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Booth, J. R.

Publications and source records attributed to Booth, J. R..

2 recordsLinked to original sources

Microbiota reduce Drosophila triacylglyceride density by providing pantothenate

Metabolism follows ground-rules that evolved in ancient bacteria: as a legacy, animal gut microbiota have abundant opportunity to modulate host metabolism, via conserved mechanisms. When these effects are beneficial, natural selection should favour hosts that reciprocally support bacterial growth. Whether microbes simply nourish hosts or more profoundly reprogram host metabolism remains to be established. Here we show that Acetobacter promote Drosophila coenzyme A, an ancient signalling metabolite, reprogramming host metabolism and generating a state that reciprocally supports bacterial growth. Impairing pathways from bacterial pantothenate to host coenzyme A increases host lipid storage, reduces nitrogen excretion, and diminishes bacterial load. These processes correspond to reprogrammed host carbohydrate handling and tissue-specific acyl-CoA pools, but live bacteria and short-chain fatty acids are dispensable. These results outline a simple microbial basis for complex host metabolic effects, via mechanisms that precede the origins of metazoa, facilitating metabolic and symbiotic homeostasis.

physiology↗

Neural trajectories reveal orchestration of cortical coding underlying natural language composition

Language is a hierarchically structured system that enables humans to communicate complex meanings. Despite recent advances, the neurocomputational mechanism underlying the composition of natural language remains unclear. Building on the neural population theory, we investigated how neural trajectories in latent spaces underpin natural language composition that integrates diverse lexical content and syntactic relations. We found that neural trajectories derived from human neocortical responses show an orchestration of distinct coding strategies during naturalistic story comprehension. Neural latent geometry is primarily associated with syntactic relations and exhibits more efficient compression relative to lexical content. We further demonstrate that these trajectories can be simulated by brain-inspired computing systems with near-critical dynamics and a preference for historical information. Overall, by positioning structure-based integration as a key computation of natural language comprehension, our findings provide a novel perspective on the mechanism underlying real-world language use and emphasize the importance of contextual information in the development of brain-inspired intelligent systems.

neuroscience↗