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Soil bacterium manipulates antifungal weapons by sensing intracellular type IVA secretion system effectors of a competitor

Soil beneficial bacteria can effectively inhibit bacterial pathogens by assembling contact-dependent killing weapons, such as the type IVA secretion system (T4ASS). It’s not clear whether these antibacterial weapons are involved in biotrophic microbial interactions in soil. Here we showed that an antifungal antibiotic 2,4-DAPG production of the soil bacterium, Pseudomonas protegens can be triggered by another soil bacterium, Lysobacter enzymogenes, via T4ASS by co-culturing on agar plates to mimic cell-to-cell contact. We demonstrated that the induced 2,4-DAPG production of P. protegens is achieved by intracellular detection of the T4ASS effector protein Le1519 translocated from L. enzymogenes. We defined Le1519 as LtaE (Lysobacter T4E triggering antifungal effects), which specifically stimulates the expression of 2,4-DAPG biosynthesis genes in P. protegens, thereby protecting soybean seedlings from infection by the fungus Rhizoctonia solani. We further found that LtaE directly bound to PhlF, a pathway-specific transcriptional repressor of the 2,4-DAPG biosynthesis, then activated the 2,4-DAPG production. Our results highlight a novel pattern of microbial interspecies and interkingdom interactions, providing a unique case for expanding the diversity of soil microbial interactions.

Ocular microbiota promotes pathological angiogenesis and inflammation in sterile injury-driven corneal neovascularization

Microbiota promotes or inhibits the pathogenesis of a range of immune-mediated disorders. Although recent studies have elucidated the role of gut microbiota in ocular disease, the effect of ocular microbiota remains unclear. Herein, we explored the role of ocular commensal bacteria in non-infectious corneal inflammation and angiogenesis in a mouse model of suture-induced corneal neovascularization. Results revealed that the ocular surface harbored a microbial community consisting mainly of Actinobacteria, Firmicutes and Proteobacteria. Elimination of the ocular commensal bacteria by oral broad-spectrum antibiotics or topical fluoroquinolone significantly suppressed corneal inflammation and neovascularization. Disease amelioration was associated with reduced numbers of CD11b+Ly6C+ and CD11b+Ly6G+ myeloid cells, not Foxp3+ regulatory T cells, in the spleen, blood, and draining lymph nodes. Therapeutic concentrations of fluoroquinolone, however, did not directly affect immune cells or vascular endothelial cells. In addition, data from a clinical study showed that antibiotic treatment in combination with corticosteroids, as compared with corticosteroid monotherapy, induced faster remission of corneal inflammation and new vessels in pediatric patients with non-infectious marginal keratitis. Altogether, our findings demonstrate a pathogenic role of ocular microbiota in non-infectious inflammatory disorders leading to sight-threatening corneal neovascularization, and suggest a therapeutic potential of targeting commensal microbes in treating ocular inflammation.

Preserving and combining knowledge in robotic lifelong reinforcement learning

Humans can continually accumulate knowledge and develop increasingly complex behaviours and skills throughout their lives, which is a capability known as ‘lifelong learning’. Although this lifelong learning capability is considered an essential mechanism that makes up general intelligence, recent advancements in artificial intelligence predominantly excel in narrow, specialized domains and generally lack this lifelong learning capability. Here we introduce a robotic lifelong reinforcement learning framework that addresses this gap by developing a knowledge space inspired by the Bayesian non-parametric domain. In addition, we enhance the agent’s semantic understanding of tasks by integrating language embeddings into the framework. Our proposed embodied agent can consistently accumulate knowledge from a continuous stream of one-time feeding tasks. Furthermore, our agent can tackle challenging real-world long-horizon tasks by combining and reapplying its acquired knowledge from the original tasks stream. The proposed framework advances our understanding of the robotic lifelong learning process and may inspire the development of more broadly applicable intelligence.

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