Impacts of a high-glucose diet or starvation on microRNA-transcription factor networks in Caenorhabditis elegans through Boolean mathematical modeling.
Roberto Carlos Martinez-Padilla, Luis Mendoza, Juan Miranda-Rios
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引用次数: 0
Abstract
We analyzed the expression of the genes of Caenorhabditis elegans that are components of the Insulin/Insulin-like growth factor (IIS) and Target of Rapamycin (TOR) signaling pathways, and the microRNAs that regulate their expression. These genes are involved in longevity and their expression is differentially regulated under starvation conditions and high-glucose diets. We inferred the regulatory network from experimental data and analyzed its dynamics using Boolean networks. The regulatory network contains 26 nodes and 60 regulatory interactions. Upon examining their dynamic behavior, we discovered the presence of six fixed-point attractors. Two attractors align with the physiological response of a normal diet and another two align with gene expression associated with starvation conditions. In the starvation conditions, pro-longevity genes were found to be active, which explains the positive effect observed when the worms are grown in these conditions. One of the attractors is consistent with the gene expression found when worms are grown in high-glucose diets. In this case, anti-longevity genes were expressed, as found in the literature. Interestingly, we found another attractor in the high-glucose diet in which pro-longevity genes were observed, suggesting that under some circumstances a high-glucose diet can have a positive effect on lifespan. We also performed simulations of knock-out (KO) experiments of critical components of the network, such as LET-7, DAF-2, DAF-16, SKN-1, HLH-30, PHA-4, and DAF-15 (a component of the TORC1 complex) and our results were similar to the reported experimental evidence.
期刊介绍:
BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.