The effect of dietary on gut microbiome and microbiota-metabolome interactions of laboratory-reared caterpillars

The gut microbiota is a dynamic component of the physiology, development and lifestyle of insects, yet how these microbial communities interact with their hosts remains poorly understood. We examined how diet impacts the gut microbiota, and metabolome of tobacco cutworm (Spodoptera litura) fed three different diets under laboratory conditions (i.e., Chinese cabbage, tobacco and an artificial diet). Our results revealed significant dietary influences on gut microbial community structure and metabolite abundance. Notably, we identified microbial biomarker taxa responsible for variations in metabolite concentrations across populations, irrespective of dietary differences, implying the prevalence of distinct microbiota-metabolome interactions in the caterpillar gut. Microbial taxa contributing to metabolites involved in vitamin B metabolism, including nicotinate and pyridoxine, experienced a complete taxonomic turnover compared to those plant-fed hosts to those fed artificial diets, despite the overall contributions of all involved taxa to the same metabolite remaining similar between diet groups. Furthermore, most microbe-host interactions observed in individuals fed artificial diets were predominantly related to metabolites associated with amino acid metabolism, such as L-methionine, L-citrulline and urea. These findings highlight the previously underestimated influences of artificial diets, affecting not only gut microbial communities or host metabolomes independently, but also microbe-insect interactions. Our study sheds light on the need to study microbial functional roles and their metabolical interactions with host insects instead of focusing solely on descriptive taxonomic research. Additionally, our findings emphasise that a thorough assessment of the often-overlooked effect of laboratory-rearing technologies using artificial diets is of great importance, when designing future experiments or drawing conclusions about real-world scenarios based on in-house experiments.