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Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network.

ABSTRACT: Gene regulatory networks (GRNs) provide insights into the mechanisms of differential gene expression at a systems level. GRNs that relate to metazoan development have been studied extensively. However, little is still known about the design principles, organization and functionality of GRNs that control physiological processes such as metabolism, homeostasis and responses to environmental cues. In this study, we report the first experimentally mapped metazoan GRN of Caenorhabditis elegans metabolic genes. This network is enriched for nuclear hormone receptors (NHRs). The NHR family has greatly expanded in nematodes: humans have 48 NHRs, but C. elegans has 284, most of which are uncharacterized. We find that the C. elegans metabolic GRN is highly modular and that two GRN modules predominantly consist of NHRs. Network modularity has been proposed to facilitate a rapid response to different cues. As NHRs are metabolic sensors that are poised to respond to ligands, this suggests that C. elegans GRNs evolved to enable rapid and adaptive responses to different cues by a concurrence of NHR family expansion and modular GRN wiring.

SUBMITTER: Arda HE

PROVIDER: S-EPMC2890327 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network.

Arda H Efsun HE Taubert Stefan S MacNeil Lesley T LT Conine Colin C CC Tsuda Ben B Van Gilst Marc M Sequerra Reynaldo R Doucette-Stamm Lynn L Yamamoto Keith R KR Walhout Albertha J M AJ

Molecular systems biology 20100501

Gene regulatory networks (GRNs) provide insights into the mechanisms of differential gene expression at a systems level. GRNs that relate to metazoan development have been studied extensively. However, little is still known about the design principles, organization and functionality of GRNs that control physiological processes such as metabolism, homeostasis and responses to environmental cues. In this study, we report the first experimentally mapped metazoan GRN of Caenorhabditis elegans metabo ...[more]

PMID: 20461074

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Project description:BackgroundComplex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments.ResultsTo facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9.ConclusionsThis work provides a strong example for a mathematical modeling approach that solely uses wild-type data to predict an underlying gene regulatory network. The modeling approach complements traditional methods of data analysis, suggesting non-intuitive network relationships and guiding future experiments.

Project description:Nuclear hormone receptors (NHRs) are transcription factors that regulate numerous physiological and developmental processes and represent important drug targets. NHR-49, an ortholog of Hepatocyte Nuclear Factor 4 (HNF4), has emerged as a key regulator of lipid metabolism and life span in the nematode worm Caenorhabditis elegans. However, many aspects of NHR-49 function remain poorly understood, including whether and how it regulates individual sets of target genes and whether its activity is modulated by a ligand. A recent study identified three gain-of-function (gof) missense mutations in nhr-49 (nhr-49(et7), nhr-49(et8), and nhr-49(et13), respectively). These substitutions all affect the ligand-binding domain (LBD), which is critical for ligand binding and protein interactions. Thus, these alleles provide an opportunity to test how three specific residues contribute to NHR-49 dependent gene regulation. We used computational and molecular methods to delineate how these mutations alter NHR-49 activity. We find that despite originating from a screen favoring the activation of specific NHR-49 targets, all three gof alleles cause broad upregulation of NHR-49 regulated genes. Interestingly, nhr-49(et7) and nhr-49(et8) exclusively affect nhr-49 dependent activation, whereas the nhr-49(et13) surprisingly affects both nhr-49 mediated activation and repression, implicating the affected residue as dually important. We also observed phenotypic non-equivalence of these alleles, as they unexpectedly caused a long, short, and normal life span, respectively. Mechanistically, the gof substitutions altered neither protein interactions with the repressive partner NHR-66 and the coactivator MDT-15 nor the subcellular localization or expression of NHR-49. However, in silico structural modeling revealed that NHR-49 likely interacts with small molecule ligands and that the missense mutations might alter ligand binding, providing a possible explanation for increased NHR-49 activity. In sum, our findings indicate that the three nhr-49 gof alleles are non-equivalent, and highlight the conserved V411 residue affected by et13 as critical for gene activation and repression alike.

Dataset Information

Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network.

Publications

Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network.

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