Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

Abstract

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

Natural gene expression variation studies in yeast

“…The cis ‐regulatory variants impact transcription in several ways, by affecting regulatory elements such as transcription factor (TF) binding sites that impact the binding of transcription factors, and chromatin regulators that impact transcription by altering chromatin structure at promoter regions (Cubillos et al ., ; Chang et al ., ; Fraser et al ., ; Salinas et al ., ; Thompson et al ., ; Wittkopp and Kalay, ). Because TF binding sites are well characterized in yeast, several instances of individual regulatory variants have now been identified, improving our understanding of the mechanisms behind natural gene expression variation (de Boer and Hughes, ; Fazlollahi et al ., ; Salinas et al ., ). Changes in trans‐ factors can contribute to expression divergence through a change in the factor's responsiveness to upstream signals, binding to newly emerging sites upstream of new targets or the factor's ability to bind different ‘non‐canonical’ sites.…”

“…Moreover, the interaction between a TF and its motif can be so sensitive that even a single nucleotide change within the motif can cause expression differences between allelic variants (Metzger et al ., ; Schaefke et al ., ). In this context, co‐factor QTLs (cQTLs) have recently been described as another source of expression variation, where a polymorphic cofactor required for the efficient transcription of a target gene by a TF can significantly influence the expression of the target gene (Fazlollahi et al ., ). The utilization of this information at a genome‐wide level can serve to predict interactions between polymorphic regulators and their targets in divergent strains, leading to expression differences and ultimately explaining phenotypic divergence.…”

Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression

“…An early attempt to map modulators of regulatory connectivity was based on expression data alone [48]; however, the need to avoid confounding between TF-target and TF-modulator correlation posed a limitation. The emergence of parallel genotype and expression data across populations provided a new opportunity to pursue this line of analysis, and allowed network connectivity QTLs (or “cQTLs”) to be mapped [49]. This led e.g.…”

“…This led e.g. to the prediction that activation of the gene expression response to mating pheromone by the transcription factor Ste12p in the yeast S. cerevisiae was modulated by a non-synonymous single-nucleotide polymorphism (SNP) in the co-factor Dig2, which was validated by reporter gene experiments on allele replacements strains [49]. A similar approach has been used in a human cancer context [28].…”

GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics

“…The identification of genetic alterations and variations that function as biological modulators and contribute to gene expression control is one of the challenging tasks in systems biology. Recently, sophisticated algorithms have been developed for this task which have successful applications in many areas [4,5,6,7,8,9]. For example, MINDy [4] formulates the problem of identifying modulators as a problem of testing if the expressions of a univariate transcription factor and its target gene, denoted by X and Y , are independent each other, conditioned on the expression levels of an univariate modulator denoted by Z in the framework of conditional mutual information.…”