Journal of Basic Microbiology最新文献

The phytopathogen Ralstonia solanacearum is responsible for the “bacterial wilt disease” in several crop species worldwide, viz., tomato, brinjal, chilli, potato, groundnut, etc. This results in the yellowing of host plants, wilting, and finally their death. Motility is an essential requirement of plant pathogens, which helps them to infect and navigate through the host tissues and spread inside the host plant. The phytopathogen R. solanacearum exhibits motility such as twitching and swimming, which are governed by pili and flagella, respectively. This study aims to delineate the role of pili and flagella encoding genes, such as pilY_1, fliP, and fliG, by recognising the genes and their associated pathways. Disruption of these genes led to the creation of three motility mutants of the R. solanacearum F1C1 strain. Motility assays showed that the ΔfliP and ΔfliG mutants were significantly altered in terms of swimming ability. Further, transcriptomic analysis identified the differentially expressed genes in the respective altered mutants.

Soil microbial diversity plays a pivotal role in sustainable agriculture by regulating nutrient cycling, organic matter turnover, and natural suppression of phytopathogens, thereby supporting crop productivity and ecosystem resilience. However, intensive agricultural practices and environmental stressors have led to a decline in soil biodiversity, compromising soil functionality and food security. Recent advances in omics technologies—including metagenomics, transcriptomics, proteomics, and metabolomics offer powerful tools to unravel the complexity, of soil microbial communities and their interactions with plants and pathogens. These integrated approaches provide high-resolution insights into microbial structure, functional dynamics, metabolic pathways, and the mechanisms underpinning plant–microbe–pathogen interactions. Furthermore, omics-driven understanding supports the development of sustainable strategies such as organic farming, conservation practices, and microbial bioinoculants, which restore microbial diversity, enhance nutrient use efficiency, reduce chemical inputs, and mitigate disease pressure. By linking soil health to crop nutritional quality and broader food system sustainability, this review highlights the potential of omics-guided approaches to optimize soil microbial ecosystems for resilient agriculture and global food security.

This study focused on Ganoderma mycelium as the experimental subject and conducted a systematic investigation into the effects of varying the addition ratio of biomass charcoal in the cultivation substrate. The objective was to thoroughly evaluate how differences in biochar proportions influence the growth and developmental characteristics of Ganoderma mycelium. Throughout the experimental process, several critical growth and physiological parameters were monitored and analyzed, including the mycelial growth rate, water retention capacity of the substrate, extracellular enzyme activity (TP, POD, SOD, CAT), as well as the levels of soluble sugar and protein within the mycelium. After a series of experiments, the results showed that biochar addition significantly enhanced mycelial growth rate (p < 0.05) and extracellular enzyme activities while decreasing protein levels. Furthermore, the group with 20 g of biochar addition significantly enhanced the extracellular enzyme activity (p < 0.05), thereby improving the ability of the mycelium to decompose and utilize the nutrients in the substrate. In conclusion, appropriately adjusting the addition of biochar has a significant impact on regulating the growth and physiological metabolism of the Ganoderma lucidum mycelium. These research results provide a scientific basis for optimizing cultivation techniques to increase the yield and quality of Ganoderma lingzhi.

Petrochemicals have emerged as an environmental hazard requiring innovative, sustainable remediation strategies. Bioremediation, specifically by genetically modified microorganisms (GMMs), is well known to produce surfactants that have emerged as a promising source to catalyze the efficiency of hydrocarbon degradation. The surfactant produced by GMMs facilitates the emulsification and dispersion of petroleum hydrocarbons. This review discusses the current state of biodegradation research, specifically on GMMs engineered to synthesize surfactant, which play an important role in enhancing the solubility of hydrophobic contaminants, such as oil components, thereby enhancing microbial access for degradation. The GMMs explored to optimize surfactant production and tailor their bioremediation efficiency. Furthermore, highlights the environmental benefits of utilizing GMMs for surfactant production, emphasizing the potential for a sustainable and cost-effective remediation strategy. Challenges and ethical considerations associated with the release of genetically modified organisms into the environment are also discussed, underscoring the importance of thorough risk assessments and regulatory frameworks. In conclusion, the integration of GMMs capable of producing surfactants presents a promising avenue for addressing petrochemicals through efficient and environmentally conscious bioremediation strategies. Future research should focus on optimizing the performance and safety of these engineered organisms to ensure their successful application in real-world environmental scenarios.

Cordycepin, a nucleoside analog derived from Cordyceps militaris, is a bioactive compound with potent pharmacological properties and growing relevance in functional food and pharmaceutical industries. However, its production is highly variable depending on cultivation conditions, making real-time and scalable prediction essential for efficient process control. This study aimed to develop a machine learning-based predictive model to estimate cordycepin content based on measurable cultivation parameters. Three machine learning algorithms—XGBoost, Random Forest, and Support Vector Machine—were trained using experimental data encompassing environmental and nutritional factors. Model validation was conducted using Tropsha's statistical criteria, and model explainability was achieved through SHAP analysis. A user-friendly GUI was also developed for real-time prediction and application. Among the models, XGBoost demonstrated the highest performance with a cross-validated Q² of 0.9087 and an R² of 0.9544, satisfying all statistical requirements for reliability. SHAP analysis identified light wavelength and carbon/nitrogen ratio as the most influential factors in cordycepin biosynthesis. The developed GUI enables end-users to input cultivation conditions and receive immediate predictions, facilitating data-driven decision-making. This approach offers a scalable and interpretable framework for optimizing bioactive compound production in edible fungi, with potential application in smart bioprocessing and precision fermentation.