1. Impact of Zygosity on Bimodal Phenotype Distributions.
- Author
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Holst-Hansen T, Abad E, Muntasell A, López-Botet M, Jensen MH, Trusina A, and Garcia-Ojalvo J
- Subjects
- Cytomegalovirus, Cytomegalovirus Infections immunology, Cytomegalovirus Infections metabolism, Feedback, Physiological, Flow Cytometry, Gene Expression, Gene Regulatory Networks physiology, Hemizygote, Homozygote, Humans, Killer Cells, Natural immunology, Models, Genetic, NK Cell Lectin-Like Receptor Subfamily C metabolism, Phenotype, Cytomegalovirus Infections genetics, Gene Dosage, Killer Cells, Natural metabolism, NK Cell Lectin-Like Receptor Subfamily C genetics
- Abstract
Allele number, or zygosity, is a clear determinant of gene expression in diploid cells. However, the relationship between the number of copies of a gene and its expression can be hard to anticipate, especially when the gene in question is embedded in a regulatory circuit that contains feedback. Here, we study this question making use of the natural genetic variability of human populations, which allows us to compare the expression profiles of a receptor protein in natural killer cells among donors infected with human cytomegalovirus with one or two copies of the allele. Crucially, the distribution of gene expression in many of the donors is bimodal, which indicates the presence of a positive feedback loop somewhere in the regulatory environment of the gene. Three separate gene-circuit models differing in the location of the positive feedback loop with respect to the gene can all reproduce the homozygous data. However, when the resulting fitted models are applied to the hemizygous donors, one model (the one with the positive feedback located at the level of gene transcription) is superior in describing the experimentally observed gene-expression profile. In that way, our work shows that zygosity can help us relate the structure and function of gene regulatory networks., (Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.)
- Published
- 2017
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