Calling Cards enable multiplexed identification of the genomic targets of DNA-binding proteins

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data

MOTIVATION

Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data.

RESULTS

We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data.

AVAILABILITY AND IMPLEMENTATION

Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

Zinc cluster transcription factors frequently activate target genes using a non-canonical half-site binding mode

Gene expression changes are orchestrated by transcription factors (TFs), which bind to DNA to regulate gene expression. It remains surprisingly difficult to predict basic features of the transcriptional process, including in vivo TF occupancy. Existing thermodynamic models of TF function are often not concordant with experimental measurements, suggesting undiscovered biology. Here, we analyzed one of the most well-studied TFs, the yeast zinc cluster Gal4, constructed a Shea-Ackers thermodynamic model to describe its binding, and compared the results of this model to experimentally measured Gal4p binding in vivo. We found that at many promoters, the model predicted no Gal4p binding, yet substantial binding was observed. These outlier promoters lacked canonical binding motifs, and subsequent investigation revealed Gal4p binds unexpectedly to DNA sequences with high densities of its half site (CGG). We confirmed this novel mode of binding through multiple experimental and computational paradigms; we also found most other zinc cluster TFs we tested frequently utilize this binding mode, at 27% of their targets on average. Together, these results demonstrate a novel mode of binding where zinc clusters, the largest class of TFs in yeast, bind DNA sequences with high densities of half sites.

A Suite of New Strain Construction Vectors for Gene Expression Knockdown in Budding Yeast

Numerous tools for gene expression knockdown have been developed and characterized in the model organism Saccharomyces cerevisiae and extended to facilitate studies in multicellular models. To comparatively evaluate the efficacy of these approaches, we systematically applied seven such published constitutive and inducible knockdown strategies to a panel of essential genes encoding nuclear-localized proteins. In this effort, we created the CEAS (C-SWAT for Essential Allele Strains) collection, a suite of tagging vectors for improved utility and ease of strain construction. Of particular note, we adapted an improved auxin inducible degron (AID) protein degradation strategy previously available only in mammalian tissue culture for one-step strain construction in budding yeast by leveraging both the C-SWAT system and CRISPR/Cas9 editing. Taken together, this work presents a toolbox for endogenous gene expression knockdown and allows us to make recommendations on the efficacy and applicability of these tools for the perturbation of essential genes.

AIDmut-Seq: a Three-Step Method for Detecting Protein-DNA Binding Specificity

Transcriptional factors (TFs) and their regulons make up the gene regulatory networks. Here, we developed a method based on TF-directed activation-induced cytidine deaminase (AID) mutagenesis in combination with genome sequencing, called AIDmut-Seq, to detect TF targets on the genome. AIDmut-Seq involves only three simple steps, including the expression of the AID-TF fusion protein, whole-genome sequencing, and single nucleotide polymorphism (SNP) profiling, making it easy for junior and interdisciplinary researchers to use. Using AIDmut-Seq for the major quorum sensing regulator LasR in Pseudomonas aeruginosa, we confirmed that a few TF-guided C-T (or G-A) conversions occurred near their binding boxes on the genome, and a number of previously characterized and uncharacterized LasR-binding sites were detected. Further verification of AIDmut-Seq using various transcriptional regulators demonstrated its high efficiency for most transcriptional activators (FleQ, ErdR, GacA, ExsA). We confirmed the binding of LasR, FleQ, and ErdR to 100%, 50%, and 86% of their newly identified promoters by using in vitro protein-DNA binding assay. And real-time RT-PCR data validated the intracellular activity of these TFs to regulate the transcription of those newly found target promoters. However, AIDmut-Seq exhibited low efficiency for some small transcriptional repressors such as RsaL and AmrZ, with possible reasons involving fusion-induced TF dysfunction as well as low transcription rates of target promoters. Although there are false-positive and false-negative results in the AIDmut-Seq data, preliminary results have demonstrated the value of AIDmut-Seq to act as a complementary tool for existing methods. IMPORTANCE Protein-DNA interactions (PDI) play a central role in gene regulatory networks (GRNs). However, current techniques for studying genome-wide PDI usually involve complex experimental procedures, which prevent their broad use by scientific researchers. In this study, we provide a in vivo method called AIDmut-Seq. AIDmut-Seq involves only three simple steps that are easy to operate for researchers with basic skills in molecular biology. The efficiency of AIDmut-Seq was tested and confirmed using multiple transcription factors in Pseudomonas aeruginosa. Although there are still some defects regarding false-positive and false-negative results, AIDmut-Seq will be a good choice in the early stage of PDI study.

NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration

MOTIVATION

Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks.

RESULTS

We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data.

AVAILABILITY AND IMPLEMENTATION

All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting which genes will respond to transcription factor perturbations

The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.

Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

Motivation

The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now.

Results

We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2.

Availability and implementation

Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573.

Supplementary information

Supplementary data are available at Bioinformatics online.

Recording of DNA-binding events reveals the importance of a repurposed Candida albicans regulatory network for gut commensalism

Candida albicans is a fungal component of the human gut microbiota and an opportunistic pathogen. C. albicans transcription factors (TFs), Wor1 and Efg1, are master regulators of an epigenetic switch required for fungal mating that also control colonization of the mammalian gut. We show that additional mating regulators, WOR2, WOR3, WOR4, AHR1, CZF1, and SSN6, also influence gut commensalism. Using Calling Card-seq to record Candida TF DNA-binding events in the host, we examine the role and relationships of these regulators during murine gut colonization. By comparing in-host transcriptomes of regulatory mutants with enhanced versus diminished commensal fitness, we also identify a set of candidate commensalism effectors. These include Cht2, a GPI-linked chitinase whose gene is bound by Wor1, Czf1, and Efg1 in vivo, that we show promotes commensalism. Thus, the network required for a C. albicans sexual switch is biochemically active in the host intestine and repurposed to direct commensalism.

Brd4-bound enhancers drive cell-intrinsic sex differences in glioblastoma

Consistent sex differences in incidence and outcome have been reported in numerous cancers including brain tumors. GBM, the most common and aggressive primary brain tumor, occurs with higher incidence and shorter survival in males compared to females. Brd4 is essential for regulating transcriptome-wide gene expression and specifying cell identity, including that of GBM. We report that sex-biased Brd4 activity drives sex differences in GBM and renders male and female tumor cells differentially sensitive to BET inhibitors. The observed sex differences in BETi treatment strongly indicate that sex differences in disease biology translate into sex differences in therapeutic responses. This has critical implications for clinical use of BET inhibitors further affirming the importance of inclusion of sex as a biological variable.

Sex can be an important determinant of cancer phenotype, and exploring sex-biased tumor biology holds promise for identifying novel therapeutic targets and new approaches to cancer treatment. In an established isogenic murine model of glioblastoma (GBM), we discovered correlated transcriptome-wide sex differences in gene expression, H3K27ac marks, large Brd4-bound enhancer usage, and Brd4 localization to Myc and p53 genomic binding sites. These sex-biased gene expression patterns were also evident in human glioblastoma stem cells (GSCs). These observations led us to hypothesize that Brd4-bound enhancers might underlie sex differences in stem cell function and tumorigenicity in GBM. We found that male and female GBM cells exhibited sex-specific responses to pharmacological or genetic inhibition of Brd4. Brd4 knockdown or pharmacologic inhibition decreased male GBM cell clonogenicity and in vivo tumorigenesis while increasing both in female GBM cells. These results were validated in male and female patient-derived GBM cell lines. Furthermore, analysis of the Cancer Therapeutic Response Portal of human GBM samples segregated by sex revealed that male GBM cells are significantly more sensitive to BET (bromodomain and extraterminal) inhibitors than are female cells. Thus, Brd4 activity is revealed to drive sex differences in stem cell and tumorigenic phenotypes, which can be abrogated by sex-specific responses to BET inhibition. This has important implications for the clinical evaluation and use of BET inhibitors.

Genome-Wide Profiling of Protein-DNA Interactions with Chromatin Endogenous Cleavage and High-Throughput Sequencing (ChEC-Seq )

Interactions between regulatory proteins and specific genomic regions are critical for all chromatin-based processes, including transcription, DNA replication, and DNA repair. Genome-wide mapping of such interactions is most commonly performed with chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq), but a number of orthogonal methods employing targeted enzymatic activity have also been introduced. We previously described a genome-wide implementation of chromatin endogenous cleavage (ChEC-Seq), wherein a protein of interest is fused to micrococcal nuclease (MNase) to enable targeted, calcium-dependent genomic cleavage. Here, we describe the ChEC-Seq protocol for use in budding yeast though it can be used in other organisms in conjunction with appropriate methods for introduction of an MNase fusion protein.

Quantitative analysis of transcription factor binding and expression using calling cards reporter arrays

We report a tool, Calling Cards Reporter Arrays (CCRA), that measures transcription factor (TF) binding and the consequences on gene expression for hundreds of synthetic promoters in yeast. Using Cbf1p and MAX, we demonstrate that the CCRA method is able to detect small changes in binding free energy with a sensitivity comparable to in vitro methods, enabling the measurement of energy landscapes in vivo. We then demonstrate the quantitative analysis of cooperative interactions by measuring Cbf1p binding at synthetic promoters with multiple sites. We find that the cooperativity between Cbf1p dimers varies sinusoidally with a period of 10.65 bp and energetic cost of 1.37 K_BT for sites that are positioned ‘out of phase’. Finally, we characterize the binding and expression of a group of TFs, Tye7p, Gcr1p and Gcr2p, that act together as a ‘TF collective’, an important but poorly characterized model of TF cooperativity. We demonstrate that Tye7p often binds promoters without its recognition site because it is recruited by other collective members, whereas these other members require their recognition sites, suggesting a hierarchy where these factors recruit Tye7p but not vice versa. Our experiments establish CCRA as a useful tool for quantitative investigations into TF binding and function.

Self-Reporting Transposons Enable Simultaneous Readout of Gene Expression and Transcription Factor Binding in Single Cells

Cellular heterogeneity confounds in situ assays of transcription factor (TF) binding. Single-cell RNA sequencing (scRNA-seq) deconvolves cell types from gene expression, but no technology links cell identity to TF binding sites (TFBS) in those cell types. We present self-reporting transposons (SRTs) and use them in single-cell calling cards (scCC), a novel assay for simultaneously measuring gene expression and mapping TFBS in single cells. The genomic locations of SRTs are recovered from mRNA, and SRTs deposited by exogenous, TF-transposase fusions can be used to map TFBS. We then present scCC, which map SRTs from scRNA-seq libraries, simultaneously identifying cell types and TFBS in those same cells. We benchmark multiple TFs with this technique. Next, we use scCC to discover BRD4-mediated cell-state transitions in K562 cells. Finally, we map BRD4 binding sites in the mouse cortex at single-cell resolution, establishing a new method for studying TF biology in situ.

A viral toolkit for recording transcription factor-DNA interactions in live mouse tissues

Transcription factors (TFs) enact precise regulation of gene expression through site-specific, genome-wide binding. Common methods for TF-occupancy profiling, such as chromatin immunoprecipitation, are limited by requirement of TF-specific antibodies and provide only end-point snapshots of TF binding. Alternatively, TF-tagging techniques, in which a TF is fused to a DNA-modifying enzyme that marks TF-binding events across the genome as they occur, do not require TF-specific antibodies and offer the potential for unique applications, such as recording of TF occupancy over time and cell type specificity through conditional expression of the TF-enzyme fusion. Here, we create a viral toolkit for one such method, calling cards, and demonstrate that these reagents can be delivered to the live mouse brain and used to report TF occupancy. Further, we establish a Cre-dependent calling cards system and, in proof-of-principle experiments, show utility in defining cell type-specific TF profiles and recording and integrating TF-binding events across time. This versatile approach will enable unique studies of TF-mediated gene regulation in live animal models.

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.

Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling

ATAC-seq has become a leading technology for probing the chromatin landscape of single and aggregated cells. Distilling functional regions from ATAC-seq presents diverse analysis challenges. Methods commonly used to analyze chromatin accessibility datasets are adapted from algorithms designed to process different experimental technologies, disregarding the statistical and biological differences intrinsic to the ATAC-seq technology. Here, we present a Bayesian statistical approach that uses latent space models to better model accessible regions, termed ChromA. ChromA annotates chromatin landscape by integrating information from replicates, producing a consensus de-noised annotation of chromatin accessibility. ChromA can analyze single cell ATAC-seq data, correcting many biases generated by the sparse sampling inherent in single cell technologies. We validate ChromA on multiple technologies and biological systems, including mouse and human immune cells, establishing ChromA as a top performing general platform for mapping the chromatin landscape in different cellular populations from diverse experimental designs.

Binding and Regulation of Transcription by Yeast Ste12 Variants To Drive Mating and Invasion Phenotypes

Amino acid substitutions are commonly found in human transcription factors, yet the functional consequences of much of this variation remain unknown, even in well-characterized DNA-binding domains. Here, we examine how six single-amino acid variants in the DNA-binding domain of Ste12-a yeast transcription factor regulating mating and invasion-alter Ste12 genome binding, motif recognition, and gene expression to yield markedly different phenotypes. Using a combination of the "calling-card" method, RNA sequencing, and HT-SELEX (high throughput systematic evolution of ligands by exponential enrichment), we find that variants with dissimilar binding and expression profiles can converge onto similar cellular behaviors. Mating-defective variants led to decreased expression of distinct subsets of genes necessary for mating. Hyper-invasive variants also decreased expression of subsets of genes involved in mating, but increased the expression of other subsets of genes associated with the cellular response to osmotic stress. While single-amino acid changes in the coding region of this transcription factor result in complex regulatory reconfiguration, the major phenotypic consequences for the cell appear to depend on changes in the expression of a small number of genes with related functions.

Homotypic cooperativity and collective binding are determinants of bHLH specificity and function

Eukaryotic cells express transcription factor (TF) paralogues that bind to nearly identical DNA sequences in vitro but bind at different genomic loci and perform different functions in vivo. Predicting how 2 paralogous TFs bind in vivo using DNA sequence alone is an important open problem. Here, we analyzed 2 yeast bHLH TFs, Cbf1p and Tye7p, which have highly similar binding preferences in vitro, yet bind at almost completely nonoverlapping target loci in vivo. We dissected the determinants of specificity for these 2 proteins by making a number of chimeric TFs in which we swapped different domains of Cbf1p and Tye7p and determined the effects on in vivo binding and cellular function. From these experiments, we learned that the Cbf1p dimer achieves its specificity by binding cooperatively with other Cbf1p dimers bound nearby. In contrast, we found that Tye7p achieves its specificity by binding cooperatively with 3 other DNA-binding proteins, Gcr1p, Gcr2p, and Rap1p. Remarkably, most promoters (63%) that are bound by Tye7p do not contain a consensus Tye7p binding site. Using this information, we were able to build simple models to accurately discriminate bound and unbound genomic loci for both Cbf1p and Tye7p. We then successfully reprogrammed the human bHLH NPAS2 to bind Cbf1p in vivo targets and a Tye7p target intergenic region to be bound by Cbf1p. These results demonstrate that the genome-wide binding targets of paralogous TFs can be discriminated using sequence information, and provide lessons about TF specificity that can be applied across the phylogenetic tree.

RNA-guided piggyBac transposition in human cells

Safer and more efficient methods for directing therapeutic genes to specific sequences could increase the repertoire of treatable conditions. Many current approaches act passively, first initiating a double-stranded break, then relying on host repair to uptake donor DNA. Alternatively, we delivered an actively integrating transposase to the target sequence to initiate gene insertion. We fused the hyperactive piggyBac transposase to the highly specific, catalytically dead SpCas9-HF1 (dCas9) and designed guide RNAs (gRNAs) to the CCR5 safe harbor sequence. We introduced mutations to the native DNA-binding domain of piggyBac to reduce non-specific binding of the transposase and cause the fusion protein to favor binding by dCas9. This strategy enabled us, for the first time, to direct transposition to the genome using RNA. We showed that increasing the number of gRNAs improved targeting efficiency. Interestingly, over half of the recovered insertions were found at a single TTAA hotspot. We also found that the fusion increased the error rate at the genome-transposon junction. We isolated clonal cell lines containing a single insertion at CCR5 and demonstrated long-term expression from this locus. These vectors expand the utility of the piggyBac system for applications in targeted gene addition for biomedical research and gene therapy.

Enzymatic methods for genome-wide profiling of protein binding sites

Genome-wide mapping of protein-DNA interactions is a staple approach in many areas of modern molecular biology. Genome-wide profiles of protein-binding sites are most commonly generated by chromatin immunoprecipitation and high-throughput sequencing (ChIP-seq). Although ChIP-seq has played a central role in studying genome-wide protein binding, recent work has highlighted systematic biases in the technique that warrant technical and interpretive caution and underscore the need for orthogonal techniques to both confirm the results of ChIP-seq studies and uncover new insights not accessible to ChIP. Several such techniques, based on genetic or immunological targeting of enzymatic activity to specific genomic loci, have been developed. Here, we review the development, applications and future prospects of these methods as complements to ChIP-based approaches and as powerful techniques in their own right.

Localization of Cdc7 Protein Kinase During DNA Replication in Saccharomyces cerevisiae

DDK, a conserved serine-threonine protein kinase composed of a regulatory subunit, Dbf4, and a catalytic subunit, Cdc7, is essential for DNA replication initiation during S phase of the cell cycle through MCM2-7 helicase phosphorylation. The biological significance of DDK is well characterized, but the full mechanism of how DDK associates with substrates remains unclear. Cdc7 is bound to chromatin in the Saccharomyces cerevisiae genome throughout the cell cycle, but there is little empirical evidence as to specific Cdc7 binding locations. Using biochemical and genetic techniques, this study investigated the specific localization of Cdc7 on chromatin. The Calling Cards method, using Ty5 retrotransposons as a marker for DNA–protein binding, suggests Cdc7 kinase is preferentially bound to genomic DNA known to replicate early in S phase, including centromeres and origins of replication. We also discovered Cdc7 binding throughout the genome, which may be necessary to initiate other cellular processes, including meiotic recombination and translesion synthesis. A kinase dead Cdc7 point mutation increases the Ty5 retrotransposon integration efficiency and a 55-amino acid C-terminal truncation of Cdc7, unable to bind Dbf4, reduces Cdc7 binding suggesting a requirement for Dbf4 to stabilize Cdc7 on chromatin during S phase. Chromatin immunoprecipitation demonstrates that Cdc7 binding near specific origins changes during S phase. Our results suggest a model where Cdc7 is loosely bound to chromatin during G₁. At the G₁/S transition, Cdc7 binding to chromatin is increased and stabilized, preferentially at sites that may become origins, in order to carry out a variety of cellular processes.

An optimized, broadly applicable piggyBac transposon induction system

The piggyBac (PB) transposon has been used in a number of biological applications. The insertion of PB transposons into the genome can disrupt genes or regulatory regions, impacting cellular function, so for many experiments it is important that PB transposition is tightly controlled. Here, we systematically characterize three methods for the post-translational control of the PB transposon in four cell lines. We investigated fusions of the PB transposase with ERT2 and two degradation domains (FKBP-DD, DHFR-DD), in multiple orientations, and determined (i) the fold-induction achieved, (ii) the absolute transposition efficiency of the activated construct and (iii) the effects of two inducer molecules on cellular transcription and function. We found that the FKBP-DD confers the PB transposase with a higher transposition activity and better dynamic range than can be achieved with the other systems. In addition, we found that the FKBP-DD regulates transposon activity in a reversible and dose-dependent manner. Finally, we showed that Shld1, the chemical inducer of FKBP-DD, does not interfere with stem cell differentiation, whereas tamoxifen has significant effects. We believe the FKBP-based PB transposon induction will be useful for transposon-mediated genome engineering, insertional mutagenesis and the genome-wide mapping of transcription factor binding.

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident processes. Formaldehyde crosslinking followed by chromatin immunoprecipitation and high-throughput sequencing (X-ChIP-seq) has been used to gain many valuable insights into genome biology. However, X-ChIP-seq has notable limitations linked to crosslinking and sonication. Native ChIP avoids these drawbacks by omitting crosslinking, but often results in poor recovery of chromatin-bound proteins. In addition, all ChIP-based methods are subject to antibody quality considerations. Enzymatic methods for mapping protein-DNA interactions, which involve fusion of a protein of interest to a DNA-modifying enzyme, have also been used to map protein-DNA interactions. We recently combined one such method, chromatin endogenous cleavage (ChEC), with high-throughput sequencing as ChEC-seq. ChEC-seq relies on fusion of a chromatin-associated protein of interest to micrococcal nuclease (MNase) to generate targeted DNA cleavage in the presence of calcium in living cells. ChEC-seq is not based on immunoprecipitation and so circumvents potential concerns with crosslinking, sonication, chromatin solubilization, and antibody quality while providing high resolution mapping with minimal background signal. We envision that ChEC-seq will be a powerful counterpart to ChIP, providing an independent means by which to both validate ChIP-seq findings and discover new insights into genomic regulation.

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genome-wide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.

Exploring Quantitative Yeast Phenomics with Single-Cell Analysis of DNA Damage Foci

A significant challenge of functional genomics is to develop methods for genome-scale acquisition and analysis of cell biological data. Here, we present an integrated method that combines genome-wide genetic perturbation of Saccharomyces cerevisiae with high-content screening to facilitate the genetic description of sub-cellular structures and compartment morphology. As proof of principle, we used a Rad52-GFP marker to examine DNA damage foci in ∼20 million single cells from ∼5,000 different mutant backgrounds in the context of selected genetic or chemical perturbations. Phenotypes were classified using a machine learning-based automated image analysis pipeline. 345 mutants were identified that had elevated numbers of DNA damage foci, almost half of which were identified only in sensitized backgrounds. Subsequent analysis of Vid22, a protein implicated in the DNA damage response, revealed that it acts together with the Sgs1 helicase at sites of DNA damage and preferentially binds G-quadruplex regions of the genome. This approach is extensible to numerous other cell biological markers and experimental systems.

Evolution of regulatory networks in Candida glabrata: learning to live with the human host

The opportunistic human fungal pathogen Candida glabrata is second only to C. albicans as the cause of Candida infections and yet is more closely related to Saccharomyces cerevisiae. Recent advances in functional genomics technologies and computational approaches to decipher regulatory networks, and the comparison of these networks among these and other Ascomycete species, have revealed both unique and shared strategies in adaptation to a human commensal/opportunistic pathogen lifestyle and antifungal drug resistance in C. glabrata. Recently, several C. glabrata sister species in the Nakeseomyces clade representing both human associated (commensal) and environmental isolates have had their genomes sequenced and analyzed. This has paved the way for comparative functional genomics studies to characterize the regulatory networks in these species to identify informative patterns of conservation and divergence linked to phenotypic evolution in the Nakaseomyces lineage.

Active promoters give rise to false positive 'Phantom Peaks' in ChIP-seq experiments

Chromatin immunoprecipitation (ChIP) is widely used to identify chromosomal binding sites. Chromatin proteins are cross-linked to their target sequences in living cells. The purified chromatin is sheared and the relevant protein is enriched by immunoprecipitation with specific antibodies. The co-purifying genomic DNA is then determined by massive parallel sequencing (ChIP-seq).

We applied ChIP-seq to map the chromosomal binding sites for two ISWI-containing nucleosome remodeling factors, ACF and RSF, in Drosophila embryos. Employing several polyclonal and monoclonal antibodies directed against their signature subunits, ACF1 and RSF-1, robust profiles were obtained indicating that both remodelers co-occupied a large set of active promoters.

Further validation included controls using chromatin of mutant embryos that do not express ACF1 or RSF-1. Surprisingly, the ChIP-seq profiles were unchanged, suggesting that they were not due to specific immunoprecipitation. Conservative analysis lists about 3000 chromosomal loci, mostly active promoters that are prone to non-specific enrichment in ChIP and appear as ‘Phantom Peaks’. These peaks are not obtained with pre-immune serum and are not prominent in input chromatin.

Mining the modENCODE ChIP-seq profiles identifies potential Phantom Peaks in many profiles of epigenetic regulators. These profiles and other ChIP-seq data featuring prominent Phantom Peaks must be validated with chromatin from cells in which the protein of interest has been depleted.

Diversification of transcription factor paralogs via noncanonical modularity in C2H2 zinc finger DNA binding

A major challenge in obtaining a full molecular description of evolutionary adaptation is to characterize how transcription factor (TF) DNA-binding specificity can change. To identify mechanisms of TF diversification, we performed detailed comparisons of yeast C2H2 ZF proteins with identical canonical recognition residues that are expected to bind the same DNA sequences. Unexpectedly, we found that ZF proteins can adapt to recognize new binding sites in a modular fashion whereby binding to common core sites remains unaffected. We identified two distinct mechanisms, conserved across multiple Ascomycota species, by which this molecular adaptation occurred. Our results suggest a route for TF evolution that alleviates negative pleiotropic effects by modularly gaining new binding sites. These findings expand our current understanding of ZF DNA binding and provide evidence for paralogous ZFs utilizing alternate modes of DNA binding to recognize unique sets of noncanonical binding sites.

Transcription factor regulation and chromosome dynamics during pseudohyphal growth

A multiplexed analysis of the transcriptional regulation of yeast pseudohyphal growth recorded the binding of 28 different transcription factors with barcoded transposons. A core set of target genes is identified, and a process of DNA looping at the FLO11 locus that provides transcriptional memory for expression of the gene is described.

Pseudohyphal growth is a developmental pathway seen in some strains of yeast in which cells form multicellular filaments in response to environmental stresses. We used multiplexed transposon “Calling Cards” to record the genome-wide binding patterns of 28 transcription factors (TFs) in nitrogen-starved yeast. We identified TF targets relevant for pseudohyphal growth, producing a detailed map of its regulatory network. Using tools from graph theory, we identified 14 TFs that lie at the center of this network, including Flo8, Mss11, and Mfg1, which bind as a complex. Surprisingly, the DNA-binding preferences for these key TFs were unknown. Using Calling Card data, we predicted the in vivo DNA-binding motif for the Flo8-Mss11-Mfg1 complex and validated it using a reporter assay. We found that this complex binds several important targets, including FLO11, at both their promoter and termination sequences. We demonstrated that this binding pattern is the result of DNA looping, which regulates the transcription of these targets and is stabilized by an interaction with the nuclear pore complex. This looping provides yeast cells with a transcriptional memory, enabling them more rapidly to execute the filamentous growth program when nitrogen starved if they had been previously exposed to this condition.

Mapping yeast transcriptional networks

The term “transcriptional network” refers to the mechanism(s) that underlies coordinated expression of genes, typically involving transcription factors (TFs) binding to the promoters of multiple genes, and individual genes controlled by multiple TFs. A multitude of studies in the last two decades have aimed to map and characterize transcriptional networks in the yeast Saccharomyces cerevisiae. We review the methodologies and accomplishments of these studies, as well as challenges we now face. For most yeast TFs, data have been collected on their sequence preferences, in vivo promoter occupancy, and gene expression profiles in deletion mutants. These systematic studies have led to the identification of new regulators of numerous cellular functions and shed light on the overall organization of yeast gene regulation. However, many yeast TFs appear to be inactive under standard laboratory growth conditions, and many of the available data were collected using techniques that have since been improved. Perhaps as a consequence, comprehensive and accurate mapping among TF sequence preferences, promoter binding, and gene expression remains an open challenge. We propose that the time is ripe for renewed systematic efforts toward a complete mapping of yeast transcriptional regulatory mechanisms.

Genetic variation in Saccharomyces cerevisiae: circuit diversification in a signal transduction network

The connection between genotype and phenotype was assessed by determining the adhesion phenotype for the same mutation in two closely related yeast strains, S288c and Sigma, using two identical deletion libraries. Previous studies, all in Sigma, had shown that the adhesion phenotype was controlled by the filamentation mitogen-activated kinase (fMAPK) pathway, which activates a set of transcription factors required for the transcription of the structural gene FLO11. Unexpectedly, the fMAPK pathway is not required for FLO11 transcription in S288c despite the fact that the fMAPK genes are present and active in other pathways. Using transformation and a sensitized reporter, it was possible to isolate RPI1, one of the modifiers that permits the bypass of the fMAPK pathway in S288c. RPI1 encodes a transcription factor with allelic differences between the two strains: The RPI1 allele from S288c but not the one from Sigma can confer fMAPK pathway-independent transcription of FLO11. Biochemical analysis reveals differences in phosphorylation between the alleles. At the nucleotide level the two alleles differ in the number of tandem repeats in the ORF. A comparison of genomes between the two strains shows that many genes differ in size due to variation in repeat length.

Global gene deletion analysis exploring yeast filamentous growth

The dimorphic switch from a single-cell budding yeast to a filamentous form enables Saccharomyces cerevisiae to forage for nutrients and the opportunistic pathogen Candida albicans to invade human tissues and evade the immune system. We constructed a genome-wide set of targeted deletion alleles and introduced them into a filamentous S. cerevisiae strain, Σ1278b. We identified genes involved in morphologically distinct forms of filamentation: haploid invasive growth, biofilm formation, and diploid pseudohyphal growth. Unique genes appear to underlie each program, but we also found core genes with general roles in filamentous growth, including MFG1 (YDL233w), whose product binds two morphogenetic transcription factors, Flo8 and Mss11, and functions as a critical transcriptional regulator of filamentous growth in both S. cerevisiae and C. albicans.

"Calling cards" for DNA-binding proteins in mammalian cells

The ability to chronicle transcription-factor binding events throughout the development of an organism would facilitate mapping of transcriptional networks that control cell-fate decisions. We describe a method for permanently recording protein-DNA interactions in mammalian cells. We endow transcription factors with the ability to deposit a transposon into the genome near to where they bind. The transposon becomes a "calling card" that the transcription factor leaves behind to record its visit to the genome. The locations of the calling cards can be determined by massively parallel DNA sequencing. We show that the transcription factor SP1 fused to the piggyBac transposase directs insertion of the piggyBac transposon near SP1 binding sites. The locations of transposon insertions are highly reproducible and agree with sites of SP1-binding determined by ChIP-seq. Genes bound by SP1 are more likely to be expressed in the HCT116 cell line we used, and SP1-bound CpG islands show a strong preference to be unmethylated. This method has the potential to trace transcription-factor binding throughout cellular and organismal development in a way that has heretofore not been possible.

Access to DNA establishes a secondary target site bias for the yeast retrotransposon Ty5

Integration sites for many retrotransposons and retroviruses are determined by interactions between retroelement-encoded integrases and specific DNA-bound proteins. The Saccharomyces retrotransposon Ty5 preferentially integrates into heterochromatin because of interactions between Ty5 integrase and the heterochromatin protein silent information regulator 4. We mapped over 14,000 Ty5 insertions onto the S. cerevisiae genome, 76% of which occurred in heterochromatin, which is consistent with the known target site bias of Ty5. Using logistic regression, associations were assessed between Ty5 insertions and various chromosomal features such as genome-wide distributions of nucleosomes and histone modifications. Sites of Ty5 insertion, regardless of whether they occurred in heterochromatin or euchromatin, were strongly associated with DNase hypersensitive, nucleosome-free regions flanking genes. Our data support a model wherein silent information regulator 4 tethers the Ty5 integration machinery to domains of heterochromatin, and then, specific target sites are selected based on DNA access, resulting in a secondary target site bias. For insertions in euchromatin, DNA access is the primary determinant of target site choice. One consequence of the secondary target site bias of Ty5 is that insertions in coding sequences occur infrequently, which may preserve genome integrity.