Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes

Abstract

Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15-80% of the genome. The gene-regulatory networks (GRNs) controlling these programs were largely identified by genetics and chromosome mapping approaches in model systems, yet it is unlikely that we have identified all of the core GRN components. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. Previous work used dynamic gene expression features to identify sets of genes with periodic behavior, our work goes further to distinguish genes by role: core versus their non-regulatory outputs. Here we present a quantitative approach that can identify nodes of GRNs controlling cell or circadian cycles across taxa. There are practical applications of the approach for network biologists, but our findings reveal something unexpected---that there are quantifiable and fundamental shared features of these unrelated GRNs controlling disparate periodic phenotypes.

Experimental Guidance for Discovering Genetic Networks through Iterative Hypothesis Reduction on Time Series

“…The current implementation of the Inherent Dynamics Pipeline focuses on discovering GRNs that produce oscillatory dynamics, such as those in cell-cycle and circadian systems. The node finding step in the Inherent Dynamics Pipeline employs the periodicity detection algorithm DL×JTK [25]. DL×JTK uses JTK CYCLE [26] and the de Lichtenberg algorithm [27] to quantify periodicity and amplitude as key features of gene expression profiles, see Methods Section 4.2.1.…”

“…The network finding step accepts a ranked list of gene interactions that are ideally enriched by regulatory connections critical to the molecular process under consideration. Although DL×JTK and LEM have a strong tendency to highly rank ground truth nodes [25] and edges [11] respectively, false positives and false negatives do exist within the lists of top-ranked nodes and edges. Furthermore, even when both tools work perfectly, there is no guarantee that the top pairwise LEM interactions will produce a network of complex interactions that faithfully reproduces the observed data.…”

“…DL×JTK uses JTK CYCLE [26] and the de Lichtenberg algorithm [27] to quantify periodicity and amplitude as key features of gene expression profiles, see Methods Section 4.2.1. These two features have been shown to be characteristic gene expression features of core genes in GRNs that produce oscillatory dynamics [25,28]. DL×JTK combines the quantification of periodicity and amplitude into one score, providing a ranked list of genes where the top of the list is enriched for core regulatory elements most critical to controlling oscillatory dynamics.…”

“…The Inherent Dynamics Pipeline network discovery tool consists of a node finding step implemented with DL×JTK [25], an edge finding step implemented with the Local Edge Machine (LEM) [11], and a network finding step dependent on the Python package Dynamic Signatures Generated by Regulatory Networks (DSGRN) [42]. The software is an iterative hypothesis reduction machine to identify core oscillators driving large scale oscillations in gene expression.…”

Inference of Gene Regulatory Network Uncovers the Linkage between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi

“…The periodicity detection methods of de Lichtenberg (DL) [ 31 , 32 ] and JTK-CYCLE (JTK) [ 33 , 34 ] are periodicity detection algorithms that take into account the amplitude of time-course gene expression and if the period of expression matches to the period length in question, respectively. Therefore, to better identify core clock genes, a new metric has been established, termed DLxJTK, that combines these two features of DL and JTK [ 35 ]. DLxJTK uses the p -values for amplitude from DL and for periodicity from JTK for each gene and has been used in mammalian, fungal and plant systems with success.…”