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

Genome-wide identification and molecular characterization of SlKRP family members in tomato and their expression profiles in response to abiotic stress

  • State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai-An 271018, Shandong, China

  • # These authors contributed equally: Genzhong Liu, Zhangfeng Guan, Mingxuan Ma

More Information
An Author Correction to this article was published on 31 January 2024, http://doi.org/10.48130/vegres-0024-0006.
  • The cell cycle has an essential role in the regulation of plant growth, development, and stress responses, which is controlled by the complex of cyclin / cyclin-dependent kinases (CDKs). Kip-related proteins (KRPs) as CDK inhibitors are involved in the precise regulation of cell cycle progression. However, the comprehensive identification of SlKRP family genes in tomato has not been achieved. Here, a total of six SlKRP proteins were identified from the tomato genome and divided into three classes via phylogenetic analysis. Chromosomal localization analysis revealed the interchromosomal segment duplication among SlKRP genes. Analyses on gene structures and conserved motifs indicated that the SlKRP genes were evolutionarily conserved. The subcellular localization analysis showed all SlKRP proteins were located in the nuclei. Six SlKRPs had distinct expression in different tissues. Their expressions were affected by the plant hormones (ABA, IAA, and ethylene) and various abiotic stresses (salt, drought, and low temperature), which were correlated with different cis-acting regulatory elements (CAREs) in the 3-Kb promoter regions of these genes. In addition, co-expression relationship and protein interaction network analysis proved that SlKRP proteins may interact with CDKs and cyclins. To further explore the function of SlKRPs in tomato, VIGS assay was performed to obtain SlKRP5-silenced plants and demonstrated that silencing of SlKRP5 increased the sensitivity to drought stress. These findings provide references for the further functional analysis of KRPs in the future.

  • 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).
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    Discussion

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

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

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

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

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

    Conclusions

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

    Acknowledgments

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

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

  • Supplemental Fig. S1 Multiple sequence alignment of the SlKRP proteins in tomato.
    Supplemental Fig. S2 Expression of the SlKRP genes in immature green fruits from 'Heinz 1706' and 'LA1589'.
    Supplemental Fig. S3 Expression of SlKRP genes in different tissues of tomato fruits at four developmental stages.
    Supplemental Fig. S4 Prediction of SlKRP protein’s secondary structure via Novopro website. Spiral and arrow represented α-helix and β-pleated sheet, respectively.
    Supplemental Fig. S5 Phosphorylation site prediction of tomato SlKRP proteins via the NetPhos-3.1 website.
    Supplemental Table S1 Primers used in this study.
    Supplemental Table S2 The identified amino acid sequences of KRP in tomato, tobacco, Arabidopsis, capsicum and eggplant.
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  • Cite this article

    Liu G, Guan Z, Ma M, Wang H, Liu X, et al. 2023. Genome-wide identification and molecular characterization of SlKRP family members in tomato and their expression profiles in response to abiotic stress. Vegetable Research 3:27 doi: 10.48130/VR-2023-0027
    Liu G, Guan Z, Ma M, Wang H, Liu X, et al. 2023. Genome-wide identification and molecular characterization of SlKRP family members in tomato and their expression profiles in response to abiotic stress. Vegetable Research 3:27 doi: 10.48130/VR-2023-0027
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Genome-wide identification and molecular characterization of SlKRP family members in tomato and their expression profiles in response to abiotic stress

  • State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai-An 271018, Shandong, China

  • Received: 27 February 2023
  • Accepted: 13 July 2023
  • Published online: 10 October 2023
Vegetable Research  3,  Article number: 27  (2023)  |  Cite this article
An Author Correction to this article was published on 31 January 2024, http://doi.org/10.48130/vegres-0024-0006.

Abstract: The cell cycle has an essential role in the regulation of plant growth, development, and stress responses, which is controlled by the complex of cyclin / cyclin-dependent kinases (CDKs). Kip-related proteins (KRPs) as CDK inhibitors are involved in the precise regulation of cell cycle progression. However, the comprehensive identification of SlKRP family genes in tomato has not been achieved. Here, a total of six SlKRP proteins were identified from the tomato genome and divided into three classes via phylogenetic analysis. Chromosomal localization analysis revealed the interchromosomal segment duplication among SlKRP genes. Analyses on gene structures and conserved motifs indicated that the SlKRP genes were evolutionarily conserved. The subcellular localization analysis showed all SlKRP proteins were located in the nuclei. Six SlKRPs had distinct expression in different tissues. Their expressions were affected by the plant hormones (ABA, IAA, and ethylene) and various abiotic stresses (salt, drought, and low temperature), which were correlated with different cis-acting regulatory elements (CAREs) in the 3-Kb promoter regions of these genes. In addition, co-expression relationship and protein interaction network analysis proved that SlKRP proteins may interact with CDKs and cyclins. To further explore the function of SlKRPs in tomato, VIGS assay was performed to obtain SlKRP5-silenced plants and demonstrated that silencing of SlKRP5 increased the sensitivity to drought stress. These findings provide references for the further functional analysis of KRPs in the future.

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    Introduction

    • Cells are the basic unit of plant organization and function[1]. The cell cycle regulates plant cell division, differentiation and expansion, which ultimately affects plant growth, development and reproduction[2]. The cell cycle is a process that a viable cell undergoes from the end of the last division to the end of the next division, which includes the mitotic cycle and the endoreduplication cycle[3]. Cell cycle progression is one of the basic characteristics of biological activities, which is closely related to the development of many higher plants. Thus, the study on the regulation of cell cycle progression has become one of the hotspots of molecular biology in recent years.

      In eukaryotes, the cell cycle is a complex regulatory process affected by multiple factors, in which cyclin-dependent kinases (CDKs) play key roles in regulating cell cycle. CDK binds to cyclin partners to activate CDK kinase activity by forming a Cyclin-CDK complex, which triggers G1/S phase to G2/M phase transition and controls cell cycle progression. Studies have shown that the activity of CDKs can be regulated by some proteins, among which Kip-related protein (KRP) as CDK inhibitors can bind to the CDKs and affect their activities[4]. KRP proteins can affect cell cycle process and regulate plant growth and development[5]. The C-terminus of the KRP proteins usually contain a conserved functional region composed of about 30 amino acids, which is necessary for ICK/KRPs to bind to the CDK-CYC complex[6]. However, the diversity of the N-terminal sequence of the KRP proteins leads to the low similarity of KRP protein sequences in plants. The cyclin-dependent kinase inhibitor KRPs can finely regulate the activity of CDK and negatively modulate the cell cycle process.

      Plant growth and development is determined by the coordination of cell division and cell expansion, which depends on the precise regulation of cell mitosis cycle and the nuclear replication cycle. Overexpressing the KRP gene in plants exhibited a number of similar phenotypes, including smaller plant biomass, serrated leaves, and reduced cell numbers[7]. In Arabidopsis, seven KRP genes were identified. AtKRP4 and AtKRP5 were mainly expressed in dividing cells, while AtKRP1 and AtKRP2 were highly expressed in differentiated cells, and AtKRP3, AtKRP6, and AtKRP7 were expressed in both cells[5]. Interestingly, when root-knot nematodes infected Arabidopsis roots, AtKRP1 and AtKRP2 genes can inhibit the mitosis of giant cells by increasing their expression level, which prevented the proliferation of adjacent cells to hinder the root-knot nematode development[8]. The AtKRP1 gene driven by the GL2 promoter in Arabidopsis not only resulted in a significant decrease in the DNA content of epidermal hair cells, but also caused programmed death of epidermal hair cells[9]. Overexpression of AtKRP3 in Arabidopsis can increase the DNA ploidy level of shoot apical meristem and leaf cells, which changed cell arrangement characteristics and reduced cell volume[10]. In tomato, overexpression of cyclin-dependent kinase inhibitors (SlKRP) in mesocarp cells can relieve the link between intracellular replication and cell growth[11]. These reports suggest that the biological function of KRPs is to inhibit cell division.

      The precise regulation of the cell cycle is also critical for plants to respond to environmental stress and changes[12]. For instance, protein kinase WEE1 affects cell numbers by inhibiting CDK1 to stop cells undergoing mitosis[13]. WEE1 protein is an important indicator of DNA replication and a damage checkpoint. When plant DNA is damaged, WEE1 is induced to express and inhibit the cell cycle to cope with abiotic stresses[14]. Also, the drought stress inhibits the enzymatic activity of CDK in Arabidopsis roots, causing the root tip meristem to stop dividing to adapt to the external environment[15]. Remarkably, few studies have been reported on how KRP responds to environmental stresses.

      Tomato is an important economic horticultural plant that is popular with consumers worldwide. However, during the growth and development of tomato, its yield and quality are severely impacted by a variety of abiotic stresses, such as drought, salinity, and chilling[16]. The study of plant resistance-related genes has important guiding significance for the production of high-quality tomatoes in abnormal environmental conditions. Although some advances in the understanding of the action of KRP have been reported in Arabidopsis thaliana, genome-wide information of SlKRP family members in tomato has not been executed. Therefore, exploring the biological function and molecular mechanism of the cell cycle important regulatory gene SlKRP in abiotic stress provides a theoretical basis for cultivating tomato varieties with strong stress resistance. In recent reports, plant KRP proteins bind to CDK protein to inhibit their activity, thereby inhibiting cell division and affecting plant growth and development[17]. Although the SlKRP genes have been identified in tomato, characteristics of the tomato SlKRP family genes have not been systemically studied. Here, we identified six SlKRP genes by genome-wide analysis, and performed bioinformatics analyses to analyze phylogenetic construction, chromosome distribution, gene structure, protein interaction network, co-expression analysis, and gene duplication. Then, the expression profiles in response to abiotic stress and in various tissues of tomato plant were characterized via qRT-PCR. Furthermore, silencing of SlKRP5 increased the sensitivity to drought stress. Therefore, this work provides a theoretical basis for further functional studies of the SlKRP family and provides potential targets for future tomato improvement.

    Materials and methods

      Genomic identification of SlKRP genes in tomato

    • To identify the SlKRP genes in the tomato genome, the KRP protein sequences in Arabidopsis were obtained from the TAIR database (https://www.Arabidopsis.org/). All Arabidopsis AtKRP genes were reported previously[5]. The conserved sequence of the CDI domain (pfam02234) was used to identify the SlKRP family genes. The genome database of tomato (SGN, https://solgenomics.net/) and the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/) was used to search for the SlKRP genes based on the CDI domain through BLAST. Further, we used HMMER software to search the SlKRP genes of tomato. A total of six SlKRP genes were identified in tomato, namely the Solyc02g090680, Solyc09g061280, Solyc12g098310, Solyc09g091780, Solyc03g044480, and Solyc01g108610. According to the existing reports, Solyc02g090680, Solyc09g061280, Solyc12g098310, and Solyc09g091780 were named as SlKRP1, SlKRP2, SlKRP3 and SlKRP4, respectively[11]. As Solyc01g108610 has high homology with AtKRP6, Solyc01g108610 was named as SlKRP6. Solyc03g044480 was named as SlKRP5.ExPASy (https://web.expasy.org/protparam) was used to calculate their physio-chemical characteristics (molecular weight, and isoelectric point). The secondary structure of SlKRP proteins was predicted using Novopro (www.novopro.cn/tools). We predicted the phosphorylation potential for SlKRPs via the NetPhos-3.1 website (https://services.healthtech.dtu.dk/service.php?NetPhos-3.1).

      Analysis of chromosomal location, gene structure and gene duplication

    • According to the chromosome location information of SlKRP genes provided by the SGN website, MG2C (http://mg2c.iask.in/mg2c_v2.1/) was performed to draw the schematic representation of SlKRP genes chromosomal positioning. The gene structures of SlKRPs were illustrated using the Gene Structure Display Server (GSDS, http://gsds.cbi.pku.edu.cn/). The Multiple Collinearity Scan toolkit (MCScanX) with default parameters was used to examine the gene duplication events of SlKRP genes[18].

      Phylogenic analyses

    • Sequence alignment of KRP proteins from tomato, tobacco, Arabidopsis, capsicum and eggplant was carried out with the ClustalW program and the integrated tool MEGAX (www.megasoftware.net). Then, a phylogenic tree was built with the neighbor-joining (NJ) algorithm, wherein the bootstrap replicate value was set as 1000.

      Identification of cis-elements of SlKRP genes

    • The 3-Kb promoters of six SlKRP genes were extracted from the SGN database and the cis-regulatory elements were analyzed using the Plant CARE website (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/). The identified CAREs visualized using the Toolkit for Biologists integrating various biological data handling tools (TBtools)[19].

      Plant materials and methods of stress treatment

    • Tomato seedings were cultured in a cave containing vermiculite: peat: perlite (1:3:1 v/v/v). The seedlings were grown in a glass greenhouse, 14 h at 28 °C/10 h at 18 °C (day/night) photocycle, and the relative humidity was 75%. Uniform seedlings at the three-leaf stage were selected and transferred to plastic pots (10 cm × 10 cm × 10 cm, one plant per pot) for the stress treatment. Tomato seedlings were treated with 150 mmol·L−1 NaCl for stress treatment. Tomato seedlings were transferred to dry substrate for drought stress treatment. Tomato seedlings were placed in 4 °C incubators to simulate low temperature stress. Both treatment group and control group were treated for 12 h. Leaves were also immediately frozen in liquid nitrogen and stored at −80 °C. The six individual tomato seedlings were treated for each experiment.

      Expression analysis for SlKRP genes

    • For analysis of SlKRP genes expression patterns under different conditions, including various abiotic stresses, growth and development, the total RNA of plant samples was isolated with an OminiPlant RNAkit (Cwbio, Beijing, China). Then, complementary DNAs were synthesized using an HiScript III RT SuperMix reverse transcriptase kit (Vazyme, Cat. #R323-01) according to the manufacturer's instruction. qRT-PCR was used to determine the gene transcription using 96-well blocks with the ABI QuantStudio 3 (Applied Biosystems, USA). The following qRT-PCR program was used: the template denaturation at 95 °C for 3 min; followed by amplification for 40 cycles with a melting temperature of 95 °C for 10 s and an annealing temperature of 68 °C for 15 s. The comparative 2−ΔΔCᴛ method was used to calculate the relative expression levels of target genes, and the β-actin gene (Soly11g008430) was used as an internal control. The primers for RT-PCR are listed in Supplemental Table S1.

      We analyzed the expression patterns of SlKRP genes under shade, sun, ABA, IAA and ACC treatments using the published RNA-seq datasets[20]. TBtools was used to draw heatmaps.

      Protein subcellular localization

    • To investigate the subcellular localizations of the SlKRP proteins, the coding sequence of SlKRP without the stop codon was amplified by PCR and then cloned into the expression vector pGWB405 with GFP under the control of the CaMV35S promoter by homologous recombination. The fusion constructs were transformed into tobacco leaves as described previously[21]. The nuclei were detected by DAPI staining. GFP fluorescence were detected at 48 h following transfection using laser confocal microscope (LSM; Carl Zeiss, Thornwood, NY, USA). The primers for subcellular localization are listed in Supplemental Table S1.

      Virus-induced gene silencing (VIGS)

    • A particular fragment of SlKRP5 was designed and amplified by PCR using specific primers (Supplemental Table S1). The fragment from SlKRP5 was inserted into the tobacco rattle virus RNA2 (TRV2) vector to construct recombinant vector TRV2:SlKRP5, and were introduced into Agrobacterium tumefaciens GV3101. A. tumefaciens cells containing TRV1 were mixed with TRV2:SlKRP5, pTRV2:00 (negative control), or pTRV2-SlPDS (positive control) vectors at a volume ratio of 1 : 1. The plants at two-leaf stage were infiltrated with inoculant of Agrobacterium suspensions (OD600 = 0.5). When leaves of pTRV2:SlPDS plants emerged photobleached phenotype, we performed qRT-PCR assay to determine the SlKRP5 expression in TRV2:SlKRP5 plants and calculate silencing efficiency. VIGS assays were conducted as previously described[22].

      Drought stress treatment

    • SlKRP5-silenced (TRV2:SlKRP5) and control (TRV2:00) plants were grown in soil and treated with drought stress. For imitation of drought stress, roots of plants were watered with 150 ml of 25% (w/v) PEG6000 solution every day and the whole treatment lasted for 3 d in total. Normally growing plants were used as controls. Drought stress treatment were conducted as previously described[23]. And then, the physiological indicators were measured. Each treatment contained six plants.

      Measurement of antioxidant enzyme activity

    • The content and activities of malondialdehyde (MAD), superoxide dismutase (SOD), and peroxidase (POD) were determined at different wavelengths using enzyme-labeled instrument according to a protocol described previously[22]. The production of hydrogen peroxide (H2O2) and superoxide anion (O−2) in leaves was detected using 3, 3′-diaminobenzidine (DAB) and nitro blue tetrazolium chloride (NBT) staining, respectively.

    Results

      Identification and chromosomal location of SlKRP genes

    • In this study, based on the amino acid sequence of the Pfam CDI domain, six SlKRP gene members were obtained via SGN, NCBI, and other public databases in the tomato genome. We further analyzed the SlKRP proteins, such as the chromosome location, the amino acid length, the molecular weight, and the theoretical isoelectric points (Table 1). We found that SlKRP genes were unevenly distributed on chromosomes 1, 2, 3, 9 and 12 (Fig. 1a). The amino acids (aa) length of SlKRP proteins ranged from 188 aa (SlKRP2) to 232 aa (SlKRP3). In addition, the molecular weights and theoretical isoelectric points of these proteins ranged from 2,1489.04 Da (SlKRP2) to 2,6152.29 Da (SlKRP3) and from 4.14 (SlKRP5) to 9.67 (SlKRP3), respectively (Table 1). Gene family is generated by either tandem repeats or large-scale fragment repeats during the evolutionary process[24]. Segmental gene duplication revealed that SlKRP1 is highly similar to SlKRP3, SlKRP5 and SlKRP6, indicating that they undergo intrachromosomal or interchromosomal fragment replication (Fig. 1b).

      Table 1.  Physico-chemical characteristics for the SlKRP family genes.

      KRP memberGene IDChr. no.StrandCDS length (bp)Protein length (aa)Molecular weight/DaPI
      SlKRP1Solyc02g090680.2.12+63921224,011.996.06
      SlKRP2Solyc09g061280.2.19−56718821,489.049.45
      SlKRP3Solyc12g098310.1.112+69923226,152.299.67
      SlKRP4Solyc09g091780.2.19−63321023,844.819.22
      SlKRP5Solyc03g044480.2.13−61220324,207.864.14
      SlKRP6Solyc01g108610.2.11+65721824,102.75.46

      Figure 1. 

      (a) Distribution and (b) duplication of SlKRP genes on tomato chromosomes. Chromosome numbers are indicated at the top of each bar.

      Structures and motifs analysis of SlKRP genes in tomato

    • We further analyzed the exon–intron structures and motif compositions of the SlKRP genes in the tomato genome (Fig. 2). The lengths of SlKRP genes were from 1,437 bp to 3,001 bp. The SlKRP1, SlKRP5 and SlKRP6 genes contained four exons and three introns, while The SlKRP2, SlKRP3 and SlKRP4 genes contained three exons and two introns. Also, conserved domain of SlKRP proteins was cyclin-dependent kinase inhibitor (CDI) and existed in C-terminal region of SlKRP proteins (Supplemental Fig. S1). We speculated that SlKRP genes had evolutionary diversity according to gene structure and gene length. Further, we identified the 20 different motifs (Motifs 1–20) from the SlKRP genes in the tomato genome using the MEME program (Table 2). The amino acid lengths of these conserved motifs ranged from 6 to 44 aa, of which motif 1 was CDI domain. The motif 1 was identified in six SlKRP proteins. We found that the SlKRP genes that classified in the same category had similar conserved domains.

      Figure 2. 

      Gene structures and conserved domains of SlKRP genes. (a) Exon-intron structure of the SlKRP genes. (b) Distributions of conserved motifs in SlKRP proteins. The 1−20 motifs in SlKRP proteins were identified by the MEME program (http://meme-suite.org/), which were displayed by different colored boxes. Motif 1 is cyclin-dependent kinase inhibitor domain.

      Table 2.  Details of conserved motifs in tomato KRP proteins.

      MotifLength (aa)Best possible match
      143IPTEAELEEFFTAAEKRQQKRFIEKYNFDFVKDEPLEGRYEWV
      244NLLEFEGRKRTTRESTPCSLIRDPDNIPTPGSSTRRTNANEANGRVPNSI
      332MGKYJRKTGKVLDVSPLGVRTRAKTLALKRLQ
      415GGCYLQLRSRRLEKP
      59PKPQIPKVC
      67CDNYHPV
      76CCSSCY
      86CAMSYS
      96MEGQKW
      108MGEFLKKC
      116PDEKCG
      127MMKKKRK
      136DEILFP
      147NFKPIDN
      159QGNGVPCEP
      167RRKHKCK
      176SGGGDG
      188INGEMKIM
      196KRDGDL
      206VAEVAI

      Phylogenetic analysis of KRPs

    • To study evolutionary patterns of KRP genes in the plants, we obtained KRP homologous of tobacco, Arabidopsis, capsicum and eggplant by sequence alignment in NCBI databases (Supplemental Table S2). Then, an unrooted phylogenetic tree with KRP protein sequences from five species was performed via MEGA software (Fig. 3). We found that the six SlKRP proteins can be classified into three classes, including Class I (SlKRP1 and SlKRP5), Class II (SlKRP2, SlKRP3 and SlKRP4), and Class III (SlKRP6). The phylogenetic analysis indicated that a closer orthologous relationship of SlKRP proteins in each clade was observed between tomato and eggplant, perhaps indicating the closest relationship between the tomato and eggplant.

      Figure 3. 

      Phylogenetic trees showing KRP genes from tomato, tobacco, Arabidopsis, capsicum and eggplant. The KRP proteins were classified into three subfamilies and distinguished by different colors.

      The expression pattern of SlKRP genes

    • To further understand the biological functions of SlKRP genes, we performed qRT-PCR to analyze the expression of six SlKRP genes in different tomato tissues, including root, stem, leaf, flower, and fruits of different developmental stages. qRT-PCR analysis showed that the six SlKRP genes had obvious tissue-specific expression (Fig. 4a). Among them, the expression of SlKRP5 and SlKRP6 was relatively higher in leaves. SlKRP1, SlKRP3, and SlKRP4 exhibited relatively higher expression in anthesis, indicating that the three genes may be involved in cell division of tomato fruit. The expression abundance of SlKRP2 gene gradually increased with fruit development, indicating that it may be involved in fruit ripening and quality formation. Moreover, we also performed transcriptomic datasets published by SGN website to analyze the differential expression of the SlKRP genes in fruits of the wild variety 'LA1589' and the cultivar 'Heinz 1706', suggesting that SlKRPs may be involved in the formation of tomato fruit size (Supplemental Figs S2 & S3). Taken together, these results suggested that the six SlKRP genes had functional divergence in regulating tomato growth and development.

      Figure 4. 

      Expression characterization of SlKRP genes. (a) Expression profiles of SlKRP genes in different tissues, including root, stem, leaf, flower, anthesis, immature green fruit (IMG), mature green fruit (MG), breaker fruit (BR), yellow fruit (YR), and red ripe fruit (RR). The expression levels of SlKRP genes were calculated using the CT method. Data shown are means ± SD (n = 3). The β-actin gene was used as an internal control. (b) Subcellular localization of SlKRP-GFP (green fluorescent protein) fusion proteins in tobacco leaf epidermal cells. The nuclei were determined by DAPI staining.

      To investigate the subcellular localization of SlKRP proteins, we constructed SlKRP-GFP fusion proteins driven by the CaMV35S promoter, which were transiently expressed in 5-week-old tobacco leaves. Confocal microscope observation showed the SlKRP-GFP fluorescence signal overlapped with that of DAPI, a nuclear localization marker[25]. Therefore, these KRP proteins were localized in the nucleus (Fig. 4b).

      Cis-regulatory elements in the promoters of SlKRP genes

    • Gene transcription is regulated by cis-regulatory elements (CAREs) in the promoter sequence[26]. To further explore the function of SlKRP genes, 131 CAREs were identified in the 3-Kb promoters of SlKRP genes, which were categorized into ten responsive groups, containing ABA-, MeJA-, light-, low temperature-, GA-, defense and stress-, SA-, Auxin-, drought-responsive element，and cell cycle regulation. It is worth noting that there was CARE involved in cell cycle regulation in the SlKRP5 promoter, indicating that its potential function may be cell cycle regulation (Fig. 5). These CAREs indicated that SlKRPs may play an important role in response to abiotic and biotic stress.

      Figure 5. 

      Cis-regulatory elements in the promoter regions of SlKRP genes.

      Expression patterns of SlKRP genes in response to abiotic stress

    • To analyze the SlKRP genes response to three abiotic challenges, such as salt, drought, and low temperature, we analyzed the expression profiles of the SlKRP genes under stress conditions. qRT-PCR analysis showed that expression abundance of SlKRP1, SlKRP2, SlKRP3, SlKRP4, and SlKRP5 genes were significantly suppressed after salt treatment (Fig. 6a). Under drought treatment, the expression level of SlKRP3 was increased by 2.6 times compared with that in tomato seedlings under normal conditions, and the expression levels of SlKRP5 and SlKRP6 decreased to 30.8% and 21.8% of the control, respectively (Fig. 6b). The expression of five SlKRPs was significantly different in response to low temperature, of which 4 SlKRP genes were significantly downregulated compared with normal temperature (Fig. 6c). Low temperature significantly increased expression level of SlKRP2 by 8.6-fold compared with the control. We further explored the expressional responses of SlKRPs to light treatment using the published transcriptomic datasets, and found that shade treatment can up-regulate the expression of SlKRP1, SlKRP3, and SlKRP6. Whereas, the expression of SlKRP2, SlKRP4, and SlKRP5 genes showed an opposite pattern to that under sunlight treatment (Fig. 6d).

      Figure 6. 

      Expression profiles of SlKRP genes under various environmental stressors and phytohormone treatment. Expression profiles of SlKRP genes in tomato plants after (a) salt , (b) drought, and (c) low temperature treatments. The β-actin gene was used as an internal control. The data are presented as the means ± SDs (n = 3). The different letters indicate statistically significant differences at a 5% level of significance according to Tukey's pairwise comparison tests. A heatmap displaying SlKRP genes expression under (d) light, (e) ABA, IAA and (f) ACC treatments. Red and blue represented up-regulated and down-regulated gene expression after treatment, respectively.

      In addition to environmental stress, plant hormones are the key factors in regulating the whole process from seed germination to fruit formation, which reveals the regulation mechanism of crop agronomic trait formation[27]. Therefore, we analyzed the expression profiles of the SlKRP genes in response to phytohormone treatments with ABA, IAA, and ACC, showing that the six SlKRP genes differentially expressed. Under ABA treatment, SlKRP1, SlKRP3, and SlKRP4 were upregulated, and other SlKRPs were significantly down-regulated (Fig. 6e). Among the six SlKRP genes, the expression of SlKRP2, SlKRP3, SlKRP5, and SlKRP6 was down-regulated after IAA application compared with CK. Under ACC treatment, the expression of four SlKRPs, including SlKRP1, SlKRP3, SlKRP4, and SlKRP5 genes exhibited differential compared with the normal condition, and SlKRP6 were less sensitive to treatment (Fig. 6f). In short, our analysis suggested that SlKRP genes played regulatory roles in response to phytohormone and environmental stress in tomato plants.

      Relationship between SlKRP family genes and cell cycle related genes

    • The ICK/KRP family genes are major regulators of cyclin-dependent kinase activities in several plants, which regulate endoreplication and cell division in plants[28]. Gene co-expression is a technical method to show the interaction between genes based on their expression data[29]. To further understand the relationship within tomato SlKRP, SlCDK and cyclin gene expression, we performed co-expression analysis using transcriptome data of these genes from tomato fruits at nine developmental stages[30]. The expression abundance of SlKRP1 was positively correlated with the most SlCDK and cyclin genes transcription, while the expression level of SlKRP1 was highly negatively correlated with the that of SlCDKD1. The expression levels of SlKRP3, SlKRP5, and SlKRP6 showed positive correlation with that of some SlCDK and cyclin genes. The expression level of SlKRP2 was highly negatively correlated with the expression of most CDK and cyclin genes. In short, these results reveal that these genes may interact with each other to regulate the cell cycle (Fig. 7).

      Figure 7. 

      Co-expression analysis of SlKRP, SlCDK and cyclin genes in tomato. The color of the circles indicates the correlation coefficient value of gene co-expression in the R environment.

      Furthermore, to gain insight into the possible biological functions of SlKRPs, we searched proteins that may interact with SlKRPs via the STRING database (Fig. 8). Interestingly, many proteins involved in cell cycle regulation were predicted to be associated with SlKRP proteins, showing that SlKRPs played essential roles in regulation of the cell cycle. Among the proteins, cyclins interacted with CDK proteins to control cell cycle progression[31]. WEE1 protein kinase is a member of the serine/threonine protein kinase family, which mainly inhibits the activity of CDC protein to regulate cell mitosis[32]. However, we did not find proteins associated with SlKRP5. Therefore, these results indicate that SlKRP proteins interacted with cell cycle-related proteins to regulate cell cycle progression.

      Figure 8. 

      The predicted protein interaction network of the SlKRP proteins in tomato using string database.

      Inhibited expression of SlKRP5 reduces drought tolerance in tomato

    • Drought treatment significantly affected SlKRP5 expression in tomato, suggesting that SlKRP5 potentially is involved in drought stress responses. To further elucidate the importance of SlKRP5 in basal drought tolerance, we performed virus-induced gene silencing (VIGS) assay to suppress SlKRP5 expression in tomato. SlPDS-silenced plants revealed a photo-bleached phenotype after 15 d of infiltration (Fig. 9a). qRT-PCR results showed that was SlKRP5 expression in the TRV2:SlKRP5 plants was significantly lower than that in control (TRV2:00) plants (Fig. 9b), illustrating that SlKRP5 was successfully silenced. We use the 25% PEG600 to treat the control and TRV2:SlKRP5 plants to simulate drought stress. Most of the leaves of the TRV2:SlKRP5 plants became withered significantly after drought stress (25% w/v PEG6000) for 3 d, nevertheless those of the control plants displayed mild curling (Fig. 9c). Under drought stress, DAB and NBT staining results showed that TRV2:SlKRP5 plants had higher accumulations of O2− and H2O2 than the control plants, reflected the greater degree of membrane damage of TRV2:SlKRP5 plants (Fig. 9d & e). Furthermore, we measured the activities of the main ROS-scavenging enzymes, including SOD and POD and found that their activities were remarkably decreased in the SlKRP5-silenced plants after drought stress (Fig. 9g & h). Collectively, these results indicate that SlKRP5 is critical for tomato resistance against drought.

      Figure 9. 

      Knockdown of SlKRP5 reduces drought stress tolerance in tomato. (a) Phenotype of SlPDS-silenced tomato plant via VIGS. (b) Expression level of SlKRP5 in SlKRP5-silenced and control plants. (c) Phenotype of SlKRP5-silenced and control plants treated with PEG6000 (25% w/v). Scale bar, 3 cm. DAB staining for (d) H2O2 and (e) NBT staining for superoxide in control and SlKRP5-silenced tomato leaves. (f) MDA content, (g) SOD activity and (h) POD activity in SlKRP5-silenced and control plants. The data are presented as the means ± SDs (n = 3). The different letters indicate statistically significant differences at a 5% level of significance according to Tukey's pairwise comparison tests.

    Discussion

    • KRP proteins are key regulators of CDK-Cyclin complex activities in endoreduplicating cells, which were identified in several plants, such as A. thaliana, O. sativa, and Z. mays[33]. However, systemic study on the roles of SlKRP genes in tomato was limited. Herein, we used the HMMER model and BLASTP to identify SlKRP family protein sequences through comparative analyses based on the Arabidopsis ICK/KRP sequences (Table 1). In maize, all of the inhibitors of cyclin-dependent kinase proteins mainly localized to the nucleus[34]. Consistent with previous reports, subcellular localization indicated that the six KRP proteins were located in the nucleus in this study. Meanwhile, these SlKRP proteins have similar secondary structures (Supplemental Fig. S4). These results provide essential information for the further functional analysis of KRPs in the plants.

      The C-terminal region of KRPs genes in plants is considered as a conserved functional motif, which is involved in interaction with CDK proteins and inhibits their activity[35]. The interaction region of KRPs with D-type cyclins and A-type CDKs is located at the C-terminus of protein[15]. In vitro enzymatic activity assay demonstrates that KRP protein can inhibit CDK activity in plant[5]. However, the tomato ICK/KRP protein SlKRP1, which lacked the conserved C-terminal region, can still interact with SlCYCD3[36]. Similarly, we performed multiple sequence alignment analysis of tomato SlKRP family and found that the C-terminal region of SlKRP proteins contain a conserved domain (Supplemental Fig. S1). On the other hand, SlKRP proteins had multiple phosphorylation sites (Supplemental Fig. S5), which indicated that SlKRP proteins may be phosphorylated by other kinases such as CDK In conclusion, the SlKRP protein structures conferred functional diversity in regulating plant growth. In this study, we identified six SlKRP genes on the five chromosomes of the tomato genome and performed phylogenetic analysis to reveal that the six SlKRP genes were classified into three classes, which is consistent with reports on Arabidopsis thaliana (Fig. 3). However, a total of seven AtKRP genes were identified in Arabidopsis[5]. The interchromosomal segment duplication and tandem duplication in plants are important driving forces for the evolution of genome and genetic systems[37]. The gene duplication analysis displayed that the SlKRP gene family is mainly characterized by interchromosomal segment duplication, suggesting that interchromosomal segmental duplication is the main expansion mechanism of these SlKRP genes in tomato (Fig. 1b). Exon-intron structure of gene is essential in the study of gene family evolution[38]. More meaningfully, we also found that the structure of interchromosomal segmental duplication genes is highly similar. These results indicated that structural and expression differences in the SlKRP gene family may confer the functional diversity in regulation of plant growth and development and tolerance to abiotic stresses. Fine-tuning of KRP expression abundance was demonstrated to be a key characteristic of cell cycle control. The expression patterns of SlKRP1-SlKRP4 in vegetative organs (roots, leaves, flowers) and developing fruits of the cherry tomato cultivar WVa106 has been reported[11]. The results showed that SlKRP4 was mostly expressed in the early stage of fruit development, while SlKRP1 was generally highly expressed in tomato vegetative organs. These findings are consistent with our results on expression patterns of SlKRPs measured in the processing tomato cultivar Heinz1706. However, our data suggested that SlKRP3 was lowly expressed in the late stage of tomato fruit development, which was different from previous reports. The difference in SlKRPs expression patterns may be due to various tomato cultivars. Here, we used qRT-PCR analysis from different tissues (root, stem, leaf, flower, and fruit) to explore the expression of the SlKRP gene family, showing that SlKRPs displayed the unique expression profiles in different tissues of tomato (Fig. 4). It is worth noting that cell division and expansion occurred in early fruit development and directly influenced the size and shape of fruit[39]. SlKRP1 had high expression mostly in young fruit development, which implied that SlKRP1 may be involved in cell division in young fruit and determine the tomato fruit weight. In addition, the cyclin-dependent kinase inhibitor KRP can inhibit the mitotic CDKA;1 kinase complex to regulate the mitosis-to-endocycle transition, which impacted the leaf size in Arabidopsis[40]. Indeed, SlKRP5 and SlKRP6 showed relatively high expression in the leaves than other organs, suggesting that these two genes may synergistically regulate tomato leaf development. The expression abundance of SlKRP2 gradually increased with the development of tomato fruits, demonstrating that SlKRP2 may affect the fruit ripening. Plant growth and development are influenced by various environmental factors and phytohormones, such as light, temperature, abscisic acid, which can regulate several cell cycle gene transcriptions to affect plant growth[6,41]. For instance, ABA treatment can significantly induce the expression abundance of AtKRP1 gene in Arabidopsis[15]. In this study, we found that most SlKRP genes had diverse cis-elements on their promoters, such as ABA-responsive, Light-responsive, and Low temperature-responsive boxes (Fig. 5). Additionally, the cell cycle regulation element existed in the promoter of the SlKRP5 gene. Interesting, the transcripts of SlKRP1, SlKRP5, and SlKRP6 were significantly suppressed by almost all tested abiotic stressors, especially the expression of SlKRP5 decreased to 53.1%, 30.9% and 17.8% of that in normal conditions after salt, drought and low temperature treatments. Also, the transcripts of SlKRP family genes were not obviously affected after the treatment of exogenous ABA, IAA and ACC (Fig. 6). These data indicated that SlKRPs might play a vital role in stress resistance.

      Drought stress is a major factor affecting plant growth and development, which limits agricultural production. The water loss can disrupt the ion balance, increase oxidation-reduction potential, produce reactive oxygen species, and even destroy macromolecules in plants. Drought causes the oxidation reaction in plant cell membrane lipids to produce malondialdehyde. Plants can quickly sense drought stress and then regulate expression of stress-related genes, which finally resist drought through self-regulation. Abiotic stress can cause damage to plant DNA, which in turn causes cell cycle retention[42]. The interaction between CDK and KRP maintains cell cycle process to alleviate damage of abiotic stress to plants. The expression of some KRP genes was induced by abiotic stress or ABA, indicating that KRP genes may play an important role in drought resistance[43]. Some cell cycle-related genes improve plant drought tolerance by regulating stomatal development. CAREs in promoter and abiotic stress induced expression analysis showed that SlKRP5 has a regulatory role in abiotic stress. In this study, we performed the VIGS experiment to obtain SlKRP5-silenced plants. Under drought treatment, MDA content was significantly increased and antioxidant enzyme activity was decreased in SlKRP5-silenced plants. Therefore, SlKRP5 is a novel target gene in genetic engineering to enhance drought tolerance in tomato.

    Conclusions

    • In this study, we identified six SlKRP genes which are located on tomato five chromosomes and classified into three main classes by phylogenetic analysis. Analyses on gene structure, protein motifs, subcellular localization and protein interaction network of SlKRP genes revealed their evolutionally conserved function. Tissue-specific expression patterns of SlKRP genes indicated specific roles of each SlKRP gene in tomato fruit development, fruit ripening, and leaf size. Both cis-element analyses in promoter regions of SlKRP genes and their expressions in various treatments suggested that SlKRP genes may play important roles in the phytohormone signaling and response to abiotic stresses. Silencing of SlKRP5 via VIGS assay reduces drought tolerance in tomato. Our studies lay foundation for the analysis of SlKRP function in tomato plant development and stress responses.

      Acknowledgments

      • This work was supported by National Natural Science Foundation of China (32202497), Taishan Scholar Foundation of Shandong Province (tsqn201812034), China Postdoctoral Science Foundation (2022M711967), and National Natural Science Foundation of China (31872951).

      Conflict of interest

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

      • # These authors contributed equally: Genzhong Liu, Zhangfeng Guan, Mingxuan Ma

      Supplementary information
      • Supplemental Fig. S1 Multiple sequence alignment of the SlKRP proteins in tomato.
      • Supplemental Fig. S2 Expression of the SlKRP genes in immature green fruits from 'Heinz 1706' and 'LA1589'.
      • Supplemental Fig. S3 Expression of SlKRP genes in different tissues of tomato fruits at four developmental stages.
      • Supplemental Fig. S4 Prediction of SlKRP protein’s secondary structure via Novopro website. Spiral and arrow represented α-helix and β-pleated sheet, respectively.
      • Supplemental Fig. S5 Phosphorylation site prediction of tomato SlKRP proteins via the NetPhos-3.1 website.
      • Supplemental Table S1 Primers used in this study.
      • Supplemental Table S2 The identified amino acid sequences of KRP in tomato, tobacco, Arabidopsis, capsicum and eggplant.
      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 (9)  Table (2) References (43)
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    Cite this article
    Liu G, Guan Z, Ma M, Wang H, Liu X, et al. 2023. Genome-wide identification and molecular characterization of SlKRP family members in tomato and their expression profiles in response to abiotic stress. Vegetable Research 3:27 doi: 10.48130/VR-2023-0027
    Liu G, Guan Z, Ma M, Wang H, Liu X, et al. 2023. Genome-wide identification and molecular characterization of SlKRP family members in tomato and their expression profiles in response to abiotic stress. Vegetable Research 3:27 doi: 10.48130/VR-2023-0027
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  • Introduction
  • Materials and methods

  • Genomic identification of SlKRP genes in tomato

  • Analysis of chromosomal location, gene structure and gene duplication

  • Phylogenic analyses

  • Identification of cis-elements of SlKRP genes

  • Plant materials and methods of stress treatment

  • Expression analysis for SlKRP genes

  • Protein subcellular localization

  • Virus-induced gene silencing (VIGS)

  • Drought stress treatment

  • Measurement of antioxidant enzyme activity

  • Results

  • Identification and chromosomal location of SlKRP genes

  • Structures and motifs analysis of SlKRP genes in tomato

  • Phylogenetic analysis of KRPs

  • The expression pattern of SlKRP genes

  • Cis-regulatory elements in the promoters of SlKRP genes

  • Expression patterns of SlKRP genes in response to abiotic stress

  • Relationship between SlKRP family genes and cell cycle related genes

  • Inhibited expression of SlKRP5 reduces drought tolerance in tomato

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