_002263358.2) and VvDCD2 (XP_002282104.1) from V. vinifera; PpDCD1 (XP_024396726.1) and PpDCD2 (XP_024361159) from P. patens; NtDCD1 (XP_016466777.1) and NtDCD2 (XP_016500459.1) from N. tabacum; OsDCD1 (XP_015626189.1) and OsDCD2 (XP_015621767.1) from O.sativa; TaDCD1 (XP_044421034.1) and TaDCD2 (XP_044357483.1) from T. aestivum; PyDCD1(XP_009349823) and PyDCD2 (XP_048427768.1) from P. bretschneideri; ZmDCD1 (NP_001130254.1) and ZmDCD2 (NP_001353762.1) from Z. mays; CaDCD1 (XP_016563957.1) and CaDCD2 (XP_016577814.1) from C. annuum; and MaDCD1 (XP_009417666.1) and MaDCD2 (XP_009409959.1) from M. acuminata were selected to construct a phylogenetic tree by the neighbor-joining method according to the parameters previously reported by Saitou & Nei[27].

      Generation and genotyping of the dcd1 mutant by CRISPR/Cas9

    • CRISPR/Cas9 mutagenesis of DCD1 in tomato was performed as previously described[28]. The primers for sgRNA are listed in Supplemental Table S1. For confirmation of the dcd1 mutant, we amplified a fragment of the sgRNA target sequence using genomic DNA from the dcd1 mutant. The amplified fragment was further used for DNA sequencing, and the genotyping of tomato plants was analyzed on the website DSDecodeM (http://skl.scau.edu.cn/dsdecode/)[29].

      Visualization of defense responses of tomato to B. cinerea

    • To compare the defense responses of wild-type (WT) and dcd1 leaves, trypan blue staining was used to detect necrosis of tomato leaf cells infected with B. cinerea[30]. Leaves were incubated in petri dishes containing staining solution (containing 10 mL 85% lactic acid, 10 mL saturated phenols, 10 mL glycerol, 10 mL distilled water, 0.4 g trypan blue) at 25 °C for 1 h and then were decolorized with alcohol at 25 °C for 12 h. The distribution of H2O2 in tomato leaf cells infected with B. cinerea was detected by DAB (3,3'-Diaminobenzidine) staining[30]. The leaves were soaked in staining solution (containing 0.5 g DAB, 25 μL TWEEN-20, 2.5 mL 200 mM Na2HPO4, 45 mL H2O, pH 3.0) and then vacuumed 2−3 times for 1 min at 0.8 kg·cm−2. Then chlorophyll was removed using ethanol, and plant leaves were photographed.

      RNA extraction and RT-qPCR

    • RNA was extracted from 0.2 g leaf or fruit and the first strand cDNA was synthesized following the method reported previously[31]. Tomato Tubulin was used as an internal reference. Gene-specific primers for RT-qPCR are listed in Supplemental Table S1. The injected fruits or leaves were sampled within 10 mm diameter of the lesion and RNA was extracted, the actin gene transcript levels of B. cinerea were used as an indicator of B. cinerea growth[30].

      Assay of antioxidant enzyme activities

    • Tomato tissue (0.5 g) was extracted using 10 mL of 50 mM phosphate buffer (pH 7.8) at 4 °C. Then samples were centrifuged at 10,000 g and 4 °C for 15 min. Supernatant is the crude antioxidant enzyme solution[32]. CAT, APX, SOD and POD (peroxidase) activity were measured and calculated spectrometrically[33−35]. An absorbance increase of 1.0 × 10−5 OD470 nm·min−1 was considered 1 U of POD activity, a decrease in absorbance of 1.0 × 10−3 at OD240 nm·min−1 was considered 1 U of CAT activity, the amount used to inhibit 5% of the photochemical reduction of NBT was considered 1 U of SOD activity, and a decrease in absorbance of 1.0 × 10−4 at OD290 nm·min−1 was considered 1 U of APX activity. The results are expressed on a FW (Fresh Weight) basis as U·g−1.

      Malondialdehyde content

    • As mentioned previously[36], 0.5 g of plant sample was homogenized, incubated, and then centrifuged to collect the supernatant. The absorbance was measured at 450, 532 and 600 nm.

      H2O2 content

    • A 0.5 g sample of plant material was homogenized and centrifuged to collect the precipitate. Then, the precipitate was added to 1.5 mL of 2 M H2SO4. The absorbance of the mixture was measured at 412 nm, and the content of H2O2 was calculated[37, 38].

      The production rate of O2.−

    • The reaction buffer was composed of 50 mM phosphate buffer (pH 7.8) containing 17 mM sulfanilic acid, 1 mM hydroxylamine hydrochloride, 7 mM 1-naphthylamine, and 50 μL sample solution. The absorbance of the mixture was measured at 530 nm, and the production rate of O2.− was calculated using previously described formulas[39].

      Principal Components Analysis

    • Principal Components Analysis (PCA) of SOD, POD, APX, CAT enzyme activities, H2O2 and MDA contents and O2.− production rate was processed using the OmicShare website (https://www.omicshare.com).

      Statistical analysis

    • Data were based on three replicates in each experiment, and the experiments were repeated independently three times. Statistical significance was assayed using a one-way analysis of variance with IBM SPSS Statistics (SPSS version 20.0; Armonk, NY, USA), and the results are expressed as the means ± SDs. Significant differences were calculated by a t test (p < 0.01 or p < 0.05).

    Results

      Response of DCDs to B. cinerea infection

    • To study the potential role of H2S in response to B. cinerea infection, we measured endogenous H2S production in infected wild-type leaves by lead acetate strips (Fig. 1a). As strips shown in Fig. 1b and gray intensity analysis in Fig. 1c, the leaves produced more H2S with D-Cys as the substrate when infected with B. cinerea for 1, 2 and 3 d compared with control leaves. Subsequently, we examined the transcript levels of DCD1/2 and LCD1/2 in the infected leaves and found that after infestation the transcript level of DCD1 significantly increased, especially at 3 DPI was three times that of 2 DPI, and the transcript level of LCD1 slightly increased, whereas the transcript levels of LCD2 and DCD2 did not change significantly (Fig. 1d). Therefore, we hypothesized that DCD1 might affect the resistance of tomato to B. cinerea. To investigate the phylogenetic relationships between DCD proteins in tomato and other plant species, the gene encoding AtDCD1 (AT1G48420) in Arabidopsis was searched in the NCBI database, and the homologous-proteins were searched in S. lycopersicum, A. thaliana, V. vinifera, P. patens, N. tabacum, O. sativa, T. aestivum, P. bretschneideri, Z. mays, C. annuum, and M. acuminata using the AtDCD1 protein sequence as the query. As shown in the phylogenetic tree in Supplemental Fig. S1, the identified DCDs could be classified to two groups. The I subfamily contained DCD1 in the above species, and the Ⅱ subfamily contained all DCD2 proteins formed a single branch. The results indicated that DCD1/2 in tomato showed higher homology with homologs in chili pepper, both belong to the Solanaceae family.

      Figure 1. 

      Response of H2S and L/DCDs expression of tomato leaves to B. cinerea infection. (a) Phenotypes of WT leaves infected by B. cinerea for 0, 1, 2, 3 DPI. (b) The endogenous H2S production in infected WT leaves, was measured by lead acetate H2S detection strips. (c) The gray intensity analysis of strips in (b). (d) Gene expression of DCD1, DCD2, LCD1, LCD2 in WT leaves infected with B. cinerea. Data indicate mean ± SD (n = 3). The symbol ** stands for p < 0.01.

      Construction of DCD1 gene-edited plant by CRISPR/Cas9

    • The sgRNA target of DCD1 was integrated into the CRISPR/Cas9 vector which was further transformed into tomato using Agrobacterium-mediated transformation. For the genotyping of positive T2 plants (Fig. 2a), the gene fragment flanking sgRNA target of DCD1 was amplified from genomic DNA of dcd1 mutant. Figure 2b indicated that 128 bp deletions in dcd1-1 near the PAM destroyed the transcription start site of DCD1, while there are 2 bp deletion in dcd1-2 which leaded to frame shift mutation, specifically, the translation stopped after the 70rd amino acid residue.

      Figure 2. 

      The overall phenotype of the two mutant lines dcd1-1 and dcd1-2 at 45 d of growth. (a), (b) Generation of dcd1 tomato lines by CRISPR/Cas9. The protospacer-adjacent motif (PAM) is indicated in red and the dashes mean deletions of bases.

      dcd1 mutant leaves showed increased susceptivity to B. cinerea infection

    • To investigate the effect of dcd1 mutation on resistance against B. cinerea, WT and dcd1 tomato leaves were inoculated with the fungal pathogen (Fig. 3a & b). Firstly, the relative expression of actin gene in B. cinerea, an index of pathogen growth, were determined at 3 and 5 DPI. Fig.3c showed that the expression actin gene increased significantly in dcd1 mutant leaves compared with WT leaves, suggesting that B. cinerea propagated more in dcd1 mutant. Moreover, trypan blue staining and DAB staining were used to observe dead cells distribution and H2O2 distribution in leaves, respectively. As shown in Fig. 3a & b, it was observed that more death cells were accumulated in dcd1 mutant leaves than WT leaves based on the trypan blue staining, and higher levels of H2O2 was found in mutant leaves according to DAB staining.

      Figure 3. 

      Effect of dcd1 mutation on the ROS metabolism in tomato leaves infected with B. cinerea. Visualization of dead cells stained by (a) trypan blue and (b) H2O2 accumulation by DAB in tomato leaves of wild-type and dcd1 mutation. The expression of (c) B. cinerea actin gene, (d) O2.− generation rate, (e) H2O2 content and (f) MDA content in wild-type and dcd1 mutant leaves after infection with B. cinerea for 0, 3 and 5 d. Trypan blue staining and DAB staining of leaves infected with a conidial suspension were performed at different time points post inoculation (0, 3, and 5 d). The results of (c) - (f) are expressed as the mean values ± SD, n = 3. The symbols * or letters above the bars stands for student's t-test at p < 0.05.

      ROS are the key feature of plant defense against invading pathogens[20]. As shown in Fig. 3d−f, O2.− generation rate, H2O2 content and MDA content displayed an increasing trend for 5 d of infection in both WT and dcd1 mutant leaves. The production of O2.− was not significantly different between WT and dcd1 (Fig. 3d), while H2O2 and MDA level in dcd1 leaves were significantly higher than that of WT leaves at 3 DPI and 5 DPI (Fig. 3e & f). These results imply that the tomato leaves defense to B. cinerea was largely suppressed in dcd1 leaves, and the dcd1 mutation caused excessive accumulation of H2O2 and MDA, suggesting that the lower level of H2S in dcd1 may lead to an imbalance in ROS metabolism and that excessive ROS may weaken the disease resistance of tomato leaves.

      Effect of dcd1 mutation on antioxidant enzymes and transcription of defense-related genes in tomato leaves infected by B. cinerea

    • Antioxidant enzymes protect plants from oxidative stress and maintain redox homeostasis through scavenging of ROS produced during pathogen attack[40]. Then, antioxidative enzymatic activities, including SOD, CAT, APX and POD, in the leaves of WT and dcd1 were determined to assess the dynamics of the antioxidant system following challenge with B. cinerea. Figure 4b & c show that CAT and APX activity in dcd1 were always lower than that in WT infected by B. cinerea, and CAT activity decreased obviously in dcd1 leaves at 3 DPI and was just one-forth of that in WT leaves. At 5 DPI, CAT activity in WT was 1.5 times that in dcd1. Compared to WT leaves, at 3 DPI and 5 DPI, the SOD (Fig. 4a) and POD (Fig. 4d) enzyme activities in dcd1 leaves were also lower than that of WT. Overall, DCD1 mutation led to decrease in antioxidant enzyme activities, suggesting that a lower level of H2S in dcd1 may lead to excessive ROS accumulation which weaken the disease resistance of tomato leaves.

      Figure 4. 

      Changes of antioxidant enzyme (a) CAT, (b) SOD, (c) APX, (d) POD activities and PCA analysis of the parameters of antioxidant enzyme activities and MDA content, content of H2O2, production rate of O2.− in tomato leaves after inoculating with B. cinerea for 0, 3 and 5 d. (g). The expression levels of pathogenesis-related genes (e) PAL and (f) PUB24 by RT-qPCR in tomato leaves after inoculating with B. cinerea for 0, 3 and 5 d. Data indicate mean ± SD (n=3). The symbols ** and * stand for p < 0.01 and p < 0.05, respectively.

      In order to explore the clustering among different parameters mentioned above in leaves and determine their effect on plant disease resistance, we conducted PCA (Fig. 4g). The PCA score plot showed the total variance (97.4%) of the two main principal components, of which 87.8% accounts for principal component one (PC1) and 9.6% is responsible for principal component two (PC2). According to the scoring plot, the CAT activity, H2O2 content and MDA content on PC1 are the key factors affecting ROS metabolism in leaves.

      Then, defense-related genes, including PAL (Fig. 4e) and PUB24 (Fig. 4f) were selected to investigate the transcript responses to B. cinerea in WT and dcd1 leaves. Phenylalanine ammonia-lyase (PAL) is a rate-limiting enzyme for the metabolism of phenylpropane substances in plants, and the infection of pathogens could induce enhanced activity of PAL, and the enhanced enzyme activity is positively correlated with disease resistance[41]. PUBs belong to U-box type E3 ligases functioned in plant defense responses[42]. As shown in Fig. 4e & f, the expression of defense-related genes PAL and PUB24 were remarkably induced by B. cinerea, while B. cinerea-triggered transcript induction of these genes were significantly depressed in dcd1 leaves. The relative expression of PAL in dcd1 leaves remained low at 3 DPI and 5 DPI compared to wild-type tomato leaves. At 0 DPI, the transcript level of PUB24 in dcd1 leaves was slightly lower than WT leaves, but the difference between the two widened significantly at 3 DPI. At 5 DPI, the transcriptional level of PUB24 in WT leaves was about 1.3 times that in dcd1 leaves. Overall, DCD1 mutation caused decreased expression of defense-related genes and excessive ROS accumulation.

      Effect of dcd1 mutation on defense to B. cinerea in tomato fruit at different ripening stages

    • B. cinerea is a major threat to the production and storage life of tomato fruit around the world[43]. To investigate whether the DCD1 gene affect fruit defense against the fungal pathogen, WT and dcd1 tomato fruits were inoculated with B. cinerea, and the relative expression of actin gene of B. cinerea were determined at 3 and 5 DPI, and meanwhile the growth diameters of B. cinerea are recorded. As shown in Fig. 5a & b, at 3 and 5 DPI, the infection process of WT and dcd1 mutant fruit at mature green stage were not obvious. When the fruit were infected for 3 or 5 d, obvious infection lesions appeared on the surface of dcd1 breaker fruit, while WT breaker fruit did not display obvious lesions. As for the red fruit of WT and dcd1 mutant, both fruit developed obvious lesions at 3 DPI, but the diameter of the lesions in dcd1 mutant was significantly larger than that of WT, as was the case on the 5 DPI. Moreover, there was little difference in B. cinerea actin transcript levels at the sites of WT and dcd1 mature green fruit lesions, while the transcription level of B. cinerea actin on the dcd1 breaker fruit surface was about 2-fold that of WT, whether at 3 DPI or 5 DPI (Fig. 5c). The above results showed that the mutation of DCD1 reduced the resistance of breaker and red tomato fruit to B. cinerea largely. Besides, fruit at more mature stages were more susceptible to fungal pathogen infection.

      Figure 5. 

      Effect of WT and dcd1 mutation on defense to B. cinerea in tomato fruit at different ripening stages. (a) Images of WT and dcd1 fruits at mature green, breaker or red stages inoculated with B. cinerea for 0, 3 and 5 d. (b) Growth diameter of B. cinerea growing on the surface of WT and dcd1 fruits and (c) B. cinerea actin gene expression were detected after inoculating with B. cinerea. Values are the means ± SDs of three replicates. The letters above the bar indicate statistical significance determined by a student's t-test at the p <0.05 level.

      Effect of dcd1 mutation on the accumulation of ROS in tomato fruit at different ripening stages infected by B. cinerea

    • The fruit tissues near the lesions were sampled for ROS determination. As shown in Fig. 6, O2.− production rate, H2O2 content and MDA content generally showed an increasing trend during B. cinerea infection for 0, 3 and 5 d in both WT and dcd1 mutant fruit at mature green, breaker or red stages. The production of O2.− was not significantly different between WT and dcd1 fruit, but fruit at red stage produced more O2.− than mature green and breaker fruit (Fig. 6a). At 3 DPI, the content of H2O2 in the mature green, breaker and red fruit of dcd1 were not significantly different from that of WT, however, H2O2 content in dcd1 mature green, breaker and red fruit was 1.4, 1.2, 1.3 times that in the counterpart WT fruit at day 0, respectively, suggesting that DCD1 mutation caused excessive H2O2 accumulation compared with WT (Fig. 6b). After infection for 5 d, dcd1 mutant fruit at all ripening stages showed significantly higher levels of H2O2 in comparison to WT. MDA content in dcd1 breaker fruit was significantly higher than that in WT fruit at 5 DPI, while the difference was not obvious between WT and dcd1 fruit at red stage at 5 DPI. MDA content in dcd1 red fruit was 1.4 times that in the counterpart WT fruit at 3 DPI (Fig. 6c). The results indicated that the deletion of DCD1 accelerated the accumulation of ROS in tomato fruit and fruit at red stage accumulated more ROS compared with un-ripened fruit.

      Figure 6. 

      Changes of (a) MDA content, (b) content of H2O2, (c) production rate of O2•− in tomato fruit of WT and dcd1 mutant at mature green, breaker and red stages after inoculating with B. cinerea at 0, 3 and 5 d. Data indicate mean ± SD (n = 3). Letters indicate statistical significance determined by a student's t-test at the p < 0.05 level.

      Deletion of DCD1 affects antioxidant enzyme activities and expression of defense-related genes in tomato fruit at different ripening stages

    • To further investigate ROS metabolism in dcd1 mutant fruit, the enzyme activities of SOD, CAT, APX and POD were determined. There was minor difference in the enzyme activity of SOD in WT and dcd1 fruit, and at 5 d after infection, the activity in dcd1 mutant was lower than that of WT at different ripening stages (Fig. 7a). In both WT and dcd1 at breaker as well as red stages, CAT activity first rose at 3 DPI and then decreased at 5 DPI, and the activity in dcd1 was significantly lower than that in WT (Fig. 7b). At 5 DPI, CAT activity in dcd1 mutant was 92.1%, 80.3%, 41.7% of that in WT at mature green, breaker and red stages, respectively, suggesting that dcd1 mutant decreased CAT activity in fruit. and dcd1 mature green fruit, gradually increased, the activity in dcd1 was significantly lower than that in WT (Fig. 7b). Similarly, dcd1 mutant also caused decreased APX and POD activity in fruit at 3 and 5 DPI compared with the counterparts of WT (Fig. 7c, d). Overall, DCD1 mutation caused excessive accumulation of H2O2 and MDA, also led to decrease in antioxidant enzyme activities, suggesting that a lower level of H2S in dcd1 may lead to an imbalance in ROS metabolism and that excessive ROS may weaken the disease resistance of tomato fruit especially red and breaker fruit. As shown in Fig. 7g, the PCA score plot showed the total variance (96.4%) of the two main principal components, of which 90.0% accounts for principal component one (PC1) and 6.4% is responsible for principal component two (PC2). According to the scoring plot, the CAT activity, H2O2 content and MDA content on PC1 contributed more for ROS metabolism in fruit.

      Figure 7. 

      Changes of antioxidant enzyme (a) CAT, (b) SOD, (c) APX, (d) POD activities and PCA analysis of enzyme activities and MDA content, content of H2O2, production rate of O2.− in tomato fruit of WT and dcd1 mutant at mature green, breaker and red stages after inoculating with B. cinerea at 0, 3 and 5 d. (g) The expression levels of pathogenesis-related genes (e) PAL and (f) PUB24 determined by RT-qPCR in WT and dcd1 tomato fruits after inoculating with B. cinerea. Data indicate mean ± SD (n = 3). Letters indicate statistical significance determined by a student's t test at the p < 0.01 level.

      Then the expression of the marker genes for evaluating plant resistance to pathogen infection were determined. In WT fruit infected with B. cinerea, the expression of PAL was always higher than that in dcd1 (Fig. 7e). As shown in the Fig. 7f, the expression of PUB24 in WT fruit at all ripening stages were generally higher than that in dcd1 fruit. At 5 DPI, the expression of PUB24 in WT breaker fruit was approximately 1.8 times that in infected dcd1 breaker fruit, and that in WT red fruit was nearly 1.5 times that in dcd1 red fruit. Generally, dcd1 mutation caused attenuated expression of defense-related genes and this was consistent to the higher sensitivity of dcd1 fruit to B. cinerea infections compared with WT.

    Discussion

    • The role of H2S in plant disease resistance has gradually been confirmed. Exogenous application of H2S helped protect pear fruit from the invasion of the fungal pathogens Aspergillus niger, Penicillium expansum by inhibiting the growth of pathogens[6]. Besides, the transcript level of DES1 was elevated after pathogen infection, and DES1 overexpressing plants showed fewer of Magnaporthe oryzae in infected tissues compared to wild-type plants, whereas DES1 mutant plants showed increased bacterial growth[44]. D-cysteine desulfhydrase (EC 4.4.1.15), which catalyzes the conversion of D-cysteine to H2S, represent a completely different enzyme both in protein structure and biochemical properties[45]. In the present work, we found that B. cinerea infection of tomato leaves resulted in a significant increase in the release of H2S from the leaves with D-cysteine as the substrate and an increase in the expression of DCD1 was observed, suggesting the potential role of DCD1 in plant response to fungal pathogen infections. To further explore the function of DCD1 in tomato resistance to B. cinerea, we constructed T2 generation of dcd1 mutant tomato plant. The results showed that dcd1 mutant increased the susceptibility of leaves to B. cinerea and more B. cinerea reproduced evidenced by the higher actin expression in dcd1 mutant leaves. It was observed that more dead cells were accumulated in dcd1 mutant leaves than WT leaves, and higher levels of H2O2 in dcd1 mutant leaves. Besides, the resistance of tomato fruit was studied at mature green, breaker and red stages. At 3 and 5 DPI, the infection of B. cinerea on WT and dcd1 mutant fruit at mature green stage are not obvious. However, dcd1 mutant at breaker and red stages showed strong B. cinerea infection and the growth diameter B. cinerea was increased when infected for 3 or 5 d. There was little difference in B. cinerea actin transcript levels at the sites of WT and dcd1 mature green fruit lesions, while the transcription level of B. cinerea actin on the dcd1 breaker fruit surface was about 2-fold that of WT, whether at 3 DPI or 5 DPI. The above results showed that the mutation of DCD1 largely reduced the resistance of tomato leaves and breaker and red fruits to B. cinerea. Moreover, the infection data indicated that fruit at ripened stage is more susceptible to fungal infections compared with green fruit, suggesting that senescence of plant tissues is more favorable to fungal infection. Consistently, senescent tobacco leaves were more sensitive to necrotrophic pathogens including B. cinerea and Alternaria alternata[46].

      Excessive accumulation of ROS has toxic effects on plants, leading to cell death and making plants more susceptible to diseases[47, 48]. It has been shown that ROS not only have direct antimicrobial activity, but also can act as a signal for defense response, causing upregulation of resistance-related genes and participating in the plant disease resistance process[49]. In the present study, we showed that the content of H2O2 in the leaves and fruit of dcd1 mutant at different ripening stages was higher than that of WT under B. cinerea infection. Besides, increasing trend of H2O2 was observed during B. cinerea infection, suggesting H2O2 is the key type of ROS that plant responses to fungal pathogen infection. Previous studies showed that H2S treatment greatly reduced H2O2 and MDA contents, and enhancing antioxidant enzyme activities and relative expression levels of defense-related genes, which in turn alleviated Fusarium head blight of wheat seedlings[50]. Previous reports suggested that H2S could delay postharvest senescence of multiple fruit and vegetables by maintaining balanced ROS homeostasis through activating antioxidant enzymes[51−53]. In the present study, significantly higher MDA and H2O2 levels were observed in dcd1 mutant fruits and leaves after B. cinerea infestation compared with WT. These data support that H2S generated by DCD1 appears to be an antioxidant signaling molecule involved in tomato resistance to B. cinerea.

      To further investigate the role of DCD1 and H2S in mitigating ROS toxicity, we determined the activities of various antioxidant enzymes, including SOD, POD, CAT and APX. Figure 4b & c show that APX and CAT activities in dcd1 leaves were always lower than that in WT leaves infected by B. cinerea, and CAT activity decreased obviously in dcd1 leaves at 3 DPI and was just one-forth of that in WT leaves (Fig. 4b). Compared to WT leaves, at 3 DPI and 5 DPI, SOD, APX and POD showed lower activity in dcd1 mutant leaves compared with control. For tomato fruit, DCD1 mutation caused decreased SOD activity at 5 DPI, and decreased APX and POD at 3 and 5 DPI in different ripening stages of tomato fruit. In both WT and dcd1 of green, breaker as well as red fruit, CAT activity rose at 3 DPI and then decreased at 5 DPI, and the activity in dcd1 was significantly lower than that in WT. Previous studies have shown that the activities of POD, APX and CAT collectively regulated ROS homeostasis in slnpr1 mutants[40]. Among the antioxidative enzymes, SOD catalyzes the reaction of O2.− to H2O2 and O2[54], then CAT and APX are responsible for the decomposition of H2O2. In the present study, the decreased activity of antioxidative enzymes especially CAT, may contribute to excessive accumulation of H2O2 as observed in leaves and fruit infected with B. cinerea. Consistently, lower CAT activity in slnpr1 mutant leads to higher H2O2 levels compared to WT[40]. By PCA, we suggest that the CAT activity, H2O2 content and MDA content are the key factors affecting ROS metabolism in tomato leaves and fruit. Due to the attenuated antioxidative enzymes in dcd1 mutant, more ROS accumulated in dcd1 mutant leaves and fruits. For necrotrophic fungal pathogens such as B. cinerea, pathogen-induced formation of cell death and ROS accumulation normally promotes pathogen growth and lesion development[55]. Therefore, more dead cells and excessive ROS observed in dcd1 mutant may facilitate the infections by B. cinerea.

      PAL is a key enzyme of phenylpropanoid metabolism and overexpressing PAL in tobacco decreased the susceptibility to fungal pathogen[56]. In our study, the relative expression of PAL in dcd1 leaves and fruit remained lower at 3 DPI and 5 DPI compared to WT leaves and in infected WT fruits, suggesting that mutation of DCD1 resulted in decreased PAL expression and diminished resistance in dcd1 mutant. PUBs belong to U-box type E3 ligases function in plant defense responses[42]. Therefore PUB24 was determined as the marker gene for disease response. In the present work, PUB24 increased significantly in leaves and fruits infected with B. cinerea, and the transcriptional level of PUB24 in dcd1 leaves or fruits was significantly lower than WT at 3 and 5 DPI. Thus we proposed that DCD1 and H2S are required for normal expression of PAL and PUB24 in response to fungal infections.

      In conclusion, the present work indicated that DCD1 plays an essential role in tomato in response to B. cinerea. The mutation of DCD1 largely reduced the resistance of tomato leaves and breaker and red fruits to B. cinerea accompanied with increased ROS accumulation. DCD1 mutation caused decreased activity of antioxidative enzymes especially CAT, which may contribute to excessive accumulation of H2O2 as observed in mutant leaves and fruits infected with B. cinerea. Moreover, DCD1 mutation caused decreased expression of defense-related genes PAL and PUB24. Thus the work emphasizes that DCD1 and H2S are required for the activation of antioxidant enzymes and for ROS homeostasis in plant response to necrotrophic fungal pathogens. In addition, the work first provides strong evidence that fruit at ripened stage is more susceptible to fungal infections compared with green fruit, suggesting that senescence of plant tissues is more favorable to fungal infection.

      Acknowledgments

      • This research was supported by the National Natural Science Foundation of China (31970312, 31970200, 32170315, 31901993), the Fundamental Research Funds for the Central Universities (JZ2021HGPA0063) and the Natural Science Foundations of Anhui Province (1908085MC72).

      Conflict of interest

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

      • # These authors contributed equally: Yuqi Zhao, Kangdi Hu

      Supplementary information
      • Supplemental Fig. S1 DCDs from S. lycopersicum, A. thaliana, V. vinifera, P. patens, N. tabacum, O.sativa, T. aestivum, P. bretschneideri, Z. mays, C. annuum, and M. acuminata were analyzed in the phylogenetic tree. The phylogenetic tree was constructed, the Neighbor-Joining was adopted using MEGA 7.0 software.
      • Supplemental Table S1 Primers used in the present study.
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      • Copyright: © 2023 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 (7)  References (56)
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    Cite this article
    Zhao Y, Hu K, Yao G, Wang S, Peng X, et al. 2023. D-cysteine desulfhydrase DCD1 participates in tomato resistance against Botrytis cinerea by modulating ROS homeostasis. Vegetable Research 3:21 doi: 10.48130/VR-2023-0021
    Zhao Y, Hu K, Yao G, Wang S, Peng X, et al. 2023. D-cysteine desulfhydrase DCD1 participates in tomato resistance against Botrytis cinerea by modulating ROS homeostasis. Vegetable Research 3:21 doi: 10.48130/VR-2023-0021
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  • Introduction
  • Materials and methods

  • Plant material growth and pathogen inoculation

  • Determination of the amount of H2S release in tomato leaves and gray intensity analysis

  • Phylogenetic analysis

  • Generation and genotyping of the dcd1 mutant by CRISPR/Cas9

  • Visualization of defense responses of tomato to B. cinerea

  • RNA extraction and RT-qPCR

  • Assay of antioxidant enzyme activities

  • Malondialdehyde content

  • H2O2 content

  • The production rate of O2.−

  • Principal Components Analysis

  • Statistical analysis

  • Results

  • Response of DCDs to B. cinerea infection

  • Construction of DCD1 gene-edited plant by CRISPR/Cas9

  • dcd1 mutant leaves showed increased susceptivity to B. cinerea infection

  • Effect of dcd1 mutation on antioxidant enzymes and transcription of defense-related genes in tomato leaves infected by B. cinerea

  • Effect of dcd1 mutation on defense to B. cinerea in tomato fruit at different ripening stages

  • Effect of dcd1 mutation on the accumulation of ROS in tomato fruit at different ripening stages infected by B. cinerea

  • Deletion of DCD1 affects antioxidant enzyme activities and expression of defense-related genes in tomato fruit at different ripening stages

  • Discussion
  • Acknowledgments
  • Conflict of interest
  • Supplementary information
  • Rights and permissions
  • About this article
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