School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK.
School of Biomedical Sciences, University of Plymouth, Derriford Research Facility, Plymouth Science Park, Plymouth, PL6 8BT, UK.
Pathog Dis. 2021 Feb 19;79(2). doi: 10.1093/femspd/ftab003.
Galleria mellonella is a recognised model to study antimicrobial efficacy; however, standardisation across the scientific field and investigations of methodological components are needed. Here, we investigate the impact of weight on mortality following infection with Methicillin-resistant Staphylococcus aureus (MRSA). Larvae were separated into six weight groups (180-300 mg at 20 mg intervals) and infected with a range of doses of MRSA to determine the 50% lethal dose (LD50), and the 'lipid weight' of larvae post-infection was quantified. A model of LD50 values correlated with weight was developed. The LD50 values, as estimated by our model, were further tested in vivo to prove our model. We establish a weight-dependent LD50 in larvae against MRSA and demonstrate that G. mellonella is a stable model within 180-260 mg. We present multiple linear models correlating weight with: LD50, lipid weight, and larval length. We demonstrate that the lipid weight is reduced as a result of MRSA infection, identifying a potentially new measure in which to understand the immune response. Finally, we demonstrate that larval length can be a reasonable proxy for weight. Refining the methodologies in which to handle and design experiments involving G. mellonella, we can improve the reliability of this powerful model.
大蜡螟被公认为是一种研究抗菌功效的模式生物；然而，仍需要在整个科学界进行标准化，并对方法学成分进行研究。在这里，我们研究了感染耐甲氧西林金黄色葡萄球菌（MRSA）后体重对死亡率的影响。幼虫被分为六个体重组（180-300mg，间隔 20mg），并用一系列剂量的 MRSA 感染，以确定 50%致死剂量（LD50），并量化感染后幼虫的“脂质重量”。建立了一个与体重相关的 LD50 值模型。通过我们的模型估计的 LD50 值在体内进一步进行了测试，以验证我们的模型。我们在幼虫中建立了一个针对 MRSA 的体重依赖性 LD50，并证明 G. mellonella 在 180-260mg 之间是一个稳定的模型。我们提出了多个线性模型，将体重与 LD50、脂质重量和幼虫长度相关联。我们证明，由于感染了 MRSA，脂质重量减少，这为理解免疫反应提供了一个潜在的新指标。最后，我们证明幼虫长度可以作为体重的合理替代指标。通过改进处理和设计涉及 G. mellonella 的实验的方法，可以提高该强大模型的可靠性。
