Incorporating Motif Analysis into Gene Co-expression Networks Reveals Novel Modular Expression Pattern and New Signaling Pathways

Abstract

Understanding of gene regulatory networks requires discovery of expression modules within gene co-expression networks and identification of promoter motifs and corresponding transcription factors that regulate their expression. A commonly used method for this purpose is a top-down approach based on clustering the network into a range of densely connected segments, treating these segments as expression modules, and extracting promoter motifs from these modules. Here, we describe a novel bottom-up approach to identify gene expression modules driven by known cis-regulatory motifs in the gene promoters. For a specific motif, genes in the co-expression network are ranked according to their probability of belonging to an expression module regulated by that motif. The ranking is conducted via motif enrichment or motif position bias analysis. Our results indicate that motif position bias analysis is an effective tool for genome-wide motif analysis. Sub-networks containing the top ranked genes are extracted and analyzed for inherent gene expression modules. This approach identified novel expression modules for the G-box, W-box, site II, and MYB motifs from an Arabidopsis thaliana gene co-expression network based on the graphical Gaussian model. The novel expression modules include those involved in house-keeping functions, primary and secondary metabolism, and abiotic and biotic stress responses. In addition to confirmation of previously described modules, we identified modules that include new signaling pathways. To associate transcription factors that regulate genes in these co-expression modules, we developed a novel reporter system. Using this approach, we evaluated MYB transcription factor-promoter interactions within MYB motif modules.

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

“…S8). The genes with the most interactions are expected to act as key components in GCN networks (Langfelder and Horvath, 2008;Allen et al, 2012), and likely represent central regulators of multiprotein biological processes (Ma et al, 2013;Du et al, 2015). The top 1000 interacting genes from all networks were analyzed in more detail, as these were potential hub genes that may regulate other expression patterns and processes.…”

Transcriptional Regulation of Arabidopsis Polycomb Repressive Complex 2 Coordinates Cell-Type Proliferation and Differentiation

“…For transactivation assays, transcription factors in PYL436 (effector) (Ma et al, 2013; collection kindly provided by Dinesh Kumar, UC Davis), promoter:GUS (reporter), 35S pro :LUCIFERASE (internal control), and p19 (RNA silencing inhibitor) constructs were transformed into Agrobacterium (strain GV3101) and used as described (TaylorTeeples et al, 2015). In Arabidopsis, 12-and 24-h treatments in liquid 1% MS supplemented with 10 mM b-estradiol (from a 10 mM stock in 100% DMSO) was used to induce the expression of each transcription factor in 5-d-old seedlings.…”

Transcript level coordination of carbon pathways during silicon starvation‐induced lipid accumulation in the diatom Thalassiosira pseudonana

“…A high degree of coordinated regulation of transcript abundances for genes involved in distinct photosynthetic and metabolic processes in different cellular compartments was documented. These clusters are sets of coordinately regulated genes under the putative control of as yet unknown master regulators such as metabolites, cellular redox state, or transcription factors (Ma et al ., 2013). Despite a limited understanding of the specific mechanisms that regulate coexpression modules, the high degree of transcriptome orchestration demonstrates that T. pseudonana utilizes a regulatory hierarchy to integrate transcript abundance of many genes involved in key energetic and carbon sources during metabolic shifts.…”