Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy.

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.

A Multichannel Gel Electrophoresis and Continuous Fraction Collection Apparatus for High-Throughput Protein Separation and Characterization.

We developed a multichannel gel electrophoresis system that continuously collects fractions as protein bands migrate to 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.

Quantitating translational control: mRNA abundance-dependent and independent contributions and the mRNA sequences that specify them.

Translation rate per mRNA molecule correlates positively with mRNA abundance. As a result, protein levels do not scale linearly with mRNA levels, but instead scale with the abundance of mRNA raised to the power of an 'amplification exponent'. Here we show that to quantitate translational control, the translation rate must be decomposed into two components. One, TRmD, depends on the mRNA level and defines the amplification exponent. The other, TRmIND, is independent of mRNA amount and impacts the correlation coefficient between protein and mRNA levels. We show that in Saccharomyces cerevisiae TRmD represents ∼20% of the variance in translation and directs an amplification exponent of 1.20 with a 95% confidence interval [1.14, 1.26]. TRmIND constitutes the remaining ∼80% of the variance in translation and explains ∼5% of the variance in protein expression. We also find that TRmD and TRmIND are preferentially determined by different mRNA sequence features: TRmIND by the length of the open reading frame and TRmD both by a ∼60 nucleotide element that spans the initiating AUG and by codon and amino acid frequency. Our work provides more appropriate estimates of translational control and implies that TRmIND is under different evolutionary selective pressures than TRmD.

Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions.

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.

Bacterial Interactomes: Interacting Protein Partners Share Similar Function and Are Validated in Independent Assays More Frequently Than Previously Reported.

Numerous affinity purification-mass spectrometry (AP-MS) 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.

System wide analyses have underestimated protein abundances and the importance of transcription in mammals.

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. (2011), 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 using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should 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. (2011), and that the measured and inferred translation rates correlate poorly (R(2) = 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. Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ∼40% of genes that are non-expressed.

Building quantitative, three-dimensional atlases of gene expression and morphology at cellular resolution.

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.

Volumetric Semantic Segmentation using Pyramid Context Features.

We present an algorithm for the per-voxel semantic segmentation of a three-dimensional volume. At the core of our algorithm is a novel "pyramid context" feature, a descriptive representation designed such that exact per-voxel linear classification can be made extremely efficient. This feature not only allows for efficient semantic segmentation but enables other aspects of our algorithm, such as novel learned features and a stacked architecture that can reason about self-consistency. We demonstrate our technique on 3D fluorescence microscopy data of Drosophila embryos for which we are able to produce extremely accurate semantic segmentations in a matter of minutes, and for which other algorithms fail due to the size and high-dimensionality of the data, or due to the difficulty of the task.

DNA regions bound at low occupancy by transcription factors do not drive patterned reporter gene expression in Drosophila.

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.

High-throughput isolation and characterization of untagged membrane protein complexes: outer membrane complexes of Desulfovibrio vulgaris.

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 multichannel gel electrophoresis and continuous fraction collection apparatus for high-throughput protein separation and characterization.

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.

Nonparametric variable selection and modeling for spatial and temporal regulatory networks.

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.

Quantitative models of the mechanisms that control genome-wide patterns of animal transcription factor binding.

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.

High-throughput SELEX determination of DNA sequences bound by transcription factors in vitro.

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.

Genome-wide in vivo cross-linking of sequence-specific transcription factors.

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.

A conserved developmental patterning network produces quantitatively different output in multiple species of Drosophila.

Differences in the level, timing, or location of gene expression can contribute to alternative phenotypes at the molecular and organismal level. Understanding the origins of expression differences is complicated by the fact that organismal morphology and gene regulatory networks could potentially vary even between closely related species. To assess the scope of such changes, we used high-resolution imaging methods to measure mRNA expression in blastoderm embryos of Drosophila yakuba and Drosophila pseudoobscura and assembled these data into cellular resolution atlases, where expression levels for 13 genes in the segmentation network are averaged into species-specific, cellular resolution morphological frameworks. We demonstrate that the blastoderm embryos of these species differ in their morphology in terms of size, shape, and number of nuclei. We present an approach to compare cellular gene expression patterns between species, while accounting for varying embryo morphology, and apply it to our data and an equivalent dataset for Drosophila melanogaster. Our analysis reveals that all individual genes differ quantitatively in their spatio-temporal expression patterns between these species, primarily in terms of their relative position and dynamics. Despite many small quantitative differences, cellular gene expression profiles for the whole set of genes examined are largely similar. This suggests that cell types at this stage of development are conserved, though they can differ in their relative position by up to 3-4 cell widths and in their relative proportion between species by as much as 5-fold. Quantitative differences in the dynamics and relative level of a subset of genes between corresponding cell types may reflect altered regulatory functions between species. Our results emphasize that transcriptional networks can diverge over short evolutionary timescales and that even small changes can lead to distinct output in terms of the placement and number of equivalent cells.

Animal transcription networks as highly connected, quantitative continua.

To understand how transcription factors function, it is essential to determine the range of genes that they each bind and regulate in vivo. Here I review evidence that most animal transcription factors each bind to a majority of genes over a quantitative series of DNA occupancy levels. These continua span functional, quasifunctional, and nonfunctional DNA binding events. Factor regulatory specificities are distinguished by quantitative differences in DNA occupancy patterns. I contrast these results with models for transcription networks that define discrete sets of direct target and nontarget genes and consequently do not fully capture the complexity observed in vivo.

Automated iterative MS/MS acquisition: a tool for improving efficiency of protein identification using a LC-MALDI MS workflow.

We have developed an information-dependent, iterative MS/MS acquisition (IMMA) tool for improving MS/MS efficiency, increasing proteome coverage, and shortening analysis time for high-throughput proteomics applications based on the LC-MALDI MS/MS platform. The underlying principle of IMMA is to limit MS/MS analyses to a subset of molecular ions that are likely to identify a maximum number of proteins. IMMA reduces redundancy of MS/MS analyses by excluding from the precursor ion peak lists proteotypic peptides derived from the already identified proteins and uses a retention time prediction algorithm to limit the degree of false exclusions. It also increases the utilization rate of MS/MS spectra by removing "low value" unidentifiable targets like nonpeptides and peptides carrying large loads of modifications, which are flagged by their "nonpeptide" excess-to-nominal mass ratios. For some samples, IMMA increases the number of identified proteins by ∼20-40% when compared to the data dependent methods. IMMA terminates an MS/MS run at the operator-defined point when "costs" (e.g., time of analysis) start to overrun "benefits" (e.g., number of identified proteins), without prior knowledge of sample contents and complexity. To facilitate analysis of closely related samples, IMMA's inclusion list functionality is currently under development.

Dynamic reprogramming of chromatin accessibility during Drosophila embryo development.

BACKGROUND: The development of complex organisms is believed to involve progressive restrictions in cellular fate. Understanding the scope and features of chromatin dynamics during embryogenesis, and identifying regulatory elements important for directing developmental processes remain key goals of developmental biology. RESULTS: We used in vivo DNaseI sensitivity to map the locations of regulatory elements, and explore the changing chromatin landscape during the first 11 hours of Drosophila embryonic development. We identified thousands of conserved, developmentally dynamic, distal DNaseI hypersensitive sites associated with spatial and temporal expression patterning of linked genes and with large regions of chromatin plasticity. We observed a nearly uniform balance between developmentally up- and down-regulated DNaseI hypersensitive sites. Analysis of promoter chromatin architecture revealed a novel role for classical core promoter sequence elements in directing temporally regulated chromatin remodeling. Another unexpected feature of the chromatin landscape was the presence of localized accessibility over many protein-coding regions, subsets of which were developmentally regulated or associated with the transcription of genes with prominent maternal RNA contributions in the blastoderm. CONCLUSIONS: Our results provide a global view of the rich and dynamic chromatin landscape of early animal development, as well as novel insights into the organization of developmentally regulated chromatin features.