SEGN: Inferring real-time gene networks mediating phenotypic plasticity

• Main Authors: Libo Jiang, Christopher H. Griffin, Rongling Wu
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: Computational and Structural Biotechnology Journal
• Subjects: Gene regulatory networks; Dynamic transcriptional plasticity; Phenotypic plasticity; Euphrates poplar; Salt stress
• Online Access: http://www.sciencedirect.com/science/article/pii/S2001037020303822

The capacity of an organism to alter its phenotype in response to environmental perturbations changes over developmental time and is a process determined by multiple genes that are co-expressed in intricate but organized networks. Characterizing the spatiotemporal change of such gene networks can offer insight into the genomic signatures underlying organismic adaptation, but it represents a major methodological challenge. Here, we integrate the holistic view of systems biology and the interactive notion of evolutionary game theory to reconstruct so-called systems evolutionary game networks (SEGN) that can autonomously detect, track, and visualize environment-induced gene networks along the time axis. The SEGN overcomes the limitations of traditional approaches by inferring context-specific networks, encapsulating bidirectional, signed, and weighted gene-gene interactions into fully informative networks, and monitoring the process of how networks topologically alter across environmental and developmental cues. Based on the design principle of SEGN, we perform a transcriptional plasticity study by culturing Euphrates poplar, a tree that can grow in the saline desert, in saline-free and saline-stress conditions. SEGN characterize previously unknown gene co-regulation that modulates the time trajectories of the trees’ response to salt stress. As a marriage of multiple disciplines, SEGN shows its potential to interpret gene interdependence, predict how transcriptional co-regulation responds to various regimes, and provides a hint for exploring the mass, energetic, or signal basis that drives various types of gene interactions.