The sprT Gene of Bacillus velezensis FZB42 Is Involved in Biofilm Formation and Bacilysin Production.

Bacillus velezensis FZB42, a representative strain of plant-growth-promoting rhizobacteria (PGPR), can form robust biofilm and produce multiple antibiotics against a wild range of phytopathogens. In this study, we observed different biofilm morphology of the mutant Y4, derived from a TnYLB-1 transposon insertion library of B. velezensis FZB42. We identified that the transposon was inserted into the sprT gene in Y4. Our bioinformatics analysis revealed that the SprT protein is an unstable hydrophilic protein located in the cytoplasm. It is highly conserved in Bacillus species and predicted to function as a metalloprotease by binding zinc ions. We also demonstrated that ΔsprT significantly reduced the swarming ability of FZB42 by ~5-fold and sporulation capacity by ~25-fold. In addition, the antagonistic experiments showed that, compared to the wild type, the ΔsprT strain exhibited significantly reduced inhibition against Staphylococcus aureus ATCC-9144 and Phytophthora sojae, indicating that the inactivation of sprT led to decreased production of the antibiotic bacilysin. The HPLC-MS analysis confirmed that bacilysin was indeed decreased in the ΔsprT strain, and qPCR analysis revealed that ΔsprT down-regulated the expression of the genes for bacilysin biosynthesis. Our results suggest that the sprT gene plays a regulatory role in multiple characteristics of B. velezensis FZB42, including biofilm formation, swarming, sporulation, and antibiotic production.

Use of remote sensing-based pressure-state-response framework for the spatial ecosystem health assessment in Langfang, China.

Land use/land cover changes are occurring at an unprecedented rate and scale because of the economic development that has jeopardized the ecosystem's health. Ecosystem health should be studied and monitored at spatiotemporal scale to promote sustainable development and ecological civilization. The goal of this study was to assess the spatial ecosystem health of Langfang at the city and administrative levels using city's regional characteristics. Remote sensing-based pressure-state-response (PSR) framework, analytical hierarchy process (AHP), and principal component analysis (PCA) were utilized for spatial ecosystem health index (SEHI) formulation, indicator weighting, and indicator selection in several epochs (1990, 2003, 2013, and 2021), respectively. SEHI was formulated by combining subindices of pressure, state and response. The spatial ecosystem pressure index (SEIP) identified that the pressure was increasing on the ecosystem. In contrast, the spatial ecosystem state index (SEIS) pointed out an improvement in the state of the ecosystem since 1990. The worst state of the ecosystem was observed for the year 2013. The spatial ecosystem response index (SEIR) indicated that the response of the ecosystem towards the exerted pressures and states remained variable; however, it was reasonably good in 1990. All the administrative units of Langfang were associated with a healthy score for the spatial ecosystem health index (SEHI) for 1990 (pre-industrialization epoch), while the SEHI significantly reduced in 2013 (industrialization epoch) however improved for the later epochs (circular economy and ecological civilization epoch). The SEHI was moderately healthy for Dachang, Dacheng, Guan, Guangyang, and Yongqing while relatively healthy for the remaining administrative units in 2021. SEHI identified that spatial health has been improving since 2003 though not reaching the 1990's level for Langfang. Therefore, efforts should be focused on minimizing pressure and stabilizing the state to improve the spatial ecosystem health of Langfang. The developed SEHI can assist policymakers in analyzing regional health, identifying development strategies, driving environmental restoration, and quantifying needed changes.

Cotton Fusarium wilt diagnosis based on generative adversarial networks in small samples.

This study aimed to explore the feasibility of applying Generative Adversarial Networks (GANs) for the diagnosis of Verticillium wilt disease in cotton and compared it with traditional data augmentation methods and transfer learning. By designing a model based on small-sample learning, we proposed an innovative cotton Verticillium wilt disease diagnosis system. The system uses Convolutional Neural Networks (CNNs) as feature extractors and applies trained GAN models for sample augmentation to improve classification accuracy. This study collected and processed a dataset of cotton Verticillium wilt disease images, including samples from normal and infected plants. Data augmentation techniques were used to expand the dataset and train the CNNs. Transfer learning using InceptionV3 was applied to train the CNNs on the dataset. The dataset was augmented using GAN algorithms and used to train CNNs. The performances of the data augmentation, transfer learning, and GANs were compared and analyzed. The results have demonstrated that augmenting the cotton Verticillium wilt disease image dataset using GAN algorithms enhanced the diagnostic accuracy and recall rate of the CNNs. Compared to traditional data augmentation methods, GANs exhibit better performance and generated more representative and diverse samples. Unlike transfer learning, GANs ensured an adequate sample size. By visualizing the images generated, GANs were found to generate realistic cotton images of Verticillium wilt disease, highlighting their potential applications in agricultural disease diagnosis. This study has demonstrated the potential of GANs in the diagnosis of cotton Verticillium wilt disease diagnosis, offering an effective approach for agricultural disease detection and providing insights into disease detection in other crops.

Relationship of the Pine Growth Promoting Pantoea eucalypti FBS135 with Type Strains P. eucalypti LMG 24197T and P. vagans 24199T.

Endophytes in woody plants are much less understood. Pantoea strain FBS135 is an endophytic bacterium isolated from Pinus massoniana with the ability to promote pine growth significantly. In this study, we demonstrated that FBS135 has the astonishing ability of low nitrogen tolerance but no ability of nitrogen fixation. To exactly determine the phylogenetic status of FBS135, we sequenced the whole genomes of P. eucalypti LMG 24197T and P. vagans 24199T, type strains of two Pantoea species, which are evolutionarily closest to FBS135. P. eucalypti LMG 24197T contained a single chromosome of 4,035,995 bp (C+G, 54.6%) plus three circular plasmids while LMG 24199T comprises a single circular chromosome of 4,050,173 bp (C+G, 55.6%) and two circular plasmids. With the genomic information, FBS135 was finally identified as a P. eucalypti strain, although it showed some different physiological traits from the two type strains. Comparative genomic analyses were performed for the three strains, revealing their common molecular basis associated with plant lifecycle as well as the differences in their gene arrangements relating to nitrogen utilization.

Cytoplasmic Lipases-A Novel Class of Fungal Defense Proteins Against Nematodes.

Fungi are an attractive food source for predators such as fungivorous nematodes. Several fungal defense proteins and their protective mechanisms against nematodes have been described. Many of these proteins are lectins which are stored in the cytoplasm of the fungal cells and bind to specific glycan epitopes in the digestive tract of the nematode upon ingestion. Here, we studied two novel nematotoxic proteins with lipase domains from the model mushroom Coprinopsis cinerea. These cytoplasmically localized proteins were found to be induced in the vegetative mycelium of C. cinerea upon challenge with fungivorous nematode Aphelenchus avenae. The proteins showed nematotoxicity when heterologously expressed in E. coli and fed to several bacterivorous nematodes. Site-specific mutagenesis of predicted catalytic residues eliminated the in-vitro lipase activity of the proteins and significantly reduced their nematotoxicity, indicating the importance of the lipase activity for the nematotoxicity of these proteins. Our results suggest that cytoplasmic lipases constitute a novel class of fungal defense proteins against predatory nematodes. These findings improve our understanding of fungal defense mechanisms against predators and may find applications in the control of parasitic nematodes in agriculture and medicine.