Exploring the DNA-binding specificities of zinc fingers with DNA microarrays

Chromatin accessibility in the Drosophila embryo is determined by transcription factor pioneering and enhancer activation

Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it is difficult to differentiate between TFs that drive accessibility and those that do not. Deep learning models that learn complex sequence rules provide an unprecedented opportunity to dissect this problem. Using zygotic genome activation in Drosophila as a model, we analyzed high-resolution TF binding and chromatin accessibility data with interpretable deep learning and performed genetic validation experiments. We identify a hierarchical relationship between the pioneer TF Zelda and the TFs involved in axis patterning. Zelda consistently pioneers chromatin accessibility proportional to motif affinity, whereas patterning TFs augment chromatin accessibility in sequence contexts where they mediate enhancer activation. We conclude that chromatin accessibility occurs in two tiers: one through pioneering, which makes enhancers accessible but not necessarily active, and the second when the correct combination of TFs leads to enhancer activation.

Stable Immobilization of DNA to Silica Surfaces by Sequential Michael Addition Reactions Developed with Insights from Confocal Raman Microscopy

The immobilization of DNA to surfaces is required for numerous biosensing applications related to the capture of target DNA sequences, proteins, or small-molecule analytes from solution. For these applications to be successful, the chemistry of DNA immobilization should be efficient, reproducible, and stable and should allow the immobilized DNA to adopt a secondary structure required for association with its respective target molecule. To develop and characterize surface immobilization chemistry to meet this challenge, it is invaluable to have a quantitative, surface-sensitive method that can report the interfacial chemistry at each step, while also being capable of determining the structure, stability, and activity of the tethered DNA product. In this work, we develop a method to immobilize DNA to silica, glass, or other oxide surfaces by carrying out the reactions in porous silica particles. Due to the high specific surface area of porous silica, the local concentrations of surface-immobilized molecules within the particle are sufficiently high that interfacial chemistry can be monitored at each step of the process with confocal Raman microscopy, providing a unique capability to assess the molecular composition, structure, yield, and surface coverage of these reactions. We employ this methodology to investigate the steps for immobilizing thiolated-DNA to thiol-modified silica surfaces through sequential Michael addition reactions with the cross-linker 1,4-phenylene-bismaleimide. A key advantage of employing a phenyl-bismaleimide over a comparable alkyl coupling reagent is the efficient conversion of the initial phenyl-thiosuccinimide to a more stable succinamic acid thioether linkage. This transformation was confirmed by in situ Raman spectroscopy measurements, and the resulting succinamic acid thioether product exhibited greater than 95% retention of surface-immobilized DNA after 12 days at room temperature in aqueous buffer. Confocal Raman microscopy was also used to assess the conformational freedom of surface-immobilized DNA by comparing the structure of a 23-mer DNA hairpin sequence under duplex-forming and unfolding conditions. We find that the immobilized DNA hairpin can undergo reversible intramolecular duplex formation based on the changes in frequencies and intensities of the phosphate backbone and base-specific vibrational modes that are informative of the hybridization state of DNA.

Current strategies employed in the manipulation of gene expression for clinical purposes

Abnormal gene expression level or expression of genes containing deleterious mutations are two of the main determinants which lead to genetic disease. To obtain a therapeutic effect and thus to cure genetic diseases, it is crucial to regulate the host's gene expression and restore it to physiological conditions. With this purpose, several molecular tools have been developed and are currently tested in clinical trials. Genome editing nucleases are a class of molecular tools routinely used in laboratories to rewire host's gene expression. Genome editing nucleases include different categories of enzymes: meganucleses (MNs), zinc finger nucleases (ZFNs), clustered regularly interspaced short palindromic repeats (CRISPR)- CRISPR associated protein (Cas) and transcription activator-like effector nuclease (TALENs). Transposable elements are also a category of molecular tools which includes different members, for example Sleeping Beauty (SB), PiggyBac (PB), Tol2 and TcBuster. Transposons have been used for genetic studies and can serve as gene delivery tools. Molecular tools to rewire host's gene expression also include episomes, which are divided into different categories depending on their molecular structure. Finally, RNA interference is commonly used to regulate gene expression through the administration of small interfering RNA (siRNA), short hairpin RNA (shRNA) and bi-functional shRNA molecules. In this review, we will describe the different molecular tools that can be used to regulate gene expression and discuss their potential for clinical applications. These molecular tools are delivered into the host's cells in the form of DNA, RNA or protein using vectors that can be grouped into physical or biochemical categories. In this review we will also illustrate the different types of payloads that can be used, and we will discuss recent developments in viral and non-viral vector technology.

A complex epigenome-splicing crosstalk governs epithelial-to-mesenchymal transition in metastasis and brain development

Epithelial-to-mesenchymal transition (EMT) renders epithelial cells migratory properties. While epigenetic and splicing changes have been implicated in EMT, the mechanisms governing their crosstalk remain poorly understood. Here we discovered that a C2H2 zinc finger protein, ZNF827, is strongly induced during various contexts of EMT, including in brain development and breast cancer metastasis, and is required for the molecular and phenotypic changes underlying EMT in these processes. Mechanistically, ZNF827 mediated these responses by orchestrating a large-scale remodelling of the splicing landscape by recruiting HDAC1 for epigenetic modulation of distinct genomic loci, thereby slowing RNA polymerase II progression and altering the splicing of genes encoding key EMT regulators in cis. Our findings reveal an unprecedented complexity of crosstalk between epigenetic landscape and splicing programme in governing EMT and identify ZNF827 as a master regulator coupling these processes during EMT in brain development and breast cancer metastasis.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

A deterministic code for transcription factor-DNA recognition through computation of binding interfaces

The recognition code between transcription factor (TF) amino acids and DNA bases remains poorly understood. Here, the determinants of TF amino acid-DNA base binding selectivity were identified through the analysis of crystals of TF-DNA complexes. Selective, high-frequency interactions were identified for the vast majority of amino acid side chains (‘structural code’). DNA binding specificities were then independently assessed by meta-analysis of random-mutagenesis studies of Zn finger-target DNA sequences. Selective, high-frequency interactions were identified for the majority of mutagenized residues (‘mutagenesis code’). The structural code and the mutagenesis code were shown to match to a striking level of accuracy (P = 3.1 × 10−33), suggesting the identification of fundamental rules of TF binding to DNA bases. Additional insight was gained by showing a geometry-dictated choice among DNA-binding TF residues with overlapping specificity. These findings indicate the existence of a DNA recognition mode whereby the physical-chemical characteristics of the interacting residues play a deterministic role. The discovery of this DNA recognition code advances our knowledge on fundamental features of regulation of gene expression and is expected to pave the way for integration with higher-order complexity approaches.

LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction

Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein-glycan interactions, it is shown that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host-microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

High five! Methyl probes at five ring positions of phenylalanine explore the hydrophobic core dynamics of zinc finger miniproteins

The elucidation of internal dynamics in proteins is essential for the understanding of their stability and functionality. Breaking the symmetry of the degenerate rotation of the phenyl side chain provides additional structural information and allows a detailed description of the dynamics. Based on this concept, we propose a combination of synthetic and computational methods, to study the rotational mobility of the Phe ring in a sensitive zinc finger motif. The systematic methyl hopping around the phenylalanine ring yields o-, m-, p-tolyl and xylyl side chains that provide a vast array of additional NOE contacts, allowing the precise determination of the orientation of the aromatic ring. MD simulations and metadynamics complement these findings and facilitate the generation of free energy profiles for each derivative. Previous studies used a wide temperature window in combination with NMR spectroscopy to elucidate the side chain mobility of stable proteins. The zinc finger moiety exhibits a limited thermodynamic stability in a temperature range of only 40 K, making this approach impractical for this compound class. Therefore, we have developed a method that can be applied even to thermolabile systems and facilitates the detailed investigation of protein dynamics.

Insight into Calcium-Binding Motifs of Intrinsically Disordered Proteins

Motifs within proteins help us categorize their functions. Intrinsically disordered proteins (IDPs) are rich in short linear motifs, conferring them many different roles. IDPs are also frequently highly charged and, therefore, likely to interact with ions. Canonical calcium-binding motifs, such as the EF-hand, often rely on the formation of stabilizing flanking helices, which are a key characteristic of folded proteins, but are absent in IDPs. In this study, we probe the existence of a calcium-binding motif relevant to IDPs. Upon screening several carefully selected IDPs using NMR spectroscopy supplemented with affinity quantification by colorimetric assays, we found calcium-binding motifs in IDPs which could be categorized into at least two groups-an Excalibur-like motif, sequentially similar to the EF-hand loop, and a condensed-charge motif carrying repetitive negative charges. The motifs show an affinity for calcium typically in the ~100 μM range relevant to regulatory functions and, while calcium binding to the condensed-charge motif had little effect on the overall compaction of the IDP chain, calcium binding to Excalibur-like motifs resulted in changes in compaction. Thus, calcium binding to IDPs may serve various structural and functional roles that have previously been underreported.

A Bit Stickier, a Bit Slower, a Lot Stiffer: Specific vs. Nonspecific Binding of Gal4 to DNA

Transcription factors regulate gene activity by binding specific regions of genomic DNA thanks to a subtle interplay of specific and nonspecific interactions that is challenging to quantify. Here, we exploit Reflective Phantom Interface (RPI), a label-free biosensor based on optical reflectivity, to investigate the binding of the N-terminal domain of Gal4, a well-known gene regulator, to double-stranded DNA fragments containing or not its consensus sequence. The analysis of RPI-binding curves provides interaction strength and kinetics and their dependence on temperature and ionic strength. We found that the binding of Gal4 to its cognate site is stronger, as expected, but also markedly slower. We performed a combined analysis of specific and nonspecific binding—equilibrium and kinetics—by means of a simple model based on nested potential wells and found that the free energy gap between specific and nonspecific binding is of the order of one kcal/mol only. We investigated the origin of such a small value by performing all-atom molecular dynamics simulations of Gal4–DNA interactions. We found a strong enthalpy–entropy compensation, by which the binding of Gal4 to its cognate sequence entails a DNA bending and a striking conformational freezing, which could be instrumental in the biological function of Gal4.

Tools for experimental and computational analyses of off-target editing by programmable nucleases

Genome editing using programmable nucleases is revolutionizing life science and medicine. Off-target editing by these nucleases remains a considerable concern, especially in therapeutic applications. Here we review tools developed for identifying potential off-target editing sites and compare the ability of these tools to properly analyze off-target effects. Recent advances in both in silico and experimental tools for off-target analysis have generated remarkably concordant results for sites with high off-target editing activity. However, no single tool is able to accurately predict low-frequency off-target editing, presenting a bottleneck in therapeutic genome editing, because even a small number of cells with off-target editing can be detrimental. Therefore, we recommend that at least one in silico tool and one experimental tool should be used together to identify potential off-target sites, and amplicon-based next-generation sequencing (NGS) should be used as the gold standard assay for assessing the true off-target effects at these candidate sites. Future work to improve off-target analysis includes expanding the true off-target editing dataset to evaluate new experimental techniques and to train machine learning algorithms; performing analysis using the particular genome of the cells in question rather than the reference genome; and applying novel NGS techniques to improve the sensitivity of amplicon-based off-target editing quantification.

The geometric influence on the Cys2His2 zinc finger domain and functional plasticity

The Cys₂His₂ zinc finger is the most common DNA-binding domain expanding in metazoans since the fungi human split. A proposed catalyst for this expansion is an arms race to silence transposable elements yet it remains poorly understood how this domain is able to evolve the required specificities. Likewise, models of its DNA binding specificity remain error prone due to a lack of understanding of how adjacent fingers influence each other's binding specificity. Here, we use a synthetic approach to exhaustively investigate binding geometry, one of the dominant influences on adjacent finger function. By screening over 28 billion protein–DNA interactions in various geometric contexts we find the plasticity of the most common natural geometry enables more functional amino acid combinations across all targets. Further, residues that define this geometry are enriched in genomes where zinc fingers are prevalent and specificity transitions would be limited in alternative geometries. Finally, these results demonstrate an exhaustive synthetic screen can produce an accurate model of domain function while providing mechanistic insight that may have assisted in the domains expansion.

Strengthening the Immune System and Reducing Inflammation and Oxidative Stress through Diet and Nutrition: Considerations during the COVID-19 Crisis

The coronavirus-disease 2019 (COVID-19) was announced as a global pandemic by the World Health Organization. Challenges arise concerning how to optimally support the immune system in the general population, especially under self-confinement. An optimal immune response depends on an adequate diet and nutrition in order to keep infection at bay. For example, sufficient protein intake is crucial for optimal antibody production. Low micronutrient status, such as of vitamin A or zinc, has been associated with increased infection risk. Frequently, poor nutrient status is associated with inflammation and oxidative stress, which in turn can impact the immune system. Dietary constituents with especially high anti-inflammatory and antioxidant capacity include vitamin C, vitamin E, and phytochemicals such as carotenoids and polyphenols. Several of these can interact with transcription factors such as NF-kB and Nrf-2, related to anti-inflammatory and antioxidant effects, respectively. Vitamin D in particular may perturb viral cellular infection via interacting with cell entry receptors (angiotensin converting enzyme 2), ACE2. Dietary fiber, fermented by the gut microbiota into short-chain fatty acids, has also been shown to produce anti-inflammatory effects. In this review, we highlight the importance of an optimal status of relevant nutrients to effectively reduce inflammation and oxidative stress, thereby strengthening the immune system during the COVID-19 crisis.

I-Block: a simple Escherichia coli-based assay for studying sequence-specific DNA binding of proteins

We have developed a simple method called I-Block assay, which can detect sequence-specific binding of proteins to DNA in Escherichia coli. The method works by detecting competition between the protein of interest and RNA polymerase for binding to overlapping target sites in a plasmid-borne lacI promoter variant. The assay utilizes two plasmids and an E. coli host strain, from which the gene of the Lac repressor (lacI) has been deleted. One of the plasmids carries the lacI gene with a unique NheI restriction site created in the lacI promoter. The potential recognition sequences of the tested protein are inserted into the NheI site. Introduction of the plasmids into the E. coliΔlacI host represses the constitutive β-galactosidase synthesis of the host bacterium. If the studied protein expressed from a compatible plasmid binds to its target site in the lacI promoter, it will interfere with lacI transcription and lead to increased β-galactosidase activity. The method was tested with two zinc finger proteins, with the lambda phage cI857 repressor, and with CRISPR-dCas9 targeted to the lacI promoter. The I-Block assay was shown to work with standard liquid cultures, with cultures grown in microplate and with colonies on X-gal indicator plates.

TFregulomeR reveals transcription factors' context-specific features and functions

Transcription factors (TFs) are sequence-specific DNA binding proteins, fine-tuning spatiotemporal gene expression. Since genomic occupancy of a TF is highly dynamic, it is crucial to study TF binding sites (TFBSs) in a cell-specific context. To date, thousands of ChIP-seq datasets have portrayed the genomic binding landscapes of numerous TFs in different cell types. Although these datasets can be browsed via several platforms, tools that can operate on that data flow are still lacking. Here, we introduce TFregulomeR (https://github.com/benoukraflab/TFregulomeR), an R-library linked to an up-to-date compendium of cistrome and methylome datasets, implemented with functionalities that facilitate integrative analyses. In particular, TFregulomeR enables the characterization of TF binding partners and cell-specific TFBSs, along with the study of TF’s functions in the context of different partnerships and DNA methylation levels. We demonstrated that TFs’ target gene ontologies can differ notably depending on their partners and, by re-analyzing well characterized TFs, we brought to light that numerous leucine zipper TFBSs derived from ChIP-seq experiments documented in current databases were inadequately characterized, due to the fact that their position weight matrices were assembled using a mixture of homodimer and heterodimer binding sites. Altogether, analyses of context-specific transcription regulation with TFregulomeR foster our understanding of regulatory network-dependent TF functions.

Cell-free gene-regulatory network engineering with synthetic transcription factors

Gene-regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein-protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here, we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription-factor binding-energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene-regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene-regulatory networks and for studying the biophysics of transcriptional regulation.

Comprehensive profiling of the ligand binding landscapes of duplexed aptamer families reveals widespread induced fit

Duplexed aptamers (DAs) are ligand-responsive constructs engineered by hybridizing an aptamer with an aptamer-complementary element (ACE, e.g., a DNA oligonucleotide). Although DAs are commonly deployed, the binding dynamics of ternary ACE-aptamer–ligand systems remain underexplored, having been conventionally described by a conformational selection framework. Here we introduce aptamer-complementary element scanning (ACE-Scan) as a method to generate comprehensive hybridization, spontaneous off-rate, and induced fit ligand-binding landscapes for entire DA families. ACE-Scan reveals induced fit in DAs engineered from small molecule- and protein-binding DNA and RNA aptamers, as well as DAs engineered from the natural add riboswitch aptamer. To validate ACE-Scan, we engineer solution-phase ATP-specific DAs from 5 ACEs with varying spontaneous and induced fit off-rates, generating aptasensors with 8-fold differences in dynamic range consistent with ACE-Scan. This work demonstrates that ACE-Scan can readily map induced fit in DAs, empowering aptamers in biosensing, synthetic biology, and DNA nanomachines.

Duplexed aptamers are a common biosensor format; however, how complementary strand sequence, length, and position modulate ligand binding is not well understood. Here, the authors introduce ACE-Scan to comprehensively map binding landscapes, uncovering hotspots of enhanced binding by induced fit.

Hox functional diversity: Novel insights from flexible motif folding and plastic protein interaction

How the formidable diversity of forms emerges from developmental and evolutionary processes is one of the most fascinating questions in biology. The homeodomain-containing Hox proteins were recognized early on as major actors in diversifying animal body plans. The molecular mechanisms underlying how this transcription factor family controls a large array of context- and cell-specific biological functions is, however, still poorly understood. Clues to functional diversity have emerged from studies exploring how Hox protein activity is controlled through interactions with PBC class proteins, also evolutionary conserved HD-containing proteins. Recent structural data and molecular dynamic simulations add further mechanistic insights into Hox protein mode of action, suggesting that flexible folding of protein motifs allows for plastic protein interaction. As we discuss in this review, these findings define a novel type of Hox-PBC interaction, weak and dynamic instead of strong and static, hence providing novel clues to understanding Hox transcriptional specificity and diversity.

Exploiting the recognition code for elucidating the mechanism of zinc finger protein-DNA interactions

Background

Engineering zinc finger protein motifs for specific binding to double-stranded DNA is critical for targeted genome editing. Most existing tools for predicting DNA-binding specificity in zinc fingers are trained on data obtained from naturally occurring proteins, thereby skewing the predictions. Moreover, these mostly neglect the cooperativity exhibited by zinc fingers.

Methods

Here, we present an ab-initio method that is based on mutation of the key α-helical residues of individual fingers of the parent template for Zif-268 and its consensus sequence (PDB ID: 1AAY). In an attempt to elucidate the mechanism of zinc finger protein-DNA interactions, we evaluated and compared three approaches, differing in the amino acid mutations introduced in the Zif-268 parent template, and the mode of binding they try to mimic, i.e., modular and synergistic mode of binding.

Results

Comparative evaluation of the three strategies reveals that the synergistic mode of binding appears to mimic the ideal mechanism of DNA-zinc finger protein binding. Analysis of the predictions made by all three strategies indicate strong dependence of zinc finger binding specificity on the amino acid propensity and the position of a 3-bp DNA sub-site in the target DNA sequence. Moreover, the binding affinity of the individual zinc fingers was found to increase in the order Finger 1 < Finger 2 < Finger 3, thus confirming the cooperative effect.

Conclusions

Our analysis offers novel insights into the prediction of ZFPs for target DNA sequences and the approaches have been made available as an easy to use web server at http://web.iitd.ac.in/~sundar/zifpredict_ihbe

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3324-8) contains supplementary material, which is available to authorized users.

Single Molecule Localization and Discrimination of DNA-Protein Complexes by Controlled Translocation Through Nanocapillaries

Through the use of optical tweezers we performed controlled translocations of DNA-protein complexes through nanocapillaries. We used RNA polymerase (RNAP) with two binding sites on a 7.2 kbp DNA fragment and a dCas9 protein tailored to have five binding sites on λ-DNA (48.5 kbp). Measured localization of binding sites showed a shift from the expected positions on the DNA that we explained using both analytical fitting and a stochastic model. From the measured force versus stage curves we extracted the nonequilibrium work done during the translocation of a DNA-protein complex and used it to obtain an estimate of the effective charge of the complex. In combination with conductivity measurements, we provided a proof of concept for discrimination between different DNA-protein complexes simultaneous to the localization of their binding sites.

Every transcription factor deserves its map: Scaling up epitope tagging of proteins to bypass antibody problems

Genome-wide identification of transcription factor binding sites with the ChIP-seq method is an extremely important scientific endeavor - one that should ideally be performed for every transcription factor in as many cell types as possible. A major hurdle on the way to this goal is the necessity for a specific, ChIP-grade antibody for each transcription factor of interest, which is often not available. Here, we describe CETCh-seq, a recently published method utilizing genome engineering with the CRISPR/Cas9 system to circumvent the need for a specific antibody. Using the CETCh-seq method, targeted genomic editing results in an epitope-tagged transcription factor, which is recognized by a well-characterized, standard antibody, efficacious for ChIP-seq. We have used CETCh-seq in human cancer cell lines as well as mouse embryonic stem cells. We find that roughly 60% of transcription factors tagged using CETCh-seq produce a high quality ChIP-seq map, a significant improvement over traditional antibody-based methods.

The Soft Touch: Low-Affinity Transcription Factor Binding Sites in Development and Evolution

Transcription factor proteins regulate gene expression by binding to specific DNA regions. Most studies of transcription factor binding sites have focused on the highest affinity sites for each factor. There is abundant evidence, however, that binding sites with a range of affinities, including very low affinities, are critical to gene regulation. Here, we present the theoretical and experimental evidence for the importance of low-affinity sites in gene regulation and development. We also discuss the implications of the widespread use of low-affinity sites in eukaryotic genomes for robustness, precision, specificity, and evolution of gene regulation.

Integrated microfluidic approach for quantitative high-throughput measurements of transcription factor binding affinities

Protein binding to DNA is a fundamental process in gene regulation. Methodologies such as ChIP-Seq and mapping of DNase I hypersensitive sites provide global information on this regulation in vivo In vitro methodologies provide valuable complementary information on protein-DNA specificities. However, current methods still do not measure absolute binding affinities. There is a real need for large-scale quantitative protein-DNA affinity measurements. We developed QPID, a microfluidic application for measuring protein-DNA affinities. A single run is equivalent to 4096 gel-shift experiments. Using QPID, we characterized the different affinities of ATF1, c-Jun, c-Fos and AP-1 to the CRE consensus motif and CRE half-site in two different genomic sequences on a single device. We discovered that binding of ATF1, but not of AP-1, to the CRE half-site is highly affected by its genomic context. This effect was highly correlated with ATF1 ChIP-seq and PBM experiments. Next, we characterized the affinities of ATF1 and ATF3 to 128 genomic CRE and CRE half-site sequences. Our affinity measurements explained that in vivo binding differences between ATF1 and ATF3 to CRE and CRE half-sites are partially mediated by differences in the minor groove width. We believe that QPID would become a central tool for quantitative characterization of biophysical aspects affecting protein-DNA binding.

A Biophysical Approach to Predicting Protein-DNA Binding Energetics

Sequence-specific interactions between proteins and DNA play a central role in DNA replication, repair, recombination, and control of gene expression. These interactions can be studied in vitro using microfluidics, protein-binding microarrays (PBMs), and other high-throughput techniques. Here we develop a biophysical approach to predicting protein-DNA binding specificities from high-throughput in vitro data. Our algorithm, called BindSter, can model alternative DNA-binding modes and multiple protein species competing for access to DNA, while rigorously taking into account all sterically allowed configurations of DNA-bound factors. BindSter can be used with a hierarchy of protein-DNA interaction models of increasing complexity, including contributions of mononucleotides, dinucleotides, and longer words to the total protein-DNA binding energy. We observe that the quality of BindSter predictions does not change significantly as some of the energy parameters vary over a sizable range. To take this degeneracy into account, we have developed a graphical representation of parameter uncertainties called IntervalLogo. We find that our simplest model, in which each nucleotide in the binding site is treated independently, performs better than previous biophysical approaches. The extensions of this model, in which contributions of longer words are also considered, result in further improvements, underscoring the importance of higher-order effects in protein-DNA energetics. In contrast, we find little evidence of multiple binding modes for the transcription factors (TFs) and experimental conditions in our data set. Furthermore, there is limited consistency in predictions for the same TF based on microfluidics and PBM data.

Understanding DNA-binding specificity by bacteria hybrid selection

Understanding how sequence-specific protein-DNA interactions direct cellular function is of great interest to the research community. High-throughput methods have been developed to determine DNA-binding specificities; one such technique, the bacterial one-hybrid (B1H) system, confers advantages including ease of use, sensitivity and throughput. In this review, we describe the evolution of the B1H system as a tool capable of screening large DNA libraries to investigate protein-DNA interactions of interest. We discuss how DNA-binding specificities produced by the B1H system have been used to predict regulatory targets. Additionally, we examine how this approach has been applied to characterize two common DNA-binding domain families-homeodomains and Cys2His2 zinc fingers-both in organism-wide studies and with synthetic approaches. In the case of the former, the B1H system has produced large catalogs of protein specificity and nuanced information about previously recovered DNA targets, thereby improving our understanding of these proteins' functions in vivo and increasing our capacity to predict similar interactions in other species. In the latter, synthetic screens of the same DNA-binding domains have further refined our models of specificity, through analyzing comprehensive libraries to uncover all proteins able to bind a complete set of targets, and, for instance, exploring how context-in the form of domain position within the parent protein-may affect specificity. Finally, we recognize the limitations of the B1H system and discuss its potential for use in the production of designer proteins and in studies of protein-protein interactions.

Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models

We present a discriminative learning method for pattern discovery of binding sites in nucleic acid sequences based on hidden Markov models. Sets of positive and negative example sequences are mined for sequence motifs whose occurrence frequency varies between the sets. The method offers several objective functions, but we concentrate on mutual information of condition and motif occurrence. We perform a systematic comparison of our method and numerous published motif-finding tools. Our method achieves the highest motif discovery performance, while being faster than most published methods. We present case studies of data from various technologies, including ChIP-Seq, RIP-Chip and PAR-CLIP, of embryonic stem cell transcription factors and of RNA-binding proteins, demonstrating practicality and utility of the method. For the alternative splicing factor RBM10, our analysis finds motifs known to be splicing-relevant.

The motif discovery method is implemented in the free software package Discrover. It is applicable to genome- and transcriptome-scale data, makes use of available repeat experiments and aside from binary contrasts also more complex data configurations can be utilized.

New Technologies Provide Quantum Changes in the Scale, Speed, and Success of SELEX Methods and Aptamer Characterization

Single-stranded oligonucleotide aptamers have attracted great attention in the past decade because of their diagnostic and therapeutic potential. These versatile, high affinity and specificity reagents are selected by an iterative in vitro process called SELEX, Systematic Evolution of Ligands by Exponential Enrichment. Numerous SELEX methods have been developed for aptamer selections; some that are simple and straightforward, and some that are specialized and complicated. The method of SELEX is crucial for selection of an aptamer with desired properties; however, success also depends on the starting aptamer library, the target molecule, aptamer enrichment monitoring assays, and finally, the analysis and characterization of selected aptamers. Here, we summarize key recent developments in aptamer selection methods, as well as other aspects of aptamer selection that have significant impact on the outcome. We discuss potential pitfalls and limitations in the selection process with an eye to aid researchers in the choice of a proper SELEX strategy, and we highlight areas where further developments and improvements are desired. We believe carefully designed multiplexed selection methods, when complemented with high-throughput downstream analysis and characterization assays, will yield numerous high-affinity aptamers to protein and small molecule targets, and thereby generate a vast array of reagents for probing basic biological mechanisms and implementing new diagnostic and therapeutic applications in the near future.

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins

Cys(2)-His(2) zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

Role of promoter DNA sequence variations on the binding of EGR1 transcription factor

In response to a wide variety of stimuli such as growth factors and hormones, EGR1 transcription factor is rapidly induced and immediately exerts downstream effects central to the maintenance of cellular homeostasis. Herein, our biophysical analysis reveals that DNA sequence variations within the target gene promoters tightly modulate the energetics of binding of EGR1 and that nucleotide substitutions at certain positions are much more detrimental to EGR1-DNA interaction than others. Importantly, the reduction in binding affinity poorly correlates with the loss of enthalpy and gain of entropy-a trend indicative of a complex interplay between underlying thermodynamic factors due to the differential role of water solvent upon nucleotide substitution. We also provide a rationale for the physical basis of the effect of nucleotide substitutions on the EGR1-DNA interaction at atomic level. Taken together, our study bears important implications on understanding the molecular determinants of a key protein-DNA interaction at the cross-roads of human health and disease.

Evolution of iron(II)-finger peptides by using a bipyridyl amino acid

We report the engineering of zinc-finger-like motifs containing the unnatural amino acid (2,2'-bipyridin-5-yl)alanine (Bpy-Ala). A phage-display library was constructed in which five residues in the N-terminal finger of zif268 were randomized to include both canonical amino acids and Bpy-Ala. Panning of this library against a nine-base-pair DNA binding site identified several Bpy-Ala-containing functional Zif268 mutants. These mutants bind the Zif268 recognition site with affinities comparable to that of the wild-type protein. Further characterization indicated that the mutant fingers bind low-spin Fe(II) rather than Zn(II) . This work demonstrates that an expanded genetic code can lead to new metal ion binding motifs that can serve as structural, catalytic, or regulatory elements in proteins.

A force-based, parallel assay for the quantification of protein-DNA interactions

Analysis of transcription factor binding to DNA sequences is of utmost importance to understand the intricate regulatory mechanisms that underlie gene expression. Several techniques exist that quantify DNA-protein affinity, but they are either very time-consuming or suffer from possible misinterpretation due to complicated algorithms or approximations like many high-throughput techniques. We present a more direct method to quantify DNA-protein interaction in a force-based assay. In contrast to single-molecule force spectroscopy, our technique, the Molecular Force Assay (MFA), parallelizes force measurements so that it can test one or multiple proteins against several DNA sequences in a single experiment. The interaction strength is quantified by comparison to the well-defined rupture stability of different DNA duplexes. As a proof-of-principle, we measured the interaction of the zinc finger construct Zif268/NRE against six different DNA constructs. We could show the specificity of our approach and quantify the strength of the protein-DNA interaction.

On the value of intra-motif dependencies of human insulator protein CTCF

The binding affinity of DNA-binding proteins such as transcription factors is mainly determined by the base composition of the corresponding binding site on the DNA strand. Most proteins do not bind only a single sequence, but rather a set of sequences, which may be modeled by a sequence motif. Algorithms for de novo motif discovery differ in their promoter models, learning approaches, and other aspects, but typically use the statistically simple position weight matrix model for the motif, which assumes statistical independence among all nucleotides. However, there is no clear justification for that assumption, leading to an ongoing debate about the importance of modeling dependencies between nucleotides within binding sites. In the past, modeling statistical dependencies within binding sites has been hampered by the problem of limited data. With the rise of high-throughput technologies such as ChIP-seq, this situation has now changed, making it possible to make use of statistical dependencies effectively. In this work, we investigate the presence of statistical dependencies in binding sites of the human enhancer-blocking insulator protein CTCF by using the recently developed model class of inhomogeneous parsimonious Markov models, which is capable of modeling complex dependencies while avoiding overfitting. These findings lead to a more detailed characterization of the CTCF binding motif, which is only poorly represented by independent nucleotide frequencies at several positions, predominantly at the 3' end.

Determining the specificities of TALENs, Cas9, and other genome-editing enzymes

The rapid development of programmable site-specific endonucleases has led to a dramatic increase in genome engineering activities for research and therapeutic purposes. Specific loci of interest in the genomes of a wide range of organisms including mammals can now be modified using zinc-finger nucleases, transcription activator-like effectornucleases, and CRISPR-associated Cas9 endonucleases in a site-specific manner, in some cases requiring relatively modest effort for endonuclease design, construction, and application. While these technologies have made genome engineering widely accessible, the ability of programmable nucleases to cleave off-target sequences can limit their applicability and raise concerns about therapeutic safety. In this chapter, we review methods to evaluate and improve the DNA cleavage activity of programmable site-specific endonucleases and describe a procedure for a comprehensive off-target profiling method based on the in vitro selection of very large (~10(12)-membered) libraries of potential nuclease substrates.

Protein-DNA force assay in a microfluidic format

The detailed study of protein-DNA interactions is a core effort to elucidate physiological processes, including gene regulation, DNA repair and the immune response. The molecular force assay (MFA) is an established method to study DNA-binding proteins. In particular, high-affinity binder dissociation is made possible by the application of force. Microfluidic lab-on-a-chip approaches have proven helpful for parallelization, small sample volumes, reproducibility, and low cost. We report the successful combination of these two principles, forming a microfluidic molecular force assay and representing a novel use for the established MITOMI chip design. We present, characterize, validate and apply this integrated method. An alternative confocal fluorescence microscopy readout and analysis method is introduced and validated. In a multiplexing application, EcoRI binding is detected and characterized. This method paves the way for quantitative on-chip force measurements. It is suited for integration with DNA micro-spotting and in vitro expression of transcription factors to form a high-throughput chip for detailed DNA-protein interaction studies.

Deep sequencing of large library selections allows computational discovery of diverse sets of zinc fingers that bind common targets

The Cys2His2 zinc finger (ZF) is the most frequently found sequence-specific DNA-binding domain in eukaryotic proteins. The ZF's modular protein-DNA interface has also served as a platform for genome engineering applications. Despite decades of intense study, a predictive understanding of the DNA-binding specificities of either natural or engineered ZF domains remains elusive. To help fill this gap, we developed an integrated experimental-computational approach to enrich and recover distinct groups of ZFs that bind common targets. To showcase the power of our approach, we built several large ZF libraries and demonstrated their excellent diversity. As proof of principle, we used one of these ZF libraries to select and recover thousands of ZFs that bind several 3-nt targets of interest. We were then able to computationally cluster these recovered ZFs to reveal several distinct classes of proteins, all recovered from a single selection, to bind the same target. Finally, for each target studied, we confirmed that one or more representative ZFs yield the desired specificity. In sum, the described approach enables comprehensive large-scale selection and characterization of ZF specificities and should be a great aid in furthering our understanding of the ZF domain.

De novo prediction of DNA-binding specificities for Cys2His2 zinc finger proteins

Proteins with sequence-specific DNA binding function are important for a wide range of biological activities. De novo prediction of their DNA-binding specificities from sequence alone would be a great aid in inferring cellular networks. Here we introduce a method for predicting DNA-binding specificities for Cys₂His₂ zinc fingers (C2H2-ZFs), the largest family of DNA-binding proteins in metazoans. We develop a general approach, based on empirical calculations of pairwise amino acid–nucleotide interaction energies, for predicting position weight matrices (PWMs) representing DNA-binding specificities for C2H2-ZF proteins. We predict DNA-binding specificities on a per-finger basis and merge predictions for C2H2-ZF domains that are arrayed within sequences. We test our approach on a diverse set of natural C2H2-ZF proteins with known binding specificities and demonstrate that for >85% of the proteins, their predicted PWMs are accurate in 50% of their nucleotide positions. For proteins with several zinc finger isoforms, we show via case studies that this level of accuracy enables us to match isoforms with their known DNA-binding specificities. A web server for predicting a PWM given a protein containing C2H2-ZF domains is available online at http://zf.princeton.edu and can be used to aid in protein engineering applications and in genome-wide searches for transcription factor targets.

Covariation between homeodomain transcription factors and the shape of their DNA binding sites

Protein–DNA recognition is a critical component of gene regulatory processes but the underlying molecular mechanisms are not yet completely understood. Whereas the DNA binding preferences of transcription factors (TFs) are commonly described using nucleotide sequences, the 3D DNA structure is recognized by proteins and is crucial for achieving binding specificity. However, the ability to analyze DNA shape in a high-throughput manner made it only recently feasible to integrate structural information into studies of protein–DNA binding. Here we focused on the homeodomain family of TFs and analyzed the DNA shape of thousands of their DNA binding sites, investigating the covariation between the protein sequence and the sequence and shape of their DNA targets. We found distinct homeodomain regions that were more correlated with either the nucleotide sequence or the DNA shape of their preferred binding sites, demonstrating different readout mechanisms through which homeodomains attain DNA binding specificity. We identified specific homeodomain residues that likely play key roles in DNA recognition via shape readout. Finally, we showed that adding DNA shape information when characterizing binding sites improved the prediction accuracy of homeodomain binding specificities. Taken together, our findings indicate that DNA shape information can generally provide new mechanistic insights into TF binding.

Experimental characterization of the human non-sequence-specific nucleic acid interactome

Background

The interactions between proteins and nucleic acids have a fundamental function in many biological processes, including gene transcription, RNA homeostasis, protein translation and pathogen sensing for innate immunity. While our knowledge of the ensemble of proteins that bind individual mRNAs in mammalian cells has been greatly augmented by recent surveys, no systematic study on the non-sequence-specific engagement of native human proteins with various types of nucleic acids has been reported.

Results

We designed an experimental approach to achieve broad coverage of the non-sequence-specific RNA and DNA binding space, including methylated cytosine, and tested for interaction potential with the human proteome. We used 25 rationally designed nucleic acid probes in an affinity purification mass spectrometry and bioinformatics workflow to identify proteins from whole cell extracts of three different human cell lines. The proteins were profiled for their binding preferences to the different general types of nucleic acids. The study identified 746 high-confidence direct binders, 139 of which were novel and 237 devoid of previous experimental evidence. We could assign specific affinities for sub-types of nucleic acid probes to 219 distinct proteins and individual domains. The evolutionarily conserved protein YB-1, previously associated with cancer and drug resistance, was shown to bind methylated cytosine preferentially, potentially conferring upon YB-1 an epigenetics-related function.

Conclusions

The dataset described here represents a rich resource of experimentally determined nucleic acid-binding proteins, and our methodology has great potential for further exploration of the interface between the protein and nucleic acid realms.

Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers

MOTIVATION

Sequence-specific transcription factors (TFs) regulate the expression of their target genes through interactions with specific DNA-binding sites in the genome. Data on TF-DNA binding specificities are essential for understanding how regulatory specificity is achieved.

RESULTS

Numerous studies have used universal protein-binding microarray (PBM) technology to determine the in vitro binding specificities of hundreds of TFs for all possible 8 bp sequences (8mers). We have developed a Bayesian analysis of variance (ANOVA) model that decomposes these 8mer data into background noise, TF familywise effects and effects due to the particular TF. Adjusting for background noise improves PBM data quality and concordance with in vivo TF binding data. Moreover, our model provides simultaneous identification of TF subclasses and their shared sequence preferences, and also of 8mers bound preferentially by individual members of TF subclasses. Such results may aid in deciphering cis-regulatory codes and determinants of protein-DNA binding specificity.

AVAILABILITY AND IMPLEMENTATION

Source code, compiled code and R and Python scripts are available from http://thebrain.bwh.harvard.edu/hierarchicalANOVA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[The databases of transcription factors.]

The control of gene transcription is a critical level of gene expression regulation. The interactions between transcription factors (TF) and their DNA binding sites (TFBS) play a key role at this level. In order to decipher the molecular mechanism of the interactions of TFs with TFBSs and construct transcription regulatory network, it is necessary to systematically collect, save, and analyze the information of discovered TFs and their TFBSs. In recent years, multiple TF and TFBS-related databases have been established. These databeses significantly promoted the TF-related studies in the fields of molecular biology, bioinformatics, and system biology. This paper summarized the contents, characteristics, access, and advances of main TFs and TFBSs-related databases, including TRANSFAC, JASPAR, TFDB, TRRD, TRED, PAZAR, MAPPER and others.

Construction of a rhythm transfer system that mimics the cellular clock

Creation of an artificial oscillating gene expression system is one of the most challenging issues in synthetic biology. Here, we constructed a simple system to manipulate gene expression patterns to be circadian, reflecting the intrinsic cellular clock, by fusing a core clock protein, BMAL1 or CLOCK, with a zinc finger-type DNA binding domain. Circadian rhythmic gene expression was induced only when the target gene contained zinc finger-binding sequences. To our knowledge, this simple approach is the first to manipulate gene expression patterns into circadian rhythms and would be applicable to various endogenous genes.

Massively parallel measurements of molecular interaction kinetics on a microfluidic platform

Quantitative biology requires quantitative data. No high-throughput technologies exist capable of obtaining several hundred independent kinetic binding measurements in a single experiment. We present an integrated microfluidic device (k-MITOMI) for the simultaneous kinetic characterization of 768 biomolecular interactions. We applied k-MITOMI to the kinetic analysis of transcription factor (TF)-DNA interactions, measuring the detailed kinetic landscapes of the mouse TF Zif268, and the yeast TFs Tye7p, Yox1p, and Tbf1p. We demonstrated the integrated nature of k-MITOMI by expressing, purifying, and characterizing 27 additional yeast transcription factors in parallel on a single device. Overall, we obtained 2,388 association and dissociation curves of 223 unique molecular interactions with equilibrium dissociation constants ranging from 2 × 10(-6) M to 2 × 10(-9) M, and dissociation rate constants of approximately 6 s(-1) to 8.5 × 10(-3) s(-1). Association rate constants were uniform across 3 TF families, ranging from 3.7 × 10(6) M(-1) s(-1) to 9.6 × 10(7) M(-1) s(-1), and are well below the diffusion limit. We expect that k-MITOMI will contribute to our quantitative understanding of biological systems and accelerate the development and characterization of engineered systems.

[DNA-binding profiles of mammalian transcription factors]

The differential gene expression is the molecular base of development and responses to stimuli of organisms. Transcription factors (TFs) play important regulatory roles in this kind of differential gene expression. Therefore, to elucidate how these TFs regulate the complex differential gene expression, it is necessary to identify all target genes of them and construct the gene transcription regulatory network controlled by them. DNA binding is a key step for TFs regulating gene transcription. Therefore, in order to identify their target genes, it is indispensable to identify all possible DNA sequences that can be recognized and bound by TFs at the molecular level of their interactions with DNA, i.e., construction of the DNA-binding profiles of TFs. In recent years, along with the development of DNA microarray and high-throughput DNA sequencing techniques, there appeared some revolutionary new techniques for constructing DNA-binding profiles of TFs, which greatly promotes studies in this field. These techniques include ChIP-chip and ChIP-Seq for constructing in vivo DNA-binding profiles of TFs, dsDNA microarray, SELEX-SAGE, Bind-n-Seq, MMP-SELEX, EMSA-Seq, and HiTS-FLIP for constructing in vitro DNA-binding profiles of TFs. This paper reviewed these techniques.

Zinc finger transcription factor zDC is a negative regulator required to prevent activation of classical dendritic cells in the steady state

Classical dendritic cells (cDCs) process and present antigens to T cells. Under steady-state conditions, antigen presentation by cDCs induces tolerance. In contrast, during infection or inflammation, cDCs become activated, express higher levels of cell surface MHC molecules, and induce strong adaptive immune responses. We recently identified a cDC-restricted zinc finger transcription factor, zDC (also known as Zbtb46 or Btbd4), that is not expressed by other immune cell populations, including plasmacytoid DCs, monocytes, or macrophages. We define the zDC consensus DNA binding motif and the genes regulated by zDC using chromatin immunoprecipitation and deep sequencing. By deleting zDC from the mouse genome, we show that zDC is primarily a negative regulator of cDC gene expression. zDC deficiency alters the cDC subset composition in the spleen in favor of CD8(+) DCs, up-regulates activation pathways in steady-state cDCs, including elevated MHC II expression, and enhances cDC production of vascular endothelial growth factor leading to increased vascularization of skin-draining lymph nodes. Consistent with these observations, zDC protein expression is rapidly down-regulated after TLR stimulation. Thus, zDC is a TLR-responsive, cDC-specific transcriptional repressor that is in part responsible for preventing cDC maturation in the steady state.