Revisiting regulatory decoherence: Accounting for temporal bias in a co-expression analysis reveals novel candidates controlling environmental response

Transcriptional Regulatory Networks (TRNs) orchestrate the timing, magnitude, and rate of organismal response to many environmental perturbations. Regulatory interactions in TRNs are dynamic but exploiting temporal variation to understand gene regulation requires a careful appreciation of both molecular biology and confounders in statistical analysis. Seeking to exploit the abundance of RNASequencing data now available, many past studies have relied upon population-level statistics from cross-sectional studies, estimating gene co-expression interactions to capture transient changes of regulatory activity. We show that population-level co-expression exhibits biases when capturing transient changes of regulatory activity in rice plants responding to elevated temperature. An apparent cause of this bias is regulatory saturation, the observation that detectable co-variance between a regulator and its target may be low as their transcript abundances are induced. This phenomenon appears to be particularly acute for rapid onset environmental stressors. However, exploiting temporal correlations appears to be a reliable means to detect transient regulatory activity following rapid onset environmental perturbations such as temperature stress. Such temporal correlation may lose information along a more gradual-onset stressor (e.g., dehydration). We here show that rice plants exposed to a dehydration stress exhibit temporal structure of coexpression in their response that can not be unveiled by temporal correlation alone. Collectively, our results point to the need to account for the nuances of molecular interactions and the possibly confounding effects that these can introduce into conventional approaches to study transcriptome datasets.