'Calling Cards' method for high-throughput identification of targets of yeast DNA-binding proteins

Mechanisms and biotechnological applications of transcription factors

Transcription factors play an indispensable role in maintaining cellular viability and finely regulating complex internal metabolic networks. These crucial bioactive functions rely on their ability to respond to effectors and concurrently interact with binding sites. Recent advancements have brought innovative insights into the understanding of transcription factors. In this review, we comprehensively summarize the mechanisms by which transcription factors carry out their functions, along with calculation and experimental-based methods employed in their identification. Additionally, we highlight recent achievements in the application of transcription factors in various biotechnological fields, including cell engineering, human health, and biomanufacturing. Finally, the current limitations of research and provide prospects for future investigations are discussed. This review will provide enlightening theoretical guidance for transcription factors engineering.

Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA Interactions Over Time in Cells and Tissues

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon "Calling Cards" into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Preparation and delivery of Calling Cards reagents Support Protocol 1: Next-generation sequencing quantification of barcode distribution within self-reporting transposon plasmid pool and adeno-associated virus genome Basic Protocol 2: Sample collection and RNA purification Support Protocol 2: Library density quantitative PCR Basic Protocol 3: Sequencing library preparation Basic Protocol 4: Library pooling and sequencing Basic Protocol 5: Data analysis.

Calling Cards: a customizable platform to longitudinally record protein-DNA interactions over time in cells and tissues

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next generation sequencing. Compared to other genomic assays, whose readout provides a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon (SRT) "Calling Cards" into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile specific transcription factor binding with custom transcription factor (TF)-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Card reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 1-2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. Basic Protocol 1: Preparation and delivery of Calling Cards reagentsBasic Protocol 2: Sample preparationBasic Protocol 3: Sequencing library preparationBasic Protocol 4: Library pooling and sequencingBasic Protocol 5: Data analysis.

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.

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.

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.

O Cdc7 kinase where art thou?

Although Cdc7 protein kinase is important for regulating DNA replication in all eukaryotes and is a target for cancer therapy, it has never been localized in cells. Recently, a novel molecular genomic method used by our laboratory to localize Cdc7 to regions of chromosomes. Originally, mutations in the CDC7 gene were found in the classic cdc mutant collection of Hartwell et al. (Genetics 74:267-286, 1973). The CDC7 gene was found to encode a protein kinase called DDK that has been studied for many years, establishing its precise role in the initiation of DNA replication at origins. Recently, clinical studies are underway with DDK inhibitors against DDK in cancer patients. However, the conundrum is that Cdc7 has never been detected at origins of replication even though many studies have suggested it should be there. We used "Calling Card" system in which DNA binding proteins are localized to the genome via retrotransposon insertion and deep-sequencing methods. We have shown that Cdc7 localizes at many regions of the genome and was enriched at functional origins of replication. These results are consistent with DDK's role in many additional genomic processes including mutagenesis, chromatid cohesion, and meiotic recombination. Thus, the main conclusion from our studies is that Cdc7 kinase is found at many locations in the genome, but is enriched at functional origins of replication. Furthermore, we propose that application of the Calling Card system to other eukaryotes should be useful in identification of functional origins in other eukaryotic cells.

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.

Retrotransposon profiling of RNA polymerase III initiation sites

Although retroviruses are relatively promiscuous in choice of integration sites, retrotransposons can display marked integration specificity. In yeast and slime mold, some retrotransposons are associated with tRNA genes (tDNAs). In the Saccharomyces cerevisiae genome, the long terminal repeat retrotransposon Ty3 is found at RNA polymerase III (Pol III) transcription start sites of tDNAs. Ty1, 2, and 4 elements also cluster in the upstream regions of these genes. To determine the extent to which other Pol III-transcribed genes serve as genomic targets for Ty3, a set of 10,000 Ty3 genomic retrotranspositions were mapped using high-throughput DNA sequencing. Integrations occurred at all known tDNAs, two tDNA relics (iYGR033c and ZOD1), and six non-tDNA, Pol III-transcribed types of genes (RDN5, SNR6, SNR52, RPR1, RNA170, and SCR1). Previous work in vitro demonstrated that the Pol III transcription factor (TF) IIIB is important for Ty3 targeting. However, seven loci that bind the TFIIIB loader, TFIIIC, were not targeted, underscoring the unexplained absence of TFIIIB at those sites. Ty3 integrations also occurred in two open reading frames not previously associated with Pol III transcription, suggesting the existence of a small number of additional sites in the yeast genome that interact with Pol III transcription complexes.

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

Transcription factors direct gene expression, so there is much interest in mapping their genome-wide binding locations. Current methods do not allow for the multiplexed analysis of TF binding, and this limits their throughput. We describe a novel method for determining the genomic target genes of multiple transcription factors simultaneously. DNA-binding proteins are endowed with the ability to direct transposon insertions into the genome near to where they bind. The transposon becomes a "Calling Card" marking the visit of the DNA-binding protein to that location. A unique sequence "barcode" in the transposon matches it to the DNA-binding protein that directed its insertion. The sequences of the DNA flanking the transposon (which reveal where in the genome the transposon landed) and the barcode within the transposon (which identifies the TF that put it there) are determined by massively parallel DNA sequencing. To demonstrate the method's feasibility, we determined the genomic targets of eight transcription factors in a single experiment. The Calling Card method promises to significantly reduce the cost and labor needed to determine the genomic targets of many transcription factors in different environmental conditions and genetic backgrounds.

Lens epithelium-derived growth factor fusion proteins redirect HIV-1 DNA integration

Lens epithelium-derived growth factor (LEDGF) fusion proteins can direct HIV-1 DNA integration to novel sites in the host genome. The C terminus of LEDGF contains an integrase binding domain (IBD), and the N terminus binds chromatin. LEDGF normally directs integrations to the bodies of expressed genes. Replacing the N terminus of LEDGF with chromatin binding domains (CBDs) from other proteins changes the specificity of HIV-1 DNA integration. We chose two well-characterized CBDs: the plant homeodomain (PHD) finger from ING2 and the chromodomain from heterochromatin binding protein 1alpha (HP1alpha). The ING2 PHD finger binds H3K4me3, a histone mark that is associated with the transcriptional start sites of expressed genes. The HP1alpha chromodomain binds H3K9me2,3, histone marks that are widely distributed throughout the genome. A fusion protein in which the ING2 PHD finger was linked to the LEDGF IBD directed integrations near the start sites of expressed genes. A similar fusion protein in which the HP1alpha chromodomain was linked to the LEDGF IBD directed integrations to sites that differed from both the PHD finger fusion-directed and LEDGF-directed integration sites. The ability to redirect HIV-1 DNA integration may help solve the problems associated with the activation of oncogenes when retroviruses are used in gene therapy.