Date of this Version
Bae, H.R.; Choi, M.-S.; Kim, S.; Young, H.A.; Gershwin, M.E.; Jeon, S.-M.; Kwon, E.-Y. IFN Is a Key Link between Obesity and Th1-Mediated AutoImmune Diseases. Int. J. Mol. Sci. 2021, 22, 208. https://dx.doi.org/ 10.3390/ijms22010208
Obesity, a characteristic of metabolic syndrome, is also associated with chronic inflammation and the development of autoimmune diseases. However, the relationship between obesity and autoimmune diseases remains to be investigated in depth. Here, we compared hepatic gene expression profiles among high-fat diet (HFD) mice using the primary biliary cholangitis (PBC) mouse model based on the chronic expression of interferon gamma (IFNγ) (ARE-Del-/- mice). The top differentially expressed genes affected by upstream transcriptional regulators IFNγ, LPS, and TNFα displayed an overlap in HFD and ARE-Del-/- mice, indicating that obesity-induced liver inflammation may be dependent on signaling via IFNγ. The top pathways altered in HFD mice were mostly involved in the innate immune responses, which overlapped with ARE-Del-/- mice. In contrast, T cell-mediated signaling pathways were exclusively altered in ARE-Del-/- mice. We further evaluated the therapeutic effect of luteolin, known as anti-inflammatory flavonoid, in HFD and ARE-Del-/- mice. Luteolin strongly suppressed the MHC I and II antigen presentation pathways, which were highly activated in both HFD and ARE-Del-/- mice. Conversely, luteolin increased metabolic processes of fatty acid oxidation and oxidative phosphorylation in the liver, which were suppressed in ARE-Del-/- mice. Luteolin also strongly induced PPAR signaling, which was downregulated in HFD and ARE-Del-/- mice. Using human GWAS data, we characterized the genetic interaction between significant obesity-related genes and IFNγ signaling and demonstrated that IFNγ is crucial for obesity-mediated inflammatory responses. Collectively, this study improves our mechanistic understanding of the relationship between obesity and autoimmune diseases. Furthermore, it provides new methodological insights into how immune network-based analyses effectively integrate RNA-seq and microarray data.