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PLoS One. 2017 Sep 12;12(9):e0184657. doi: 10.1371/journal.pone.0184657. eCollection 2017.

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network.

Author information

1
Theoretical Department, Ioffe Institute, Saint Petersburg, Russia.
2
Systems Biology and Bioinformatics Laboratory, Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russia.
3
Engelhardt Institute of Molecular Biology, Moscow, Russia.
4
Vavilov Institute of General Genetics, Moscow, Russia.
5
Center for Data-Intensive Biomedicine and Biotechnology, Skolkovo Institute of Science and Technology, Moscow, Russia.
6
Molecular and Computational Biology, University of Southern California, Los Angeles, California, United States of America.

Abstract

Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects.

PMID:
28898266
PMCID:
PMC5595321
DOI:
10.1371/journal.pone.0184657
[Indexed for MEDLINE]
Free PMC Article

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