Assigning function to natural allelic variation via dynamic modeling of gene network induction. Issue 1 (15th January 2018)

• Record Type: Journal Article
• Authors: Richard, Magali; Chuffart, Florent; Duplus‐Bottin, Hélène; Pouyet, Fanny; Spichty, Martin; Fulcrand, Etienne; Entrevan, Marianne; Barthelaix, Audrey; Springer, Michael; Jost, Daniel; Yvert, Gaël
• Is Part Of: Molecular systems biology. Volume 14:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-01-15
• Subjects: galactose -- personalized medicine -- SNP function -- stochastic model -- yeast; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.20177803
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be "personalized" according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non‐synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants. Synopsis: An approach based on genotype‐specific gene regulatory network models is used to examine the functional consequences of yeast GAL3 sequence variants. This framework can be more generally applied to the mechanistic interpretation of genetic variants. The principle of the proposed approach is linking genetic variation to informative changes of parameter values of a regulatory network model. Experimental analyses of the yeast GAL network shows that GAL3 natural variation is sufficient to convert a gradual response into a binary switch. Dynamic network modeling successfully maps alleles to specific locations of the parameter space, allowing functional inference of DNA polymorphisms. Abstract : An approach based on genotype‐specific gene regulatory network models is used to examine the functional consequences of yeast GAL3 sequence variants. This framework can be more generally applied to the mechanistic interpretation of genetic variants.