Design of a combinatorial DNA microarray for protein-DNA interaction studies

BACKGROUND: Discovery of precise specificity of transcription factors is an important step on the way to understanding the complex mechanisms of gene regulation in eukaryotes. Recently, double-stranded protein-binding microarrays were developed as a potentially scalable approach to tackle transcription factor binding site identification. RESULTS: Here we present an algorithmic approach to experimental design of a microarray that allows for testing full specificity of a transcription factor binding to all possible DNA binding sites of a given length, with optimally efficient use of the array. This design is universal, works for any factor that binds a sequence motif and is not species-specific. Furthermore, simulation results show that data produced with the designed arrays is easier to analyze and would result in more precise identification of binding sites. CONCLUSION: In this study, we present a design of a double stranded DNA microarray for protein-DNA interaction studies and show that our algorithm allows optimally efficient use of the arrays for this purpose. We believe such a design will prove useful for transcription factor binding site identification and other biological problems

Notch and MAML-1 Complexation Do Not Detectably Alter the DNA Binding Specificity of the Transcription Factor CSL

Canonical Notch signaling is initiated when ligand binding induces proteolytic release of the intracellular part of Notch (ICN) from the cell membrane. ICN then travels into the nucleus where it drives the assembly of a transcriptional activation complex containing the DNA-binding transcription factor CSL, ICN, and a specialized co-activator of the Mastermind family. A consensus DNA binding site motif for the CSL protein was previously defined using selection-based methods, but whether subsequent association of Notch and Mastermind-like proteins affects the DNA binding preferences of CSL has not previously been examined.Here, we utilized protein-binding microarrays (PBMs) to compare the binding site preferences of isolated CSL with the preferred binding sites of CSL when bound to the CSL-binding domains of all four different human Notch receptors. Measurements were taken both in the absence and in the presence of Mastermind-like-1 (MAML1). Our data show no detectable difference in the DNA binding site preferences of CSL before and after loading of Notch and MAML1 proteins.These findings support the conclusion that accrual of Notch and MAML1 promote transcriptional activation without dramatically altering the preferred sites of DNA binding, and illustrate the potential of PBMs to analyze the binding site preferences of multiprotein-DNA complexes

Reverse Engineering the Yeast RNR1 Transcriptional Control System

Transcription is controlled by multi-protein complexes binding to short non-coding regions of genomic DNA. These complexes interact combinatorially. A major goal of modern biology is to provide simple models that predict this complex behavior. The yeast gene RNR1 is transcribed periodically during the cell cycle. Here, we present a pilot study to demonstrate a new method of deciphering the logic behind transcriptional regulation. We took regular samples from cell cycle synchronized cultures of Saccharomyces cerevisiae and extracted nuclear protein. We tested these samples to measure the amount of protein that bound to seven different 16 base pair sequences of DNA that have been previously identified as protein binding locations in the promoter of the RNR1 gene. These tests were performed using surface plasmon resonance. We found that the surface plasmon resonance signals showed significant variation throughout the cell cycle. We correlated the protein binding data with previously published mRNA expression data and interpreted this to show that transcription requires protein bound to a particular site and either five different sites or one additional sites. We conclude that this demonstrates the feasibility of this approach to decipher the combinatorial logic of transcription

A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays

Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge

Protein-Binding Microarray Analysis of Tumor Suppressor AP2α Target Gene Specificity

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 AP2α 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 AP2α target genes and it revealed genes whose direct or indirect interactions with AP2α are affected in the diseased tissues. AP2α 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 AP2α 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

A Feature-Based Approach to Modeling Protein–DNA Interactions

Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models (FMMs), a novel probabilistic method for modeling TF–DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation (ChIP) dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie.weizmann.ac.il/

An Integrated Approach to Identifying Cis-Regulatory Modules in the Human Genome

In eukaryotic genomes, it is challenging to accurately determine target sites of transcription factors (TFs) by only using sequence information. Previous efforts were made to tackle this task by considering the fact that TF binding sites tend to be more conserved than other functional sites and the binding sites of several TFs are often clustered. Recently, ChIP-chip and ChIP-sequencing experiments have been accumulated to identify TF binding sites as well as survey the chromatin modification patterns at the regulatory elements such as promoters and enhancers. We propose here a hidden Markov model (HMM) to incorporate sequence motif information, TF-DNA interaction data and chromatin modification patterns to precisely identify cis-regulatory modules (CRMs). We conducted ChIP-chip experiments on four TFs, CREB, E2F1, MAX, and YY1 in 1% of the human genome. We then trained a hidden Markov model (HMM) to identify the labels of the CRMs by incorporating the sequence motifs recognized by these TFs and the ChIP-chip ratio. Chromatin modification data was used to predict the functional sites and to further remove false positives. Cross-validation showed that our integrated HMM had a performance superior to other existing methods on predicting CRMs. Incorporating histone signature information successfully penalized false prediction and improved the whole performance. The dataset we used and the software are available at http://nash.ucsd.edu/CIS/

Integrating Phosphorylation Network with Transcriptional Network Reveals Novel Functional Relationships

Phosphorylation and transcriptional regulation events are critical for cells to transmit and respond to signals. In spite of its importance, systems-level strategies that couple these two networks have yet to be presented. Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets. Based on this understanding, we introduce a HeRS score (hetero-regulatory similarity score) to systematically characterize the functional relevance of kinase/phosphatase involvement with transcription factor, and present an algorithm that predicts hetero-regulatory modules. When extended to signaling network, this approach confirmed the structure and cross talk of MAPK pathways, inferred a novel functional transcription factor Sok2 in high osmolarity glycerol pathway, and explained the mechanism of reduced mating efficiency upon Fus3 deletion. This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors