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Chemical fungicides have been instrumental in protecting crops from fungal diseases. However, increasing fungal resistance to many of the single‐site chemical fungicides calls for the development of new antifungal agents with novel modes of action (MoA). The sequence‐divergent cysteine‐rich antifungal defensins with multisite MoA are promising starting templates for design of novel peptide‐based fungicides. Here, we experimentally tested such a set of 17‐amino‐acid peptides containing the γ‐core motif of the antifungal plant defensin MtDef4. These designed peptides exhibited antifungal properties different from those of MtDef4. Focused analysis of a lead peptide, GMA4CG_V6, showed that it was a random coil in solution with little or no secondary structure elements. Additionally, it exhibited potent cation‐tolerant antifungal activity against the plant fungal pathogen Botrytis cinerea, the causal agent of grey mould disease in fruits and vegetables. Its multisite MoA involved localization predominantly to the plasma membrane, permeabilization of the plasma membrane, rapid internalization into the vacuole and cytoplasm, and affinity for the bioactive phosphoinositides phosphatidylinositol 3‐phosphate (PI3P), PI4P, and PI5P. The sequence motif RRRW was identified as a major determinant of the antifungal activity of this peptide. While topical spray application of GMA4CG_V6 on Nicotiana benthamiana and tomato plants provided preventive and curative suppression of grey mould disease symptoms, the peptide was not internalized into plant cells. Our findings open the possibility that truncated and modified defensin‐derived peptides containing the γ‐core sequence could serve as promising candidates for further development of bio‐inspired fungicides. A short variant of a plant defensin‐derived peptide containing the γ‐core motif exhibits potent antifungal activity, multifaceted modes of action, and potential for development into a bio‐inspired fungicide.

Botrytis cinerea is the most widely studied necrotrophic phytopathogenic fungus. It causes economic losses that are difficult to calculate due to the large number of hosts. While there are a wide array of fungicides on the market to control this phytopathogen, they are not considered sustainable in terms of the environment and human health. The search for new alternatives to control this phytopathogen has led to the use of endophytic microorganisms as biological control agents. Endophytic bacteria and endophytic fungi have been isolated from different plant species and some have proven effective in inhibiting B. cinerea . Furthermore, a significant number of fungistatic or fungicidal metabolites which could be used as alternative complementary chemical controls have been isolated from these fungi and bacteria. In this review, in addition to the metabolites which have shown fungicide activity against this phytopathogen, the different genera and species of endophytic bacteria and fungi are also considered. These have been isolated from various plant species and have displayed antagonistic activity against B. cinerea .

The use of compost extracts is steadily increasing, offering an attractive way for plant growth enhancement and disease management replacing chemical pesticides. In this study, potential mechanisms involved in plant growth promotion and suppressive activity against fungal diseases, of a compost extract produced from poultry manure/olive husk compost, were investigated. Results of physico-chemical and microbiological investigations showed high ability to reduce Fusarium oxysporum, Alternaria alternata, Aspergillus niger and Botrytis cinerea growth. The suppressive ability detected using confrontation test and the phytostimulatory effect tested on tomato seeds were related mainly to its microbial population content. Among 150 bacterial strains, isolated from the compost extract, 13 isolates showed antifungal activity against the four tested plant pathogenic fungi. Their identification based on 16S rRNA gene sequence revealed they belonged to different species of the genus Bacillus, Alcaligenes, Providencia and Ochrobactrum. When tested for their ability to produce cell wall degradation enzymes using specific media, the majority of the 13 isolates were shown to synthesize proteases, lipases and glucanases. Similarly, the best part of them showed positive reaction for plant growth promoting substances liberation, biosurfactant production and biofilm formation. In vivo tests were carried out using tomato seeds and fruits and proved that 92% of strains improved tomato plants vigor indexes when compared to the control and 6 among them were able to reduce decay severity caused by B. cinerea over 50%. Principal component analysis showed an important correlation between in vitro and in vivo potentialities and that Bacillus siamensis CEBZ11 strain was statistically the most effective strain in protecting tomato plants from gray mould disease. This study revealed the selected strains would be useful for plant pathogenic fungi control and plant growth promotion.

Botrytis cinerea can infect almost all of the important horticultural crops and cause severe economic losses globally every year. Modifying candidate genes and studying the phenotypic changes are among the most effective ways to unravel the pathogenic mechanism of this crop killer. However, few effective positive selection markers are used for B. cinerea genetic transformation, which limits multiple modifications to the genome, especially genes involving redundant functions. Here, we optimized a geneticin resistance gene, BcNPTII, based on the codon usage preference of B. cinerea. We found that BcNPTII can greatly increase the transformation efficiency of B. cinerea under G418 selection, with approximately 30 times higher efficiency than that of NPTII, which is applied efficiently to transform Magnaporthe oryzae. Using the gene replacement method, we successfully knocked out the second gene BOT2, with BcNPTII as the selection marker, from the mutant ΔoahA, in which OAHA was first replaced by the hygromycin resistance gene HPH in a field strain. We obtained the double knockout mutant ΔoahA Δbot2. Our data show that the codon-optimized BcNPTII is an efficient positive selection marker for B. cinerea transformation and can be used for various genetic manipulations in B. cinerea, including field wild-type strains.

Background Wall-associated kinase (WAK)/WAK-like (WAKL) is one of the subfamily of receptor like kinases (RLK). Although previous studies reported that WAK/WAKL played an important role in plant cell elongation, response to biotic and abiotic stresses, there are no systematic studies on RcWAK/RcWAKL in rose. Results In this study, we identified a total of 68 RcWAK/RcWAKL gene family members within rose ( Rosa chinensis ) genome. The RcWAKs contained the extracellular galacturonan-binding domain and calcium-binding epidermal growth factor (EGF)-like domain, as well as an intracellular kinase domains. The RcWAKLs are missing either calcium-binding EGF-like domain or the galacturonan-binding domain in their extracellular region. The phylogenetic analysis showed the RcWAK/RcWAKL gene family has been divided into five groups, and these RcWAK/RcWAKL genes were unevenly distributed on the 7 chromosomes of rose. 12 of RcWAK/RcWAKL genes were significantly up-regulated by Botrytis cinerea -inoculated rose petals, where RcWAK4 was the most strongly expressed. Virus induced gene silencing of RcWAK4 increased the rose petal sensitivity to B. cinerea. The results indicated RcWAK4 is involved in the resistance of rose petal against B. cinerea . Conclusion Our study provides useful information to further investigate the function of the RcWAK/RcWAKL gene family and breeding research for resistance to B. cinerea in rose.

In the indeterminate nodules of a model legume Medicago truncatula, ∼700 nodule-specific cysteine-rich (NCR) peptides with conserved cysteine signature are expressed. NCR peptides are highly diverse in sequence, and some of these cationic peptides exhibit antimicrobial activity in vitro and in vivo. However, there is a lack of knowledge regarding their structural architecture, antifungal activity, and modes of action against plant fungal pathogens. Here, the three-dimensional NMR structure of the 36-amino acid NCR044 peptide was solved. This unique structure was largely disordered and highly dynamic with one four-residue α-helix and one three-residue antiparallel β-sheet stabilized by two disulfide bonds. NCR044 peptide also exhibited potent fungicidal activity against multiple plant fungal pathogens, including Botrytis cinerea and three Fusarium spp. It inhibited germination in quiescent spores of B. cinerea. In germlings, it breached the fungal plasma membrane and induced reactive oxygen species. It bound to multiple bioactive phosphoinositides in vitro. Time-lapse confocal and superresolution microscopy revealed strong fungal cell wall binding, penetration of the cell membrane at discrete foci, followed by gradual loss of turgor, subsequent accumulation in the cytoplasm, and elevated levels in nucleoli of germlings. Spray-applied NCR044 significantly reduced gray mold disease symptoms caused by the fungal pathogen B. cinerea in tomato and tobacco plants, and postharvest products. Our work illustrates the antifungal activity of a structurally unique NCR peptide against plant fungal pathogens and paves the way for future development of this class of peptides as a spray-on fungistat/fungicide.

Gray mold caused by necrotrophic fungus pathogen (Botrytis cinerea) is a lethal disease, which affects various plants. It is also a common disease in strawberry, limiting the yield. Therefore, the detection and quantification of gray mold disease in the field is indispensable. Most of the deep convolutional neural networks (CNNs) were used for the plant disease identification and classification based on the highest probability value scored by the network. However, pixel-level segmentation allows quantifying the disease severity in the plant, which is crucial to determine the pesticides’ dose. Disease severity is also a useful parameter for monitoring the plant's resistance against a particular disease. Therefore, accurate quantification of plant disease is of utmost necessity in agriculture. In this circumstance, a deep learning-based semantic segmentation model was designed and tested to detect and measure the strawberry plants’ gray mold disease. For this purpose, three concentrations of 1 × 103, 1 × 105, 1 × 107 CFU/mL pathogen were inoculated on each group of 10 strawberry plants, and consequent occurrence of disease and its intensity over time were observed. The model performance was evaluated using pixel accuracy, dice accuracy, and intersection over union (IoU) metrics using fivefold cross-validation method. The results were compared with the results obtained from the XGBoost model, K-means, and Otsu image processing algorithms. The pixel, dice, and IoU accuracies were achieved the highest from the Unet model followed by the XGBoost model on 80 test images. Results showed that the Unet model surpasses the conventional XGBoost, K-means, and image processing technique in detecting and quantifying gray mold disease. Thus, a deep learning Unet can be a nifty tool assisting the farmers and agronomists in disease severity measurement.

High CO 2 (> 20 kPa) conditions are beneficial for suppressing spoilage caused by Botrytis cinerea in strawberry fruit; however, these conditions are often accompanied by discoloration, off-flavors, and faster softening. Stepwise increments of CO 2 concentrations have been proposed to alleviate injuries in fruits caused by high CO 2 . In this study, we investigated whether stepwise increments of CO 2 , up to 30 kPa and under a reduced O 2 concentration, are beneficial for reducing fungal spoilage without inducing CO 2 injury symptoms in strawberry fruit. Based on recommended settings (5–10 kPa O 2 with 15–20 kPa CO 2 ), we first selected optimal O 2 and CO 2 concentrations that best-reduced spoilage caused by B. cinerea in red ripe “Sonsation” strawberry fruit. We found that higher O 2 (10 kPa) and CO 2 (20 kPa) concentrations were most beneficial for prolonging strawberry fruit shelf life. Subsequently, we studied the performance of red ripe “Arabella” strawberry fruit stored at 5°C under different controlled atmosphere (CA) conditions (10 kPa O 2 with either 0, 20, or 30 kPa CO 2 ). The CO 2 concentrations were achieved either within 8 h or in a stepwise manner within the first 4 days of storage. As a control, 21 kPa O 2 and 0 kPa CO 2 were used. Following storage for up to 11 days, the spoilage incidence was assessed at 12°C for 5 days. The application of high CO 2 (20 and 30 kPa) combined with 10 kPa O 2 greatly suppressed fruit spoilage during storage and subsequent shelf life. High CO 2 suppressed respiration as well as maintained a higher pH and firmness in treated fruit. The level of total sugars did not change, but during storage, a substantial part of sucrose was converted into glucose and fructose, especially under high CO 2 conditions. High CO 2 did not affect ascorbic acid and anthocyanin levels. The stepwise increments of CO 2 did not result in beneficial effects compared to the static application of high CO 2 . Our results show that “Arabella” strawberry fruit are highly tolerant to elevated CO 2 and can be stored under 30 kPa CO 2 to prolong the shelf life.

Gray mold disease caused by Botrytis cinerea is a damaging postharvest disease in tomato plants, and it is known to be a limiting factor in tomato production. This study aimed to evaluate antifungal activities of Vernonia amygdalina leaf extracts against B. cinerea and to screen the phytochemical compound in the crude extract that had the highest antifungal activity. In this study, crude extracts of hexane, dichloromethane, methanol, and water extracts with concentration levels at 100, 200, 300, 400, and 500 mg/mL were shown to significantly affect the inhibition of B. cinerea. Among the crude extracts, dichloromethane extract was shown to be the most potent in terms of antifungal activities. The SEM observation proved that the treatment altered the fungal morphology, which leads to fungal growth inhibition. For the in vivo bioassay, the fruits treated with dichloromethane extract at 400 and 500 mg/mL showed the lowest disease incidence with mild severity of infection. There were 23 chemical compounds identified in V. amygdalina dichloromethane extract using GCMS analysis. The top five major compounds were dominated by squalene (16.92%), phytol (15.05%), triacontane (11.31%), heptacosane (7.14%), and neophytadiene (6.28%). Some of these significant compounds possess high antifungal activities. This study proved that V. amygdalina from dichloromethane extract could be useful for inhibiting gray mold disease on tomato fruit and has potential as a natural antifungal agent.

Hyperspectral imaging (HSI) and deep learning techniques have been widely applied to predict postharvest quality and shelf life in multiple horticultural crops such as vegetables, mushrooms, and fruits; however, few studies show the application of these techniques to evaluate the quality issues of cut flowers. Therefore, in this study, we developed a non-contact and rapid detection technique for the emergence of gray mold disease (GMD) and the potential longevity of cut roses using deep learning techniques based on HSI data. Cut flowers of two rose cultivars ('All For Love' and 'White Beauty') underwent either dry transport (thus impaired cut flower hydration), ethylene exposure, or inoculation, in order to identify the characteristic light wavelengths that are closely correlated with plant physiological states based on HSI. The flower bud of cut roses was selected for HSI measurement and the development of a vase life prediction model utilizing YOLOv5. The HSI results revealed that spectral reflectance between 470 to 680 nm was strongly correlated with gray mold disease (GMD), whereas those between 700 to 900 nm were strongly correlated with flower wilting or vase life. To develop a YOLOv5 prediction model that can be used to anticipate flower longevity, the vase life of cut roses was classed into two categories as over 5 d (+5D) and under 5 d (-5D), based on scoring a grading standard on the flower quality. A total of 3000 images from HSI were forwarded to the YOLOv5 model for training and prediction of GMD and vase life of cut flowers. Validation of the prediction model using independent data confirmed its high predictive accuracy in evaluating the vase life of both 'All For Love' (r = 0.86) and 'White Beauty' (r = 0.83) cut flowers. The YOLOv5 model also accurately detected and classified GMD in the cut rose flowers based on the image data. Our results demonstrate that the combination of HSI and deep learning is a reliable method for detecting early GMD infection and evaluating the longevity of cut roses.