Global analysis of Drosophila Cys2-His2 zinc finger proteins reveals a multitude of novel recognition motifs and binding determinants.

Cys2-His2 zinc finger proteins (ZFPs) are the largest group of transcription factors in higher metazoans. A complete characterization of these ZFPs and their associated target sequences is pivotal to fully annotate transcriptional regulatory networks in metazoan genomes. As a first step in this process, we have characterized the DNA-binding specificities of 129 zinc finger sets from Drosophila using a bacterial one-hybrid system. This data set contains the DNA-binding specificities for at least one encoded ZFP from 70 unique genes and 23 alternate splice isoforms representing the largest set of characterized ZFPs from any organism described to date. These recognition motifs can be used to predict genomic binding sites for these factors within the fruit fly genome. Subsets of fingers from these ZFPs were characterized to define their orientation and register on their recognition sequences, thereby allowing us to define the recognition diversity within this finger set. We find that the characterized fingers can specify 47 of the 64 possible DNA triplets. To confirm the utility of our finger recognition models, we employed subsets of Drosophila fingers in combination with an existing archive of artificial zinc finger modules to create ZFPs with novel DNA-binding specificity. These hybrids of natural and artificial fingers can be used to create functional zinc finger nucleases for editing vertebrate genomes.

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

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

Recognition models to predict DNA-binding specificities of homeodomain proteins.

MOTIVATION: Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C(2)H(2) zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes. RESULTS: Using extensive experimental data, we have tested several machine learning approaches and find that both support vector machines and random forests (RFs) can produce recognition models for HD proteins that are significant improvements over KNN-based methods. Cross-validation analyses show that the resulting models are capable of predicting specificities with high accuracy. We have produced a web-based prediction tool, PreMoTF (Predicted Motifs for Transcription Factors) (http://stormo.wustl.edu/PreMoTF), for predicting position frequency matrices from protein sequence using a RF-based model.

FlyFactorSurvey: a database of Drosophila transcription factor binding specificities determined using the bacterial one-hybrid system.

FlyFactorSurvey (http://pgfe.umassmed.edu/TFDBS/) is a database of DNA binding specificities for Drosophila transcription factors (TFs) primarily determined using the bacterial one-hybrid system. The database provides community access to over 400 recognition motifs and position weight matrices for over 200 TFs, including many unpublished motifs. Search tools and flat file downloads are provided to retrieve binding site information (as sequences, matrices and sequence logos) for individual TFs, groups of TFs or for all TFs with characterized binding specificities. Linked analysis tools allow users to identify motifs within our database that share similarity to a query matrix or to view the distribution of occurrences of an individual motif throughout the Drosophila genome. Together, this database and its associated tools provide computational and experimental biologists with resources to predict interactions between Drosophila TFs and target cis-regulatory sequences.

Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data.

Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, in a step-by-step process. Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide snapshots of high-dimensional expression profiles but have fundamental limits on revealing temporal information, and fluorescence-based live-cell imaging approaches provide temporal information but are technically challenging for multiplex long-term imaging. We first developed a live-cell imaging platform that tracks cellular status change through combining endogenous fluorescent labeling that minimizes perturbation to cell physiology and/or live-cell imaging of high-dimensional cell morphological and texture features. With our platform and an A549 VIM-RFP epithelial-to-mesenchymal transition (EMT) reporter cell line, live-cell trajectories reveal parallel paths of EMT missing from snapshot data due to cell-cell dynamic heterogeneity. Our results emphasize the necessity of extracting dynamical information of phenotypic transitions from multiplex live-cell imaging.

Dissociation of halted T7 RNA polymerase elongation complexes proceeds via a forward-translocation mechanism.

A recent model for the mechanism of intrinsic transcription termination involves dissociation of the RNA from forward-translocated (hypertranslocated) states of the complex [Yarnell WS, Roberts JW (1999) Science, 284:611-615]. The current study demonstrates that halted elongation complexes of T7 RNA polymerase in the absence of termination signals can also dissociate via a forward-translocation mechanism. Shortening of the downstream DNA or the introduction of a stretch of mismatched DNA immediately downstream of the halt site reduces a barrier to forward translocation and correspondingly reduces the lifetime of halted complexes. Conversely, introduction of a cross-link downstream of the halt site increases the same barrier and leads to an increase in complex lifetime. Introduction of a mismatch within the bubble reduces a driving force for forward translocation and correspondingly increases the lifetime of the complex, but only for mismatches at the upstream edge of the bubble, as predicted by the model. Mismatching only the two most upstream of the eight bases in the bubble provides a maximal increase in complex stability, suggesting that dissociation occurs primarily from early forward-translocated states. Finally, addition in trans of an oligonucleotide complementary to the nascent RNA just beyond the hybrid complements the loss of driving force derived from placement of a mismatch within the bubble, confirming the expected additivity of effects. Thus, forward translocation is likely a general mechanism for dissociation of elongation complexes, both in the presence and absence of intrinsic termination signals.