Homotypic cooperativity and collective binding are determinants of bHLH specificity and function

Abstract

Eukaryotic cells express transcription factor (TF) paralogues that bind to nearly identical DNA sequences in vitro but bind at different genomic loci and perform different functions in vivo. Predicting how 2 paralogous TFs bind in vivo using DNA sequence alone is an important open problem. Here, we analyzed 2 yeast bHLH TFs, Cbf1p and Tye7p, which have highly similar binding preferences in vitro, yet bind at almost completely nonoverlapping target loci in vivo. We dissected the determinants of specificity for these 2 proteins by making a number of chimeric TFs in which we swapped different domains of Cbf1p and Tye7p and determined the effects on in vivo binding and cellular function. From these experiments, we learned that the Cbf1p dimer achieves its specificity by binding cooperatively with other Cbf1p dimers bound nearby. In contrast, we found that Tye7p achieves its specificity by binding cooperatively with 3 other DNA-binding proteins, Gcr1p, Gcr2p, and Rap1p. Remarkably, most promoters (63%) that are bound by Tye7p do not contain a consensus Tye7p binding site. Using this information, we were able to build simple models to accurately discriminate bound and unbound genomic loci for both Cbf1p and Tye7p. We then successfully reprogrammed the human bHLH NPAS2 to bind Cbf1p in vivo targets and a Tye7p target intergenic region to be bound by Cbf1p. These results demonstrate that the genome-wide binding targets of paralogous TFs can be discriminated using sequence information, and provide lessons about TF specificity that can be applied across the phylogenetic tree.

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

“…It does not require crosslinking, sonication, or affinity purification (Wang et al 2011;Ryan et al 2012;Mayhew and Mitra 2016). Here, we analyze previously published calling cards data on seven TFs (Wang et al 2011;Shively et al 2019) and new, never-before-analyzed data on eight TFs. Binding data from ChIP-chip and calling cards were compared to perturbation-response data from TFKO and ZEV15, using the 12 TFs present in all four data sets (Fig.…”

“…S5). We combined calling cards data from Wang et al (2011) and Shively et al (2019) on Cbf1, Cst6, Gal4, Gcr1, Gcr2, Rgm1, and Tye7 with new data on Eds1, Gcn4, Ino4, Leu3, Lys14, Rgt1, Sfp1, and Zap1 (Supplemental File S10).…”

“…The data sets we focus on are those that aim to determine the binding locations of TFs and those that attempt to measure the transcriptional response to perturbations of TF activity, such as overexpressing the TF or deleting the gene that encodes it. The binding location data are derived from either chromatin immunoprecipitation (ChIP) or transposon calling cards (Wang et al 2008;Ryan et al 2012;Mayhew and Mitra 2016;Shively et al 2019).…”

Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

“…We anticipate improvements coming from better network maps. One likely source of better maps is new, more accurate methods for measuring TF binding locations (53,(75)(76)(77)(78). The input network could also be improved by obtaining TF perturbation data from cells grown in new conditions.…”

The qBED track: a novel genome browser visualization for point processes

“…We have recently used calling cards to study TF binding in bulk populations of cells in vivo (Cammack et al, 2020), and we have also combined calling cards with single cell RNA-seq to simultaneously profile cell identity in complex organs and heterogenous disease states (Moudgil et al, 2019). Calling cards has also been used to dissect TF binding in both steady state and dynamic contexts (Mayhew and Mitra, 2014;Shively et al, 2019). As the scope and application of the calling card technique grows, we anticipate greater interest and increasingly complex visualization demands.…”