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Metabolomic, Lipidomic and Proteomic Characterisation of Lipopolysaccharide-induced Inflammation Mouse Model

2022; Elsevier BV; Volume: 496; Linguagem: Inglês

10.1016/j.neuroscience.2022.05.030

1873-7544

Elena Puris, Štěpán Kouřil, Lukáš Najdekr, Seppo Auriola, Sanna Loppi, Paula Korhonen, Mireia Gómez‐Budia, Gert Fricker, Katja M. Kanninen, Tarja Malm, David Friedecký, Mikko Gynther,

Alzheimer's disease research and treatments

Neuroinflammation is an important feature in the pathogenesis and progression of central nervous system (CNS) diseases including Alzheimer’s disease (AD). One of the widely used animal models of peripherally induced neuroinflammation and neurodegeneration is a lipopolysaccharide (LPS)-induced inflammation mouse model. An acute LPS administration has been widely used for investigation of inflammation-associated disease and testing inflammation-targeting drug candidates. In the present metabolomic, lipidomic and proteomic study, we investigated short-term effects of systemic inflammation induced by LPS administration on the mouse plasma and brain cortical and hippocampal metabolome, lipidome as well as expression of the brain cortical proteins which were shown to be involved in inflammation-associated CNS diseases. From a global perspective, the hippocampus was more vulnerable to the effects of LPS-induced systemic inflammation than the cortex. In addition, the study revealed several brain region-specific changes in metabolic pathways and lipids, such as statistically significant increase in several cortical and hippocampal phosphatidylcholines/phosphatidylethanolamines, and significantly decreased levels of brain cortical betaine after LPS treatment in mice. Moreover, LPS treatment in mice caused significantly increased protein expression of GluN1 receptor in the brain cortex. The revealed perturbations in the LPS-induced inflammation mouse model may give insight into the mechanisms underlying inflammation-associated CNS diseases. In addition, the finding of the study provide important information about the appropriate use of the model during target validation and drug candidate testing.