Poly(I:C) source, molecular weight and endotoxin contamination affect dam and prenatal outcomes, implications for

H M Kowash1, H G Potter2, M E Edye3

  • 1Division of Developmental Biology and Medicine, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9WL, UK.

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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