Temporal correlations robustly reveal regulatory coherence upon environmental perturbation

Transcriptional Regulatory Networks (TRNs) orchestrate the timing, magnitude, and rate of organismal response to many environmental perturbations. Regulatory interactions in TRNs are context-dependent, affording the opportunity to detect responsive links upon environmental perturbation. To measure the transition between regulatory status directly, one powerful approach is chromatin immuno-precipitation binding, ChIP-Seq. However, genome-wide ChIP-Seq is costly and currently inaccessible for many transcription factors (TFs) across multiple conditions in most organisms. Seeking to exploit the abundance of RNASequencing data now available, many past studies have relied upon population-level statistics from cross-sectional studies, generating pairwise gene co-expression, to capture transient regulatory activity. Here, we employ a minimal stochastic model of transcriptional regulation and demonstrate that population correlations from cross-sectional studies may fail to capture transient regulatory activities. To characterize network rewiring in response to environmental perturbations, we use the dynamic correlation between time-series gene expression profiles in Oryza sativa in a network prior. Overall, stronger regulatory interactions are observed following environmental perturbation, which we term regulatory coherence. Previously known regulators with changing regulatory activities are reliably identified with our method. Exploiting dynamic correlations has the potential to prioritize stress-responsive regulators, affording greater detection power as compared to traditional differential expression approaches.