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The chromosome of Escherichia coli is riddled with multi-faceted complexity. The emergence of chromosome conformation capture techniques are providing newer ways to explore chromosome organization. Here we combine a beads-on-a-spring polymer-based framework with recently reported Hi–C data for E. coli chromosome, in rich growth condition, to develop a comprehensive model of its chromosome at 5 kb resolution. The investigation focuses on a range of diverse chromosome architectures of E. coli at various replication states corresponding to a collection of cells, individually present in different stages of cell cycle. The Hi–C data-integrated model captures the self-organization of E. coli chromosome into multiple macrodomains within a ring-like architecture. The model demonstrates that the position of oriC is dependent on architecture and replication state of chromosomes. The distance profiles extracted from the model reconcile fluorescence microscopy and DNA-recombination assay experiments. Investigations into writhe of the chromosome model reveal that it adopts helix-like conformation with no net chirality, earlier hypothesized in experiments. A genome-wide radius of gyration map captures multiple chromosomal interaction domains and identifies the precise locations of rrn operons in the chromosome. We show that a model devoid of Hi–C encoded information would fail to recapitulate most genomic features unique to E. coli.

Degenerate splice site sequences mark the intron boundaries of pre-mRNA transcripts in multicellular eukaryotes. The essential pre-mRNA splicing factor U2AF(65) is faced with the paradoxical tasks of accurately targeting polypyrimidine (Py) tracts preceding 3' splice sites while adapting to both cytidine and uridine nucleotides with nearly equivalent frequencies. To understand how U2AF(65) recognizes degenerate Py tracts, we determined six crystal structures of human U2AF(65) bound to cytidine-containing Py tracts. As deoxy-ribose backbones were required for co-crystallization with these Py tracts, we also determined two baseline structures of U2AF(65) bound to the deoxy-uridine counterparts and compared the original, RNA-bound structure. Local structural changes suggest that the N-terminal RNA recognition motif 1 (RRM1) is more promiscuous for cytosine-containing Py tracts than the C-terminal RRM2. These structural differences between the RRMs were reinforced by the specificities of wild-type and site-directed mutant U2AF(65) for region-dependent cytosine- and uracil-containing RNA sites. Small-angle X-ray scattering analyses further demonstrated that Py tract variations select distinct inter-RRM spacings from a pre-existing ensemble of U2AF(65) conformations. Our results highlight both local and global conformational selection as a means for universal 3' splice site recognition by U2AF(65).

Zinc-finger nucleases (ZFNs) have been used for genome engineering in a wide variety of organisms; however, it remains challenging to design effective ZFNs for many genomic sequences using publicly available zinc-finger modules. This limitation is in part because of potential finger-finger incompatibility generated on assembly of modules into zinc-finger arrays (ZFAs). Herein, we describe the validation of a new set of two-finger modules that can be used for building ZFAs via conventional assembly methods or a new strategy-finger stitching-that increases the diversity of genomic sequences targetable by ZFNs. Instead of assembling ZFAs based on units of the zinc-finger structural domain, our finger stitching method uses units that span the finger-finger interface to ensure compatibility of neighbouring recognition helices. We tested this approach by generating and characterizing eight ZFAs, and we found their DNA-binding specificities reflected the specificities of the component modules used in their construction. Four pairs of ZFNs incorporating these ZFAs generated targeted lesions in vivo, demonstrating that stitching yields ZFAs with robust recognition properties.

Zinc finger nucleases (ZFNs) facilitate tailor-made genomic modifications in vivo through the creation of targeted double-stranded breaks. They have been employed to modify the genomes of plants and animals, and cell-based therapies utilizing ZFNs are undergoing clinical trials. However, many ZFNs display dose-dependent toxicity presumably due to the generation of undesired double-stranded breaks at off-target sites. To evaluate the parameters influencing the functional specificity of ZFNs, we compared the in vivo activity of ZFN variants targeting the zebrafish kdrl locus, which display both high on-target activity and dose-dependent toxicity. We evaluated their functional specificity by assessing lesion frequency at 141 potential off-target sites using Illumina sequencing. Only a minority of these off-target sites accumulated lesions, where the thermodynamics of zinc finger-DNA recognition appear to be a defining feature of active sites. Surprisingly, we observed that both the specificity of the incorporated zinc fingers and the choice of the engineered nuclease domain could independently influence the fidelity of these ZFNs. The results of this study have implications for the assessment of likely off-target sites within a genome and point to both zinc finger-dependent and -independent characteristics that can be tailored to create ZFNs with greater precision.

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.

We examine the use of high-throughput sequencing on binding sites recovered using a bacterial one-hybrid (B1H) system and find that improved models of transcription factor (TF) binding specificity can be obtained compared to standard methods of sequencing a small subset of the selected clones. We can obtain even more accurate binding models using a modified version of B1H selection method with constrained variation (CV-B1H). However, achieving these improved models using CV-B1H data required the development of a new method of analysis--GRaMS (Growth Rate Modeling of Specificity)--that estimates bacterial growth rates as a function of the quality of the recognition sequence. We benchmark these different methods of motif discovery using Zif268, a well-characterized C(2)H(2) zinc-finger TF on both a 28 bp randomized library for the standard B1H method and on 6 bp randomized library for the CV-B1H method for which 45 different experimental conditions were tested: five time points and three different IPTG and 3-AT concentrations. We find that GRaMS analysis is robust to the different experimental parameters whereas other analysis methods give widely varying results depending on the conditions of the experiment. Finally, we demonstrate that the CV-B1H assay can be performed in liquid media, which produces recognition models that are similar in quality to sequences recovered from selection on solid media.