Studying transcription factor function in the genome at molecular resolution

Mouse promoters are characterised by low occupancy and high turnover of RNA polymerase II

The general transcription machinery and its occupancy at promoters are highly conserved across metazoans. This contrasts with the kinetics of mRNA production that considerably differ between model species such as Drosophila and mouse. The molecular basis for these kinetic differences is currently unknown. Here, we used Single-Molecule Footprinting to measure RNA Polymerase II (Pol II) occupancy, the fraction of DNA molecules bound, at promoters in mouse and Drosophila cell lines. Single-molecule data reveals that Pol II occupancy is on average 3-5 times more frequent at transcriptionally active Drosophila promoters than active mouse promoters. Kinetic modelling of the occupancy states suggests that these differences in Pol II occupancy are determined by the ratio between the transcription initiation and Pol II turnover rates. We used chemical perturbation of transcription initiation to determine Pol II turnover rate in both species. Integration of these data into the model shows that infrequent Pol II occupancy in mouse is explained by the combination of high Pol II turnover and low transcription initiation rates.

Programming of synthetic regulatory DNA for cell-type targeting in humans

In a recent study in Nature, Gosai et al.1 introduce a framework to engineer and validate synthetic DNA regulatory elements showing cell-type-specific activity in human cell lines, closing the distance to the machine-driven design of functional regulatory sequences with therapeutic applications in humans.

Profiling the epigenome using long-read sequencing

The advent of single-molecule, long-read sequencing (LRS) technologies by Oxford Nanopore Technologies and Pacific Biosciences has revolutionized genomics, transcriptomics and, more recently, epigenomics research. These technologies offer distinct advantages, including the direct detection of methylated DNA and simultaneous assessment of DNA sequences spanning multiple kilobases along with their modifications at the single-molecule level. This has enabled the development of new assays for analyzing chromatin states and made it possible to integrate data for DNA methylation, chromatin accessibility, transcription factor binding and histone modifications, thereby facilitating comprehensive epigenomic profiling. Owing to recent advancements, alternative, nascent and translating transcripts can be detected using LRS approaches. This Review discusses LRS-based experimental and computational strategies for characterizing chromatin states and highlights their advantages over short-read sequencing methods. Furthermore, we demonstrate how various long-read methods can be integrated to design multi-omics studies to investigate the relationship between chromatin states and transcriptional dynamics.

Crosstalk between paralogs and isoforms influences p63-dependent regulatory element activity

The p53 family of transcription factors (p53, p63 and p73) regulate diverse organismal processes including tumor suppression, maintenance of genome integrity and the development of skin and limbs. Crosstalk between transcription factors with highly similar DNA binding profiles, like those in the p53 family, can dramatically alter gene regulation. While p53 is primarily associated with transcriptional activation, p63 mediates both activation and repression. The specific mechanisms controlling p63-dependent gene regulatory activity are not well understood. Here, we use massively parallel reporter assays (MPRA) to investigate how local DNA sequence context influences p63-dependent transcriptional activity. Most regulatory elements with a p63 response element motif (p63RE) activate transcription, although binding of the p63 paralog, p53, drives a substantial proportion of that activity. p63RE sequence content and co-enrichment with other known activating and repressing transcription factors, including lineage-specific factors, correlates with differential p63RE-mediated activities. p63 isoforms dramatically alter transcriptional behavior, primarily shifting inactive regulatory elements towards high p63-dependent activity. Our analysis provides novel insight into how local sequence and cellular context influences p63-dependent behaviors and highlights the key, yet still understudied, role of transcription factor paralogs and isoforms in controlling gene regulatory element activity.

Identifying Markov Chain Models from Time-to-Event Data: An Algebraic Approach

Many biological and medical questions can be modeled using time-to-event data in finite-state Markov chains, with the phase-type distribution describing intervals between events. We solve the inverse problem: given a phase-type distribution, can we identify the transition rate parameters of the underlying Markov chain? For a specific class of solvable Markov models, we show this problem has a unique solution up to finite symmetry transformations, and we outline a recursive method for computing symbolic solutions for these models across any number of states. Using the Thomas decomposition technique from computer algebra, we further provide symbolic solutions for any model. Interestingly, different models with the same state count but distinct transition graphs can yield identical phase-type distributions. To distinguish among these, we propose additional properties beyond just the time to the next event. We demonstrate the method's applicability by inferring transcriptional regulation models from single-cell transcription imaging data.

BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells

Understanding cell destiny requires unraveling the intricate mechanism of gene regulation, where transcription factors (TFs) play a pivotal role. However, the actual contribution of TFs, that is TF activity, is not only determined by TF expression, but also accessibility of corresponding chromatin regions. Therefore, we introduce BIOTIC, an advanced Bayesian model with a well-established gene regulation structure that harnesses the power of single-cell multi-omics data to model the gene expression process under the control of regulatory elements, thereby defining the regulatory activity of TFs with variational inference. We demonstrated that the TF activity inferred by BIOTIC can serve as a characterization of cell identity, and outperforms baseline methods for the tasks of cell typing, cell development tracking, and batch effect correction. Additionally, BIOTIC trained on multi-omics data can flexibly be applied to the scenario where merely single-cell transcriptome sequencing is available, to infer TF activity and annotate the cell type by mapping the query cell into the reference TF activity space, as an emerging application of cell atlases. The structure of BIOTIC has been determined to be adaptable for the inclusion of additional biological factors, allowing for flexible and more comprehensive gene regulation analysis. BIOTIC introduces a pioneering biological-mechanism-driven framework to infer TF activity and elucidate cell identity states at gene regulatory level, paving the way for a deeper understanding of the complex interplay between TFs and gene expression in living systems.

guidedNOMe-seq quantifies chromatin states at single allele resolution for hundreds of custom regions in parallel

Since the introduction of next generation sequencing technologies, the field of epigenomics has evolved rapidly. However, most commonly used assays are enrichment-based methods and thus only semi-quantitative. Nucleosome occupancy and methylome sequencing (NOMe-seq) allows for quantitative inference of chromatin states with single locus resolution, but this requires high sequencing depth and is therefore prohibitively expensive to routinely apply to organisms with large genomes. To overcome this limitation, we introduce guidedNOMe-seq, where we combine NOMe profiling with large scale sgRNA synthesis and Cas9-mediated region-of-interest (ROI) liberation. To facilitate quantitative comparisons between multiple samples, we additionally develop an R package to standardize differential analysis of any type of NOMe-seq data. We extensively benchmark guidedNOMe-seq in a proof-of-concept study, dissecting the interplay of ChAHP and CTCF on chromatin. In summary we present a cost-effective, scalable, and customizable target enrichment extension to the existing NOMe-seq protocol allowing genome-scale quantification of nucleosome occupancy and transcription factor binding at single allele resolution.

Hold out the genome: a roadmap to solving the cis-regulatory code

Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The 'cis-regulatory code' - how cells interpret DNA sequences to determine when, where and how much genes should be expressed - has proven to be exceedingly complex. Recently, advances in the scale and resolution of functional genomics assays and machine learning have enabled substantial progress towards deciphering this code. However, the cis-regulatory code will probably never be solved if models are trained only on genomic sequences; regions of homology can easily lead to overestimation of predictive performance, and our genome is too short and has insufficient sequence diversity to learn all relevant parameters. Fortunately, randomly synthesized DNA sequences enable testing a far larger sequence space than exists in our genomes, and designed DNA sequences enable targeted queries to maximally improve the models. As the same biochemical principles are used to interpret DNA regardless of its source, models trained on these synthetic data can predict genomic activity, often better than genome-trained models. Here we provide an outlook on the field, and propose a roadmap towards solving the cis-regulatory code by a combination of machine learning and massively parallel assays using synthetic DNA.

Transcriptional Stochasticity as a Key Aspect of HIV-1 Latency

This review summarizes current advances in the role of transcriptional stochasticity in HIV-1 latency, which were possible in a large part due to the development of single-cell approaches. HIV-1 transcription proceeds in bursts of RNA production, which stem from the stochastic switching of the viral promoter between ON and OFF states. This switching is caused by random binding dynamics of transcription factors and nucleosomes to the viral promoter and occurs at several time scales from minutes to hours. Transcriptional bursts are mainly controlled by the core transcription factors TBP, SP1 and NF-κb, the chromatin status of the viral promoter and RNA polymerase II pausing. In particular, spontaneous variability in the promoter chromatin creates heterogeneity in the response to activators such as TNF-α, which is then amplified by the Tat feedback loop to generate high and low viral transcriptional states. This phenomenon is likely at the basis of the partial and stochastic response of latent T cells from HIV-1 patients to latency-reversing agents, which is a barrier for the development of shock-and-kill strategies of viral eradication. A detailed understanding of the transcriptional stochasticity of HIV-1 and the possibility to precisely model this phenomenon will be important assets to develop more effective therapeutic strategies.

Evaluation of site-specific methylation of the CMV promoter and its role in CHO cell productivity of a recombinant monoclonal antibody

We previously demonstrated that increased monoclonal antibody productivity in dihydrofolate reductase (DHFR)-amplified CHO cells correlates with phosphorylated transcription factor-cytomegalovirus (CMV) promoter interactions. In this article, we extend the characterization to include CMV promoter methylation and its influence on NFκB and CREB1 transcription factor binding to the CMV promoter in two families of DHFR-amplified CHO cell lines. CMV promoter methylation was determined using bisulfite sequencing. To overcome Sanger-sequencing limitations due to high CG bias and multiple transgenes copies, pyrosequencing was used to determine the frequency of methylated cytosines in regions proximal to and containing the NFκB and CREB1 transcription-factor consensus binding sites. Chromatin immunoprecipitation was performed to interrogate transcription factor–DNA interactions. Antibodies to CREB1 and NFκB were used to immunoprecipitate formaldehyde-crosslinked protein-DNA fractions, followed by reverse transcription quantitative real-time polymerase chain reaction to quantitate the number of copies of CMV-promoter DNA bound to the various transcription factors. The relative unmethylated fraction at the CREB1 and NFκB consensus binding sites determined by pyrosequencing was correlated with transcription factor binding as determined by chromatin immunoprecipitation. Azacytidine treatment reduced methylation in all treated samples, though not at all methylation sites, while increasing transcription. Distinct promoter methylation patterns arise upon clonal selection in different families of cell lines. In both cell line families, increased methylation was observed upon amplification. In one family, the NFκB binding-site methylation was accompanied by increased CREB1 interaction with the promoter. In the other cell line family, lower methylation frequency at the NFκB consensus binding site was accompanied by more NFκB recruitment to the promoter region.

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting

Precise control of gene expression requires the coordinated action of multiple factors at cis-regulatory elements. We recently developed single-molecule footprinting to simultaneously resolve the occupancy of multiple proteins including transcription factors, RNA polymerase II and nucleosomes on single DNA molecules genome-wide. The technique combines the use of cytosine methyltransferases to footprint the genome with bisulfite sequencing to resolve transcription factor binding patterns at cis-regulatory elements. DNA footprinting is performed by incubating permeabilized nuclei with recombinant methyltransferases. Upon DNA extraction, whole-genome or targeted bisulfite libraries are prepared and loaded on Illumina sequencers. The protocol can be completed in 4-5 d in any laboratory with access to high-throughput sequencing. Analysis can be performed in 2 d using a dedicated R package and requires access to a high-performance computing system. Our method can be used to analyze how transcription factors cooperate and antagonize to regulate transcription.

Reading the chromatinized genome

Eukaryotic DNA-binding proteins operate in the context of chromatin, where nucleosomes are the elementary building blocks. Nucleosomal DNA is wrapped around a histone core, thereby rendering a large fraction of the DNA surface inaccessible to DNA-binding proteins. Nevertheless, first responders in DNA repair and sequence-specific transcription factors bind DNA target sites obstructed by chromatin. While early studies examined protein binding to histone-free DNA, it is only now beginning to emerge how DNA sequences are interrogated on nucleosomes. These readout strategies range from the release of nucleosomal DNA from histones, to rotational/translation register shifts of the DNA motif, and nucleosome-specific DNA binding modes that differ from those observed on naked DNA. Since DNA motif engagement on nucleosomes strongly depends on position and orientation, we argue that motif location and nucleosome positioning co-determine protein access to DNA in transcription and DNA repair.