Existing theories explain why operons are advantageous in prokaryotes, but their occurrence in metazoans is an enigma. Nematode operon genes, typically consisting of growth genes, are significantly upregulated during recovery from growth-arrested states. This expression pattern is anticorrelated to nonoperon genes, consistent with a competition for transcriptional resources. We find that transcriptional resources are initially limiting during recovery and that recovering animals are highly sensitive to any additional decrease in transcriptional resources. We provide evidence that operons become advantageous because, by clustering growth genes into operons, fewer promoters compete for the limited transcriptional machinery, effectively increasing the concentration of transcriptional resources and accelerating recovery. Mathematical modeling reveals how a moderate increase in transcriptional resources can substantially enhance transcription rate and recovery. This design principle occurs in different nematodes and the chordate C. intestinalis. As transition from arrest to rapid growth is shared by many metazoans, operons could have evolved to facilitate these processes.
Bibliographical noteFunding Information:
We are grateful to Tom Blumenthal, Erich Schwarz, Hillel Schwartz, Shalev Itzkovitz, Ronen Zaidel-Bar, and Ron Milo for critical review and helpful comments. We thank Elodie Ghedin for sharing the list of operon genes in B. malayi, the Mitani laboratory and the Caenorhabditis Genetics Center for deletion strains, WormBase, and the Genome BC C. elegans Gene Expression Consortium http://elegans.bcgsc.bc.ca/ for SAGE data (produced at the Michael Smith Genome Sciences Centre with funding from Genome Canada). A.Z. was supported by the European Molecular Biology Organization (EMBO), the Human Frontier Science Program (HFSP), and the Caltech Center for Biological Circuit Design. L.R.B. was an ACS postdoctoral fellow. P.W.S. is an Investigator with the HHMI, which supported this work. A.Z. conceived the idea of this study, designed and performed all of the experiments and the mathematical modeling, and carried out all of the data and bioinformatics analyses. L.R.B. contributed experimental tools, shared unpublished data, and helped to analyze and interpret the data. A.Z. and P.W.S. wrote the paper.