Project description:For shade-intolerant species, shade light indicates the close proximity of neighboring plants and triggers the shade avoidance syndrome (SAS), which causes exaggerated growth and reduced crop yield. We report that microbiotal root commensals(Pseudomonas fluorescens and Root918) alleviate the shade avoidance responses in Arabidopsis. To identify the functions of Pseudomonas fluorescens and Root918 during SAS, we performed RNA-seq to search for differentially expressed genes (DEGs) in different tissues by comparing the transcript levels of shoot and root parts of col0 in germ-free, Pseudomonas fluorescens and Root918 during white light and shade conditions.
Project description:We acquired the largest bacterial proteomic resource, covering 303 species, 119 genera, and five phyla. The proteome coverage is, on average, over 50%. Additionally, we acquired further datasets for bacterial identification algorithm validation: i) 303 species at a 30-minute gradient (38 samples per day throughput), ii) 303 species at a 10-minute gradient (80 samples per day throughput), iii) reproducibility dataset, iv) genus-specific Pseudomonas spp. dataset (94 Pseudomonas spp. strains), v) genus-specific Bacillus spp. dataset (28 Bacillus cereus s.l. strains), vi) food routine dataset (60 dairy product isolates), and vii) clinical routine dataset (570 clinical isolates).
Project description:The microbiota plays a crucial role in protecting plants from pests and pathogens. The protection provided by the microbiota constitutes not just the plant’s first line of defense, but possibly its most potent one, as experimental disruptions to the microbiota cause plants to succumb to otherwise asymptomatic infections. To understand how microbial plant defense is deployed, we applied a complex and tractable plant-soil-microbiome microcosm. This system, consisting of Arabidopsis plants and a 150-member bacterial synthetic community, provides a platform for the discovery of novel bacterial plant-beneficial traits, under a realistically complex microbial community context. To identify which components of the plant microbiota are critical for plant defense, we deconstructed this microcosm top-down, removing different microbial groups from the community to examine their protective effect on the plant when challenged with the leaf pathogen Pseudomonas syringae. This process of community deconstruction revealed a critical role for the genus Bacillus in protecting the plant from infection. Using plant RNA-seq and bacterial co-culturing experiments, we demonstrated that Bacillus-provided plant protection is independent of plant immune system activation. We also show that the level of plant protection is strongly dependent on the diversity of the protective inoculum. We show that deconstructing the microbiome top-down is a powerful tool for identifying and prioritizing microbial taxa with specific functions within it.
Project description:Pseudouridine (Ψ) modifications play crucial roles in RNA regulation, yet their distribution and functional significance in bacteria remain largely unexplored compared to eukaryotic systems. Here, we present the first comprehensive transcriptome-wide mapping of Ψ modifications across five diverse bacterial species (Klebsiella pneumoniae, Escherichia coli, Bacillus cereus, Pseudomonas aeruginosa, Pseudomonas syringae) using bisulfte-induced deletion sequencing at single-base resolution. Ψ modifications exhibit enriched distribution in coding sequences and notable conservation across orthologous genes involved in central metabolism. Comparative analysis revealed evolutionarily conserved modification patterns in operons, such as the atp operon, and growth phase-dependent dynamics, particularly elevated modification levels in transfer RNA T-arms and transfer-messenger RNAs under stress conditions. We uncovered that Ψ modifications modulate mRNA translation in Pseudomonas syringae under nutrition-limited condition and enhance interactions with the RNA chaperone Hfq in Pseudomonas aeruginosa under stationary growth phase. We found local RNA architecture significantly influences modification levels, with highly modified sites sharing distinct structural features reminiscent of different type of RNA. To facilitate broader studies, we developed an integrated deep learning framework combining convolutional neural networks with transformer architecture to effectively capture both sequence patterns and RNA secondary structural features, enabling accurate prediction of Ψ modification sites across bacterial transcriptomes. Overall, our study provides fundamental insights into bacterial RNA Ψ modification landscapes and establishes a foundation for future mechanistic studies.