Metabolic Profiling to Identify the Latent Infection of Strawberry by Botrytis cinerea

Continuous cropping disorders of eggplants (Solanum melongena L.) and tomatoes (Solanum lycopersicum L.) in suburban agriculture: Microbial structure and assembly processes

Deciphering the intricate relationships between microorganisms and plants remains a formidable challenge in plant microbial ecology, an area that holds promise for optimizing microbial interventions to enhance stress resilience and agricultural yields. In our investigation, we procured samples during 2019 and 2022 from a suburban agricultural greenhouse. Our study delineated the composition of bacterial and fungal communities across various ecological niches-namely, the rhizosphere soil, bulk soil, and phyllosphere of healthy, Ralstonia solanacearum-infected, and dead eggplants and tomatoes. The structure and composition of both fungal and bacterial communities change significantly under the influence of the host genotype across all samples. In the tomato or eggplant groups, bacterial wilt exerts a more pronounced impact on the bacterial community than on the fungal community. We speculate that the rhizosphere of healthy eggplants and tomatoes harbored more antibiotic-producing (e.g., Amycolatopsis and Penicillium) and biocontrol (e.g., Bacillus) strains, which can lead to have lower absolute abundance of R. solanacearum. In the context of R. solanacearum invasion, deterministic processes were responsible for shaping 70.67 % and 80.63 % of the bacterial community assembly in the rhizosphere of eggplants and tomatoes, respectively. Deterministic processes dominated the assembly of fungal communities in the rhizosphere of R. solanacearum-infected eggplants, whereas the opposite was true in the tomatoes. Homogeneous selection emerged as the predominant force governing the bacterial community assembly in the rhizospheres of R. solanacearum-infected eggplants and tomatoes. The bacterial co-occurrence networks in healthy rhizosphere soil were characterized by reduced vulnerability and enhanced stability (i.e., robustness index) and complexity (i.e., cohesion index), compared to their infected counterparts. In summary, complex microbial networks in rhizosphere soils are more resistant to invasion by soil-borne pathogens. The dynamics of bacterial interactions and community assembly processes are pivotal for effective microbiome management and offer predictive insights into the ecological ramifications of R. solanacearum invasions.

Hot water treatment in combination with silicate salts dipping for controlling apple gray mold caused by Botrytis cinerea Pers.:Fr

Background

Gray mold is the most prevalent postharvest disease of apple fruits in Egypt. In this study, five isolates of Botrytis cinerea were isolated from apple fruits that had postharvest decay symptoms. Investigations were made into the pathogenicity tests of these isolates as well as the molecular identification of the most virulent isolate. A study was done in vitro to see how B. cinerea's mycelial growth and conidial germination would be affected by hot water treatments (HWT) at temperatures of 25, 50, 52, 54, or 56 °C for 10, 20, 30, or 40 s as well as silicate salts (SS), specifically potassium silicate and sodium silicate at 0.0, 2.0, 4.0, and 6.0%. The effectiveness of hot water treatment and silicate salts dipping (SSD), both separately and together, for preventing B. cinerea infection and preserving the natural qualities of apple fruits was investigated in vivo.

Results

Pathogenicity tests on apples (Anna cv.) revealed that B. cinerea isolate (Bc-1) was found to be the most virulent. This isolate was identified as belonging to the fungus B. cinerea through molecular testing using internal transcribed spacer (ITS) sequencing and phylogenetic analyses, and it has since been added to Gene Bank with the accession number ON1498639.1. The lethal temperature for B. cinerea mycelial growth and spore germination in vitro was 54 °C/30 s and 54 °C/10 s, respectively. At a 6.0%, the SS, specifically potassium silicate and sodium silicate, completely prevented pathogen growth. When applied separately, HWT (60 °C/30 s) and SSD (6.0%/1 min) significantly reduced B. cinerea decay of apple fruits stored at 20 ± 2 °C for 15 days. In terms of control efficacy, the HWT (60 °C/30 s) and SSD (6.0%/1 min) combination performed better.

Conclusions

When apple fruits are stored at 20 ± 2 °C for 15 days, the combination of HWT (60 °C/30 s) and SSD (6.0%/1 min) may be an efficient way to control the gray mold disease. The amount of total soluble solids (TSS) in apple fruits was unaffected by these treatments, but they significantly lessened fruit weight loss after 40 days of storage at 20 ± 2 °C.

Early detection of Botrytis cinerea in strawberry fruit during quiescent infection using selected ion flow tube mass spectrometry (SIFT-MS)

Botrytis cinerea is a devastating pathogen that can cause huge postharvest losses of strawberry. Although this fungus usually infects strawberries through their flowers, symptoms mainly appear when fruit are fully mature. A fast and sensitive method to detect and quantify the fungal infection, prior to symptom development, is, therefore, needed. In this study, we explore the possibility of using the strawberry volatilome to identify biomarkers for B. cinerea infection. Strawberry flowers were inoculated with B. cinerea to mimic the natural infection. First, quantitative polymerase chain reaction (qPCR) was used to quantify B. cinerea in the strawberry fruit. The detection limit of qPCR for B. cinerea DNA extracted from strawberries was 0.01 ng. Subsequently, changes in the fruit volatilome at different fruit developmental stages were characterized using gas chromatography - mass spectrometry (GC-MS) and selected ion flow tube mass spectrometry (SIFT-MS). Based on GC-MS data, 1-octen-3-ol produced by B. cinerea was confirmed as a potential biomarker of B. cinerea infection. Moreover, the product ion NO⁺ 127, obtained by SIFT-MS measurements, was proposed as a potential biomarker for B. cinerea infection by comparing its relative level with that of 1-octen-3-ol (obtained by GC-MS) and B. cinerea (obtained by qPCR). Separate PLS regressions were carried out for each developmental stages, and 11 product ions were significantly altered at all developmental stages. Finally, PLS regressions using these 11 ions as variables allowed the discrimination between samples containing different amount of B. cinerea. This work showed that profiling the fruit's volatilome using SIFT-MS can be used as a potential alternative to detect B. cinerea during the quiescent stage of B. cinerea infection prior to symptom development. Moreover, the corresponding compounds of potential biomarkers suggest that the volatile changes caused by B. cinerea infection may contribute to strawberry defense.

Constitutive Defense Mechanisms Have a Major Role in the Resistance of Woodland Strawberry Leaves Against Botrytis cinerea

The necrotrophic fungus Botrytis cinerea is a major threat to strawberry cultivation worldwide. By screening different Fragaria vesca genotypes for susceptibility to B. cinerea, we identified two genotypes with different resistance levels, a susceptible genotype F. vesca ssp. vesca Tenno 3 (T3) and a moderately resistant genotype F. vesca ssp. vesca Kreuzkogel 1 (K1). These two genotypes were used to identify the molecular basis for the increased resistance of K1 compared to T3. Fungal DNA quantification and microscopic observation of fungal growth in woodland strawberry leaves confirmed that the growth of B. cinerea was restricted during early stages of infection in K1 compared to T3. Gene expression analysis in both genotypes upon B. cinerea inoculation suggested that the restricted growth of B. cinerea was rather due to the constitutive resistance mechanisms of K1 instead of the induction of defense responses. Furthermore, we observed that the amount of total phenolics, total flavonoids, glucose, galactose, citric acid and ascorbic acid correlated positively with higher resistance, while H₂O₂ and sucrose correlated negatively. Therefore, we propose that K1 leaves are more resistant against B. cinerea compared to T3 leaves, prior to B. cinerea inoculation, due to a lower amount of innate H₂O₂, which is attributed to a higher level of antioxidants and antioxidant enzymes in K1. To conclude, this study provides important insights into the resistance mechanisms against B. cinerea, which highly depend on the innate antioxidative profile and specialized metabolites of woodland strawberry leaves.

Development of a single-cell atlas for woodland strawberry (Fragaria vesca) leaves during early Botrytis cinerea infection using single cell RNA-seq

Pathogen invasion leads to fast, local-to-systemic signal transduction that initiates plant defense responses. Despite tremendous progress in past decades, aspects of this process remain unknown, such as which cell types respond first and how signals are transferred among cell types. Here, we used single-cell RNA-seq of more than 50 000 single cells to document the gene expression landscape in leaves of woodland strawberry during infection by Botrytis cinerea and identify major cell types. We constructed a single-cell atlas and characterized the distinct gene expression patterns of hydathode, epidermal, and mesophyll cells during the incubation period of B. cinerea infection. Pseudotime trajectory analysis revealed signals of the transition from normal functioning to defense response in epidermal and mesophyll cells upon B. cinerea infection. Genes related to disease resistance showed different expression patterns among cell types: disease resistance-related genes and gene encoding transcription factors were highly expressed in individual cell types and interacted to trigger plant systemic immunity to B. cinerea. This is the first report to document the of single-cell transcriptional landscape of the plant pathogenic invasion process, it provides new insights into the wholistic dynamics of host-pathogen interactions and can guide the identification of genes and the formulation of strategies for resistant cultivar development.

Transcriptional differentiation driving Cucumis sativus-Botrytis cinerea interactions based on the Skellam model and Bayesian networks

Robust statistical tools such as the Skellam model and Bayesian networks can capture the count properties of transcriptome sequencing data and clusters of genes among treatments, thereby improving our knowledge of gene functions and networks. In this study, we successfully implemented a model to analyze a transcriptome dataset of Cucumis sativus and Botrytis cinerea before and after their interaction. First, 4200 differentially expressed genes (DEGs) from C. sativus were clustered into 17 distinct groups, and 670 DEGs from B. cinerea were clustered into 12 groups. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied on these DEGs to assess the interactions between C. sativus and B. cinerea. In C. sativus, more DEGs were divided into terms in the molecular function and biological process domains than into cellular components, and 277 DEGs were allocated to 19 KEGG pathways. In B. cinerea, more DEGs were divided into terms in the biological process and cellular component domains than into molecular functions, and 150 DEGs were allocated to 26 KEGG pathways. In this study, we constructed networks of genes that interact with each other to screen hub genes based on a directed graphical model known as Bayesian networks. Through a detailed GO analysis, we excavated hub genes which were biologically meaningful. These results verify that availability of Skellam model and Bayesian networks in clustering gene expression data and sorting out hub genes. These models are instrumental in increasing our knowledge of gene functions and networks in plant–pathogen interaction.

Unravelling Plant Responses to Stress-The Importance of Targeted and Untargeted Metabolomics

Climate change and an increasing population, present a massive global challenge with respect to environmentally sustainable nutritious food production. Crop yield enhancements, through breeding, are decreasing, whilst agricultural intensification is constrained by emerging, re-emerging, and endemic pests and pathogens, accounting for ~30% of global crop losses, as well as mounting abiotic stress pressures, due to climate change. Metabolomics approaches have previously contributed to our knowledge within the fields of molecular plant pathology and plant-insect interactions. However, these remain incredibly challenging targets, due to the vast diversity in metabolite volatility and polarity, heterogeneous mixtures of pathogen and plant cells, as well as rapid rates of metabolite turn-over. Unravelling the systematic biochemical responses of plants to various individual and combined stresses, involves monitoring signaling compounds, secondary messengers, phytohormones, and defensive and protective chemicals. This demands both targeted and untargeted metabolomics approaches, as well as a range of enzymatic assays, protein assays, and proteomic and transcriptomic technologies. In this review, we focus upon the technical and biological challenges of measuring the metabolome associated with plant stress. We illustrate the challenges, with relevant examples from bacterial and fungal molecular pathologies, plant-insect interactions, and abiotic and combined stress in the environment. We also discuss future prospects from both the perspective of key innovative metabolomic technologies and their deployment in breeding for stress resistance.

Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

Strawberry FaWRKY25 Transcription Factor Negatively Regulated the Resistance of Strawberry Fruits to Botrytis cinerea

WRKY genes and jasmonic acid (JA) play a crucial role in plants’ responses against biotic and abiotic stress. However, the regulating mechanism of WRKY genes on strawberry fruits’ resistance against Botrytis cinerea is largely unknown, and few studies have been performed on their effect on the JA-mediated defense mechanism against B. cinerea. This study explored the effect of FaWRKY25 on the JA-mediated strawberry resistance against B. cinerea. Results showed that the JA content decreased significantly as the fruits matured, whereas the FaWRKY25 expression rose substantially, which led to heightened susceptibility to B. cinerea and in strawberries. External JA treatment significantly increased the JA content in strawberries and reduced the FaWRKY25 expression, thereby enhancing the fruits’ resistance against B. cinerea. FaWRKY25 overexpression significantly lowered the fruits’ resistance against B. cinerea, whereas FaWRKY25 silencing significantly increased resistance. Moreover, FaWRKY25 overexpression significantly lowered the JA content, whereas FaWRKY25 silencing significantly increased it. FaWRKY25 expression level substantially affects the expression levels of genes related to JA biosynthesis and metabolism, other members of the WRKY family, and defense genes. Accordingly, FaWRKY25 plays a crucial role in regulating strawberries’ resistance against B. cinerea and may negatively regulate their JA-mediated resistance mechanism against B. cinerea.

Advances of Metabolomics in Fungal Pathogen-Plant Interactions

Plant disease caused by fungus is one of the major threats to global food security, and understanding fungus-plant interactions is important for plant disease control. Research devoted to revealing the mechanisms of fungal pathogen-plant interactions has been conducted using genomics, transcriptomics, proteomics, and metabolomics. Metabolomics research based on mass spectrometric techniques is an important part of systems biology. In the past decade, the emerging field of metabolomics in plant pathogenic fungi has received wide attention. It not only provides a qualitative and quantitative approach for determining the pathogenesis of pathogenic fungi but also helps to elucidate the defense mechanisms of their host plants. This review focuses on the methods and progress of metabolomics research in fungal pathogen-plant interactions. In addition, the prospects and challenges of metabolomics research in plant pathogenic fungi and their hosts are addressed.