<p>The gut microbiota is a dynamic component of insects’ physiology and development, yet how these microbes interact with their hosts remains poorly understood. We examined how diet impacts the gut microbiota and metabolome of <i>Spodoptera litura</i> fed three different diets under laboratory conditions (i.e., Chinese cabbage, tobacco and an artificial diet). Our results revealed significant dietary influences on microbial community structure and metabolite abundance. We also identified the prevalence of microbe–metabolome interactions across populations with distinct metabolite variations. Notably, dietary-driven patterns in these interactions were observed, including a complete turnover of microbial taxa contributing to metabolites related to vitamin B metabolism (such as nicotinate) when comparing natural plant-fed hosts to those fed artificial diets, despite the identical overall contributions of all taxa to this metabolism between dietary groups. Furthermore, most microbe–host interactions observed in individuals fed artificial diets were predominantly related to metabolites associated with amino acid metabolisms, such as L-methionine, L-citrulline and urea, likely due to the high quantity and variety of proteins contained in their diets. These results underscore the previously overlooked impacts of artificial diets, affecting not only gut microbial communities or host metabolomes independently, but also microbe–insect interactions. Our study highlights the importance of investigating microbial functions and their metabolic interactions with hosts 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 important when designing future experiments or drawing conclusions about real-world scenarios based on in-house experiments.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Meta-omics reveals dietary-dependent microbiota–metabolome interactions and often-overlooked impacts of artificial diets on laboratory-reared caterpillars

  • Jianfeng Liu,
  • Jieyun Wu,
  • Gavin Lear,
  • Liang Wang,
  • Yuanchan Yu,
  • Jian-Yu Gou,
  • Chun-Yang Huang,
  • Maofa Yang

摘要

The gut microbiota is a dynamic component of insects’ physiology and development, yet how these microbes interact with their hosts remains poorly understood. We examined how diet impacts the gut microbiota and metabolome of 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 microbial community structure and metabolite abundance. We also identified the prevalence of microbe–metabolome interactions across populations with distinct metabolite variations. Notably, dietary-driven patterns in these interactions were observed, including a complete turnover of microbial taxa contributing to metabolites related to vitamin B metabolism (such as nicotinate) when comparing natural plant-fed hosts to those fed artificial diets, despite the identical overall contributions of all taxa to this metabolism between dietary groups. Furthermore, most microbe–host interactions observed in individuals fed artificial diets were predominantly related to metabolites associated with amino acid metabolisms, such as L-methionine, L-citrulline and urea, likely due to the high quantity and variety of proteins contained in their diets. These results underscore the previously overlooked impacts of artificial diets, affecting not only gut microbial communities or host metabolomes independently, but also microbe–insect interactions. Our study highlights the importance of investigating microbial functions and their metabolic interactions with hosts 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 important when designing future experiments or drawing conclusions about real-world scenarios based on in-house experiments.