Search
Submit Manuscript
1.8CiteScore
Advanced Search
MAP  >  Vegetable Research
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
About this article
Next Previous
ARTICLE   Open Access    

Comprehensive analysis of CYP78A family genes reveals the involvement of CYP78A5 and CYP78A10 in fruit development in eggplant

  • College of Horticulture, State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, Hebei Agricultural University, Baoding, Hebei 071000, China

  • # These authors contributed equally: Mengya Zhou, Liying Zhang

More Information
  • The CYP78A family is a plant-specific family, members of which have been considered as promising targets for yield improvement due to their important roles in regulating organ size. Eggplant is an important vegetable cultivated worldwide. However, little information about the eggplant CYP78As (SmCYP78As) limits the potential utilization of SmCYP78As for crop improvement. In this study, we identified six CYP78A genes in the eggplant genome named SmCYP78A5 to SmCYP78A10 according to the phylogenetic relationships to Arabidopsis CYP78As. The phylogenetic analysis of CYP78As from eggplant, Arabidopsis, rice and tomato classified the 27 CYP78As into five clades. SmCYP78As were found in three of the five clades. This classification is consistently supported by their gene structures, domains and conserved motifs. Segmental duplication events were found to contribute to the expansion of the SmCYP78A family. Comparative syntenic analysis provided further insight into the phylogenetic relationships of CYP78A genes from the four plants. qRT-PCR analysis revealed that the expression of the six SmCYP78As was detected in at least one of the eight tissues, showing a tissue-specific pattern. Notably, SmCYP78A5 and SmCYP78A10 were highly expressed in developing ovaries, indicating the involvement of fruit development in eggplant. Co-expression clustering and GO enrichment analysis suggested that SmCYP78A5 and SmCYP78A10 regulate fruit development likely through different pathways. In addition, six transcription factors were identified as promising candidates that may directly bind promoters of SmCYP78A5 and SmCYP78A10. This study provides a comprehensive overview of the SmCYP78As family, which would lay a foundation for further understanding of evolution and function of the SmCYP78A family.

  • 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 Table S1 The primers used for qRT-PCR.
    Supplemental Table S2 Co-expression genes in the 12 clusters.
    Supplemental Table S3 The list of TFs in Cluster 6.
    Supplemental Table S4 The list of TFs in Cluster 11.
    Supplemental Table S5 The sequences of CYP78A genes in eggplant.
    Supplemental Fig. S1 Motif sequences identified in CYP78A proteins from eggplant, Arabidopsis, rice and tomato by MEME.
  • [1]

    Mizutani M, Ohta D. 2010. Diversification of P450 genes during land plant evolution. Annual Review of Plant Biology 61:291−315

    doi: 10.1146/annurev-arplant-042809-112305

    CrossRef   Google Scholar

    [2]

    Hansen CC, Nelson DR, Møller BL, Werck-Reichhart D. 2021. Plant cytochrome P450 plasticity and evolution. Molecular Plant 14:1244−65

    doi: 10.1016/j.molp.2021.06.028

    CrossRef   Google Scholar

    [3]

    Nelson D, Werck-Reichhart D. 2011. A P450-centric view of plant evolution. The Plant Journal 66:194−211

    doi: 10.1111/j.1365-313X.2011.04529.x

    CrossRef   Google Scholar

    [4]

    Adamski NM, Anastasiou E, Eriksson S, O'Neill CM, Lenhard M. 2009. Local maternal control of seed size by KLUH/CYP78A5-dependent growth signaling. PNAS 106:20115−20

    doi: 10.1073/pnas.0907024106

    CrossRef   Google Scholar

    [5]

    Anastasiou E, Kenz S, Gerstung M, MacLean D, Timmer J, et al. 2007. Control of plant organ size by KLUH/CYP78A5-dependent intercellular signaling. Developmental Cell 13:843−56

    doi: 10.1016/j.devcel.2007.10.001

    CrossRef   Google Scholar

    [6]

    Jiang L, Yoshida T, Stiegert S, Jing Y, Alseekh S, et al. 2021. Multi-omics approach reveals the contribution of KLU to leaf longevity and drought tolerance. Plant Physiology 185:352−68

    doi: 10.1093/plphys/kiaa034

    CrossRef   Google Scholar

    [7]

    Wang J, Schwab R, Czech B, Mica E, Weigel D. 2008. Dual effects of miR156-targeted SPL genes and CYP78A5/KLUH on plastochron length and organ size in Arabidopsis thaliana. The Plant Cell 20:1231−43

    doi: 10.1105/tpc.108.058180

    CrossRef   Google Scholar

    [8]

    Poretska O, Yang S, Pitorre D, Poppenberger B, Sieberer T. 2020. AMP1 and CYP78A5/7 act through a common pathway to govern cell fate maintenance in Arabidopsis thaliana. PLoS Genetics 16:e1009043

    doi: 10.1371/journal.pgen.1009043

    CrossRef   Google Scholar

    [9]

    Nobusawa T, Kamei M, Ueda H, Matsushima N, Yamatani H, et al. 2021. Highly pleiotropic functions of CYP78As and AMP1 are regulated in non-cell-autonomous/organ-specific manners. Plant Physiology 186:767−81

    doi: 10.1093/plphys/kiab067

    CrossRef   Google Scholar

    [10]

    Fang W, Wang Z, Cui R, Li J, Li Y. 2012. Maternal control of seed size by EOD3/CYP78A6 in Arabidopsis thaliana. The Plant Journal 70:929−39

    doi: 10.1111/j.1365-313X.2012.04907.x

    CrossRef   Google Scholar

    [11]

    Chakrabarti M, Zhang N, Sauvage C, Muños S, Blanca J, et al. 2013. A cytochrome P450 regulates a domestication trait in cultivated tomato. PNAS 110:17125−30

    doi: 10.1073/pnas.1307313110

    CrossRef   Google Scholar

    [12]

    Li Q, Chakrabarti M, Taitano NK, Okazaki Y, Saito K, et al. 2021. Differential expression of SlKLUH controlling fruit and seed weight is associated with changes in lipid metabolism and photosynthesis-related genes. Journal of Experimental Botany 72:1225−44

    doi: 10.1093/jxb/eraa518

    CrossRef   Google Scholar

    [13]

    Li Q, Feng Q, Snouffer A, Zhang B, Rodríguez GR, et al. 2022. Increasing fruit weight by editing a cis-regulatory element in tomato KLUH promoter using CRISPR/Cas9. Frontiers in Plant Science 13:879642

    doi: 10.3389/fpls.2022.879642

    CrossRef   Google Scholar

    [14]

    Alonge M, Wang X, Benoit M, Soyk S, Pereira L, et al. 2020. Major impacts of widespread structural variation on gene expression and crop improvement in tomato. Cell 182:145−161.e23

    doi: 10.1016/j.cell.2020.05.021

    CrossRef   Google Scholar

    [15]

    Sun X, Cahill J, Van Hautegem T, Feys K, Whipple C, et al. 2017. Altered expression of maize PLASTOCHRON1 enhances biomass and seed yield by extending cell division duration. Nature Communications 8:14752

    doi: 10.1038/ncomms14752

    CrossRef   Google Scholar

    [16]

    Guo L, Ma M, Wu L, Zhou M, Li M, et al. 2022. Modified expression of TaCYP78A5 enhances grain weight with yield potential by accumulating auxin in wheat (Triticum aestivum L.). Plant Biotechnology Journal 20:168−82

    doi: 10.1111/pbi.13704

    CrossRef   Google Scholar

    [17]

    Zhou M, Peng H, Wu L, Li M, Guo L, et al. 2022. TaKLU plays as a time regulator of leaf growth via auxin signaling. International Journal of Molecular Sciences 23:4219

    doi: 10.3390/ijms23084219

    CrossRef   Google Scholar

    [18]

    Wang X, Li Y, Zhang H, Sun G, Zhang W, et al. 2015. Evolution and association analysis of GmCYP78A10 gene with seed size/weight and pod number in soybean. Molecular Biology Reports 42:489−96

    doi: 10.1007/s11033-014-3792-3

    CrossRef   Google Scholar

    [19]

    Dai AH, Yang SX, Zhou HK, Tang KQ, Li G, et al. 2018. Evolution and expression divergence of the CYP78A subfamily genes in soybean. Genes 9:611

    doi: 10.3390/genes9120611

    CrossRef   Google Scholar

    [20]

    Zhao B, Dai A, Wei H, Yang S, Wang B, et al. 2016. Arabidopsis KLU homologue GmCYP78A72 regulates seed size in soybean. Plant Molecular Biology 90:33−47

    doi: 10.1007/s11103-015-0392-0

    CrossRef   Google Scholar

    [21]

    Qi X, Liu C, Song L, Li Y, Li M. 2017. PaCYP78A9, a cytochrome P450, regulates fruit size in sweet cherry (Prunus avium L. ). Frontiers in Plant Science 8:2076

    doi: 10.3389/fpls.2017.02076

    CrossRef   Google Scholar

    [22]

    Dong Y, Qi X, Liu C, Song L, Ming L. 2022. A sweet cherry AGAMOUS-LIKE transcription factor PavAGL15 affects fruit size by directly repressing the PavCYP78A9 expression. Scientia Horticulturae 297:110947

    doi: 10.1016/j.scienta.2022.110947

    CrossRef   Google Scholar

    [23]

    Nagasawa N, Hibara KI, Heppard EP, Vander Velden KA, Luck S, et al. 2013. GIANT EMBRYO encodes CYP78A13, required for proper size balance between embryo and endosperm in rice. The Plant Journal 75:592−605

    doi: 10.1111/tpj.12223

    CrossRef   Google Scholar

    [24]

    Yang W, Gao M, Yin X, Liu J, Xu Y, et al. 2013. Control of rice embryo development, shoot apical meristem maintenance, and grain yield by a novel cytochrome p450. Molecular Plant 6:1945−60

    doi: 10.1093/mp/sst107

    CrossRef   Google Scholar

    [25]

    Yarmohammadi F, Ghasemzadeh Rahbardar M, Hosseinzadeh H. 2021. Effect of eggplant (Solanum melongena) on the metabolic syndrome: A review. Iranian Journal of Basic Medical Sciences 24:420−27

    Google Scholar

    [26]

    Ogunsuyi OB, Olagoke OC, Afolabi BA, Loreto JS, Ademiluyi AO, et al. 2022. Effect of Solanum vegetables on memory index, redox status, and expressions of critical neural genes in Drosophila melanogaster model of memory impairment. Metabolic Brain Disease 37:729−41

    doi: 10.1007/s11011-021-00871-9

    CrossRef   Google Scholar

    [27]

    Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, et al. 2020. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Molecular Plant 13:1194−202

    doi: 10.1016/j.molp.2020.06.009

    CrossRef   Google Scholar

    [28]

    Bailey TL, Boden M, Buske FA, Frith M, Grant CE, et al. 2009. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Research 37:W202−W208

    doi: 10.1093/nar/gkp335

    CrossRef   Google Scholar

    [29]

    Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, et al. 2009. Circos: an information aesthetic for comparative genomics. Genome Research 19:1639−45

    doi: 10.1101/gr.092759.109

    CrossRef   Google Scholar

    [30]

    Wang Y, Tang H, DeBarry JD, Tan X, Li J, et al. 2012. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Research 40:e49

    doi: 10.1093/nar/gkr1293

    CrossRef   Google Scholar

    [31]

    Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. 2016. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols 11:1650

    doi: 10.1038/nprot.2016.095

    CrossRef   Google Scholar

    [32]

    Futschik ME, Carlisle B. 2005. Noise-robust soft clustering of gene expression time-course data. Journal of Bioinformatics and Computational Biology 3:965−88

    doi: 10.1142/S0219720005001375

    CrossRef   Google Scholar

    [33]

    Wickham H. 2016. ggplot2: elegant graphics for data analysis. New York: Springer. https://doi.org/10.1007/978-0-387-98141-3
    [34]

    Yu G, Wang LG, Han Y, He QY. 2012. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics:a journal of integrative biology 16:284−7

    doi: 10.1089/omi.2011.0118

    CrossRef   Google Scholar

    [35]

    Randall RS, Sornay E, Dewitte W, Murray JAH. 2015. AINTEGUMENTA and the D-type cyclin CYCD3;1 independently contribute to petal size control in Arabidopsis: evidence for organ size compensation being an emergent rather than a determined property. Journal of Experimental Botany 66:3991−4000

    doi: 10.1093/jxb/erv200

    CrossRef   Google Scholar

    [36]

    Li YJ, Yu Y, Liu X, Zhang XS, Su YH. 2021. The Arabidopsis MATERNAL EFFECT EMBRYO ARREST45 protein modulates maternal auxin biosynthesis and controls seed size by inducing AINTEGUMENTA. The Plant Cell 33:1907−26

    doi: 10.1093/plcell/koab084

    CrossRef   Google Scholar

    [37]

    Simmons AR, Davies KA, Wang W, Liu Z, Bergmann DC. 2019. SOL1 and SOL2 regulate fate transition and cell divisions in the Arabidopsis stomatal lineage. Development 146:dev171066

    doi: 10.1242/dev.171066

    CrossRef   Google Scholar

    [38]

    Noh M, Shin JS, Hong JC, Kim SY, Shin JS. 2021. Arabidopsis TCX8 functions as a senescence modulator by regulating LOX2 expression. Plant Cell Reports 40:677−89

    doi: 10.1007/s00299-021-02663-y

    CrossRef   Google Scholar

    [39]

    Li C, Potuschak T, Colón-Carmona A, Gutiérrez RA, Doerner P. 2005. Arabidopsis TCP20 links regulation of growth and cell division control pathways. PNAS 102:12978−83

    doi: 10.1073/pnas.0504039102

    CrossRef   Google Scholar

    [40]

    Bueso E, Muñoz-Bertomeu J, Campos F, Brunaud V, Martínez L, et al. 2014. ARABIDOPSIS THALIANA HOMEOBOX25 uncovers a role for Gibberellins in seed longevity. Plant physiology 164:999−1010

    doi: 10.1104/pp.113.232223

    CrossRef   Google Scholar

    [41]

    Vigeolas H, Hühn D, Geigenberger P. 2011. Nonsymbiotic hemoglobin-2 leads to an elevated energy state and to a combined increase in polyunsaturated fatty acids and total oil content when overexpressed in developing seeds of transgenic Arabidopsis plants. Plant physiology 155:1435−44

    doi: 10.1104/pp.110.166462

    CrossRef   Google Scholar

    [42]

    Leister D. 2004. Tandem and segmental gene duplication and recombination in the evolution of plant disease resistance genes. Trends in genetics 20:116−22

    doi: 10.1016/j.tig.2004.01.007

    CrossRef   Google Scholar

    [43]

    Wei Q, Wang J, Wang W, Hu T, Hu H, et al. 2020. A high-quality chromosome-level genome assembly reveals genetics for important traits in eggplant. Horticulture Research 7:153

    doi: 10.1038/s41438-020-00391-0

    CrossRef   Google Scholar

    [44]

    Mimura M, Itoh JI. 2014. Genetic interaction between rice PLASTOCHRON genes and the gibberellin pathway in leaf development. Rice 7:25

    doi: 10.1186/s12284-014-0025-2

    CrossRef   Google Scholar

    [45]

    Sotelo-Silveira M, Cucinotta M, Chauvin AL, Chávez Montes RA, Colombo L, et al. 2013. Cytochrome P450 CYP78A9 is involved in Arabidopsis reproductive development. Plant Physiology 162:779−99

    doi: 10.1104/pp.113.218214

    CrossRef   Google Scholar

    [46]

    Miyoshi K, Ahn BO, Kawakatsu T, Ito Y, Itoh JI, et al. 2004. PLASTOCHRON1, a timekeeper of leaf initiation in rice, encodes cytochrome P450. PNAS 101:875−80

    doi: 10.1073/pnas.2636936100

    CrossRef   Google Scholar

    [47]

    Imaishi H, Matsuo S, Swai E, Ohkawa H. 2000. CYP78A1 preferentially expressed in developing inflorescences of Zea mays encoded a cytochrome P450-dependent lauric acid 12-monooxygenase. Bioscience, Biotechnology, and Biochemistry 64:1696−701

    doi: 10.1271/bbb.64.1696

    CrossRef   Google Scholar

    [48]

    Kai K, Hashidzume H, Yoshimura K, Suzuki H, Sakurai N, et al. 2009. Metabolomics for the characterization of cytochromes P450-dependent fatty acid hydroxylation reactions in Arabidopsis. Plant Biotechnology 26:175−82

    doi: 10.5511/plantbiotechnology.26.175

    CrossRef   Google Scholar

    [49]

    Shi L, Song J, Guo C, Wang B, Guan Z, et al. 2019. A CACTA-like transposable element in the upstream region of BnaA9.CYP78A9 acts as an enhancer to increase silique length and seed weight in rapeseed. The Plant Journal 98:524−39

    doi: 10.1111/tpj.14236

    CrossRef   Google Scholar

    [50]

    Zhang Y, Du L, Xu R, Cui R, Hao J, et al. 2015. Transcription factors SOD7/NGAL2 and DPA4/NGAL3 act redundantly to regulate seed size by directly repressing KLU expression in Arabidopsis thaliana. The Plant Cell 27:620−32

    doi: 10.1105/tpc.114.135368

    CrossRef   Google Scholar

  • Cite this article

    Zhou M, Zhang L, Luo S, Song L, Shen S, et al. 2023. Comprehensive analysis of CYP78A family genes reveals the involvement of CYP78A5 and CYP78A10 in fruit development in eggplant. Vegetable Research 3:5 doi: 10.48130/VR-2023-0005
    Zhou M, Zhang L, Luo S, Song L, Shen S, et al. 2023. Comprehensive analysis of CYP78A family genes reveals the involvement of CYP78A5 and CYP78A10 in fruit development in eggplant. Vegetable Research 3:5 doi: 10.48130/VR-2023-0005
    shu

Figures(6)  /  Tables(2)

Download PDF

Article Metrics

Article views(6678) PDF downloads(788)

Other Articles By Authors

ARTICLE   Open Access    

Comprehensive analysis of CYP78A family genes reveals the involvement of CYP78A5 and CYP78A10 in fruit development in eggplant

  • College of Horticulture, State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, Hebei Agricultural University, Baoding, Hebei 071000, China

  • Received: 19 November 2022
  • Accepted: 27 December 2022
  • Published online: 14 February 2023
Vegetable Research  3,  Article number: 5  (2023)  |  Cite this article

Abstract: The CYP78A family is a plant-specific family, members of which have been considered as promising targets for yield improvement due to their important roles in regulating organ size. Eggplant is an important vegetable cultivated worldwide. However, little information about the eggplant CYP78As (SmCYP78As) limits the potential utilization of SmCYP78As for crop improvement. In this study, we identified six CYP78A genes in the eggplant genome named SmCYP78A5 to SmCYP78A10 according to the phylogenetic relationships to Arabidopsis CYP78As. The phylogenetic analysis of CYP78As from eggplant, Arabidopsis, rice and tomato classified the 27 CYP78As into five clades. SmCYP78As were found in three of the five clades. This classification is consistently supported by their gene structures, domains and conserved motifs. Segmental duplication events were found to contribute to the expansion of the SmCYP78A family. Comparative syntenic analysis provided further insight into the phylogenetic relationships of CYP78A genes from the four plants. qRT-PCR analysis revealed that the expression of the six SmCYP78As was detected in at least one of the eight tissues, showing a tissue-specific pattern. Notably, SmCYP78A5 and SmCYP78A10 were highly expressed in developing ovaries, indicating the involvement of fruit development in eggplant. Co-expression clustering and GO enrichment analysis suggested that SmCYP78A5 and SmCYP78A10 regulate fruit development likely through different pathways. In addition, six transcription factors were identified as promising candidates that may directly bind promoters of SmCYP78A5 and SmCYP78A10. This study provides a comprehensive overview of the SmCYP78As family, which would lay a foundation for further understanding of evolution and function of the SmCYP78A family.

    HTML

    INTRODUCTION

    • Cytochrome P450 (P450) is an important superfamily in plants, the members of which play important roles in a wide range of biochemical pathways to produce phytohormones, including auxin, brassinosteroids (BRs) and gibberellins (GAs), as well as secondary metabolites, such as phenylpropanoids and fatty acids[1,2]. Plant P450s were grouped into 11 clans in two categories: multi-family clans (CYP71, CYP72, CYP85, CYP86) and single-family clans (CYP51, CYP74, CYP97, CYP710, CYP711, CYP727, CYP746)[2,3].

      The CYP78A family is one of the families in the CYP71 clan and a plant-specific gene family[3]. The first CYP78A gene, CYP78A5/KLUH, was characterized in Arabidopsis, which is a positive regulator of organ size by promoting cell proliferation. While loss-of-function of Arabidopsis CYP78A5/KLUH results in smaller leaves and floral organs, over-expression of CYP78A5/KLUH produces larger organs, including leaves, seeds and petals[4,5]. Recently, AtKLUH was shown to play positive roles in regulating leaf longevity and drought tolerance by promoting cytokinin signaling and proline metabolism[6]. In addition, cyp78a5 mutants also exhibit reduced plastochron length and early flowering[7,8], indicating the pleiotropic roles of AtKLUH in regulating Arabidopsis growth and development. There are five other Arabidopsis CYP78A members, CYP78A6, CYP78A7, CYP78A8, CYP78A9 and CYP78A10. CYP78A7 and CYP78A5 play redundant roles in regulating plastochron length, leaf size and apical dominance[7,9]. CYP78A6 (EOD3) and CYP78A9 were shown to redundantly regulate seed size and leaf senescence[9,10]. The functions of CYP78A10 are still unknown.

      The orthologs of AtCYP78A5 have been shown to regulate seed or fruit size in other plants. For example, increased expression of tomato KLUH leads to larger fruits and seeds by stimulating cell division[11−14]. Over-expression of maize PLASTOCHRON1 (ZmPLA1) stimulates leaf growth by extending cell division duration, leading to increased seed yield[15]. Recent studies in wheat showed that constitutive overexpression of TaCYP78A5 enhances grain weight by accumulating auxin[16,17]. In addition, soybean CYP78A10 and CYP78A72, sweet cherry CYP78A9 and rice GIANT EMBRYO (GE; CYP78A13) have also been demonstrated as key regulators of organ size[18−24], showing great potential in yield improvement.

      Eggplant is an important vegetable crop that is cultivated worldwide. Eggplant is also known as an important medical plant due to its high phenolic and alkaloid contents, including chlorogenic acid and acetylcholine (ACh), which can be used to treat human diseases, such as diabetes and high blood pressure[25,26]. With increasing worldwide population, the yield of eggplants is needed to be increased to meet the demands. Considering the potential of CYP78A family members in yield improvement, it would be useful to identify and characterize the CYP78A family in the eggplant genome. However, none of the CYP78A genes have been cloned or characterized in eggplant to date.

      In the present study, the CYP78A gene family was identified in the eggplant genome. Detailed information of the SmCYP78As, including chromosomal locations, phylogenetic relationships, gene structures, conserved motifs, synteny and candidate transcription factors which might directly bind the promoter of SmCYP78As, were investigated. qRT-PCR was performed to analyze the tissue-specific expression patterns of the SmCYP78As. Expression levels of SmCYP78As in young flower buds and developing ovaries were also analyzed using RNA-seq data. Co-expression clustering and GO enrichment analysis were conducted to gain further insights into the functions of SmCYP78As. The results of the present study will be helpful for further functional study of the SmCYP78A genes.

    MATERIALS AND METHODS

      Identification of CYP78A genes in eggplant

    • Amino acid sequences of Arabidopsis CYP78As were downloaded from The Arabidopsis Information Resource (TAIR) database (www.arabidopsis.org). The six AtCYP78A proteins were used as query sequences and searched against the eggplant proteins in the Eggplant Genome Database (http://eggplant-hq.cn/Eggplant/home/index). Then, the Pfam (http://pfam.janelia.org/) and Simple Modular Architecture Research Tool (SMART) (http://smart.embl-heidelberg.de/) programs were used to confirm the existence of the P450 domain. The number of amino acids, isoelectric points (PIs) and molecular weights (MWs) of the 27 CYP78A proteins were determined using ExPASy (https://web.expasy.org/protparam/), and their subcellular localizations were predicted with Cell-PLoc 2.0 (www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc-2/).

      Phylogenetic analysis, gene structure and conserved motif recognition

    • Multiple sequence alignments were performed and the phylogenetic trees were constructed with the full protein sequences of the 27 CYP78As from eggplant, Arabidopsis, rice and tomato using MEGA 7.0. The neighbor-joining (NJ) method was used for the construction of the phylogenetic tree with the following parameters: Poisson correction, pairwise deletion and 1000 bootstrap replicates. Gene structures were analyzed using TBtools[27]. Conserved motifs were identified using MEME (Multiple Expectation Maximization for Motif Elicitation)[28].

      Chromosomal distribution and synteny analysis

    • All SmCYP78A genes were mapped to chromosomes based on physical location information from the Eggplant Genome Database using Circos[29]. Multiple Collinearity Scan toolkit (MCScanX) is employed for scanning multiple genomes to align syntenic blocks[30].

      Plant materials

    • An inbred line '14-345' with round and purple black fruits was used in this study for gene expression analysis and RNA-seq. The plants were grown in the greenhouse at Hebei Agricultural University in Baoding (38° N, 115° E), China.

      Real-time quantitative RT-PCR (qRT-PCR)

    • Roots, stems, leaves, young flower buds, petals and sepals of 0 DPA (Days post anthesis) flowers, pericarp of 0 DPA ovary, fruit flesh of 10 DPA fruits were collected separately from at least 2−3 individual plants as one biological replicate. Three biological replicates for each tissue sample were carried out for qRT-PCR. Total RNAs were extracted using TRIzol reagent (Invitrogen, USA) and treated with DNase I (Fermentas, Canada) following the manufacturer's protocol. First strand cDNA was synthesized from 1 μg total RNA using PrimeScript 1st Strand cDNA Synthesis Kit (TaKaRa, Japan). qRT-PCR was performed on LightCycler 96 (Roche, Switzerland) using the ChamQ Universal SYBR qPCR Master Mix (Vazyme, China). Clathrin adaptor complexes medium subunit (CAC) gene (Smechr0800014) was used as the housekeeping gene to normalize the gene expression. Primers used for qRT-PCR are listed in Supplemental Table S1. The relative expression level was calculated using the 2− ΔΔCᴛ method.

      Generation and analysis of RNA-seq data

    • Young flower buds and developing ovaries, including 10 DBA (Days before anthesis), 7 DBA and 0 DPA, were collected with four biological replicates for each tissue sample. The cDNA library preparation and sequencing were conducted by the Novogene Bioinformatics Technology Company (Beijing, China) on the Illumina HiSeq TM4000 platform (Illumina Inc., San Diego, CA, USA). The read mapping was performed using the latest version of the Tuxedo protocol with HISAT2 and StringTie[31]. The clean reads from each library were aligned to the eggplant genome of 'HQ-1315' using HISAT2. The mapping results were normalized via Stringtie to compute TPM (Transcripts Per Kilobase Million) values of genes.

      Co-expression genes were clustered using fuzzy C means in the Mfuzz package[32] and gene clusters were visualized by plotting of the normalized expression profiles of each cluster using ggplot2 package in R[33]. For GO enrichment analysis, eggplant geneIDs were converted to Arabidopsis geneIDs using BLASTP and then GO enrichment analysis was performed using clusterProfiler with cnetplot function in R[34]. The significant GO terms were identified with the adjusted value smaller than 0.05.

    RESULTS

      Genome-wide identification and analysis of CYP78A genes in eggplant

    • The six AtCYP78A proteins were employed as a query to search the SmCYP78As in the eggplant genome via BlastP program, resulting in six putative SmCYP78A genes. The presence of the P450 domains in the six putative SmCYP78A proteins was confirmed via Pfam and SMART, indicating that the six proteins are members of the eggplant CYP78A family (Table 1).

      Table 1.  Summary information of CYP78A family genes in eggplant, Arabidopsis, rice and tomato.

      Gene IDGene nameLocationDeduced polypeptidePSL
      ChrStartEndLength (aa)MW (KDa)pI
      Smechr0101302CYP78A6Chr1126405811264324553860.358.90ER
      Smechr0302971CYP78A5Chr3898868008988918451658.196.79ER
      Smechr0400048CYP78A7Chr442056642292052558.989.23ER
      Smechr0500049CYP78A8Chr578327278534652859.769.30ER
      Smechr0502180CYP78A9Chr5749692627497117753760.557.52ER
      Smechr1100733CYP78A10Chr11105348191053696755161.708.23ER
      AT1G01190CYP78A8Chr1830458494654160.918.22ER
      AT1G13710CYP78A5Chr14702657470469451857.648.57ER
      AT1G74110CYP78A10Chr1278666672786836853860.187.84ER
      AT2G46660CYP78A6Chr2191533281915557953159.578.29ER
      AT3G61880CYP78A9Chr3229058682290795855662.629.04ER
      AT5G09970CYP78A7Chr53111945311423953759.496.69ER
      LOC_Os10g26340CYP78A11/PLA1Chr10136587901366054355659.087.06ER
      LOC_Os11g29720CYP78A5Chr11172342851723817853959.6410.19ER
      LOC_Os03g04190CYP78A9Chr31920043192189651655.808.10ER
      LOC_Os03g30420CYP78A6/GL3.2Chr3173404151734228451656.059.00ER
      LOC_Os03g40600CYP78A7Chr3225676702256868519420.297.99ER
      LOC_Os03g40610CYP78A8Chr3225727062257400830832.8410.12ER
      LOC_Os07g41240CYP78A13/GEChr7247137782471581352655.898.68ER
      LOC_Os08g43390CYP78A15/BSR2Chr8274205012742283655259.799.38ER
      LOC_Os09g35940CYP78A10Chr9206913062069311655460.749.07ER
      Solyc01g096280CYP78A6Chr1796222667962461853961.058.60ER
      Solyc03g114940CYP78A5/KLUHChr3592173895921973051758.386.21ER
      Solyc05g015350CYP78A8Chr5104750281047926733237.856.08ER
      Solyc05g047680CYP78A7Chr5585063905850829253260.499.20ER
      Solyc10g009310CYP78A9Chr103224421322712352661.649.05ER
      Solyc12g056810CYP78A10Chr12625109416251267653760.717.50ER

      The information of the six SmCYP78As, including chromosomal locations, amino acids number (length), PIs, MWs and predicted subcellular localizations (PSL), was listed in Table 1. To gain further insights into the CYP78A family genes in plants, the information of CYP78As from Arabidopsis, rice and tomato was also included in Table 1. The amino acids number of SmCYP78A proteins varies from 516 (SmCYP78A5) to 551 (SmCYP78A10), the PI ranges from 6.79 (SmCYP78A5) to 9.30 (SmCYP78A8) and the MW ranges from 58.19 (SmCYP78A5) to 61.70 KDa (SmCYP78A10). Interestingly, the amino acids lengths of rice CYP78A7 and CYP78A8 as well as tomato CYP78A8 (SlCYP78A8) were much smaller than other CYP78As from Arabidopsis, rice, tomato and eggplant. It would be interesting to know whether the three short CYP78A proteins show similar functions with other CYP78As. Notably, the subcellular localizations of all CYP78As were predicted to be localized in endoplasmic reticulum (ER), which is in agreement with the subcellular localizations of AtCYP78A5 and TaCYP78A5 in vivo[4,16].

      To elucidate the evolutionary relationships of SmCYP78As, an unrooted neighbor-joining (NJ) phylogenetic tree was constructed using the full protein sequences from the six AtCYP78As, nine OsCYP78As, six SlCYP78As and six SmCYP78As. The resulting tree contained five distinct clades (C1-C5) (Fig. 1a). Phylogenetic analysis revealed that C1 and C4 were shared in all the four species. There was equal number of the CYP78A proteins from eggplant, Arabidopsis, tomato and rice in C1 (Fig. 1a). C4 contained two SmCYP78As and one CYP78A from each of tomato, Arabidopsis and rice, indicating the expansion of SmCYP78As in this clade compared with the other three species. C2 and C3 didn't contain SmCYP78As. C2 was shared by rice and tomato, but not in Arabidopsis and eggplant, suggesting the unique roles of the CYP78As in C2 that were likely acquired or expanded in tomato and rice after divergence from the last common ancestor with eggplant and Arabidopsis. It is worth noting that C3 only contains rice CYP78As and OsCYP78A8 didn't fit into any clades, which may have evolved following divergence and have special roles in rice. Moreover, C5 didn't include any rice CYP78As but only members from eggplant, tomato and Arabidopsis, suggesting that the CYP78As in C5 may have been lost in rice during evolution.

      Figure 1. 

      Phylogenetic relationships, gene structure and conserved protein motifs of CYP78A genes from eggplant, Arabidopsis, rice and tomato. (a) The phylogenetic tree was constructed based on the full-length protein sequences of six AtCYP78As, nine OsCYP78As, six SlCYP78As and six SmCYP78As proteins using MEGA 7.0 software. Eggplant, Arabidopsis, rice and tomato CYP78As were labeled by red, black, pink and green dots. (b) Exon-intron structure of CYP78A genes. Black lines indicate introns. The number indicates the phases of corresponding introns. (c) The motif composition of CYP78A proteins. The motifs, numbers 1–10, are displayed in different colored boxes. The sequence logos and E values for each motif are given in Supplemental Fig. S1.

      Gene structure analysis showed that the number of exons in the 27 CYP78A genes was conserved and most of them contain two exons except OsCYP78A7 and AtCYP78A9, which contains only one and three exons, respectively (Fig. 1b). In addition, the introns of the 27 CYP78As are a phase 0 intron (Fig. 1b), further suggesting the highly conservation of CYP78A genes during the evolution of the four plants.

      Ten conserved motifs that are shared among the 27 CYP78A proteins were identified using the MEME (Fig. 1c; Supplemental Fig. S1). Twenty-four CYP78A proteins contain all 10 motifs with motif 6, 2, 9, 7 and 10 at N terminal and motif 1, 5, 3, 4 and 8 at C terminal (Fig. 1c), suggesting the similar function of these CYP78As. The other three CYP78A proteins with shortest amino acid length did not include some motifs (Fig. 1c). For example, SlCYP78A8 does not have motif 2, 6 and 10. While OsCYP78A7 does not include motif 1, 6, 2, 7, 9 and 10, OsCYP78A8 does not contain motif 1, 5, 3, 4 and 8. Further studies are required to investigate the roles of these motifs regarding the functions of CYP78As.

      Synteny analysis of CYP78A genes from eggplant, Arabidopsis, tomato and rice

    • The six SmCYP78A genes were mapped on five chromosomes, i.e. E01, E03, E04, E05 and E11 (Fig. 2a). Interestingly, all the six SmCYP78A genes were located at the end of the five chromosomes. Similar locations of CYP78As were also found in Arabidopsis, tomato and rice genomes (Fig. 2). Syntenic analysis of the eggplant genome were performed using MCscanX to identify duplication events among SmCYP78As. Only one gene pair, SmCYP78A6 and SmCYP78A7, were identified in the eggplant genome, indicating that segmental duplication contributes to the expansion of the CYP78A family in eggplant.

      Figure 2. 

      Gene duplication and synteny analysis of SmCYP78A genes. (a) Schematic representations for the chromosomal distribution and interchromosomal relationships of SmCYP78A genes. Gray lines indicate all synteny blocks in the eggplant genome, and the red lines indicate segmental duplicated SmCYP78A gene pairs. (b) Synteny analysis of CYP78A genes between eggplant and Arabidopsis. (c) Synteny analysis of CYP78A genes between eggplant and tomato. (d) Synteny analysis of CYP78A genes between eggplant and rice. Gray lines in the background indicate the collinear blocks between genomes, while the red lines highlight the syntenic blocks harboring CYP78A gene pairs.

      Comparative syntenic analyses of eggplant genome were performed with genomes of Arabidopsis, tomato and rice. Three (SmCYP78A5, SmCYP78A6 and SmCYP78A7), four (SmCYP78A5, SmCYP78A6, SmCYP78A5, SmCYP78A8 and SmCYP78A9) and one (SmCYP78A5) SmCYP78A gene show syntenic relationships with those in Arabidopsis, tomato and rice, respectively (Fig. 2). Interestingly, SmCYP78A6 and SmCYP78A7 were syntenic with three Arabidopsis CYP78A genes (AtCYP78A6, AtCYP78A8 and AtCYP78A9), respectively. Notably, SmCYP78A5 showed a syntenic relationship with AtCYP78A10, SlCYP78A5/KLUH and OsCYP78A13/GE, indicating that these orthologous pairs likely have existed before the ancestral divergence with conserved functions.

      Expression profiles for SmCYP78A genes in different tissues

    • Real-time quantitative RT-PCR were used to detect the expression patterns for the six SmCYP78A genes in the roots, stems, leaves, young flower buds, petals, sepals, pericarp and fruit flesh. The six SmCYP78A genes showed different patterns of tissue-specific expression and exhibited relatively low expression levels in most tissues (Fig. 3a). SmCYP78A5, SmCYP78A7, SmCYP78A8, SmCYP78A9 and SmCYP78A10 was specifically expressed in young flower buds, roots, petals, roots and stems, respectively (Fig. 3a). SmCYP78A6 showed high levels of transcript abundance in roots and pericarp (Fig. 3a). The different expression patterns of the SmCYP78As indicate their distinct roles in various aspects of physiological and developmental processes.

      Figure 3. 

      Expression profiles of the six SmCYP78A genes in different tissues. (a) Relative transcript abundances of the SmCYP78A genes examined by qRT-PCR. (b) Expression of the SmCYP78A genes in young flower buds and developing ovaries detected by RNA-seq. Rt, Root; St, Stems; Le, Leaf; Pet, Petal; Se, Sepal; Per, Pericarp; FF, Fruit flesh; YB, Young flower buds; DBA, Days before anthesis; DPA, Days post anthesis.

      Considering the important roles of CYP78A genes in fruit development and the fact that fruit size was largely determined at the early developmental stages, we analyzed the RNA-seq data of young flower buds and developing ovaries in eggplant. The six SmCYP78A genes showed different expression patterns in developing ovaries (Fig. 3b). While SmCYP78A5 and SmCYP78A10 showed high expression, the other four SmCYP78A genes were barely expressed in young flower buds and developing ovaries (Fig. 3b). Moreover, SmCYP78A5 showed highest expression in young flower buds and gradually decreased with the development of eggplant ovary and showed no expression at 0 DPA (Fig. 3b). Interestingly, SmCYP78A5 showed similar expression patterns with tomato KLUH, the closest ortholog of SmCYP78A5, in developing ovaries[11], indicating their conserved roles in regulating fruit size. SmCYP78A10 abundantly expressed in developing ovaries with the expression peak at 10 DBA and very low expression in 0 DPA (Fig. 3b).

      Analysis of co-expression genes of SmCYP78A5 and SmCYP78A10

    • The high expression of SmCYP78A5 and SmCYP78A10 in developing ovaries (Fig. 3b) indicated their important roles in regulating fruit development in eggplant. To gain further insight into the functions of SmCYP78A5 and SmCYP78A10, co-expression analysis was performed using fuzzy C-means clustering. Twelve co-expressed clusters were identified with Cluster 6 and 11 representing SmCYP78A10 and SmCYP78A5, respectively (Fig. 4; Supplemental Table S2).

      Figure 4. 

      Twelve co-expressed clusters are clustered using fuzzy C-means clustering in Mfuzz with normalized expression values (z-scores). The red lines represent the average of expression values, whereas the gray lines represent the expression values of the co-expressed genes. YB, Young flower buds; DBA, Days before anthesis; DPA, Days post anthesis.

      Cluster 6 represented genes that expressed at higher levels in ovaries at 10 DBA and 7 DBA than young flower buds and ovaries at 0 DPA (Fig. 4). Cluster 6 was significantly enriched with genes involved in cellular processes, such as 'Cell cycle process', 'Organelle fission' and 'Microtubule-based process' (Fig. 5). Genes involved in these processes included SmCYP78A10 and putative orthologs of Arabidopsis SUN1, TON1, TUA6 and NEK1 (Fig. 5). Genes in Cluster 11 showed highest expression in young flower buds and low expression in developing ovaries (Fig. 4). Cluster 11 was enriched with genes involved in photosynthesis related processes, including 'photosynthesis', 'carbon fixation' and 'response to high light intensity'. Genes involved in these processes included NDFs, PRK and PPH1 (Fig. 5). The GO enrichment analysis indicated that SmCYP78A10 and SmCYP78A5 regulate fruit development likely through different mechanisms.

      Figure 5. 

      Significantly enriched GO terms (biological process) of co-expression genes in (a) Cluster 6 and (b) Cluster 11. Only the top five enriched GO terms are shown. The color of lines represents different GO terms.

      Since transcription factors (TFs) are the main regulators of gene expression, we sought out the TFs in the two clusters. Cluster 6 harbored 77 TFs (7.60%) which were classified into 29 families (Fig. 6a; Supplemental Table S3). The 10 most abundant TF families in cluster 6 were HB (8), GRAS (6), MYB (5), bHLH (5), B3 (5), ERF (4), zf-HD (3), NAC (3), MYB-related (3) and GRF (3) (Fig. 6a; Supplemental Table S3). The Cluster 11 contained 63 TFs (6.52%) mainly from families classified as HB (8), bHLH (5), MIKC (4), MYB (4), NF-YA (4), bZIP (3), C2C2-CO-like (3), C2C2-YABBY (3), C3H (3) and HSF (3) (Fig. 6b; Supplemental Table S4). Interestingly, HB, MYB and bHLH TFs were found in both Cluster 6 and 11, suggesting that HB, MYB and bHLH TFs might play important roles in regulating the expression of CYP78As in eggplant.

      Figure 6. 

      Overview of distribution of TF families that were co-expressed with (a) SmCYP78A10 in Cluster 6 and (b) SmCYP78A5 in Cluster 11. The Plant Transcription Factor Database v5.0 (http://planttfdb.gao-lab.org) was used to identify TFs in the eggplant genome.

      Transcription factor binding site analysis in the promoters of SmCYP78As

    • To gain further insight into the transcriptional regulation of the SmCYP78A5 and SmCYP78A10, we selected a 1.5 kb regulatory region upstream of the ATG of SmCYP78A5 and SmCYP78A10 (Supplemental Table S5) to scan transcription factor binding sites (TFBSs) using PlantRegMap. Interestingly, two HB TFs, Smechr0402062 and Smechr0101299, that are co-expressed with SmCYP78A5 in Cluster 11 were predicted to directly target SmCYP78A5 (Table 2). SmCYP78A10 was identified as candidate target of Smechr0402092 (AP2), Smechr0902218 (Cysteine-rich polycomb-like protein, CPP), Smechr0801604 (MYB) and Smechr0201168 (TCP) that are co-expressed genes of SmCYP78A10 in Cluster 6. Some orthologs of the TFs were known from other studies to be involved in organ size regulation in plants. For example, AINTEGUMENTA (ANT) is an ortholog of Smechr0402092 in Arabidopsis and has been demonstrated as a positive organ size regulator by stimulating cell proliferation and modulating auxin biosynthesis[35,36]. Smechr0902218 encodes a CPP TF and is closely related to Arabidopsis TCX2/SOL2 that has been reported to regulate both cell fate and cell division[37,38]. Smechr0201168 is a putative ortholog of Arabidopsis TCP20 which has been proposed to control cell division and growth by directly binding to the GCCCR element in the promoters of cyclin CYCB1;1[39]. In addition, studies from Arabidopsis have shown the important roles of HB TFs in regulating organ size[40,41]. Therefore, the TFs may function as regulators of eggplant fruit development by directly binding the promoters of CYP78As.

      Table 2.  Candidate transcription factors binding promoters of SmCYP78As identified by PlantRegMap.

      Gene IDTF familyArabidopsis
      ortholog      		Binding sequenceStrandP value
      Smechr0402062HBAT4G08150CACTTCCCTTCTCTCTCTCT+1.71E-05
      Smechr0101299HBAT2G46680TCATTTATTGAAC−9.07E-05
      GGAATGATTGTAA−9.88E-05
      Smechr0402092AP2AT4G37750CATCACAAATTCCAAAATCCC+2.73E-05
      AAACACTCTCCCCCACGTATA−7.73E-05
      Smechr0902218CPPAT4G14770TAAAATTTTAAAA−7.34E-05
      TGAAATTTAAAAA−8.37E-05
      TCAAATTTAAAAA+8.47E-05
      Smechr0801604MYBCTTGAAGACCGTTGA+9.42E-05
      Smechr0201168TCPAT3G27010TTGCCCCAC+5.27E-05
    DISCUSSION

    • The members of P450 superfamily encodes enzymes presenting in the kingdoms of life with functional diversity[2]. CYP78A is a plant-specific P450 family and has been well studied in Arabidopsis, in which CYP78A genes play important roles in growth and development, including plastochron and organ size[9]. However, no related information has been reported in eggplant. In the present study, we identified six CYP78A members in eggplant genome (Table 1), which is same to the number of CYP78A genes in Arabidopsis and tomato. However, compared to rice, the size of CYP78A families was small in Arabidopsis, tomato and eggplant. Considering the number of CYP78A family members was not correlated with genome size, suggesting differential expansion events occurred during the evolution of the CYP78A family between rice and Arabidopsis, tomato and eggplant. Gene duplication events contribute to the expansion and evolution of gene families[42] and one segmental duplication event (SmCYP78A6 and SmCYP78A7) was identified in eggplant genome (Fig. 2a), which may result from the ancient whole genome duplication (WGD) in eggplant before the divergence of asterids and rosids[43].

      Although CYP78A genes have been reported in many plant species, genome-wide identification of CYP78A family were only performed in Arabidopsis[9]. To better understand the phylogenetic relationships of CYP78As in eggplant with those of model plant species, such as Arabidopsis, tomato and rice, we also identified CYP78A genes in the genomes of tomato and rice (Table 1). Phylogenetic trees combining eggplant, Arabidopsis, tomato and rice CYP78As were constructed, which divided the 27 CYP78As into five clades and the six SmCYP78A members into three clades (Fig. 1a). Interestingly, lineage-specific gene loss and expansions were observed in some clades, indicating the divergence of the clades during the evolution of the species. For example, C2 and C3 did not include any eggplant CYP78As, suggesting that the two clades were either lost in eggplant or were acquired in rice and tomato after divergence from the last common ancestor. Similar reasons could explain why none of the rice CYP78As were found in C5 (Fig. 1a).

      To further obtain insight of the evolutionary relationship and diversity/conservativeness of CYP78A genes in eggplant, Arabidopsis, tomato and rice, the gene structure, P450 domain and motif analyses were also conducted (Fig. 1b & c). The results showed that CYP78A members from the four species showed similar gene structure, P450 domain and motif compositions. In addition, all the CYP78A proteins were proposed to be ER-localized (Table 1). These results indicated that the CYP78A family was highly conserved in plants. More importantly, many studies from other plants, such as rice, tomato and maize, also suggested the highly conserved roles of CYP78As in plants[11,15,44].

      Notably, phylogenetic analysis showed that SmCYP78A5 was grouped into the same clade with AtCYP78A5, AtCYP78A10 and SlCYP78A5/KLUH (Fig. 1a). Moreover, syntenic analysis indicated that SmCYP78A5 was an ortholog of AtCYP78A10, SlCYP78A5/KLUH and OsCYP78A13/GE (Fig. 2), which have been identified as key positive organ size regulators in Arabidopsis, tomato and rice, respectively. Therefore, it is reasonable to hypothesize that SmCYP78A5 may positively control organ size in eggplant. SmCYP78A6, SmCYP78A7 and SmCYP78A8 were classified into the same clade with AtCYP78A6, AtCYP78A8 and AtCYP78A9 (Fig. 1a). Furthermore, SmCYP78A6 and SmCYP78A7 were syntenic orthologs of AtCYP78A6, AtCYP78A8 and AtCYP78A9 (Fig. 2b). AtCYP78A6 and AtCYP78A9 have been found to redundantly regulate seed size and leaf senescence, whereas AtCYP78A8 was shown to be involved in seed color regulation but not leaf senescence[9,10,45]. It would be interesting to know whether SmCYP78A6, SmCYP78A7 and SmCYP78A8 play roles in the regulation of organ size, seed color and leaf senescence. Phylogenetic analysis indicated the closest relationships between SmCYP78A9 and SmCYP78A10 with AtCYP78A7 and OsPLA1 (Fig. 1a). Loss of function of AtCYP78A7 and OsPLA1 led to short plastochron in Arabidopsis and rice, respectively[9,46], indicating the important roles of SmCYP78A9 and SmCYP78A10 in plastochron regulation in eggplant, which needs to be confirmed in future studies. In addition, we found that SmCYP78A6, 7, 8 and 9 were barely expressed in developing ovaries but highly expressed in other tissues, such as roots and petals, indicating that they may play important roles in these tissues.

      Tomato KLUH underlies fruit weight QTL fw3.2 and is highly expressed in vegetative meristems and young flower buds[11]. Our previous study indicated that tomato KLUH positively regulates fruit size by promoting cell proliferation in the pericarp at the early stages of fruit development, which has been proposed to be associated with lipid metabolism and photosynthesis[12]. In sweet cherry, PaCYP78A9 showed highest expression in floral organs and was shown to regulate fruit size by affecting cell proliferation and expansion in pericarp. Interestingly, SmCYP78A5 showed similar expression pattern with SlCYP78A5/KLUH and PaCYP78A9 (Fig. 3b)[11,12]. Moreover, gene co-expression analysis in the present study suggested the tight links between SmCYP78A5 and photosynthesis, further supporting the conserved roles of SmCYP78A5 and SlKLUH in regulating fruit development. In the present study, although SmCYP78A5 and SmCYP78A10 had high expression in developing ovaries, they showed different expression patterns. Moreover, GO enrichment of the co-expression genes of SmCYP78A5 and SmCYP78A10 suggested that the two genes regulate fruit development through different pathways. These results indicated the functional divergence of eggplant CYP78A family members. Over-expression and knock-out of SmCYP78A5 and 10 were required to confirm their roles in regulating organ size, especially in fruit size or length in eggplant.

      It has been shown that AtKLUH regulate organ size in non-cell autonomous manner by producing a mobile molecule[4,5,9]. Several lines of evidence suggest that the mobile signal might be fatty acid-derived molecules[5,12,47,48]. A recent study in Arabidopsis showed that AtKLUH promotes organ growth as well as drought tolerance mainly by affecting cytokinin signaling and proline metabolism[6]. However, the studies from maize, wheat and rapeseed indicated the involvement of CYP78As in auxin metabolism[49]. Therefore, more direct evidence is needed to determine the mobile signal generated by CYP78As.

      Identification of TFs that directly target CYP78As would be helpful to gain further insight into the transcriptional regulation of the pathway mediated by CYP78As in eggplant. In Arabidopsis, suppressor of da1-1 (SOD7) encodes B3 domain transcription factor NGATHA-like protein 2 (NGAL2). SOD7/NGAL2 directly binds the promoter of KLUH to repress its expression and thereby negatively regulates seed size[50]. In sweet cherry, AGAMOUS-LIKE transcription factor PavAGL15 regulates fruit size by directly repressing the expression of PavCYP78A9[22]. In this study, several putative TFs binding to the promoters of SmCYP78As were identified through bioinformatic prediction and co-expression analysis (Fig. 4; Fig. 6; Table 2). More interestingly, the orthologs of these TFs in Arabidopsis, including ANT, TCX2/SOL2 and TCP20, have been shown to be associated with organ size regulation[35−41]. However, the involvement of these TFs in fruit development needs to be confirmed in eggplant and further studies, such as Yeast One Hybrid and Electrophoretic Mobility Shift Assay (EMSA), are required to dissect the relationships between the TFs and SmCYP78As.

    CONCLUSIONS

    • In this work, we identified six CYP78A family genes in the eggplant genome and provided comprehensive analysis of CYP78A genes from eggplant, Arabidopsis, rice and tomato. The results indicated the close evolutionary relationship and functional conservation of CYP78A genes in plants. The high expression of SmCYP78A5 and SmCYP78A10 in young flower buds and developing ovaries suggested their important roles in controlling fruit development. Co-expression clustering, GO enrichment analysis and TF binding site analysis indicated the different mechanisms underlying fruit development regulation between SmCYP78A5 and SmCYP78A10 and identified six potential upstream TFs that directly bind to the promoters of SmCYP78A5 and SmCYP78A10.

      Acknowledgments

      • This work was supported by Natural Science Foundation of Hebei Province (C2021204015), 2021 Project for the Introduction of Oversea Students in Hebei (C20210510), Science and technology research projects of colleges and universities in Hebei Province (ZD2022111), the Introduction of Talents Start-up fund of State Key Laboratory of North China Crop Improvement and Regulation (NCCIR2020RC-13), the Introduction of Talents Start-up fund of Hebei Agricultural University (YJ2020067), Hebei Fruit Vegetables Seed Industry Science and Technology Innovation Team Project (21326309D) and Vegetable Innovation Team Project of Hebei Modern Agricultural Industrial Technology System (HBCT2018030203).

      Conflict of interest

      • Jianjun Zhao is an Editorial Board member of the journal Vegetable Research. He 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 the Editorial Board member and his research group.

      • # These authors contributed equally: Mengya Zhou, Liying Zhang

      Supplementary information
      Rights and permissions
      • 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 (6)  Table (2) References (50)
  • About this article
    Cite this article
    Zhou M, Zhang L, Luo S, Song L, Shen S, et al. 2023. Comprehensive analysis of CYP78A family genes reveals the involvement of CYP78A5 and CYP78A10 in fruit development in eggplant. Vegetable Research 3:5 doi: 10.48130/VR-2023-0005
    Zhou M, Zhang L, Luo S, Song L, Shen S, et al. 2023. Comprehensive analysis of CYP78A family genes reveals the involvement of CYP78A5 and CYP78A10 in fruit development in eggplant. Vegetable Research 3:5 doi: 10.48130/VR-2023-0005
    shu

Catalog

  • Html

  • Introduction
  • Materials and methods

  • Identification of cyp78a genes in eggplant

  • Phylogenetic analysis, gene structure and conserved motif recognition

  • Chromosomal distribution and synteny analysis

  • Plant materials

  • Real-time quantitative rt-pcr (qrt-pcr)

  • Generation and analysis of rna-seq data

  • Results

  • Genome-wide identification and analysis of cyp78a genes in eggplant

  • Synteny analysis of cyp78a genes from eggplant, arabidopsis, tomato and rice

  • Expression profiles for smcyp78a genes in different tissues

  • Analysis of co-expression genes of smcyp78a5 and smcyp78a10

  • Transcription factor binding site analysis in the promoters of smcyp78as

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

Contents

Policies

Services

Partnerships

© 2025 Maximum Academic Press, except Open Access articles. MAP is a member of ALPSP, COPE, CrossRef, cOAlition S, OASPA and STM.

Privacy Policy  |  Site Map  |  Terms and Conditions

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return