Identification of Key Tissue-Specific, Biological Processes by Integrating Enhancer Information in Maize Gene Regulatory Networks

Abstract

Enhancers are key players in the spatio-temporal coordination of gene expression during numerous crucial processes, including tissue differentiation across development. Characterizing the transcription factors (TFs) and genes they connect, and the molecular functions underpinned is important to better characterize developmental processes. In plants, the recent molecular characterization of enhancers revealed their capacity to activate the expression of several target genes. Nevertheless, identifying these target genes at a genome-wide level is challenging, particularly for large-genome species, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to plant regulatory networks remains poorly understood. Here, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage (V2-IST) and husks (bracts) at flowering. Using systems biology, we integrate genomic, epigenomic, and transcriptomic data to model the regulatory relationships between TFs and their potential target genes, and identify regulatory modules specific to husk and V2-IST. We show that leaves at the V2-IST stage are characterized by the response to hormones and macromolecules biogenesis and assembly, which are regulated by the BBR/BPC and AP2/ERF TF families, respectively. In contrast, husks are characterized by cell wall modification and response to abiotic stresses, which are, respectively, orchestrated by the C2C2/DOF and AP2/EREB families. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped part of the regulatory network in each tissue, and that MITEs have provided potential new TF binding sites involved in husk tissue-specificity.

Cis-regulatory sequences in plants: Their importance, discovery, and future challenges

“…Interestingly, it was noted that ∼40% of distal putative CRMs in maize did not interact with one of the immediate flanking genes, i.e. they tended to skip at least one gene and interact with more distal genes ( Li et al, 2019a ; Fagny et al, 2021 ). This is reminiscent of how DICE, a distal CRM, controls the expression of the maize bx1 gene ( Zheng et al, 2015 ).…”

“…It is increasingly recognized that TEs provide a source of CRMs ( Lisch, 2013 ; Chuong et al, 2017 ). It is estimated that about 25% and 30% of regulatory sequences could be TE-derived (TE-CRMs) in humans and maize, respectively ( Oka et al, 2017 ; Zhao et al, 2018 ; Pehrsson et al, 2019 ; Fagny et al, 2021 ). Accordingly, several TEs are bound by TFs in mammals and plants ( Bourque et al, 2008 ; Kunarso et al, 2010 ; Lynch et al, 2011 ; Batista et al, 2019 ).…”

Reconstructing Sample-Specific Networks using LIONESS

“…Because LIONESS can be applied to very different types of network reconstruction algorithms and data, we recommend that the user explores how the data and data preprocessing may affect the output networks, as well as identify the downstream network analysis approach that is most appropriate to answer their question of interest. These decisions are critical to gaining important biological insights, as we and others have in our previous applications of the method (Kuijjer et al, 2019a; Sonawane et al, 2019; Fagny et al, 2020; Lopes-Ramos et al, 2018, 2020, 2021, Pham et al, 2021).…”

“…For example, the topology of each of the single-sample networks could be analyzed using community structure detection methods to identify modules of strongly connected nodes. These modules may be enriched for specific biological properties (Platig et al, 2016;Fagny et al, 2017Fagny et al, , 2020. In addition, community structure comparison methods (Padi and Quackenbush, 2018;Lim et al, 2020) can be used to identify differences between pairs of single-sample networks.…”

“…The single-sample networks predicted by LIONESS provide a way to unite (i) the extensive literature and methodologies for estimating complex network relationships using genomic data with (ii) statistical analysis techniques that use sample-level information to model heterogeneity and compare phenotypic groups. Applications of LIONESS have included analyzing the yeast cell cycle (Kuijjer et al, 2019a), studying biological processes in lymphobastoid cell lines (Kuijjer et al, 2019a), investigating the relationship between the host transcriptome and nasal microbiome in infants with respiratory syncytial virus infection (Sonawane et al, 2019), investigating microbiome co-occurrence in respiratory infections (Mac Aogáin et al, 2021), identifying regulatory processes associated with brain cancer survival (Lopes-Ramos et al, 2021) and with sexual dimorphism in colorectal cancer chemotherapy response (Lopes-Ramos et al, 2018), characterizing sex differences in twenty-nine tissues from the Genotype-Tissue Expression Project (Lopes-Ramos et al, 2020), and identifying tissue-specific regulatory processes in maize (Fagny et al, 2020). LIONESS has also been integrated into a method to identify cancer driver genes (Pham et al, 2021).…”