Effect of iron on cholesterol 7α-hydroxylase expression in alcohol-induced hepatic steatosis in mice.

Both iron and lipids are involved in the progression of alcoholic fatty liver disease (AFLD), but the interaction between iron and lipids in AFLD is unclear. Here, we tested the hypothesis that iron regulates the expression of genes involved in lipid metabolism through iron regulatory proteins (IRPs), which interact with the iron-responsive elements (IREs) in the untranslated regions (UTRs) of genes, resulting in lipid accumulation. Using "RNA structure software", we predicted the mRNA secondary structures of more than 100 genes involved in lipid metabolism to investigate whether the IRE structure exists in novel mRNAs. Cholesterol 7α-hydroxylase (Cyp7a1) has an IRE-like stem-loop, a noncanonical IRE structure, in its 3'-UTR. Cyp7a1 expression can be regulated by in vivo and in vitro iron treatment. In addition, the noncanonical IRE motif can efficiently bind both to IRP1 and IRP2. The results indicate that hepatic iron overloading in AFLD mice decreased Cyp7a1 expression and resulted in cholesterol accumulation, providing a new mechanism of iron-regulated gene transcription and translation through the interaction between iron and a noncanonical IRE structure in Cyp7a1 mRNA. This finding has significant implications in studying a proposed mechanism for the regulation of cholesterol homeostasis by an Fe/IRP/noncanonical IRE axis.

Structural Controllability of Temporal Networks with a Single Switching Controller.

Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance.