Search

BACKGROUNDGeneral translationalcis-elements are present in the mRNAs of all genes and affect the recruitment, assembly, and progress of preinitiation complexes and the ribosome under many physiological states. These elements are: mRNA folding, upstream open reading frames, specific nucleotides flanking the initiating AUG codon, protein coding sequence length, and codon usage. The quantitative contributions of these sequence features and how and why they coordinate together to control translation rates are not well understood.RESULTSHere we show that these sequence features specify 42%–81% of the variance in translation rates inS.cerevisiae, S.pombe, Arabidopsis thaliana, M.musculus, andH.Sapiens. We establish that control by RNA secondary structure is chiefly mediated by highly folded 25–60 nucleotide segments within mRNA 5’ regions; that changes in tri-nucleotide frequencies between highly and poorly translated 5’ regions are correlated between all species; and that control by distinct biochemical processes is extensively correlated as is regulation by a single process acting in different parts of the same mRNA.CONCLUSIONSOur work shows that the general features control a much larger fraction of the variance in translation rates than previously realized. We provide a more detailed and accurate understanding of the aspects of RNA structure that direct translation in diverse eukaryotes. In addition, we note that the strongly correlated regulation between and withincis-control features will cause more even densities of translational complexes along each mRNA and therefore more efficient use of the translation machinery by the cell.

Significance We demonstrate a high-accuracy method for predicting enhancers genome-wide with >85% precision as validated by transgenic reporter assays in Drosophila embryos. This accuracy in a metazoan system enables us to predict with high confidence 1,640 enhancers genome-wide that participate in body segmentation during early development. The predicted enhancers are demarcated by heterogeneous collections of epigenetic marks; many strong enhancers are free from classic indicators of activity, including H3K27ac, but are bound by key transcription factors., Identifying functional enhancer elements in metazoan systems is a major challenge. Large-scale validation of enhancers predicted by ENCODE reveal false-positive rates of at least 70%. We used the pregrastrula-patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held-out data results from heterogeneity of functional signatures in enhancer elements. We show that at least two classes of enhancers are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, greater than 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well-predicted elements is composed predominantly of enhancers driving multistage segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome. An analysis of 32 SDEs using whole-mount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.

© 2016 by The American Society for Biochemistry and Molecular Biology, Inc. Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.

We developed a multichannel gel electrophoresis system that continuously collects fractions as protein bands migrate off the bottom of gel columns. The device uses several short linear gel columns, each of a different percent acrylamide, to achieve a separation power similar to that of a long gradient gel. A "Counter Free-Flow" elution technique allows continuous and simultaneous fraction collection from multiple channels at low cost. Using the system with SDS-PAGE, 300 μg samples of protein can be separated and eluted into 48-96 fractions over a mass range of 10-150 kDa in 2.5 h. Each eluted protein can be recovered at 50% efficiency or higher in ∼500 μL. The system can also be used for native gel electrophoresis, but protein aggregation limits the loading capacity to about 50 μg per channel and reduces resolution. This system has the potential to be coupled with mass spectrometry to achieve high-throughput protein identification.

We developed a multichannel gel electrophoresis system that continuously collects fractions as protein bands migrate off the bottom of gel columns. The device uses several short linear gel columns, each of a different percent acrylamide, to achieve a separation power similar to that of a long gradient gel. A "Counter Free-Flow" elution technique allows continuous and simultaneous fraction collection from multiple channels at low cost. Using the system with SDS-PAGE, 300 μg samples of protein can be separated and eluted into 48-96 fractions over a mass range of 10-150 kDa in 2.5 h. Each eluted protein can be recovered at 50% efficiency or higher in ∼500 μL. The system can also be used for native gel electrophoresis, but protein aggregation limits the loading capacity to about 50 μg per channel and reduces resolution. This system has the potential to be coupled with mass spectrometry to achieve high-throughput protein identification.

Identifying functional enhancers elements in metazoan systems is a major challenge. For example, large-scale validation of enhancers predicted by ENCODE reveal false positive rates of at least 70%. Here we use the pregrastrula patterning network ofDrosophila melanogasterto demonstrate that loss in accuracy in held out data results from heterogeneity of functional signatures in enhancer elements. We show that two classes of enhancer are active during earlyDrosophilaembryogenesis and that by focusing on a single, relatively homogeneous class of elements, over 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well predicted elements is composed predominantly of enhancers driving multi-stage, segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome, 916 of which are novel. An analysis of 32 novel SDEs using wholemount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.Significance StatementWe demonstrate a high accuracy method for predicting enhancers genome wide with > 85% precision as validated by transgenic reporter assays inDrosophilaembryos. This is the first time such accuracy has been achieved in a metazoan system, allowing us to predict with high-confidence 1640 enhancers, 916 of which are novel. The predicted enhancers are demarcated by heterogeneous collections of epigenetic marks; many strong enhancers are free from classical indicators of activity, including H3K27ac, but are bound by key transcription factors. H3K27ac, often used as a one-dimensional predictor of enhancer activity, is an uninformative parameter in our data.

Animals comprise dynamic three-dimensional arrays of cells that express gene products in intricate spatial and temporal patterns that determine cellular differentiation and morphogenesis. A rigorous understanding of these developmental processes requires automated methods that quantitatively record and analyze complex morphologies and their associated patterns of gene expression at cellular resolution. Here we summarize light microscopy-based approaches to establish permanent, quantitative datasets-atlases-that record this information. We focus on experiments that capture data for whole embryos or large areas of tissue in three dimensions, often at multiple time points. We compare and contrast the advantages and limitations of different methods and highlight some of the discoveries made. We emphasize the need for interdisciplinary collaborations and integrated experimental pipelines that link sample preparation, image acquisition, image analysis, database design, visualization, and quantitative analysis.

© 2016 by The American Society for Biochemistry and Molecular Biology, Inc. Numerous affinity purification-mass spectrometry (APMS) and yeast two-hybrid screens have each defined thousands of pairwise protein-protein interactions (PPIs), most of which are between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here, we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial yeast two-hybrid and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli. Compared with the nine published interactomes, our two networks are smaller, are much less highly connected, and have significantly lower false discovery rates. In addition, our interactomes are much more enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays than the pairs reported in prior studies. Our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.

The ability to conduct advanced functional genomic studies of the thousands of sequenced bacteria has been hampered by the lack of available tools for making high-throughput chromosomal manipulations in a systematic manner that can be applied across diverse species. In this work, we highlight the use of synthetic biological tools to assemble custom suicide vectors with reusable and interchangeable DNA “parts” to facilitate chromosomal modification at designated loci. These constructs enable an array of downstream applications, including gene replacement and the creation of gene fusions with affinity purification or localization tags. We employed this approach to engineer chromosomal modifications in a bacterium that has previously proven difficult to manipulate genetically, Desulfovibrio vulgaris Hildenborough, to generate a library of over 700 strains. Furthermore, we demonstrate how these modifications can be used for examining metabolic pathways, protein-protein interactions, and protein localization. The ubiquity of suicide constructs in gene replacement throughout biology suggests that this approach can be applied to engineer a broad range of species for a diverse array of systems biological applications and is amenable to high-throughput implementation.

The cohesin complex is a chromosomal component required for sister chromatid cohesion that is conserved from yeast to man. The similarly conserved Nipped-B protein is needed for cohesin to bind to chromosomes. In higher organisms, Nipped-B and cohesin regulate gene expression and development by unknown mechanisms. Using chromatin immunoprecipitation, we find that Nipped-B and cohesin bind to the same sites throughout the entire non-repetitive Drosophila genome. They preferentially bind transcribed regions and overlap with RNA polymerase II. This contrasts sharply with yeast, where cohesin binds almost exclusively between genes. Differences in cohesin and Nipped-B binding between Drosophila cell lines often correlate with differences in gene expression. For example, cohesin and Nipped-B bind the Abd-B homeobox gene in cells in which it is transcribed, but not in cells in which it is silenced. They bind to the Abd-B transcription unit and downstream regulatory region and thus could regulate both transcriptional elongation and activation. We posit that transcription facilitates cohesin binding, perhaps by unfolding chromatin, and that Nipped-B then regulates gene expression by controlling cohesin dynamics. These mechanisms are likely involved in the etiology of Cornelia de Lange syndrome, in which mutation of one copy of the NIPBL gene encoding the human Nipped-B ortholog causes diverse structural and mental birth defects.

Polycomb group (PcG) complexes are multiprotein assemblages that bind to chromatin and establish chromatin states leading to epigenetic silencing. PcG proteins regulate homeotic genes in flies and vertebrates, but little is known about other PcG targets and the role of the PcG in development, differentiation and disease. Here, we determined the distribution of the PcG proteins PC, E(Z) and PSC and of trimethylation of histone H3 Lys27 (me3K27) in the D. melanogaster genome. At more than 200 PcG target genes, binding sites for the three PcG proteins colocalize to presumptive Polycomb response elements (PREs). In contrast, H3 me3K27 forms broad domains including the entire transcription unit and regulatory regions. PcG targets are highly enriched in genes encoding transcription factors, but they also include genes coding for receptors, signaling proteins, morphogens and regulators representing all major developmental pathways.

During late embryogenesis, the expression domains of homeotic genes are maintained by two groups of ubiquitously expressed regulators: the Polycomb repressors and the Trithorax activators. It is not known how the activities of the two maintenance systems are initially targeted to the correct genes. Zeste and GAGA are sequence-specific DNA-binding proteins previously shown to be Trithorax group activators of the homeotic gene Ultrabithorax (Ubx). We demonstrate that Zeste and GAGA DNA-binding sites at the proximal promoter are also required to maintain, but not to initiate, repression of Ubx. Furthermore, the repression mediated by Zeste DNA-binding site is abolished in zeste null embryos. These data imply that Zeste and probably GAGA mediate Polycomb repression. We present a model in which the dual transcriptional activities of Zeste and GAGA are an essential component of the mechanism that chooses which maintenance system is to be targeted to a given promoter.

Large scale surveys in mammalian tissue culture cells suggest that the protein expressed at the median abundance is present at 8,000 - 16,000 molecules per cell and that differences in mRNA expression between genes explain only 10-40% of the differences in protein levels. We find, however, that these surveys have significantly underestimated protein abundances and the relative importance of transcription. Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al., we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances, showing that mRNA levels explain at least 56% of the differences in protein abundance for the genes detected by Schwanhausser et al., though because one major source of error could not be estimated the true percent contribution could be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhausser et al. and that the measured and inferred translation rates correlate only poorly (R2=0.13). Based on this, our second strategy suggests that mRNA levels explain ~81% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role., Comment: v2 adds a model of all gene's protein and mRNA expression. v3 corrects the omission of dataset files. v4 and 5 extends models of all gene's expression. v6 adds two new ribosome footprint datasets. v7 The final version accepted at PeerJ. Adds NIH3T3 ribosome footprint data and removes modeling of all genes protein expression levels

We showed previously that homeoproteins bind to multiple DNA sites throughout the length of most genes in Drosophila embryos. Based on a compari­son of in vivo and in vitro DNA binding specificities, we suggested that homeoprotein binding sites on actively transcribed genes are largely accessible, whereas the binding of homeoproteins to inactive and poorly transcribed genes may be significantly inhibited at most sites, perhaps by chromatin structure. To test this model, we have measured the accessibility of restriction enzyme sites in a number of genes in isolated nuclei. Surprisingly, our data indicate that there is no difference in the overall accessibility of sites for several restriction enzymes on active versus inactive genes. However, consistent with our model, restriction enzyme recognition sequences that overlap with homeoprotein binding sites are less accessible on inactive genes than they are on active genes. We propose that transcriptional activation in all animals may involve a localized increase in accessibility at the AT-rich regions bound by homeo­proteins and perhaps at a few other regions, rather than a generalized effect on all sites throughout a gene.

Little is known about the range of DNA sequences bound by transcription factors in vivo. Using a sensitive UV cross-linking technique, we show that three classes of homeoprotein bind at significant levels to the majority of genes in Drosophila embryos. The three classes bind with specificities different from each other; however, their levels of binding on any single DNA fragment differ by no more than 5- to 10-fold. On actively transcribed genes, there is a good correlation between the in vivo DNA-binding specificity of each class and its in vitro DNA-binding specificity. In contrast, no such correlation is seen on inactive or weakly transcribed genes. These genes are bound poorly in vivo, even though they contain many high affinity homeoprotein-binding sites. Based on these results, we suggest how the in vivo pattern of homeoprotein DNA binding is determined.

Mammalian proteins are expressed at ∼103 to 108 molecules per cell ( 1 ). Differences between cell types, between normal and disease states, and between individuals are largely defined by changes in the abundance of proteins, which are in turn determined by rates of transcription, messenger RNA (mRNA) degradation, translation, and protein degradation. If the rates for one of these steps differ much more than the rates of the other three, that step would be dominant in defining the variation in protein expression. Over the past decade, system-wide studies have claimed that in animals, differences in translation rates predominate ( 2 – 5 ). On page 1112 of this issue, Jovanovic et al. ( 6 ), as well as recent studies by Battle et al. ( 7 ) and Li et al. ( 1 ), challenge this conclusion, suggesting that transcriptional control makes the larger contribution.

The selector homeoproteins are a highly conserved group of transcription factors found throughout the Eumetazoa. Previously, the Drosophila selector homeoproteins Eve and Ftz were shown to bind with similar specificities to all genes tested, including four genes chosen because they were thought to be unlikely targets of Eve and Ftz. Here, we demonstrate that the expression of these four unexpected targets is controlled by Eve and probably by the other selector homeoproteins as well. A correlation is observed between the level of DNA binding and the degree to which gene expression is regulated by Eve. Suspecting that the selector homeoproteins may affect many more genes than previously thought, we have characterized the expression of randomly selected genes at different stages of embryogenesis. At cellular blastoderm, 25-50% of genes whose transcription can be monitored are regulated by both Eve and Ftz. In late embryogenesis, 87% of genes are directly or indirectly controlled by most or all selector homeoproteins. We argue that this broad control of gene expression is essential to coordinate morphogenesis. Our results raise the possibility that each selector homeoprotein may directly regulate the expression of most genes.

Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies—such as efficient data management—supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.

Many biological processes appear to be controlled by functionally redundant genes or pathways, but it has proven difficult to understand the nature of this redundancy. Here, we have analyzed a redundant regulatory interaction between the Drosophila transcription factor zeste and the homeotic gene Ultrabithorax. Mutations in zeste do not affect the cis-regulation of the endogenous Ultrabithorax gene; however, the expression of small Ultrabithorax promoter constructs is strongly dependent upon zeste. We show that this difference is due to redundant cis-regulatory elements in the Ultrabithorax gene, which presumably contain binding sites for factors that share the function of zeste. We also provide evidence suggesting that zeste and the gene encoding the GAGA factor have an overlapping function in regulating Ultrabithorax. Furthermore, we show that the zeste protein is bound at equal levels in vivo to a Ultrabithorax promoter construct, which zeste strongly activates, and to the identical promoter region in the endogenous Ultrabithorax gene, which zeste redundantly regulates. These results suggest that zeste is significantly active in the wild-type animal and not simply a factor that is induced as a back-up when other activators fail.

We examined the mechanism by which the C-terminal 236 amino acids of the even-skipped protein (region CD) repress transcription. A fusion protein, CDGB, was created that contains region CD fused to the glucocorticoid receptor DNA binding domain. This protein repressed transcription in an in vitro system containing purified fractions of the RNA polymerase II general transcription factors, and repression was dependent upon the presence of high-affinity glucocorticoid receptor binding sites in the promoter. Repression by CDGB was prevented when the promoter DNA was preincubated with TFIID or TBP, whereas preincubation of the template DNA with CDGB prevented TFIID binding. Together, these results strongly imply that CDGB represses transcription by inhibiting TFIID binding, and further experiments suggested a mechanism by which this may occur. Region CD can mediate cooperative interactions between repressor molecules such that molecules bound at the glucocorticoid receptor binding sites stabilize binding of additional CDGB molecules to low-affinity binding sites throughout the basal promoter. Binding to some of these low-affinity sites was shown to contribute to repression. Further experiments suggested that the full-length eve protein also represses transcription by the same mechanism. We speculate that occupancy of secondary sites within the basal promoter by CDGB or the eve protein inhibits subsequent TFIID binding to repress transcription, a mechanism we term cooperative blocking.

In animals, each sequence-specific transcription factor typically binds to thousands of genomic regions in vivo. Our previous studies of 20 transcription factors show that most genomic regions bound at high levels in Drosophila blastoderm embryos are known or probable functional targets, but genomic regions occupied only at low levels have characteristics suggesting that most are not involved in the cis -regulation of transcription. Here we use transgenic reporter gene assays to directly test the transcriptional activity of 104 genomic regions bound at different levels by the 20 transcription factors. Fifteen genomic regions were selected based solely on the DNA occupancy level of the transcription factor Kruppel. Five of the six most highly bound regions drive blastoderm patterns of reporter transcription. In contrast, only one of the nine lowly bound regions drives transcription at this stage and four of them are not detectably active at any stage of embryogenesis. A larger set of 89 genomic regions chosen using criteria designed to identify functional cis -regulatory regions supports the same trend: genomic regions occupied at high levels by transcription factors in vivo drive patterned gene expression, whereas those occupied only at lower levels mostly do not. These results support studies that indicate that the high cellular concentrations of sequence-specific transcription factors drive extensive, low-occupancy, nonfunctional interactions within the accessible portions of the genome.

Cell membranes represent the "front line" of cellular defense and the interface between a cell and its environment. To determine the range of proteins and protein complexes that are present in the cell membranes of a target organism, we have utilized a "tagless" process for the system-wide isolation and identification of native membrane protein complexes. As an initial subject for study, we have chosen the Gram- negative sulfate-reducing bacterium Desulfovibrio vulgaris. With this tagless methodology, we have identified about two-thirds of the outer membrane- associated proteins anticipated. Approximately three-fourths of these appear to form homomeric complexes. Statistical and machine-learning methods used to analyze data compiled over multiple experiments revealed networks of additional protein−protein interactions providing insight into heteromeric contacts made between proteins across this region of the cell. Taken together, these results establish a D. vulgaris outer membrane protein data set that will be essential for the detection and characterization of environment-driven changes in the outer membrane proteome and in the modeling of stress response pathways. The workflow utilized here should be effective for the global characterization of membrane protein complexes in a wide range of organisms.

A Systematic Evolution of Ligands by EXponential enrichment (SELEX) experiment begins in round one with a random pool of oligonucleotides in equilibrium solution with a target. Over a few rounds, oligonucleotides having a high affinity for the target are selected. Data from a high throughput SELEX experiment consists of lists of thousands of oligonucleotides sampled after each round. Thus far, SELEX experiments have been very good at suggesting the highest affinity oligonucleotide, but modeling lower affinity recognition site variants has been difficult. Furthermore, an alignment step has always been used prior to analyzing SELEX data. We present a novel model, based on a biochemical parametrization of SELEX, which allows us to use data from all rounds to estimate the affinities of the oligonucleotides. Most notably, our model also aligns the oligonucleotides. We use our model to analyze a SELEX experiment containing double stranded DNA oligonucleotides and the transcription factor Bicoid as the target. Our SELEX model outperformed other published methods for predicting putative binding sites for Bicoid as indicated by the results of an in-vivo ChIP-chip experiment., Comment: Published in at http://dx.doi.org/10.1214/12-AOAS537 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Animal transcription factors drive complex spatial and temporal patterns of gene expression during development by binding to a wide array of genomic regions. While the in vivo DNA binding landscape and in vitro DNA binding affinities of many such proteins have been characterized, our understanding of the forces that determine where, when, and the extent to which these transcription factors bind DNA in cells remains primitive. In this chapter, we describe computational thermodynamic models that predict the genome-wide DNA binding landscape of transcription factors in vivo and evaluate the contribution of biophysical determinants, such as protein-protein interactions and chromatin accessibility, on DNA occupancy. We show that predictions based only on DNA sequence and in vitro DNA affinity data achieve a mild correlation (r=0.4) with experimental measurements of in vivo DNA binding. However, by incorporating direct measurements of DNA accessibility in chromatin, it is possible to obtain much higher accuracy (r=0.6-0.9) for various transcription factors across known target genes. Thus, a combination of experimental DNA accessibility data and computational modeling of transcription factor DNA binding may be sufficient to predict the binding landscape of any animal transcription factor with reasonable accuracy.

Animal transcription factors drive complex spatial and temporal patterns of gene expression during development by binding to a wide array of genomic regions. While the in vivo DNA binding landscape and in vitro DNA binding affinities of many such proteins have been characterized, our understanding of the forces that determine where, when, and the extent to which these transcription factors bind DNA in cells remains primitive. In this chapter, we describe computational thermodynamic models that predict the genome-wide DNA binding landscape of transcription factors in vivo and evaluate the contribution of biophysical determinants, such as protein–protein interactions and chromatin accessibility, on DNA occupancy. We show that predictions based only on DNA sequence and in vitro DNA affinity data achieve a mild correlation ( r = 0.4) with experimental measurements of in vivo DNA binding. However, by incorporating direct measurements of DNA accessibility in chromatin, it is possible to obtain much higher accuracy ( r = 0.6–0.9) for various transcription factors across known target genes. Thus, a combination of experimental DNA accessibility data and computational modeling of transcription factor DNA binding may be sufficient to predict the binding landscape of any animal transcription factor with reasonable accuracy.

Because of the increasing diversity of data sets and measurement techniques in biology, a growing spectrum of modeling methods is being developed. It is generally recognized that it is critical to pick the appropriate method to exploit the amount and type of biological data available for a given system. Here, we describe a method for use in situations where temporal data from a network is collected over multiple time points, and in which little prior information is available about the interactions, mathematical structure, and statistical distribution of the network. Our method results in models that we term Nonparametric exterior derivative estimation Ordinary Differential Equation (NODE) model's. We illustrate the method's utility using spatiotemporal gene expression data from Drosophila melanogaster embryos. We demonstrate that the NODE model's use of the temporal characteristics of the network leads to quantifiable improvements in its predictive ability over nontemporal models that only rely on the spatial characteristics of the data. The NODE model provides exploratory visualizations of network behavior and structure, which can identify features that suggest additional experiments. A new extension is also presented that uses the NODE model to generate a comb diagram, a figure that presents a list of possible network structures ranked by plausibility. By being able to quantify a continuum of interaction likelihoods, this helps to direct future experiments.

Three-dimensional gene expression PointCloud data generated by the Berkeley Drosophila Transcription Network Project (BDTNP) provides quantitative information about the spatial and temporal expression of genes in early Drosophila embryos at cellular resolution. The BDTNP team visualizes and analyzes Point-Cloud data using the software application PointCloudXplore (PCX). To maximize the impact of novel, complex data sets, such as PointClouds, the data needs to be accessible to biologists and comprehensible to developers of analysis functions. We address this challenge by linking PCX and Matlab via a dedicated interface, thereby providing biologists seamless access to advanced data analysis functions and giving bioinformatics researchers the opportunity to integrate their analysis directly into the visualization application. To demonstrate the usefulness of this approach, we computationally model parts of the expression pattern of the gene even skipped using a genetic algorithm implemented in Matlab and integrated into PCX via our Matlab interface.

We have used in vivo UV cross-linking to directly measure DNA binding by the homeo domain proteins even-skipped (eve) and fushi tarazu (ftz) in Drosophila embryos. Strikingly, these two proteins bind at uniformly high levels throughout the length of their genetically identified target genes and at lower, but significant, levels to genes that they are not expected to regulate. The data also suggest that these two proteins have very similar DNA-binding specificities in vivo. In contrast, a non-homeo domain transcription factor, zeste, is only detected on short DNA elements within a target promoter and not on other genes. These results are consistent with the in vitro properties of these various proteins, their respective concentrations in the nucleus, and with earlier predictions of how transcription factors bind DNA in vivo. We propose that these data favor the model that eve, ftz, and closely related homeo domain proteins act by directly regulating mostly the same target genes.

Immunoprecipitation of cross-linked chromatin in combination with microarrays (ChIP-chip) or ultra high-throughput sequencing (ChIP-seq) is widely used to map genome-wide in vivo transcription factor binding. Both methods employ initial steps of in vivo cross-linking, chromatin isolation, DNA fragmentation, and immunoprecipitation. For ChIP-chip, the immunoprecipitated DNA samples are then amplified, labeled, and hybridized to DNA microarrays. For ChIP-seq, the immunoprecipitated DNA is prepared for a sequencing library, and then the library DNA fragments are sequenced using ultra high-throughput sequencing platform. The protocols described here have been developed for ChIP-chip and ChIP-seq analysis of sequence-specific transcription factor binding in Drosophila embryos. A series of controls establish that these protocols have high sensitivity and reproducibility and provide a quantitative measure of relative transcription factor occupancy. The quantitative nature of the assay is important because regulatory transcription factors bind to highly overlapping sets of thousands of genomic regions and the unique regulatory specificity of each factor is determined by relative moderate differences in occupancy between factors at commonly bound regions.

Gene expression and in vivo DNA binding data provide important information for understanding gene regulatory networks: in vivo DNA binding data indicate genomic regions where transcription factors are bound, and expression data show the output resulting from this binding. Thus, there must be functional relationships between these two types of data. While visualization and data analysis tools exist for each data type alone, there is a lack of tools that can easily explore the relationship between them. We propose an approach that uses the average expression driven by multiple of cis-control regions to visually relate gene expression and in vivo DNA binding data. We demonstrate the utility of this tool with examples from the network controlling early Drosophila development. The results obtained support the idea that the level of occupancy of a transcription factor on DNA strongly determines the degree to which the factor regulates a target gene, and in some cases also controls whether the regulation is positive or negative.

Immunoprecipitation of cross-linked chromatin in combination with microarrays (ChIP-chip) or ultra high-throughput sequencing (ChIP-seq) is widely used to map genome-wide in vivo transcription factor binding. Both methods employ initial steps of in vivo cross-linking, chromatin isolation, DNA fragmentation, and immunoprecipitation. For ChIP-chip, the immunoprecipitated DNA samples are then amplified, labeled, and hybridized to DNA microarrays. For ChIP-seq, the immunoprecipitated DNA is prepared for a sequencing library, and then the library DNA fragments are sequenced using ultra high-throughput sequencing platform. The protocols described here have been developed for ChIP-chip and ChIP-seq analysis of sequence-specific transcription factor binding in Drosophila embryos. A series of controls establish that these protocols have high sensitivity and reproducibility and provide a quantitative measure of relative transcription factor occupancy. The quantitative nature of the assay is important because regulatory transcription factors bind to highly overlapping sets of thousands of genomic regions and the unique regulatory specificity of each factor is determined by relative moderate differences in occupancy between factors at commonly bound regions.

SELEX (systematic evolution of ligands by exponential enrichment) was created 20 years ago as a method to enrich small populations of bound DNAs from a random sequence pool by PCR amplification. It provides a powerful way to determine the in vitro binding specificities of DNA-binding proteins such as transcription factors. Here, we present a robust version of the SELEX protocol for high-throughput analysis. Protein-bound beads prepared from insoluble recombinant 6× HIS-tagged transcription factor protein are used in a simple pull-down assay. To allow efficient determination of the enriched DNA sequences, bound oligonucleotides are concatenated, allowing approximately 1,000 oligonucleotides to be sequenced from one 96-well format plate. Successive rounds of SELEX data are statistically useful for understanding the full range of moderate affinity and high-affinity binding sites.