Transcription Factor Activity Mapping of a Tissue-Specific In Vivo Gene Regulatory Network

Abstract

A wealth of physical interaction data between transcription factors (TFs) and DNA has been generated, but these interactions often do not have apparent regulatory consequences. Thus, equating physical interaction data with gene regulatory networks (GRNs) is problematic. Here, we comprehensively assay TF activity, rather than binding, to construct a network of gene regulatory interactions in the C. elegans intestine. By manually observing the in vivo tissue-specific knockdown of 921 TFs on a panel of 19 fluorescent transcriptional reporters, we identified a GRN of 411 interactions between 19 promoters and 177 TFs. This GRN shows only modest overlap with physical interactions, indicating that many regulatory interactions are indirect. We applied nested effects modeling to uncover information flow between TFs in the intestine that converges on a small set of physical TF-promoter interactions. We found numerous cell nonautonomous regulatory interactions, illustrating tissue-to-tissue communication. Altogether, our study illuminates the complexity of gene regulation in the context of a living animal.

Multi-level regulation of cellular glycosylation: from genes to transcript to enzyme to structure

“…In this regard, the methylation of the α(2,3)sialyltransferase ST6Gal1 promoter was shown to silence expression of the ST6Gal1 transcript in bladder cancer [18], confirming an earlier report in breast cancer showing methylation-dependent silencing of this glycogene [22]. Mapping transcription factors and epigenetic regulation onto glycogene regulatory networks will require a combination of methods from traditional Chromatin Immunoprecipitation (ChIP) assays to more sophisticated promoter-reporter assays that can reveal indirect and direct effects, to more sophisticated analysis of genome accessibility and silencing [15,23]. All of this is important to our understanding of the glycome and its regulation.…”

Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity

“…In silico models of mammalian TF networks have also so far tended to investigate limited sub-networks within the larger TF network. By moving towards larger-scale comprehensive analysis of enhancer activity and TF network interactions, such as methods used in other organisms (Arnold et al, 2013, MacNeil et al, 2015), modeling of entire mammalian TF regulatory networks should be possible. For example, Arnold et al recently developed a powerful genome-wide assay called STARR-seq to quantitiate enhancer activity genome-wide in Drosophila (Arnold et al, 2013).…”

PRIMA: a gene-centered, RNA-to-protein method for mapping RNA-protein interactions

“…33 Such strains can then be used with RNAi knockdown screening to identify or characterize proteins that regulate that promoter or 3 0 UTR either directly or indirectly. 27,33,34 We predicted that the C. elegans genome contains up to 887 RBPs, and this estimate has largely been verified by proteomic findings. 8,20 In vitro assays have been used to determine the binding specificities of several C. elegans RBPs.…”

“…Indeed, more evidence is becoming available that not all physical transcription factor-DNA interactions, detected either in vivo or by yeastbased methods, have a (measurable) regulatory consequence in vivo. 27,43,57 This finding could be because the potential regulatory effects were examined under irrelevant physiologic conditions, because the interaction effect is masked by redundantly functioning RBPs, or because the interaction is harmless, and can occur without any regulatory consequence (and thus would not be selected for or against).…”

“…22 The nematode Caenorhabditis elegans is a powerful model organism for the study of biologic interactome networks. [23][24][25][26][27] C. elegans transgenic strains can be generated that express a fluorescent reporter protein under the control of a promoter (with fixed 3 0 UTR), [28][29][30][31][32] or 3 0 UTR (with fixed promoter) of interest. 33 Such strains can then be used with RNAi knockdown screening to identify or characterize proteins that regulate that promoter or 3 0 UTR either directly or indirectly.…”