Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models

Many transcription factors (TFs) have the ability to cooperate on DNA elements as heterodimers. Despite the significance of TF heterodimerization for gene regulation, a quantitative understanding of cooperativity between various TF dimer partners and its impact on heterodimer DNA binding specificity models is still lacking. Here, we used a novel integrative approach, combining microfluidics-steered measurements of dimer-DNA assembly with mechanistic modeling of the implicated protein-protein-DNA interactions to quantitatively interrogate the cooperative DNA binding behavior of the adipogenic peroxisome proliferator-activated receptor gamma (PPAR gamma): retinoid X receptor alpha (RXR alpha) heterodimer. Using the high throughput MITOMI (mechanically induced trapping of molecular interactions) platform, we derived equilibrium DNA binding data for PPAR gamma, RXR alpha, as well as the PPAR gamma: RXR alpha heterodimer to more than 300 target DNA sites and variants thereof. We then quantified cooperativity underlying heterodimer-DNA binding and derived an integrative heterodimer DNA binding constant. Using this cooperativity-inclusive constant, we were able to build a heterodimer-DNA binding specificity model that has superior predictive power than the one based on a regular one-site equilibrium. Our data further revealed that individual nucleotide substitutions within the target site affect the extent of cooperativity in PPAR gamma:RXR alpha-DNA binding. Our study therefore emphasizes the importance of assessing cooperativity when generating DNA binding specificity models for heterodimers.

Modeling and analysis of parts and devices of genetic regulation

Yves Berset

DNA encrypts the composition of the cellular material that is synthesized through transcription and translation. Never-theless, gene regulation mechanisms determine the final amount of transcribed and translated material. Transcription factors (TF) are a class of proteins that bind to DNA motifs and can either facilitate or prevent the RNA polymerase to transcribe a strand of DNA into an mRNA. These mechanisms allow the cell to sense the environment and adapt to different environmental conditions, e.g. presence of toxic compounds, oxidative stress or absence of nutrients. TF interactions with DNA are depicted by networks of molecular interactions. Some TFs bind to very specific DNA sites, and others have a broad range of binding sites. Moreover, TFs often interact each other, e.g. through heterodimerization prior to bind to DNA, leading to changes in their binding specificities. However, these effects remain poorly understood. By combining detailed mathematical modeling and high-throughput experimental techniques for quantification of molecular interactions, we built a heterodimer-DNA specificity model with higher predictive power than a one-site model. Bioreporters are living cells that emit a signal in the presence of a chemical compound. In arsenic bioreporters, a TF triggers the detoxification response in presence of arsenic. To better understand the key mechanisms involved in the response, we built a detailed mechanistic model of the gene regulatory circuits of different bioreporters. In this study, we used mathematical modeling (ODE, SSA) to create, calibrate and analyze detailed networks of molecular interactions involved in gene regulations. We quantified the cooperative binding of transcription factors forming heterodimers on a DNA library, and optimized bioreporters for the detection of arsenic by modeling feedback, uncoupled and toggle switched-based gene regulatory circuits.

EPFL2018

Exploring on Protein-DNA interactions

Alexandra Styliani Kalantzi

A variety of DNA-binding proteins organizes the chromosomal DNA and regulates gene transcription, and DNA replication and recombination. In particular, for gene regulation there is a category of DNA-binding proteins, the transcription factors, which can detect and bind to a specific set of DNA motifs. For the protein-DNA complexes there are some solved structures, but for some cases, it is difficult to define experimentally these structures at the atomistic level. However, there are various in silico methods, that alone or in combination with experimental techniques, are capable of predicting proteins structures with high accuracy. DNA-binding proteins contain DNA-binding domains and unfortunately, there are few protein-DNA complexes that have been characterized at atomistic resolution. Here we have developed artificial neural networks to predict the binding sites and the interface between proteins and the DNA, based on a structure-based approach. Specifically, we used different interface descriptors and we included a variety of protein-DNA complexes, in order to predict the correct localizations of the DNA on a protein. A large group of proteins that can recognize specific DNA sequences is the KRAB-ZFP family. DNA recognition by zinc finger proteins (ZFPs) plays an important role in gene regulation. However, the molecular determinants defining the recognition of specific DNA nucleotides by ZFP finger repeats has not yet been decoded. Here, we present a method that can predict which ZF repeats specifically bind to a DNA target sequence and what is the most probable DNA target sequence for these repeats. Our method is based on the structural analysis of the binding network of resolved protein DNA complexes, and is validated on a benchmark set of solved ZFP-DNA complexes. We also characterized the binding specificity of two KRAB-ZFPs (ZFP14 and ZNF145) integrating our predictions with SMiLE-seq (Selective Microfluidics-based Ligand Enrichment followed by sequencing) data. Subsequently, we use our recognition code on ChIP-exo data, in order to give an insight into the poly-ZF domains binding patterns. We determined the most probable binding motifs and we separated the ZFPs in two groups: the ones that have a potential canonical and non-canonical binding. Altogether, this work gives an insight into the proteins that interact with the DNA, through a better understanding of the various protein-DNA interfaces and the predictions of these complexes at the atomistic level.

EPFL2019

Protein-Binding Microarray Analysis of Tumor Suppressor AP2 alpha Target Gene Specificity

Tanja Egener-Kuhn, Luigino Grasso, Jan Kerschgens, Hans-Anton Lehr, Nicolas Mermod, Horst Vogel

Cheap and massively parallel methods to assess the DNA-binding specificity of transcription factors are actively sought, given their prominent regulatory role in cellular processes and diseases. Here we evaluated the use of protein-binding microarrays (PBM) to probe the association of the tumor suppressor AP2 alpha with 6000 human genomic DNA regulatory sequences. We show that the PBM provides accurate relative binding affinities when compared to quantitative surface plasmon resonance assays. A PBM-based study of human healthy and breast tumor tissue extracts allowed the identification of previously unknown AP2 alpha target genes and it revealed genes whose direct or indirect interactions with AP2 alpha are affected in the diseased tissues. AP2 alpha binding and regulation was confirmed experimentally in human carcinoma cells for novel target genes involved in tumor progression and resistance to chemotherapeutics, providing a molecular interpretation of AP2 alpha role in cancer chemoresistance. Overall, we conclude that this approach provides quantitative and accurate assays of the specificity and activity of tumor suppressor and oncogenic proteins in clinical samples, interfacing genomic and proteomic assays.