Faculty Bibliography

Cell Systems invited 16 experts to share their views on the field of systems genetics. In questions repeated in the headings, we asked them to define systems genetics, highlight its relevance to researchers outside the field, discuss what makes a strong systems genetics paper, and paint a picture of where the field is heading in the coming years. Their responses, ordered by the journal but otherwise unedited, make it clear that deciphering genotype to phenotype relationships is a central challenge of systems genetics and will require understanding how networks and higher-order properties of biological systems underlie complex traits. In addition, our experts illuminate the applications and relevance of systems genetics to human disease, the gut microbiome, development of tools that connect the global research community, sustainability, drug discovery, patient-specific disease and network models, and personalized treatments. Finally, a table of suggested reading provides a sample of influential work in the field.

Engineered nucleases have transformed biological research and offer great therapeutic potential by enabling the straightforward modification of desired genomic sequences. While many nuclease platforms have proven functional, all can produce unanticipated off-target lesions and have difficulty discriminating between homologous sequences, limiting their therapeutic application. Here we describe a multi-reporter selection system that allows the screening of large protein libraries to uncover variants able to discriminate between sequences with substantial homology. We have used this system to identify zinc-finger nucleases that exhibit high cleavage activity (up to 60% indels) at their targets within the CCR5 and HBB genes and strong discrimination against homologous sequences within CCR2 and HBD. An unbiased screen for off-target lesions using a novel set of CCR5-targeting nucleases confirms negligible CCR2 activity and demonstrates minimal off-target activity genome wide. This system offers a straightforward approach to generate nucleases that discriminate between similar targets and provide exceptional genome-wide specificity.

Cys2His2 zinc fingers (C2H2-ZFs) comprise the largest class of metazoan DNA-binding domains. Despite this domain's well-defined DNA-recognition interface, and its successful use in the design of chimeric proteins capable of targeting genomic regions of interest, much remains unknown about its DNA-binding landscape. To help bridge this gap in fundamental knowledge and to provide a resource for design-oriented applications, we screened large synthetic protein libraries to select binding C2H2-ZF domains for each possible three base pair target. The resulting data consist of >160 000 unique domain-DNA interactions and comprise the most comprehensive investigation of C2H2-ZF DNA-binding interactions to date. An integrated analysis of these independent screens yielded DNA-binding profiles for tens of thousands of domains and led to the successful design and prediction of C2H2-ZF DNA-binding specificities. Computational analyses uncovered important aspects of C2H2-ZF domain-DNA interactions, including the roles of within-finger context and domain position on base recognition. We observed the existence of numerous distinct binding strategies for each possible three base pair target and an apparent balance between affinity and specificity of binding. In sum, our comprehensive data help elucidate the complex binding landscape of C2H2-ZF domains and provide a foundation for efforts to determine, predict and engineer their DNA-binding specificities.

Understanding how sequence-specific protein-DNA interactions direct cellular function is of great interest to the research community. High-throughput methods have been developed to determine DNA-binding specificities; one such technique, the bacterial one-hybrid (B1H) system, confers advantages including ease of use, sensitivity and throughput. In this review, we describe the evolution of the B1H system as a tool capable of screening large DNA libraries to investigate protein-DNA interactions of interest. We discuss how DNA-binding specificities produced by the B1H system have been used to predict regulatory targets. Additionally, we examine how this approach has been applied to characterize two common DNA-binding domain families-homeodomains and Cys2His2 zinc fingers-both in organism-wide studies and with synthetic approaches. In the case of the former, the B1H system has produced large catalogs of protein specificity and nuanced information about previously recovered DNA targets, thereby improving our understanding of these proteins' functions in vivo and increasing our capacity to predict similar interactions in other species. In the latter, synthetic screens of the same DNA-binding domains have further refined our models of specificity, through analyzing comprehensive libraries to uncover all proteins able to bind a complete set of targets, and, for instance, exploring how context-in the form of domain position within the parent protein-may affect specificity. Finally, we recognize the limitations of the B1H system and discuss its potential for use in the production of designer proteins and in studies of protein-protein interactions.

The Cys2His2 zinc finger (ZF) is the most frequently found sequence-specific DNA-binding domain in eukaryotic proteins. The ZF's modular protein-DNA interface has also served as a platform for genome engineering applications. Despite decades of intense study, a predictive understanding of the DNA-binding specificities of either natural or engineered ZF domains remains elusive. To help fill this gap, we developed an integrated experimental-computational approach to enrich and recover distinct groups of ZFs that bind common targets. To showcase the power of our approach, we built several large ZF libraries and demonstrated their excellent diversity. As proof of principle, we used one of these ZF libraries to select and recover thousands of ZFs that bind several 3-nt targets of interest. We were then able to computationally cluster these recovered ZFs to reveal several distinct classes of proteins, all recovered from a single selection, to bind the same target. Finally, for each target studied, we confirmed that one or more representative ZFs yield the desired specificity. In sum, the described approach enables comprehensive large-scale selection and characterization of ZF specificities and should be a great aid in furthering our understanding of the ZF domain.

Synthetic biology aims to rationally design and build synthetic circuits with desired quantitative properties, as well as provide tools to interrogate the structure of native control circuits. In both cases, the ability to program gene expression in a rapid and tunable fashion, with no off-target effects, can be useful. We have constructed yeast strains containing the ACT1 promoter upstream of a URA3 cassette followed by the ligand-binding domain of the human estrogen receptor and VP16. By transforming this strain with a linear PCR product containing a DNA binding domain and selecting against the presence of URA3, a constitutively expressed artificial transcription factor (ATF) can be generated by homologous recombination. ATFs engineered in this fashion can activate a unique target gene in the presence of inducer, thereby eliminating both the off-target activation and nonphysiological growth conditions found with commonly used conditional gene expression systems. A simple method for the rapid construction of GFP reporter plasmids that respond specifically to a native or artificial transcription factor of interest is also provided.

A general method for the dynamic control of single gene expression in eukaryotes, with no off-target effects, is a long-sought tool for molecular and systems biologists. We engineered two artificial transcription factors (ATFs) that contain Cys(2)His(2) zinc-finger DNA-binding domains of either the mouse transcription factor Zif268 (9 bp of specificity) or a rationally designed array of four zinc fingers (12 bp of specificity). These domains were expressed as fusions to the human estrogen receptor and VP16 activation domain. The ATFs can rapidly induce a single gene driven by a synthetic promoter in response to introduction of an otherwise inert hormone with no detectable off-target effects. In the absence of inducer, the synthetic promoter is inactive and the regulated gene product is not detected. Following addition of inducer, transcripts are induced >50-fold within 15 min. We present a quantitative characterization of these ATFs and provide constructs for making their implementation straightforward. These new tools allow for the elucidation of regulatory network elements dynamically, which we demonstrate with a major metabolic regulator, Gcn4p.

The homeobox transcription factor Mohawk (Mkx) is a potent transcriptional repressor expressed in the embryonic precursors of skeletal muscle, cartilage, and bone. MKX has recently been shown to be a critical regulator of musculoskeletal tissue differentiation and gene expression; however, the genetic pathways through which MKX functions and its DNA-binding properties are currently unknown. Using a modified bacterial one-hybrid site selection assay, we determined the core DNA-recognition motif of the mouse monomeric Mkx homeodomain to be A-C-A. Using cell-based assays, we have identified a minimal Mkx-responsive element (MRE) located within the Mkx promoter, which is composed of a highly conserved inverted repeat of the core Mkx recognition motif. Using the minimal MRE sequence, we have further identified conserved MREs within the locus of Sox6, a transcription factor that represses slow fiber gene expression during skeletal muscle differentiation. Real-time PCR and immunostaining of in vitro differentiated muscle satellite cells isolated from Mkx-null mice revealed an increase in the expression of Sox6 and down-regulation of slow fiber structural genes. Together, these data identify the unique DNA-recognition properties of MKX and reveal a novel role for Mkx in promoting slow fiber type specification during skeletal muscle differentiation.

The recognition potential of most families of DNA-binding domains (DBDs) remains relatively unexplored. Homeodomains (HDs), like many other families of DBDs, display limited diversity in their preferred recognition sequences. To explore the recognition potential of HDs, we utilized a bacterial selection system to isolate HD variants, from a randomized library, that are compatible with each of the 64 possible 3' triplet sites (i.e., TAANNN). The majority of these selections yielded sets of HDs with overrepresented residues at specific recognition positions, implying the selection of specific binders. The DNA-binding specificity of 151 representative HD variants was subsequently characterized, identifying HDs that preferentially recognize 44 of these target sites. Many of these variants contain novel combinations of specificity determinants that are uncommon or absent in extant HDs. These novel determinants, when grafted into different HD backbones, produce a corresponding alteration in specificity. This information was used to create more explicit HD recognition models, which can inform the prediction of transcriptional regulatory networks for extant HDs or the engineering of HDs with novel DNA-recognition potential. The diversity of recovered HD recognition sequences raises important questions about the fitness barrier that restricts the evolution of alternate recognition modalities in natural systems.

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.