Poly(I:C) source, molecular weight and endotoxin contamination affect dam and prenatal outcomes, implications for models of maternal immune activation

表型 免疫系统 垃圾箱 污染 胎儿 化学 怀孕 生物 医学 免疫学 生物化学 遗传学 基因 生态学
作者
Hager M. Kowash,Harry G. Potter,Michelle E. Edye,Eric Prinssen,S. Bandinelli,Joanna C. Neill,Reinmar Hager,Jocelyn D. Glazier
出处
期刊:Brain Behavior and Immunity [Elsevier]
卷期号:82: 160-166 被引量:44
标识
DOI:10.1016/j.bbi.2019.08.006
摘要

The viral mimetic polyinosinic:polycytidylic acid (poly(I:C)) is increasingly used to induce maternal immune activation (mIA) to model neurodevelopmental disorders (NDDs). Robust and reproducible phenotypes across studies are essential for the generation of models that will enhance our understanding of NDDs and enable the development of improved therapeutic strategies. However, differences in mIA-induced phenotypes using poly(I:C) have been widely observed, and this has prompted the reporting of useful and much needed methodological guidelines. Here, we perform a detailed investigation of molecular weight and endotoxin variations in poly(I:C) procured from two of the most commonly used suppliers, Sigma and InvivoGen. We demonstrate that endotoxin contamination and molecular weight differences in poly(I:C) composition lead to considerable variability in maternal IL-6 response in rats treated on gestational day (GD)15 and impact on fetal outcomes. Specifically, both endotoxin contamination and molecular weight predicted reductions in litter size on GD21. Further, molecular weight predicted a reduction in placental weight at GD21. While fetal body weight at GD21 was not affected by poly(I:C) treatment, male fetal brain weight was significantly reduced by poly(I:C), dependent on supplier. Our data are in agreement with recent reports of the importance of poly(I:C) molecular weight, and extend this work to demonstrate a key role of endotoxin on relevant phenotypic outcomes. We recommend that the source and batch numbers of poly(I:C) used should always be stated and that molecular weight variability and endotoxin contamination should be minimised for more robust mIA modelling.
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