Identifying genetic modulators of the connectivity PNAS PLUS between transcription factors and their transcriptional targets Mina Fazlollahia,b, Ivor Muroffa, Eunjee Leea,1, Helen C. Caustonc,2, and Harmen J. Bussemakera,b,2 aDepartment of Biological Sciences, Columbia University, New York, NY 10027; bDepartment of Systems Biology, Columbia University, New York, NY 10032; and cDepartment of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032 Edited by Steven Henikoff, Fred Hutchinson Cancer Research Center, Seattle, WA, and approved February 9, 2016 (received for review August 28, 2015) Regulation of gene expression by transcription factors (TFs) is highly At the simplest level, regulation of gene expression is char- dependent on genetic background and interactions with cofactors. acterized by binding of a transcription factor (TF) to a promoter Identifying specific context factors is a major challenge that requires and the concomitant activation or repression of the target gene. new approaches. Here we show that exploiting natural variation is Variation in responsiveness of a target gene to a regulator, either a potent strategy for probing functional interactions within gene due to genetic variation or due to a change in the environment, regulatory networks. We developed an algorithm to identify genetic can affect its expression and the resulting cellular phenotype. polymorphisms that modulate the regulatory connectivity between One possible strategy for tuning gene-specific responsiveness is specific transcription factors and their target genes in vivo. As a proof modulation of TF–target connectivity by specific cofactors. The of principle, we mapped connectivity quantitative trait loci (cQTLs) interaction between a TF and cofactor can take a number of using parallel genotype and gene expression data for segregants from different forms. It may require a specific cofactor(s) to tether a cross between two strains of the yeast Saccharomyces cerevisiae.We effectively to the promoter of a target gene; for example, Met4p is DIG2 identified a nonsynonymous mutation in the gene as a cQTL for recruited to the promoters of genes involved in sulfur metabolism the transcription factor Ste12p and confirmed this prediction empiri- via interaction with Cbf1p and several other DNA-binding proteins cally. We also identified three polymorphisms in TAF13 as putative (17–23). Cofactors may also influence TF-dependent recruitment of BIOPHYSICS AND modulators of regulation by Gcn4p. Our method has potential for re- the transcription machinery to the transcription start site; for ex- COMPUTATIONAL BIOLOGY vealing how genetic differences among individuals influence gene ample, multiprotein bridging factor 1 (Mbf1p) enhances Gcn4p- regulatory networks in any organism for which gene expression and dependent transcriptional activation by simultaneously binding the genotype data are available along with information on binding pref- DNA-binding domain of general control nonderepressible 4 (Gcn4p) erences for transcription factors. and a subunit of RNA polymerase II complex (24), or a cofactor may prevent a TF from binding to the promoters of its targets. In transcription factors | cofactor interactions | genetic variation in nutrient-rich conditions in the absence of mating pheromone, the gene expression | quantitative trait locus mapping activator of the mating response, Ste12p, is bound by a down- regulator of invasive growth protein (Dig2p), which inhibits he ability to predict the phenotype of an organism from its binding of Ste12p to the promoters of its target genes (25, 26). Tgenotype is a long-term goal of biology. The increasing avail- In the presence of pheromone, Dig2p is phosphorylated and ability of high-throughput genomic data and systems biology-based approaches is bringing this closer to reality. Linkage or association Significance analyses have been used to map quantitative trait loci (QTLs) that explain phenotypic variation in terms of genetic polymorphism. – However, detectable QTLs typically only account for a small We present a transcription-factor centric computational method fraction of heritable trait variation. Even when most of the addi- that utilizes data on natural variation in mRNA abundance to tive contribution to the heritability can be explained in terms of identify genetic loci that influence the responsiveness of genes QTLs, as is the case for yeast, most of the contribution from gene– to variation in the activities of their regulators. We call these gene interactions remains unexplained (1). Consequently, there connectivity quantitative trait loci, or cQTLs. When testing our has been a growing appreciation for the importance of interactions method in yeast, we identified a polymorphism that modulates among genetic loci. For instance, a gene deletion study comparing the mating response and confirmed our prediction experimen- two yeast strains that differ by 3–5%, similar to the difference tally. Our approach provides a sophisticated and nuanced ap- between unrelated human individuals, identified subsets of genes proach to understanding the influence of genetic variation on that were essential in one strain background but not the other (2). interactions within regulatory networks. Applying our approach This study exemplifies one of the computational challenges that to human data may improve our understanding of the impact will have to be addressed before phenotype can reliably be pre- of genetic variation in contributing to phenotypic differences dicted from genotype. among individuals. The identification of modifier genes contributing to phenotype is Author contributions: M.F., H.C.C., and H.J.B. designed research; M.F. and I.M. performed difficult, because individual contributions are usually too weak to research; M.F. and E.L. contributed new analytic tools; M.F., I.M., H.C.C., and H.J.B. analyzed be detected. Considering genome-wide mRNA abundance along data; and M.F., I.M., H.C.C., and H.J.B. wrote the paper. with information on genotypic variation represents the combined The authors declare no conflict of interest. – effect of the latter on the regulatory state of the cell (3 7) and can This article is a PNAS Direct Submission. yield better prediction of phenotype (8, 9), the combined effect of Freely available online through the PNAS open access option. allelic variation on transcript abundance (8, 10), the effect of ge- 1Present address: Department of Genetics and Genomic Sciences, Mount Sinai School of netic variation on transcription factoractivity(11,12),ortranscript Medicine, New York, NY 10029. stability (13). However, there are few methods that systematically 2To whom correspondence may be addressed. Email: [email protected] or identify the effect of genetic modifiers on the strength of the [email protected]. “ ” connection ( connectivity ) between a transcription factor and its This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. target genes (14–16). 1073/pnas.1517140113/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1517140113 PNAS Early Edition | 1of9 Downloaded by guest on September 28, 2021 dissociates from Ste12p, allowing Ste12p to activate transcription of information (33), which we exploit to estimate the susceptibility the mating genes (26). (i.e., responsiveness) of each gene to variation in the differential In all of the above cases, the magnitude and/or kinetics of the regulatory activity of a particular TF. We subsequently treat the change in transcription rate promoted by a TF may be affected by average difference in susceptibility between genetic backgrounds natural variation in the amino acid sequence or protein level of a containing each allele for a given locus as a quantitative trait and cofactor. In this work, we explore the effect of genetic variation at aggregate these differences over the targets of the TF to con- trans-acting loci on the regulatory interaction between a TF and its struct a (χ2) statistic that can be used to map cQTLs. Applica- targets. This is illustrated in Fig. 1, where allelic variation of a tion of our algorithm to yeast data uncovered a cQTL modulating cofactor modulates the efficiency of interaction between a TF and the pheromone-induced mating response, which we confirmed ex- the promoter to which it binds. Many studies rely on knowledge of perimentally, as well as a cQTL modulating amino acid biosynthesis. TF–target interactions to decipher the transcriptional network (27– 30). However, few algorithms are able to identify modulators of Results TF–target connectivity at a network level (14–16). These typically The goal of our analysis is to detect genetic loci that modulate the derive information from statistical relationships across conditions strength of the functional connection between a TF and the ex- between the mRNA abundance of gene triplets: the TF of interest, pression of its target genes. We used genome-wide mRNA ex- a candidate modulator, and a target of the TF. However, regula- pression data for 108 segregants resulting from a genetic cross tion of TF activity and interaction with cofactors often occurs at the between two yeast strains: a laboratory strain (BY4716) and a wild protein level. A complementary approach is therefore needed. strain from a vineyard in California (RM11-1a) (10). We also used Our method identifies genetic loci whose allelic variation mod- a genotype map that specifies the parental allele (BY or RM) ulates the regulatory connectivity