An Insulin-to-Insulin Regulatory Network Orchestrates Phenotypic Specificity in Development and Physiology

Diana Andrea Fernandes de Abreu, Antonio Caballero, Pascal Fardel, Nicholas Stroustrup, Zhunan Chen, Kyung Hwa Lee, William D. Keyes, Zachary M. Nash, Isaac F. López-Moyado, Federico Vaggi, Astrid Cornils, Martin Regenass, Anca Neagu, Ivan Ostojic, Chang Liu, Yongmin Cho, Deniz Sifoglu, Yu Shen, Walter Fontana, Hang LuAttila Csikasz-Nagy, Coleen T. Murphy, Adam Antebi, Eric Blanc, Javier Apfeld, Yun Zhang, Joy Alcedo, Quee Lim Ch'ng

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


Insulin-like peptides (ILPs) play highly conserved roles in development and physiology. Most animal genomes encode multiple ILPs. Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes, including development, reproduction, longevity and several specific stress responses. Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy. Notably, we show that ILPs regulate each other transcriptionally, uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family. Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated. A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk, feedback and compensation. This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe. Our findings also provide insights into how large hormonal networks regulate diverse traits.

Original languageEnglish (US)
Article numbere1004225
JournalPLoS genetics
Issue number3
StatePublished - Mar 2014

All Science Journal Classification (ASJC) codes

  • Genetics(clinical)
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Cancer Research


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