Protein binding microarrays for the characterization of DNA-protein interactions

Delaying production with prokaryotic inducible expression systems

BACKGROUND

Engineering bacteria with the purpose of optimizing the production of interesting molecules often leads to a decrease in growth due to metabolic burden or toxicity. By delaying the production in time, these negative effects on the growth can be avoided in a process called a two-stage fermentation.

MAIN TEXT

During this two-stage fermentation process, the production stage is only activated once sufficient cell mass is obtained. Besides the possibility of using external triggers, such as chemical molecules or changing fermentation parameters to induce the production stage, there is a renewed interest towards autoinducible systems. These systems, such as quorum sensing, do not require the extra interference with the fermentation broth to start the induction. In this review, we discuss the different possibilities of both external and autoinduction methods to obtain a two-stage fermentation. Additionally, an overview is given of the tuning methods that can be applied to optimize the induction process. Finally, future challenges and prospects of (auto)inducible expression systems are discussed.

CONCLUSION

There are numerous methods to obtain a two-stage fermentation process each with their own advantages and disadvantages. Even though chemically inducible expression systems are well-established, an increasing interest is going towards autoinducible expression systems, such as quorum sensing. Although these newer techniques cannot rely on the decades of characterization and applications as is the case for chemically inducible promoters, their advantages might lead to a shift in future inducible expression systems.

Identification of Plant Co-regulatory Modules Using CoReg

Regulatory network is often characterized by complex interactions between transcription factors (TFs) and target genes. The synergistic regulations among multiple TFs may co-induce/co-suppress the expressions of the similar target genes. Such information is important for understanding stress response signaling pathways in plants. In this chapter, we present a computational tool, CoReg, for mining co-regulatory gene modules from network topology. The analysis results can be used to interpret co-regulation effects in regulatory networks generated by high-through TF-DNA interaction screenings such as yeast-one-hybrid, ChIP-seq, and DAP-seq.

Identification of Plant Transcription Factor DNA-Binding Sites Using seq-DAP-seq

The identification of genome-wide transcription factor binding sites (TFBS) is a critical step in deciphering gene and transcriptional regulatory networks. However, determining the genome-wide binding of specific TFs or TF complexes remains a technical challenge. DNA affinity purification sequencing (DAP-seq) and modifications such as sequential DAP-seq (seq-DAP-seq) are robust in vitro methods for mapping individual TF or TF complex binding sites in a genome-wide manner. DAP-seq protocols use a genomic DNA (gDNA) library from any target organism with or without amplification, allowing the determination of TF binding on naked or endogenously modified DNA, respectively. As a first step, the gDNA is fragmented to ~200 bp, end-repaired, and sequencing adaptors are added. This gDNA library can be used directly or an amplification step may be performed to remove DNA modifications such as cytosine methylation. DNA libraries are then incubated with an affinity-tagged TF or TF- complex immobilized on magnetic beads. The TF or TF complex of interest is generally produced using recombinant protein expression and purified prior to DNA affinity purification. After incubation of the DNA library with the immobilized TF of interest, multiple wash steps are performed to reduce non-specific DNA binding and the TF-DNA complexes eluted. The eluted DNA is PCR-amplified and sequenced using next-generation sequencing. The resulting sequence reads are mapped to the corresponding reference genome, identifying direct potential bound regions and binding sites of the TF or TF complex of interest. Predictive TFBS models are generated from the bound regions using downstream bioinformatics analysis pipelines. Here, we present a detailed protocol outlining the steps required for seq-DAP-seq of a heterooligomeric TF complex (Fig. 1) and briefly describe the downstream bioinformatics pipeline used to develop a robust TFBS model from sequencing data generated from a DAP-seq experiment.

Landscape of allele-specific transcription factor binding in the human genome

Sequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.

Quantitative analysis of transcription factor binding and expression using calling cards reporter arrays

We report a tool, Calling Cards Reporter Arrays (CCRA), that measures transcription factor (TF) binding and the consequences on gene expression for hundreds of synthetic promoters in yeast. Using Cbf1p and MAX, we demonstrate that the CCRA method is able to detect small changes in binding free energy with a sensitivity comparable to in vitro methods, enabling the measurement of energy landscapes in vivo. We then demonstrate the quantitative analysis of cooperative interactions by measuring Cbf1p binding at synthetic promoters with multiple sites. We find that the cooperativity between Cbf1p dimers varies sinusoidally with a period of 10.65 bp and energetic cost of 1.37 K_BT for sites that are positioned ‘out of phase’. Finally, we characterize the binding and expression of a group of TFs, Tye7p, Gcr1p and Gcr2p, that act together as a ‘TF collective’, an important but poorly characterized model of TF cooperativity. We demonstrate that Tye7p often binds promoters without its recognition site because it is recruited by other collective members, whereas these other members require their recognition sites, suggesting a hierarchy where these factors recruit Tye7p but not vice versa. Our experiments establish CCRA as a useful tool for quantitative investigations into TF binding and function.

Improved linking of motifs to their TFs using domain information

Motivation

A central aim of molecular biology is to identify mechanisms of transcriptional regulation. Transcription factors (TFs), which are DNA-binding proteins, are highly involved in these processes, thus a crucial information is to know where TFs interact with DNA and to be aware of the TFs’ DNA-binding motifs. For that reason, computational tools exist that link DNA-binding motifs to TFs either without sequence information or based on TF-associated sequences, e.g. identified via a chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiment.

In this paper, we present MASSIF, a novel method to improve the performance of existing tools that link motifs to TFs relying on TF-associated sequences. MASSIF is based on the idea that a DNA-binding motif, which is correctly linked to a TF, should be assigned to a DNA-binding domain (DBD) similar to that of the mapped TF. Because DNA-binding motifs are in general not linked to DBDs, it is not possible to compare the DBD of a TF and the motif directly. Instead we created a DBD collection, which consist of TFs with a known DBD and an associated motif. This collection enables us to evaluate how likely it is that a linked motif and a TF of interest are associated to the same DBD. We named this similarity measure domain score, and represent it as a P-value. We developed two different ways to improve the performance of existing tools that link motifs to TFs based on TF-associated sequences: (i) using meta-analysis to combine P-values from one or several of these tools with the P-value of the domain score and (ii) filter unlikely motifs based on the domain score.

Results

We demonstrate the functionality of MASSIF on several human ChIP-seq datasets, using either motifs from the HOCOMOCO database or de novo identified ones as input motifs. In addition, we show that both variants of our method improve the performance of tools that link motifs to TFs based on TF-associated sequences significantly independent of the considered DBD type.

Availability and implementation

MASSIF is freely available online at https://github.com/SchulzLab/MASSIF.

Supplementary information

Supplementary data are available at Bioinformatics online.

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

BACKGROUND

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task.

RESULTS

We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4).

CONCLUSION

Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

HOT or not: examining the basis of high-occupancy target regions

High-occupancy target (HOT) regions are segments of the genome with unusually high number of transcription factor binding sites. These regions are observed in multiple species and thought to have biological importance due to high transcription factor occupancy. Furthermore, they coincide with house-keeping gene promoters and consequently associated genes are stably expressed across multiple cell types. Despite these features, HOT regions are solely defined using ChIP-seq experiments and shown to lack canonical motifs for transcription factors that are thought to be bound there. Although, ChIP-seq experiments are the golden standard for finding genome-wide binding sites of a protein, they are not noise free. Here, we show that HOT regions are likely to be ChIP-seq artifacts and they are similar to previously proposed ‘hyper-ChIPable’ regions. Using ChIP-seq data sets for knocked-out transcription factors, we demonstrate presence of false positive signals on HOT regions. We observe sequence characteristics and genomic features that are discriminatory of HOT regions, such as GC/CpG-rich k-mers, enrichment of RNA–DNA hybrids (R-loops) and DNA tertiary structures (G-quadruplex DNA). The artificial ChIP-seq enrichment on HOT regions could be associated to these discriminatory features. Furthermore, we propose strategies to deal with such artifacts for the future ChIP-seq studies.

Enhancer Dysfunction in 3D Genome and Disease

Spatiotemporal patterns of gene expression depend on enhancer elements and other factors during individual development and disease progression. The rapid progress of high-throughput techniques has led to well-defined enhancer chromatin properties. Various genome-wide methods have revealed a large number of enhancers and the discovery of three-dimensional (3D) genome architecture showing the distant interacting mechanisms of enhancers that loop to target gene promoters. Whole genome sequencing projects directed at cancer have led to the discovery of substantial enhancer dysfunction in misregulating gene expression and in tumor initiation and progression. Results from genome-wide association studies (GWAS) combined with functional genomics analyses have elucidated the functional impacts of many cancer risk-associated variants that are enriched within the enhancer regions of chromatin. Risk variants dysregulate the expression of enhancer variant-associated genes via 3D genomic interactions. Moreover, these enhancer variants often alter the chromatin binding affinity for cancer-relevant transcription factors, which in turn leads to aberrant expression of the genes associated with cancer susceptibility. In this review, we investigate the extent to which these genetic regulatory circuits affect cancer predisposition and how the recent development of genome-editing methods have enabled the determination of the impacts of genomic variation and alteration on cancer phenotype, which will eventually lead to better management plans and treatment responses to human cancer in the clinic.

Low-Affinity Binding Sites and the Transcription Factor Specificity Paradox in Eukaryotes

Eukaryotic transcription factors (TFs) from the same structural family tend to bind similar DNA sequences, despite the ability of these TFs to execute distinct functions in vivo. The cell partly resolves this specificity paradox through combinatorial strategies and the use of low-affinity binding sites, which are better able to distinguish between similar TFs. However, because these sites have low affinity, it is challenging to understand how TFs recognize them in vivo. Here, we summarize recent findings and technological advancements that allow for the quantification and mechanistic interpretation of TF recognition across a wide range of affinities. We propose a model that integrates insights from the fields of genetics and cell biology to provide further conceptual understanding of TF binding specificity. We argue that in eukaryotes, target specificity is driven by an inhomogeneous 3D nuclear distribution of TFs and by variation in DNA binding affinity such that locally elevated TF concentration allows low-affinity binding sites to be functional.

Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions

Charting a temporal path in gene networks requires linking early transcription factor (TF)-triggered events to downstream effects. We scale-up a cell-based TF-perturbation assay to identify direct regulated targets of 33 nitrogen (N)-early response TFs encompassing 88% of N-responsive Arabidopsis genes. We uncover a duality where each TF is an inducer and repressor, and in vitro cis-motifs are typically specific to regulation directionality. Validated TF-targets (71,836) are used to refine precision of a time-inferred root network, connecting 145 N-responsive TFs and 311 targets. These data are used to chart network paths from direct TF₁-regulated targets identified in cells to indirect targets responding only in planta via Network Walking. We uncover network paths from TGA1 and CRF4 to direct TF₂ targets, which in turn regulate 76% and 87% of TF₁ indirect targets in planta, respectively. These results have implications for N-use and the approach can reveal temporal networks for any biological system.

Temporal control of transcriptional networks enables organisms to adapt to changing environment. Here, the authors use a scaled-up cell-based assay to identify direct targets of nitrogen-early responsive transcription factors and validate a network path mediating dynamic nitrogen signaling in Arabidopsis.

A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain

Transcriptional activation domains are essential for gene regulation, but their intrinsic disorder and low primary sequence conservation have made it difficult to identify the amino acid composition features that underlie their activity. Here, we describe a rational mutagenesis scheme that deconvolves the function of four activation domain sequence features-acidity, hydrophobicity, intrinsic disorder, and short linear motifs-by quantifying the activity of thousands of variants in vivo and simulating their conformational ensembles using an all-atom Monte Carlo approach. Our results with a canonical activation domain from the Saccharomyces cerevisiae transcription factor Gcn4 reconcile existing observations into a unified model of its function: the intrinsic disorder and acidic residues keep two hydrophobic motifs from driving collapse. Instead, the most-active variants keep their aromatic residues exposed to the solvent. Our results illustrate how the function of intrinsically disordered proteins can be revealed by high-throughput rational mutagenesis.

Single-Molecule Patterning via DNA Nanostructure Assembly: A Reusable Platform

Here we describe a facile strategy of general applicability for controlling the immobilization of individual nanomoieties on nanopatterned surfaces with single-molecule control. We combine the ability of DNA nanostructures as programmable platforms, with a one-step Focused Ion Beam nanopatterning, to demonstrate the controlled immobilization of DNA origami functionalized with individual quantum dots (QDs) at predesigned positions on glass coverslips and silicon substrates. Remarkably, the platform developed is reusable after a simple cleaning process, and can be designed to display different geometrical arrangements.

High-Resolution Single Particle Zeta Potential Characterisation of Biological Nanoparticles using Tunable Resistive Pulse Sensing

Physicochemical properties of nanoparticles, such as size, shape, surface charge, density, and porosity play a central role in biological interactions and hence accurate determination of these characteristics is of utmost importance. Here we propose tunable resistive pulse sensing for simultaneous size and surface charge measurements on a particle-by-particle basis, enabling the analysis of a wide spectrum of nanoparticles and their mixtures. Existing methodologies for measuring zeta potential of nanoparticles using resistive pulse sensing are significantly improved by including convection into the theoretical model. The efficacy of this methodology is demonstrated for a range of biological case studies, including measurements of mixed anionic, cationic liposomes, extracellular vesicles in plasma, and in situ time study of DNA immobilisation on the surface of magnetic nanoparticles. The high-resolution single particle size and zeta potential characterisation will provide a better understanding of nano-bio interactions, positively impacting nanomedicine development and their regulatory approval.

Determination of Zeta Potential via Nanoparticle Translocation Velocities through a Tunable Nanopore: Using DNA-modified Particles as an Example

Nanopore technologies, known collectively as Resistive Pulse Sensors (RPS), are being used to detect, quantify and characterize proteins, molecules and nanoparticles. Tunable resistive pulse sensing (TRPS) is a relatively recent adaptation to RPS that incorporates a tunable pore that can be altered in real time. Here, we use TRPS to monitor the translocation times of DNA-modified nanoparticles as they traverse the tunable pore membrane as a function of DNA concentration and structure (i.e., single-stranded to double-stranded DNA). TRPS is based on two Ag/AgCl electrodes, separated by an elastomeric pore membrane that establishes a stable ionic current upon an applied electric field. Unlike various optical-based particle characterization technologies, TRPS can characterize individual particles amongst a sample population, allowing for multimodal samples to be analyzed with ease. Here, we demonstrate zeta potential measurements via particle translocation velocities of known standards and apply these to sample analyte translocation times, thus resulting in measuring the zeta potential of those analytes. As well as acquiring mean zeta potential values, the samples are all measured using a particle-by-particle perspective exhibiting more information on a given sample through sample population distributions, for example. Of such, this method demonstrates potential within sensing applications for both medical and environmental fields.

Genetics of juvenile idiopathic arthritis: new tools bring new approaches

PURPOSE OF REVIEW

In juvenile idiopathic arthritis (JIA), there are now more than 25 regions represented by single nucleotide polymorphisms that show strong genetic associations. The causal variants and corresponding functions have not yet been defined for the majority of these regions. Here, we review current JIA association findings and the recent progress in the annotation of noncoding portion of the human genome as well as the new technologies necessary to apply this knowledge to JIA association findings.

RECENT FINDINGS

An international collaboration was able to amass sufficient numbers of JIA and control samples to identify significantly robust genetic associations for JIA. The Encyclopedia of DNA Elements project and the National Institutes of Health (NIH) Roadmap Epigenetics Program have now annotated more than 80% of the noncoding genome, important in understanding the impact of risk loci, the majority of which fall outside of protein coding regions. Recent technological advances in high throughput sequencing, chromatin structure determination, transcription factor and enhancer binding site mapping and genome editing will likely provide a basis for understanding JIA genetic risk.

SUMMARY

Understanding the role of genetic variation in the cause of JIA will provide insight for disease mechanism and may explain disease heterogeneity between JIA subtypes and between autoimmune diseases in general.

Latest developments in experimental and computational approaches to characterize protein-lipid interactions

Understanding the functional roles of all the molecules in cells is an ultimate goal of modern biology. An important facet is to understand the functional contributions from intermolecular interactions, both within a class of molecules (e.g. protein-protein) or between classes (e.g. protein-DNA). While the technologies for analyzing protein-protein and protein-DNA interactions are well established, the field of protein-lipid interactions is still relatively nascent. Here, we review the current status of the experimental and computational approaches for detecting and analyzing protein-lipid interactions. Experimental technologies fall into two principal categories, namely solution-based and array-based methods. Computational methods include large-scale data-driven analyses and predictions/dynamic simulations based on prior knowledge of experimentally identified interactions. Advances in the experimental technologies have led to improved computational analyses and vice versa, thereby furthering our understanding of protein-lipid interactions and their importance in biological systems.

SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates

Identification of genomic regulatory elements is essential for understanding the dynamics of cellular processes. This task has been substantially facilitated by the availability of genome sequences for many species and high-throughput data of transcripts and transcription factor (TF) binding. However, rigorous computational methods are necessary to derive accurate genome-wide annotations of regulatory sites from such data. SwissRegulon (http://swissregulon.unibas.ch) is a database containing genome-wide annotations of regulatory motifs, promoters and TF binding sites (TFBSs) in promoter regions across model organisms. Its binding site predictions were obtained with rigorous Bayesian probabilistic methods that operate on orthologous regions from related genomes, and use explicit evolutionary models to assess the evidence of purifying selection on each site. New in the current version of SwissRegulon is a curated collection of 190 mammalian regulatory motifs associated with ∼340 TFs, and TFBS annotations across a curated set of ∼35 000 promoters in both human and mouse. Predictions of TFBSs for Saccharomyces cerevisiae have also been significantly extended and now cover 158 of yeast’s ∼180 TFs. All data are accessible through both an easily navigable genome browser with search functions, and as flat files that can be downloaded for further analysis.

TATA binding proteins can recognize nontraditional DNA sequences

We demonstrate an accurate, quantitative, and label-free optical technology for high-throughput studies of receptor-ligand interactions, and apply it to TATA binding protein (TBP) interactions with oligonucleotides. We present a simple method to prepare single-stranded and double-stranded DNA microarrays with comparable surface density, ensuring an accurate comparison of TBP activity with both types of DNA. In particular, we find that TBP binds tightly to single-stranded DNA, especially to stretches of polythymine (poly-T), as well as to the traditional TATA box. We further investigate the correlation of TBP activity with various lengths of DNA and find that the number of TBPs bound to DNA increases >7-fold as the oligomer length increases from 9 to 40. Finally, we perform a full human genome analysis and discover that 35.5% of human promoters have poly-T stretches. In summary, we report, for the first time to our knowledge, the activity of TBP with poly-T stretches by presenting an elegant stepwise analysis of multiple techniques: discovery by a novel quantitative detection of microarrays, confirmation by a traditional gel electrophoresis, and a full genome prediction with computational analyses.

Analysis of variation at transcription factor binding sites in Drosophila and humans

BACKGROUND

Advances in sequencing technology have boosted population genomics and made it possible to map the positions of transcription factor binding sites (TFBSs) with high precision. Here we investigate TFBS variability by combining transcription factor binding maps generated by ENCODE, modENCODE, our previously published data and other sources with genomic variation data for human individuals and Drosophila isogenic lines.

RESULTS

We introduce a metric of TFBS variability that takes into account changes in motif match associated with mutation and makes it possible to investigate TFBS functional constraints instance-by-instance as well as in sets that share common biological properties. We also take advantage of the emerging per-individual transcription factor binding data to show evidence that TFBS mutations, particularly at evolutionarily conserved sites, can be efficiently buffered to ensure coherent levels of transcription factor binding.

CONCLUSIONS

Our analyses provide insights into the relationship between individual and interspecies variation and show evidence for the functional buffering of TFBS mutations in both humans and flies. In a broad perspective, these results demonstrate the potential of combining functional genomics and population genetics approaches for understanding gene regulation.

Bayesian analysis of high-throughput quantitative measurement of protein-DNA interactions

Transcriptional regulation depends upon the binding of transcription factor (TF) proteins to DNA in a sequence-dependent manner. Although many experimental methods address the interaction between DNA and proteins, they generally do not comprehensively and accurately assess the full binding repertoire (the complete set of sequences that might be bound with at least moderate strength). Here, we develop and evaluate through simulation an experimental approach that allows simultaneous high-throughput quantitative analysis of TF binding affinity to thousands of potential DNA ligands. Tens of thousands of putative binding targets can be mixed with a TF, and both the pre-bound and bound target pools sequenced. A hierarchical Bayesian Markov chain Monte Carlo approach determines posterior estimates for the dissociation constants, sequence-specific binding energies, and free TF concentrations. A unique feature of our approach is that dissociation constants are jointly estimated from their inferred degree of binding and from a model of binding energetics, depending on how many sequence reads are available and the explanatory power of the energy model. Careful experimental design is necessary to obtain accurate results over a wide range of dissociation constants. This approach, which we call Simultaneous Ultra high-throughput Ligand Dissociation EXperiment (SULDEX), is theoretically capable of rapid and accurate elucidation of an entire TF-binding repertoire.

Selective biomolecular nanoarrays for parallel single-molecule investigations

The ability to direct the self-assembly of biomolecules on surfaces with true nanoscale control is key for the creation of functional substrates. Herein we report the fabrication of nanoscale biomolecular arrays via selective self-assembly on nanopatterned surfaces and minimized nonspecific adsorption. We demonstrate that the platform developed allows for the simultaneous screening of specific protein-DNA binding events at the single-molecule level. The strategy presented here is generally applicable and enables high-throughput monitoring of biological activity in real time and with single-molecule resolution.

Statistical tests for natural selection on regulatory regions based on the strength of transcription factor binding sites

Background

Although cis-regulatory changes play an important role in evolution, it remains difficult to establish the contribution of natural selection to regulatory differences between species. For protein coding regions, powerful tests of natural selection have been developed based on comparisons of synonymous and non-synonymous substitutions, and analogous tests for regulatory regions would be of great utility.

Results

Here, tests for natural selection on regulatory regions are proposed based on nucleotide substitutions that occur in characterized transcription factor binding sites (an important type functional element within regulatory regions). In the absence of selection, these substitutions will tend to reduce the strength of existing binding sites. On the other hand, purifying selection will act to preserve the binding sites in regulatory regions, while positive selection can act to create or destroy binding sites, as well as change their strength. Using standard models of binding site strength and molecular evolution in the absence of selection, this intuition can be used to develop statistical tests for natural selection. Application of these tests to two well-characterized regulatory regions in Drosophila provides evidence for purifying selection.

Conclusion

This demonstrates that it is possible to develop tests for selection on regulatory regions based on the specific functional constrains on these sequences.

End-specific strategies of attachment of long double stranded DNA onto gold-coated nanofiber arrays

We report the effective and site-specific binding of long double stranded (ds)DNA to high aspect ratio carbon nanofiber arrays. The carbon nanofibers were first coated with a thin gold layer to provide anchorage for two controllable binding methods. One method was based on the direct binding of thiol end-labeled dsDNA. The second and enhanced method used amine end-labeled dsDNA bound with crosslinkers to a carboxyl-terminated self-assembled monolayer. The bound dsDNA was first visualized with a fluorescent, dsDNA-intercalating dye. The specific binding onto the carbon nanofiber was verified by a high resolution detection method using scanning electron microscopy in combination with the binding of neutravidin-coated fluorescent microspheres to the immobilized and biotinylated dsDNA. Functional activity of thiol end-labeled dsDNA on gold-coated nanofiber arrays was verified with a transcriptional assay, whereby Chinese hamster lung cells (V79) were impaled upon the DNA-modified nanofibers and scored for transgene expression of the tethered template. Thiol end-labeled dsDNA demonstrated significantly higher expression levels than nanofibers prepared with control dsDNA that lacked a gold-binding end-label. Employing these site-specific and robust techniques of immobilization of dsDNA onto nanodevices can be of advantage for the study of DNA/protein interactions and for gene delivery applications.

Gel-based oligonucleotide microarray approach to analyze protein-ssDNA binding specificity

Gel-based oligonucleotide microarray approach was developed for quantitative profiling of binding affinity of a protein to single-stranded DNA (ssDNA). To demonstrate additional capabilities of this method, we analyzed the binding specificity of ribonuclease (RNase) binase from Bacillus intermedius (EC 3.1.27.3) to ssDNA using generic hexamer oligodeoxyribonucleotide microchip. Single-stranded octamer oligonucleotides were immobilized within 3D hemispherical gel pads. The octanucleotides in individual pads 5'-{N}N(1)N(2)N(3)N(4)N(5)N(6){N}-3' consisted of a fixed hexamer motif N(1)N(2)N(3)N(4)N(5)N(6) in the middle and variable parts {N} at the ends, where {N} represent A, C, G and T in equal proportions. The chip has 4096 pads with a complete set of hexamer sequences. The affinity was determined by measuring dissociation of the RNase-ssDNA complexes with the temperature increasing from 0 degrees C to 50 degrees C in quasi-equilibrium conditions. RNase binase showed the highest sequence-specificity of binding to motifs 5'-NNG(A/T/C)GNN-3' with the order of preference: GAG > GTG > GCG. High specificity towards G(A/T/C)G triplets was also confirmed by measuring fluorescent anisotropy of complexes of binase with selected oligodeoxyribonucleotides in solution. The affinity of RNase binase to other 3-nt sequences was also ranked. These results demonstrate the applicability of the method and provide the ground for further investigations of nonenzymatic functions of RNases.