Cytokine changes associated with the maternal immune activation (MIA) model of autism: A penalized regression
Cristina Paraschivescu1,2, Susana Barbosa1,2, Thomas Lorivel1,2
1Centre National de la Recherche Scientifique, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne, France.
Maternal immune activation (MIA) during pregnancy induces a cytokine storm that alters neurodevelopment and behavior in the progeny. In humans, MIA increases the odds of developing neuropsychiatric disorders such as autism spectrum disorder (ASD). In mice, MIA can be induced by injecting the viral mimic polyinosinic:polycytidylic acid (poly(I:C)) to pregnant dams. Although the murine model of MIA has been extensively studied, it is not clear whether MIA results in cytokine changes in the progeny at early postnatal stages. Further, the murine model of MIA suffers from a lack of reproducibility and high inter-individual variability. Multivariable (MV) statistical analysis is widely used in human studies to control for confounders and covariates such as sex, age and exposure to environmental factors. We therefore reasoned that animal studies in general and studies on the MIA model in particular could benefit from MV analyses to account for complex phenotype interactions and high inter-individual variability. Here, we used MV statistical analysis to identify cytokines associated with MIA after adjustment for covariates. Besides confirming the association between previously described variables and MIA, we identified new cytokines that could play a role in behavioural alterations in the progeny during the early postnatal period.
Maternal immune activation (MIA) during pregnancy induces a cytokine storm that alters neurodevelopment and behavior in the progeny. In humans, MIA increases the odds of developing neuropsychiatric disorders such as autism spectrum disorder (ASD). In mice, MIA can be induced by injecting the viral mimic polyinosinic:polycytidylic acid (poly(I:C)) to pregnant dams. Although the murine model of MIA has been extensively studied, it is not clear whether MIA results in cytokine changes in the progeny at early postnatal stages. Further, the murine model of MIA suffers from a lack of reproducibility and high inter-individual variability. Multivariable (MV) statistical analysis is widely used in human studies to control for confounders and covariates such as sex, age and exposure to environmental factors. We therefore reasoned that animal studies in general and studies on the MIA model in particular could benefit from MV analyses to account for complex phenotype interactions and high inter-individual variability. Here, we used MV statistical analysis to identify cytokines associated with MIA after adjustment for covariates. Besides confirming the association between previously described variables and MIA, we identified new cytokines that could play a role in behavioural alterations in the progeny during the early postnatal period.
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