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Banana somatic embryogenesis and biotechnological application

  • # These authors contributed equally: Jingyi Wang, Shanshan Gan

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  • As one of the most important economic crops for both staple food and fruit widely cultivated in the tropics and subtropics, banana (Musa spp.) is susceptible to a plethora of abiotic and biotic stresses. Breeding cultivars resistant to abiotic and biotic stressors without adverse effects on yield and fruit quality are the objectives of banana improvement programs. However, conventional breeding approaches are time-consuming and severely hampered by inherent banana problems (polyploidy and sterility). Therefore, genetic transformation is becoming increasingly popular and can provide rapid solutions. Numerous efforts have been made to develop superior banana cultivars with better resistance to abiotic and biotic stresses and optimum yields using genetic modification strategies. Somatic embryogenesis (SE) through embryogenic cell suspension (ECS) cultures is an ideal recipient system for genetic transformation in banana. The purpose of this paper is to review the current status of banana somatic embryo research, clarify the process of banana somatic embryo induction and culture, and summarize the main influencing factors in the process of somatic embryogenesis. At the same time, their applications in breeding technologies such as cryopreservation, protoplast culture, genetic transformation and gene editing were also summarized, in order to provide reference for the research and practical application of banana somatic embryogenesis in the future.
  • 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[24]. 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[10,1517]. 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.

    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.

    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.

    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.

    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

    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 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

    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.

    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.

    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).

    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. 3cf).

    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.
    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.

    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.
    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.

    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.

    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).

    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[3840]. 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.

    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.

    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.

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  • Cite this article

    Wang J, Gan S, Zheng Y, Jin Z, Cheng Y, et al. 2022. Banana somatic embryogenesis and biotechnological application. Tropical Plants 1:12 doi: 10.48130/TP-2022-0012
    Wang J, Gan S, Zheng Y, Jin Z, Cheng Y, et al. 2022. Banana somatic embryogenesis and biotechnological application. Tropical Plants 1:12 doi: 10.48130/TP-2022-0012

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Banana somatic embryogenesis and biotechnological application

Tropical Plants  1 Article number: 12  (2022)  |  Cite this article

Abstract: As one of the most important economic crops for both staple food and fruit widely cultivated in the tropics and subtropics, banana (Musa spp.) is susceptible to a plethora of abiotic and biotic stresses. Breeding cultivars resistant to abiotic and biotic stressors without adverse effects on yield and fruit quality are the objectives of banana improvement programs. However, conventional breeding approaches are time-consuming and severely hampered by inherent banana problems (polyploidy and sterility). Therefore, genetic transformation is becoming increasingly popular and can provide rapid solutions. Numerous efforts have been made to develop superior banana cultivars with better resistance to abiotic and biotic stresses and optimum yields using genetic modification strategies. Somatic embryogenesis (SE) through embryogenic cell suspension (ECS) cultures is an ideal recipient system for genetic transformation in banana. The purpose of this paper is to review the current status of banana somatic embryo research, clarify the process of banana somatic embryo induction and culture, and summarize the main influencing factors in the process of somatic embryogenesis. At the same time, their applications in breeding technologies such as cryopreservation, protoplast culture, genetic transformation and gene editing were also summarized, in order to provide reference for the research and practical application of banana somatic embryogenesis in the future.

    • Banana (Musa spp.), the most vital economic crop for both staple food and fruit extensively planted in the tropics and subtropics, are a perennial herbaceous monocotyledonous plant pertaining to the Musaceae family of the order Scitamineae. Based on the data of the United Nations Food and Agriculture Organization, bananas are planted in 138 countries and regions around the world. As a staple food for the largely impoverished continent of Africa, it is the fourth largest staple food crop after rice, wheat and maize. As a fresh fruit, it stands the second largest fruit in the world after citrus, and the consumption and trade volume of fresh fruit rank first in the world[1].

      The most vital cultivated banana cultivars globally are triploids, originating from interspecific or intraspecific hybridization of two wild diploid species, Musa acuminata (A genome) and M. balbisiana (B genome)[1]. Owing to their parthenocarpy and polyploidy, it is very hard to cultivate new varieties through conventional breeding[2]. Banana plants are extremely threatened by diverse biotic and abiotic stresses, such as diseases, salt, drought, and cold. Currently, fusarium wilt (commonly known as panama disease) caused by Fusarium oxysporum f. sp. cubense (Foc) seriously threatens global banana production. At present, there are a lack of banana cultivars with both excellent production and Foc-resistance[2].

      It is a fundamental way out for the sustainable development of global banana production to improve the new varieties with excellent production and Foc-resistance. Biotechnology involving plant tissue culture is a powerful complementary strategy in conventional plant breeding programs[3]. There are two processes of plant regeneration, namely organogenesis and somatic embryogenesis (SE). In general, organogenesis involves the sequential formation of shoots and roots from tissues, relying on the appropriate culture conditions. On the other hand, SE is a totipotent embryonic stem cell formed by dedifferentiation of plant somatic cells. This new embryo can go on to develop into a complete plant[4]. Currently, there are two different ways to induce explants to form SE: direct SE and indirect SE. In the direct SE pathway, explants directly form somatic embryos without callus formation. SE also can be formed indirectly through a callus stage.

      Banana plant regeneration via organogenesis based on meristemic tissue, such as shoot tips and floral apices, are widely used for clonal propagation. Although the regeneration system based on organogenesis has also been applied to genetic transformation, it has the problems of low efficiency of genetic transformation and high proportion of chimeric plants. SE through embryogenic cell suspension (ECS) cultures is an ideal recipient system for genetic transformation in several plants, including banana, due to their oocyte characteristics, strong ability to accept foreign genes and fewer chimeras[5, 6]. Genetic transformation through ECS is a most widely used strategy in different banana varieties[5].

      The purpose of this paper is to review the current status of research on the process of SE for Musa spp. Indirect SE from IMFs and scalps are the focus of this review. At the same time, their applications in breeding technologies were also summarized in order to provide reference for the research and practical application of banana SE in the future.

    • The regenerative system of banana somatic embryogenesis based on ECS provides an ideal raw material for mutation breeding, somatic hybridization and genetic transformation[5, 6]. Based on the type of explant, there are four recognized procedures for the establishment of banana EC and proliferation of embryogenic cell suspension (ECS). In majority of the reports, the immature male flowers (IMFs) and/or shoot-tip derived scalps are a preferred choice for developing ECS cultures. Similar to other plants, banana plant regeneration via somatic embryogenesis based on ECS mainly includes callus induction, embryogenic callus selection, embryogenic callus proliferation and initiation of cell suspension culture, development and maturation of somatic embryo, and plant regeneration[7, 8] (Fig. 1).

      Figure 1. 

      The main phases and time required for each phase in the somatic embryogenesis of banana and critical questions (Q). (SE) somatic embryo; (IMFs) immature male flowers.

      Although somatic embryogenesis in banana is now a well-established method, the initiation of a 'genotype-independent' embryogenic cell culture is still far from routine. There are still some problems that need to be solved in the reported protocols for banana SE. These problems include either all or some of the following: low SE initiation frequency from the explants, reduction or loss of embryogenic competence concomitant with the increased time of subculture, and low embryo germination and plant conversion rates (Fig. 1).

    • In banana, indirect SE was mostly observed. The main culture stages of indirect SE are induction and proliferation of embryogenic callus (EC), maturation and germination of somatic embryos[7, 8]. Therefore, various factors affect the efficiency and quality of EC formation, including the explant type, the genotype of the donor plant, plant growth regulators, and the media and other additives, etc.

    • The selection of suitable explants is one of the key factors for the success of EC induction. Since early reports in the late 1980s, a series of explants have been successfully used in banana EC. In a word, there are mainly four different types of explants used in banana: immature and mature zygotic embryos[914], Rhizome slices and leaf sheaths[15], IMFs and female flowers[5, 1621], and scalps[22, 23]. Recently, somatic embryos were also successfully induced by secondary explants from male buds and bracts in medium containing TDZ[24, 25]. While, direct somatic embryos developed from split shoot tips under a combination of picloram and 6-benzyladenine (BA)[26]. Despite many options, the most used explants to establish a renewable ECS for seedless banana are still limited to scalps[2731] and IMFs[3234].

      It is reported that factors such as the developmental and physiological state of the explant and the location of the material can affect SE. Strosse et al.[35] suggested that the immature flowers should be taken from position 8 to 16, which were the most responsive ones in terms of embryogenesis. From the reports, sensitive positions are mainly concentrated in 7-13[17]; 8-15[36], 4-11[37], and 6-11[34]. Interestingly, higher efficiency and taking a short time for EC formation were observed by spraying exogenous 2,4-dichlorophenoxyacetic acid (2,4-D) on immature male flower buds[38].

    • SE is highly genome dependent as the efficiency varies with cultivars. Using various explants, SE has been achieved for some genotypes of banana varieties (AA, BB, AB, AAA, ABB, and AAB). Using IMFs as starting materials, three genotypes including six cultivars were tested[17]. The efficiency of EC obtained from different genotypes ranged from 0 to 7%. Even for the different variety of the same genotype, the frequency of EC formation varied differently. Musa AAB cvs. 'French Plantain', 'Mysore' and 'Silk' showed the efficiency of 2%, 3%, and 7%, respectively. As for Musa AAA cv. 'Grande Naine', it had the highest induction rate (37%) of all tested varieties[17]. Among the reported genotypes, two cultivars from Cavendish subgroup (AAA) ranged from 0.7% to 10%, responses[39]. Using the scalps, the mean embryogenic frequency was 6.0%, 3.8%, and 1.8% for cooking bananas (ABB), Cavendish-type bananas (AAA), and plantains (AAB), respectively[30]. Whereas, using inflorescence proliferation for SE induction, the embryogenic frequency was 12.5% and 25% under semisolid and liquid inductive medium, respectively[24].

    • PGRs are crucial in the process of callus formation, proliferation, somatic embryo formation, plant regeneration and rooting. Auxins and cytokinins act a decisive role in somatic embryogenesis in various plant species. At present, the commonly used auxins are 2,4-dichlorophenoxyacetic acid (2,4-D), 1-naphtaleneacetic acid (NAA), indole-3-acetic acid (IAA), Indole-3-butyric acid (IBA), and picloram (4-amino-3,5,6-trichloropicolinic acid). As for CKs, 6-benzylaminopurine (BA), kinetin (KN), and zeatin are the mostly used. 2,4-D is used for EC induction, establishment and proliferation of ECS in most banana cultivars. It is often applied at 1–4 mg L−1, and is combined with low concentrations of cytokinins to control SE. For plant regeneration, BA is often used at concentrations of 0.1–3 mg L−1, and low concentrations of NAA added, or sometimes hormone-free media.

      Different concentrations and various combinations of PGRs were required for different explants. For IMFs method, even though a high level of 2,4-D is needed for the EC induction, prolonged exposure will reduce the embryogenic nature of the callus. At the proliferation of EC and initiation of cell suspension cultures, reduction of the concentration of the sole auxin 2.4-D is improtant for proliferation of somatic embryogenic callus and expression of somatic embryos[18, 37, 40]. However, Nandhakumar et al.[34] reported a MS based ECS medium with 10 mg L−1 resulted in the rapid multiplication of embryogenic cells. In addition, picloram also plays a vital role in SE. It was reported that the induction percentage of EC of M. acuminata cv. 'Mas' (AA) reached 15.6% when 2.4-D in the callus induction medium was substituted by 8.28 μM picloram. The induction efficiency of IMFs on medium with picloram was more than twice that of 2.4-D[41]. On the contrary, the opposite results were observed when the effects of different concentrations of 2,4-D and picloram on callus initiation of M. acuminata cv. 'Berangan' (AAA) were studied[40]. It may be induced by the different genotypes of the explants. As for the other explants, both auxin and cytokinin were used and maintained in the medium. Embryogenic callus (17.5%) was induced from scalps of Musa AAB Silk 'Guoshanxiang' on MS medium with 5 μM 2,4-D and 1 μM Zeatin[28]. Using split shoot tips as explants, maximum embryo induction (100%) for M. acuminata AAA cv. 'Grand Naine' occurred in medium with 4.14 μM picloram and 0.22 μM BA. The plant regeneration (2%–3%) occurred in MS medium with NAA (0.53–2.68 μM) and BA (2.22–44.39 μM), or TDZ (4.54 μM) plus glutamine (200 mg/L)[26].

    • Medium is the basic substance for in vitro plant culture. According to the components, it can be divided into Murashige and Skoog (MS) medium[42], Gamborg's B5[43], Woody Plant Medium (WPM)[44], and Schenk and Hildebrandt (SH) medium[45], etc. The basal medium may be solid, semi-solid, or liquid. The commonly used media for SE in Musa spp. include MS and SH. MS is the preferred medium for callus initiation, establishment of ECS, and plant regeneration. SH medium with MS vitamins or 1/2 MS is often used for the development and maturation of somatic embryos of Musa spp.

      Medium additives, used along with basal media and PGRs, commonly include carbon source, various amino acids, malt extract (ME), and coconut water (CW), etc. Carbon source plays a major role in plant energy metabolism and regulates the osmotic potential of the cell. The most preferred carbon source for banana was sucrose (2%–4.5%; w/v). In addition, maltose, dextrose, fructose, lactose and galactose are also used as carbon sources in some studies. Adding maltose in the medium promoted the formation of banana ECS[32, 34]. The effect of different amino acids (L-Proline, L-Glutamine and L-Asparagine) on somatic embryo production was compared. Among the tested amino acids, L-Glutamine (400 mg L−1) had a significant strengthening effect on primary and secondary somatic embryos in M3 medium[34]. It was in concert with the early report[46]. The presence of 400 mg L−1 L-Glutamine resulted in optimum somatic embryo development and high regeneration efficiency in banana cv. Berangan (AAA). Although L-Glutamine and L-Proline have been shown to promote the embryo development, high concentrations of proline (400 mg L−1) in liquid media caused abnormal embryo differentiation[46]. CW and ME play a promoting role in banana callus induction[32, 47]. To avoid rapid browning of the explants, anti-oxidant like ascorbate (10 mg L−1), melatonin (50 mg L−1), and L-Glutamine (100 mg L−1) were also added to the medium[34].

    • Exploring the molecular regulatory mechanism of plant SE can not only reveal the process of somatic embryo development, but also afford a basis molecular mechanism for somatic embryo development. In most banana genotypes, the potency of explant to develop EC is highly inefficient. Therefore, it is important to find the molecular regulators that can be explored to enhance the SE potential in banana.

      Based on the banana genome database, the differential transcribed fragments between zygotic and somatic embryogenesis were compared by cDNA-AFLP[48]. The role of genes including transcription factors (TFs) was identified in banana SE. The results showed that MaBBM2 and MaWUS2 maybe the prospective candidate TFs and MaPIN1 could be a hopeful gene marker for the embryogenicity in banana[49, 50].

      Recently, differentially expressed proteins during the SE in banana were identified by proteome technology[5153]. Based on comparative proteomics, it is indicated that EC was related to excessive accumulation of ROS scavenging proteins, heat shock proteins (HSP), and growth-regulator related proteins[51]. Furthermore, calcium signaling and PGRs were also involved in the development and germination of somatic embryos. The important role of calcium and PGRs (IAA, BAP, and kinetin) were confirmed by proper induction of five recalcitrant banana cultivars[52]. Based on these results, the medium for optimal SE efficiency in several cultivars could be customized.

    • As the basal materials, ECS is very important for banana germplasm innovation. However, the establishment of banana ECS is very difficult, and after establishment, it needs to be subcultured regularly. Frequent subculture not only consumes a lot of manpower and material resources, but also leads to somatic mutation and the loss of embryogenic characteristics. Furthermore, it is susceptible to bacterial and fungal contamination. Therefore, it is of great significance to study the preservation methods of banana ECS. Cryopreservation is an effective technology that can not only reduce the risk of contamination and gene mutation, but also effectively store plant material for a long time. There are three main methods for cryopreservation of banana germplasm, namely slow-freezing (two-step method), quick-freezing and vitrification. Panis et al.[54] successfully used a two-step method to preserve banana ECS for the first time. In 2010, Li et al. successfully cryopreserved banana ECS by vitrification[55].

    • Protoplast fusion and somatic hybridization offers the potential to produce novel crops and overcome breeding obstacles in polyploid and apomictic banana cultivars. In 1993, the isolation and regeneration of protoplasts from an embryogenic cell suspension culture in banana were successfully received[56, 57]. Science then, a number of banana cultivars including various genotypes (AA, AAA, AAB, ABB) were effectively regenerated through protoplast culture[58].

      Plant regeneration via protoplast culture opens up feasible opportunities for somatic hybridization and protoplast transformation, and eventually leads to genetic modification and breeding of new varieties. Somatic hybridization between triploid (Musa spp. AAB group, cv. 'Maçã') and diploid (Musa spp. AA group, cv. 'Lidi') bananas was attempted using protoplast electrofusion and nurse culture techniques. Somatic hybrids showed different ploidy levels by RAPD and flow cytometric ploidy analyses[59]. Assani et al.[60] successfully obtained banana somatic hybrid plants from Musa spp. triploid cv. 'Gros Michel' (AAA) and diploid cv. 'SF265' (AA). By the comparison of chemical (PEG: polyethylene glycol) and electrical fusion technique, it was found that electric fusion was better for mitotic activities, somatic embryogenesis and plantlet, and chemical procedure was better for the frequency of binary fusion. Xiao et al.[58] developed an asymmetric protoplast fusion with 20% (w/v) PEG and obtained somatic hybrids between Musa Silk cv. 'Guoshanxiang' (AAB) and Musa acuminata cv. 'Mas' (AA). Recently, Wu et al.[61] established a PEG-mediated protoplast transformation, which can serve as an effective and rapid tool for transient expression assays and sgRNA validation in banana.

    • As a perennial fruit crop, banana is susceptible to a plethora of abiotic and biotic stresses[6, 62, 63]. The objectives of banana improvement programs are breeding cultivars resistant to abiotic and biotic stressors without adverse effects on yield and fruit quality. Numerous efforts have been made to breed superior banana cultivars with better resistance to abiotic and biotic stresses and optimum yields at the same time using conventional breeding and genetic modification strategies. However, conventional breeding approaches are time-consuming and severely hampered by inherent banana problems (polyploidy and sterility). Therefore, genetic transformation is becoming increasingly popular and can provide rapid solutions.

      In summary, the recipients used for banana genetic transformation are usually ECS, apical meristem, corm slices, thin cell layers from shoot tips, multiple shoot clumps etc. Among them, genetic transformation through ECS is a most commonly used method in different cultivars of banana owing to its strong ability to accept foreign genes and the lower frequency of chimeras shoot production. The transformation method is mainly mediated by gene gun and Agrobacterium. The flow chart of banana genetic transformation using ECS is shown in Fig. 2. The transformation efficiency was between 1.25% and 50.00%, with a large range of changes. Except for NPTII, GUS, GFP and other screening genes and reporter genes, the transformed functional genes mainly involved in banana fruit quality, disease resistance, drought tolerance, dwarfing and other traits improvement[62]. In this part, the studies for banana genetic transformation with added value from 2000 on were mainly summarized as below.

      Figure 2. 

      Schematic representation of genetic transformation steps of banana using embryogenic cell suspension. Scalps and immature male flowers (IMFs) are the most used explants to establish a renewable ECS for seedless banana cultivars. The photos of scalps and friable embryogenic callus are cited from Tripathi et al.[31].

    • In banana, the most serious diseases are fungal (Fusarium wilt, black Sigatoka), bacterial (banana Xanthomonas wilt, BXW), and viral (banana bunchy top disease, and banana streak disease)[63, 64]. Researchers have been working to improve disease and pest resistance in bananas using transgenic technology[6466].

      Various transgenes have been used to develop genetically engineered banana and many conferred significant levels of resistance to fungal pathogens (Table 1). Functional genes used to develop Foc-resistance bananas mainly included the antimicrobial peptides belonging to plant or animal origin[67, 68, 70, 72, 75, 78, 80], apoptosis-inhibition-related animal genes (Bcl-xL, Ced-9 and Bcl-2 3' UTR, Ced9)[69, 74, 81], different cell-death-related genes (MusaDAD1, MusaBAG1 and MusaBI1)[72], and defense-related protein[82]. In addition, Foc-resistance has also been conformed using RNAi silencing of key genes of Foc[71, 84]. Although the above studies demonstrate the transgenic plants resistance to Foc in the greenhouse, field evaluation remains to be seen. Recently, transgenic bananas with resistance gene analog 2 (RGA2), isolated from a seedling of Musa acuminata ssp. malaccensis with resistance to TR4, showed promising resistance against Fusarium wilt after a 3-year field trial in Australia[81]. Similarly, two native genes (MaLYK1 and MabHLH) from banana germplasms with Foc resistance were introduced back to Cavendish banana cv. Brazil, had shown increased resistance to TR4[83, 85]. Several studies from the researchers at the International Institute of Tropical Agriculture (IITA) reported transgenic bananas resistant to BXW disease[8892]. Other studies also dealt with the production of resistance to Black Sigatoka[86, 87] and banana bunchy top disease[9395] (Table 1).

      Table 1.  Genetic transformations of banana (Musa spp.).

      TraitGeneSourcesTransformation methodResultCultivarReference
      Fungal
      Foc Race2 resistanceMSI-99SyntheticAgrobacterium/EHA105/ ECSImproved disease resistance against Foc and black leaf streak diseaseRasthali (AAB)[67]
      Foc Race1 resistanceβ–1,3–endoglucanaseSoybeanAgrobacterium/LBA4404/Single budsIncreased tolerance to Foc Race 1Rasthali (AAB)[68]
      Bcl-xL, Ced-9, Bcl-2 3' UTRAnimalAgrobacterium/LBA4404/ECSApoptosis-inhibition-related genes confer resistance to Foc Race 1Lady Finger (AAB)[69]
      PhDef1 and PhDef2PetuniaAgrobacterium/EHA105/ECSImproved fungal resistance with normal growth and no stunting phenotypeRasthali (AAB)[70]
      ihpRNA-VEL and ihpRNA-FTF1Agrobacterium/EHA105/ECSIncreased resistance to Foc Race 1Rasthali (AAB)[71]
      MusaDAD1, MusaBAG1 and MusaBI1BananaAgrobacterium/EHA105/ECSIncreased resistance to Foc Race 1Rasthali (AAB)[72]
      Sm-AMP-D1Stellaria mediaAgrobacterium/EHA105/ECSImproved resistance against Foc Race 1 and no gross growth abnormalitiesRasthali (AAB)[73]
      Ced9SyntheticIncreased resistance against Fusarium wiltSukali Ndiizi (AAB)[74]
      Ace-AMP1Onion seedsAgrobacterium/LBA4404/ECSEnhanced resistance to Foc race 1Rasthali (AAB)[75]
      Ace-AMP1 + Ca-pflpAllium cepa L; Capsicum annum L.Agrobacterium/AGL1/ ECSStacked Ace-AMP1 and pflp transgenic plants showed resistance to Foc race 1Rasthali (AAB)[76]
      Foc TR4 resistanceHuman lysozyme (HL)Agrobacterium/EHA105/corm slicesImproved resistance to Foc TR4Taijiao (AAA)[77]
      PflpSweet pepperAgrobacterium/EHA105/
      multiple bud clumps
      Enhanced resistance to Foc TR4Pei Chiao (AAA) and Gros Michel (AAA)[78]
      TLP or PR-5RiceBiolistics/Single
      cauliflower-like bodies
      Enhanced resistance to Foc TR4Pisang Nangka (AAB)[79]
      ThChit42Trichoderma harzianumAgrobacterium/EHA105/ECSEnhanced resistance to Foc TR4Furenzhi (AA)[80]
      RGA2 or Ced9BananaAgrobacterium/EHA105/ECSImproved promising resistance against Fusarium wiltGrand Nain (AAA)[81]
      MaPR-10bananaAgrobacterium/−/ECSImproved tolerance against Fusarium infectionBerangan[82]
      MaLYK1BananaAgrobacterium/EHA105/ECSIncreased resistance to Foc TR4Cavendish (AAA)[83]
      Synthesis of ergosterol (ERG6/11)Agrobacterium/EHA105/ECSstrong resistance to Fusarium wiltBrazil (AAA)[84]
      MpbHLHBananaAgrobacterium/EHA105/ECSEnhanced Foc TR4-resistance of Cavendish bananaBrazil (AAA)[85]
      Sigatoka resistanceThEn-42 + StSy + Cu,Zn-SOD co-transformationTrichoderma harzianum
      + grape + tomato
      Biolistics/ECSEnhanced tolerance to SigatokaGrand Nain (AAA)[86]
      rcc2 or rcg3RiceAgrobacterium/EHA105/ ECSEnhanced host plant resistance to black SigatokaGros Michel (AAA)[87]
      Bacterial
      BXW resistanceHrapSweet pepperAgrobacterium/AGL1/ ECSAbout 20% of the Hrap lines showed
      100% resistance for both mother and
      ratoon crops under field conditions
      Sukali Ndiizi (AAB) and Mpologoma (AAA)[88, 89]
      PflpSweet pepperAgrobacterium/EHA105/ ECSAbout 16% of the Pflp lines showed
      100% resistance for both mother and
      ratoon crops under field conditions
      Sukali Ndiizi (AAB) and Nakinyika (AAA)[89, 90]
      Stacked Harp and PflpSweet pepperAgrobacterium/AGL1/ ECSStacked Harp and Pflp transgenic plants
      had higher resistance to X cm
      Gonja manjaya (AAB)[91]
      Xa21RiceAgrobacterium/EHA105/ ECS50% of the transgenic lines showed complete resistance to X cmGonja manjaya (AAB)[92]
      ViralRepBBTVCompletely resistant to BBTV infection was found under glasshouse conditionsBrazilian (AAA)[93]
      BBTV-G- cpBBTVBiolistics/apical meristemWilliams (AAA)[94]
      Rep, ProRepBBTVRasthali (AAB)[95]
      Abiotic stress
      Salt, oxidative stressMusaWRKY71bananaAgrobacterium/EHA105/ ECSEnhanced tolerance towards oxidative and salt stressRasthali (AAB)[96]
      Cold, drought, saltMusabZIP53bananaAgrobacterium/EHA105/ ECSTransgenic plants displayed severe growth retardationRasthali (AAB)[97]
      ColdMpMYBS3bananaAgrobacterium/EHA105/ ECSThe transgenic lines had higher cold toleranceBrazil (AAA)[98]
      Salt, droughtMusaNAC042bananaAgrobacterium/EHA105/ ECSMusaNAC042 is positively associated with drought and salinity toleranceRasthali (AAB)[99]
      Drought, saltMusa-DHN-1bananaAgrobacterium/-/ ECSImproved tolerance to drought and salt-stressRasthali (AAB)[100]
      Salt, droughtAhSIPR10Arachis hypogaeaAgrobacterium/EHA105/
      multiple shoot clump
      Transgenic plants showed better tolerance of salt and drought conditionsMatti (AA)[101]
      Drought, salt, oxidative stressMusaSAP1bananaAgrobacterium/EHA105/ ECSTransgenic plants displayed better stress endurance characteristicsRasthali (AAB)[102]
      Cold, salt, droughtMusaPIP1;2bananaAgrobacterium/EHA105/ ECSTransgenic plants showed better abiotic stress survival characteristicsRasthali (AAB)[103]
      SaltMusaPIP2;6bananaAgrobacterium/EHA105/ ECSTransgenic plants showed better tolerance under salt stressRasthali (AAB)[104]
      Salt, droughtMaPIP1;1bananaAgrobacterium/EHA105/
      thin cell layers from shoot tips
      Improved tolerance to salt and drought stressesBrazilian (AAA)[105, 106]
      Drought, cold ,saltMaPIP2-7bananaAgrobacterium/EHA105/
      thin cell layers from shoot tips
      Improved tolerance to salt, drought, and cold stressesBrazilian (AAA)[107]
      MaSIP2-1bananaAgrobacterium/EHA105/
      thin cell layers from shoot tips
      Transgenic plants had a stronger drought and cold tolerance than the controlBrazilian (AAA)[108]
      Salt, droughtAhcAPXArachis hypogeaAgrobacterium/EHA105/
      multiple shoot clump
      Enhanced the tolerance to drought and salt stressGrand naine (AAA)[109]
      Fruit quality and others
      Biofortified Iron ferritinsoybeanAgrobacterium/EHA105/ ECSA 6.32-fold increase in iron accumulation and a 4.58-fold increase in the zinc levels were noted in the leaves of transgenic plantsRasthali (AAB)[110]
      Biofortified pro-vitamin AMtPsy2abanaanAgrobacterium/AGL1/ ECSA high content of β-CE (75.1 µg/g dw) was found in the fourth generation with no variation in critical agronomical features such as yield and cycle timeDwarf Cavendish (AAA)[111]
      Fruit ripeningMaMADS1 and MaMADS2bananaAgrobacterium/-/ ECSRepression of either MaMADS1 or
      MaMADS2, resulted in delayed ethylene
      synthesis and maturation
      Grand Nain (AAA)[112]
      Sense and anti-sense MaMADS36bananaAgrobacterium/GV3101/
      thin cell layers from shoot tips
      MaMADS36 represents a central molecular switch in regulating banana fruit ripeningRed banana (AAA)[113]
      Foc, Fusarium oxysporum f. sp. cubense; ECS, embryogenic cell suspension; BBTV, Banana bunchy top virus; NM, not mention.
    • Many transcription factors (TFs) and downstream genes which respond to abiotic stress have been identified in banana. They mainly include WRKY[96], bZIP[97], MYB[98], NAC[99], dehydrins (DHN)[100], SAP1[102], and aquaporins (AQP)[103108], and so on (Table 1). In transgenic plants, overexpression of these TFs let them withstand and survive under stress conditions. Because of their important role in plant growth and development, it can cause abnormal growth in transgenic plants by the constitutive overexpression of these TFs, such as bZIP53[97]. Identifying and overexpressing a key gene participated in stress tolerance is a good option. Ectopic expression of stress-related genes has been introduced into banana to enhance the tolerance to some abiotic stresses[100109]. However, the majority of these studies have been reported from a glasshouse evaluation. Trials in the field are necessary to further prove their worthiness.

    • Recently, genetic engineering was also employed to improve fruit nutrient content and control fruit ripening (Table 1). Transgenic bananas with biofortified iron content and pro-vitamin A were tested in the green house and field, respectively. The transgenic banana plants overexpressing soybean ferritin accumulated the higher levels of iron and zinc under in vitro conditions as well as in the green house[110]. PVA-biofortified transgenic Cavendish bananas were also developed[111].

      Banana MaMADS transcription factors are necessary for fruit ripening and molecular tools to promote shelf-life. Repression of either MaMADS1 or MaMADS2, resulted in delayed ethylene synthesis and maturation[112]. Similarly, transgenic red bananas were obtained with sense and anti-sense constructs of MaMADS36. Further study demonstrated that MaMADS36 directly binds to the CA/T(r)G box of the MaBAM9b promoter to regulate enzyme activity and starch degradation during ripening[113].

    • Genome-editing technologies using various site-directed nucleases (SDNs) have become powerful tools for modifying plant genomes. SDNs include meganucleases, ZFNs (zinc finger nucleases), TALENs (transcription activator-like effector nucleases), and CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein)[114]. The CRISPR/Cas system has been widely adopted for plants genetic improvement due to its simplicity and high-efficiency[115].

      Although the application of the CRISPR/Cas9 system in banana is still at the preliminary stage, CRISPR/Cas9 mediated genome editing has been applied to improve banana nutrient contents, storage time, disease resistance, and alter the plant architecture[6, 114, 116, 117]. In summary, the CRISPR/Cas9-based genome editing utilized in banana are outline in Table 2.

      Table 2.  Application of CRISPR/Cas9 gene editing technology in Musa.

      CultivarExplantStrategy of transformationCas9 promotersgRNA promoterTarget geneTarget traitResultEditing efficiencyReference
      Baxi; AAAECSAgrobacterium-mediated transformation
      EHA105
      UbiOsU6aMaPDSAlbino and variegated phenotypeMurtation in target genes; Albino phenotype in transgenic plants55%[118]
      Rasthali; AABECSAgrobacterium-mediated transformation
      Agl1
      2 × CaMV35SOsU3MaPDSAlbino and variegated phenotypeMurtation in target genes; Albino phenotype in transgenic plants59%[119]
      Williams; AAAECSAgrobacterium-mediated transformation
      EHA105
      Ubi;CaMV35sOsU3MaPDSAlbino and dwarf phenotypeMurtation in target genes; Albino and dwarfing phenotype in transgenic plants63%[120]
      Sukali Ndiizi; AAB;Gonja Manjaya; AABECSAgrobacterium-mediated transformation
      EHA105
      2 × CaMV35SOsU6MaPDSalbino and variegated phenotypesGeneration of mutants with albino and variegated phenotypes100%[121]
      Williams; AAAECSAgrobacterium-mediated transformationNMNMMaCHAOSPale-green phenotypesMurtation in target genes; Pale-green phenotypes and normal growthNM[122]
      Baxi; AAAprotoplastPEG-mediated transformationUbiOsU3MaPDS-The efficiency of CRISPR/Cas9-mediated mutagenesis was higher than that of CRISPR/Cas12a, and RNP-CRISPR-Cas91.04% (Cas9), 0.92% (RNP), 0.39% (Cas12a)[61]
      Brazilian; AAAprotoplastPEG-mediated transformationUbiMaU6MaPDS-Increased mutation efficiency of CRISPR/Cas9 genome editing in banana by optimized construct4-fold[123]
      Gonja Manjaya; AABECSAgrobacterium-mediated transformation
      EHA105
      UbiOsU6Viral genesBanana streak virus (BSV)Inactivation of endogenous banana streak virus (eBSV) intergated in host genome and generated resistant banana plants against eBSV95%[124]
      Sukali Ndiizi; AABECSAgrobacterium-mediated transformation
      EHA105
      2 × CaMV35SOsU6MusaDMR6Banana Xanthomonas wilt (BXW)Improved resistance to BXW disease in mutants with normal growth100%[125]
      Grand Naine; AAAECSAgrobacterium-mediated transformation
      Agl1
      CaMV35SOsU3MaLCYεRegulation synthesis of β-caroteneImproved nutritional trait in transgenic plants with normal growthNM[127]
      Rasthali; AABprotoplasts and ECSelectroporation-mediated transformation;
      particle bombardment method
      CaMV35SOsU3MaCCD4Regulatory mechanism of β-carotene homeostasisCCD4 negatively regulates β-carotene biosynthesisNM[128]
      Brazilian; AAAECSAgrobacterium-mediated transformationUbiOsU6aMaACO1Shelf lifeMore vitamin C and improved shelf life in transgenic plants98%[129]
      Gros Michel; AAAECSAgrobacterium-mediated transformationUbiOsU6a/
      OsU3
      MaGA20ox2Semi-dwarf phenotypeA lower active GA content and a semi-dwarf phenotype in transgenic plantsNM[130]
      NM, not mentioned.
    • Plant albino phenotype is a classic and indicative phenotype for testing and judging whether the CRISPR/Cas9 system is effective. As an indicator gene, phytoene desaturase (PDS) can easily obtain the target albino trait and has been knocked out in most fruit trees. Recently, CRISPR/Cas9-based genome editing in banana has been established using the PDS as a marker gene[118121]. However, knockout of PDS has adverse effects on plant growth. Optionally, RP43/CHAOS39-edited banana plants were obtained with pale-green phenotype and no negative effects on plant growth[122]. Recently, the transient delivery system by a PEG-mediated protoplast was established[61]. The editing efficiency of the CRISPR/Cas9, CRISPR/Cas12a, and ribonucleoprotein-CRISPR-Cas9 (RNP-CRISPR-Cas9) for targeting the PDS gene in banana protoplasts was compared. The results showed that the efficiency of CRISPR/Cas9-mediated mutagenesis was higher than that of the other two systems. In addition, it was the first report by a RNP-CRISPR-Cas9 system for genome editing in banana[61]. In comparison to the previous report in banana, using endogenous U6promoter and banana codon-optimized Cas9 in CRISPR/Cas9 cassette, the mutagenesis efficiency has a fourfold increase[123].

    • Banana streak virus (BSV), a double stranded DNA Badnavirus which integrated in the B genome derived from M. balbisiana, is called endogenous BSV (eBSV). It severely affects production of plantain (AAB) in Africa. To inactivate the virus, a multiplexed gRNA strategy targeting all three ORFs of eBSV was constructed and transformed into Gonja Manjaya (AAB). Compared with the controls, the eBSV-edited plants exhibited resistance against eBSV and normal growth. A very high mutation effciency of 95% using three gRNAs were observed[124]. Recently, CRISPR/Cas9 mediated gene editing for banana resistance against bacteria was also reported. To obtain the banana cultivar against Xan­thomonas Wilt (BXW), a Musadmr6 gene was edited[125]. The edited plants had a higher resistance to BXW without adverse affecting on plant growth. Researchers are also trying to breed TR4 resistance cultivars by CRISPR[126].

    • Fruit quality is an important indicator to measure the value of fruit commodities. Carotenoids are essential for human nutrition. Most Cavendish group cultivars have low β-carotene content in the fruit pulp. Using CRISPR/Cas9 technology, β-carotene-enriched banana plants were created by editing the fifth exon of LCYε gene from A genome, which determines a high α-/β-carotene ratio[127]. Compared with the unedited fruits, the β-carotene count of the fruit pulp of the edited lines increased by 6-fold. More recently, CRISPR/Cas9-mediated editing of CCD4 was conducted in Rasthali. In comparison to the controls, the accumulation of β-carotene in roots was increased in the CCD4-edited plants[128].

      The shelf life of post-harvest fruits is an important factor affecting fruit quality. The production of ethylene is closely related to the storage time of banana fruits. Thus, it is the first consideration for developing postharvest preservation technology. MaACO1 encodes for an O2-activating ascorbate-dependent non-heme iron enzyme that catalyzes the last step in ethylene biosynthesis. The MaACO1-editted banana fruit extended shelf life and had more Vitamin C compared with the wild-type fruit[129].

    • Developing semi-dwarf and dwarf banana varieties is also one of the objectives of banana improvement programs. Gibberellin (GA) is a key gene which determines plant height and the mutations in its biosynthesis genes often leads to dwarf plants. CRISPR/Cas9 technology was applied to generate a semi-dwarf banana cultivar 'Gros Michel' by manipulating the M. acuminata gibberellin 20ox2 (MaGA20ox2) gene, disrupting the gibberellin (GA) pathway[130].

    • At present, extensive advances has been made on banana SE. Reports of banana genetic improvement using ECS in the past five years has increased dramatically. Nevertheless, there are still many problems to be solved in the research on banana SE and genetic modification. Little information is available on the molecular mechanisms of banana SE. The embryogenic capacity and efficiently propagated plantlets are very low. The repeatability of the protocols early reported for SE in banana is poor. So far, SE in bananas is far from being considered a conventional technique and has not even been successfully used in some varieties.

      Hence, an important consideration for future work is to explore the basic molecular mechanism of banana embryogenic potency. The gradual application of multi-omics technique in plant SE provides the feasibility to uncover the regulatory mechanism of SE development at the molecular level. Further continuous work is needed for optimizing a highly effcient and versatile transformation and regeneration system which independent on genotype. And the genetic improvement at present only aims at single gene or single trait. More genes associated with disease-resistance, as well as with other important agronomic traits, should be characterized and utilized in target breeding programs. Molecular designing breeding with multi-gene superposition should be carried out to breed new banana varieties with good comprehensive characters.

      Despite the rapid progress of banana transgenic, there are no commercial transgenic varieties applied to the production. With the continuous optimization and improvement of CRISPR/Cas and other gene editing technologies, it is possible to obtain an ideal mutant by accurately targeting target sites. In addition, the key technology of modern biotechnology breeding is the delivery system of plant genetic modification. The application of nanocarriers in plant genetic engineering shows a broad application prospect. The introduction of nanotechnology into banana tissue culture showed significant positive effects on callus induction, somatic embryogenesis and other regeneration aspects. More recently, a cut-dip-budding delivery (CBD) system enables genetic modifications in plants without tissue culture[131]. It overcomes the difficulties posed by the traditional technology due to the plant tissue culture process. Therefore, it would be very interesting to explore a simple, fast and efficient method for banana genetic transformation or genome editing without the need for tissue culture.

      • This research was supported by the specific research fund of The Innovation Platform for Academicians of Hainan Province (YSPTZX202101), the Hainan Provincial Natural Science Foundation (321RC638), the National Natural Science Foundation of China (32172269, 31501043) and the Earmarked Fund for Modern Agro-industry Technology Research System (CARS-31).

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

      • Received 30 September 2022; Accepted 29 November 2022; Published online 23 December 2022

      • # These authors contributed equally: Jingyi Wang, Shanshan Gan

      • Copyright: © 2022 by the author(s). Published by Maximum Academic Press on behalf of Hainan University. 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 (2)  Table (2) References (131)
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    Wang J, Gan S, Zheng Y, Jin Z, Cheng Y, et al. 2022. Banana somatic embryogenesis and biotechnological application. Tropical Plants 1:12 doi: 10.48130/TP-2022-0012
    Wang J, Gan S, Zheng Y, Jin Z, Cheng Y, et al. 2022. Banana somatic embryogenesis and biotechnological application. Tropical Plants 1:12 doi: 10.48130/TP-2022-0012

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