The analysis of novel distal Cebpa enhancers and silencers using a transcriptional model reveals the complex regulatory logic of hematopoietic lineage specification

The contributions of DNA accessibility and transcription factor occupancy to enhancer activity during cellular differentiation

During gene regulation, DNA accessibility is thought to limit the availability of transcription factor (TF) binding sites, while TFs can increase DNA accessibility to recruit additional factors that upregulate gene expression. Given this interplay, the causative regulatory events in the modulation of gene expression remain unknown for the vast majority of genes. We utilized deeply sequenced ATAC-Seq data and site-specific knock-in reporter genes to investigate the relationship between the binding-site resolution dynamics of DNA accessibility and the expression dynamics of the enhancers of Cebpa during macrophage-neutrophil differentiation. While the enhancers upregulate reporter expression during the earliest stages of differentiation, there is little corresponding increase in their total accessibility. Conversely, total accessibility peaks during the last stages of differentiation without any increase in enhancer activity. The accessibility of positions neighboring C/EBP-family TF binding sites, which indicates TF occupancy, does increase significantly during early differentiation, showing that the early upregulation of enhancer activity is driven by TF binding. These results imply that a generalized increase in DNA accessibility is not sufficient, and binding by enhancer-specific TFs is necessary, for the upregulation of gene expression. Additionally, high-coverage ATAC-Seq combined with time-series expression data can infer the sequence of regulatory events at binding-site resolution.

Deep molecular learning of transcriptional control of a synthetic CRE enhancer and its variants

Massively parallel reporter assay measures transcriptional activities of various cis-regulatory modules (CRMs) in a single experiment. We developed a thermodynamic computational model framework that calculates quantitative levels of gene expression directly from regulatory DNA sequences. Using the framework, we investigated the molecular mechanisms of cis-regulatory mutations of a synthetic enhancer that cause abnormal gene expression. We found that, in a human cell line, competitive binding between family transcription factors (TFs) with slightly different binding preferences significantly increases the accuracy of recapitulating the transcriptional effects of thousands of single- or multi-mutations. We also discovered that even if various harmful mutations occurred in an activator binding site, CRM could stably maintain or even increase gene expression through a certain form of competitive binding between family TFs. These findings enhance understanding the effect of SNPs and indels on CRMs and would help building robust custom-designed CRMs for biologics production and gene therapy.

LucFlow: A method to measure Luciferase reporter expression in single cells

Reporter assays, in which the expression of an inert protein is driven by gene regulatory elements such as promoters and enhancers, are a workhorse for investigating gene regulation. Techniques for measuring reporter gene expression vary from single-cell or single-molecule approaches having low throughput to bulk Luciferase assays that have high throughput. We developed a Luciferase Reporter Assay using Flow-Cytometry (LucFlow), which measures reporter expression in single cells immunostained for Luciferase. We optimized and tested LucFlow with a murine cell line that can be differentiated into neutrophils, into which promoter-reporter and enhancer-promoter-reporter constructs have been integrated in a site-specific manner. The single-cell measurements are comparable to bulk ones but we found that dead cells have no detectable Luciferase protein, so that bulk assays underestimate reporter expression. LucFlow is able to achieve a higher accuracy than bulk methods by excluding dead cells during flow cytometry. Prior to fixation and staining, the samples are spiked with stained cells that can be discriminated during flow cytometry and control for tube-to-tube variation in experimental conditions. Computing fold change relative to control cells allows LucFlow to achieve a high level of precision. LucFlow, therefore, enables the accurate and precise measurement of reporter expression in a high throughput manner.

The contributions of DNA accessibility and transcription factor occupancy to enhancer activity during cellular differentiation

The upregulation of gene expression by enhancers depends upon the interplay between the binding of sequence-specific transcription factors (TFs) and DNA accessibility. DNA accessibility is thought to limit the ability of TFs to bind to their sites, while TFs can increase accessibility to recruit additional factors that upregulate gene expression. Given this interplay, the causative regulatory events underlying the modulation of gene expression during cellular differentiation remain unknown for the vast majority of genes. We investigated the binding-site resolution dynamics of DNA accessibility and the expression dynamics of the enhancers of an important neutrophil gene, Cebpa, during macrophage-neutrophil differentiation. Reporter genes were integrated in a site-specific manner in PUER cells, which are progenitors that can be differentiated into neutrophils or macrophages in vitro by activating the pan-leukocyte TF PU.1. Time series data show that two enhancers upregulate reporter expression during the first 48 hours of neutrophil differentiation. Surprisingly, there is little or no increase in the total accessibility, measured by ATAC-Seq, of the enhancers during the same time period. Conversely, total accessibility peaks 96 hrs after PU.1 activation-consistent with its role as a pioneer-but the enhancers do not upregulate gene expression. Combining deeply sequenced ATAC-Seq data with a new bias-correction method allowed the profiling of accessibility at single-nucleotide resolution and revealed protected regions in the enhancers that match all previously characterized TF binding sites and ChIP-Seq data. Although the accessibility of most positions does not change during early differentiation, that of positions neighboring TF binding sites, an indicator of TF occupancy, did increase significantly. The localized accessibility changes are limited to nucleotides neighboring C/EBP-family TF binding sites, showing that the upregulation of enhancer activity during early differentiation is driven by C/EBP-family TF binding. These results show that increasing the total accessibility of enhancers is not sufficient for upregulating their activity and other events such as TF binding are necessary for upregulation. Also, TF binding can cause upregulation without a perceptible increase in total accessibility. Finally, this study demonstrates the feasibility of comprehensively mapping individual TF binding sites as footprints using high coverage ATAC-Seq and inferring the sequence of events in gene regulation by combining with time-series gene expression data.

EBF1 primes B-lymphoid enhancers and limits the myeloid bias in murine multipotent progenitors

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) generate all cells of the blood system. Despite their multipotency, MPPs display poorly understood lineage bias. Here, we examine whether lineage-specifying transcription factors, such as the B-lineage determinant EBF1, regulate lineage preference in early progenitors. We detect low-level EBF1 expression in myeloid-biased MPP3 and lymphoid-biased MPP4 cells, coinciding with expression of the myeloid determinant C/EBPα. Hematopoietic deletion of Ebf1 results in enhanced myelopoiesis and reduced HSC repopulation capacity. Ebf1-deficient MPP3 and MPP4 cells exhibit an augmented myeloid differentiation potential and a transcriptome with an enriched C/EBPα signature. Correspondingly, EBF1 binds the Cebpa enhancer, and the deficiency and overexpression of Ebf1 in MPP3 and MPP4 cells lead to an up- and downregulation of Cebpa expression, respectively. In addition, EBF1 primes the chromatin of B-lymphoid enhancers specifically in MPP3 cells. Thus, our study implicates EBF1 in regulating myeloid/lymphoid fate bias in MPPs by constraining C/EBPα-driven myelopoiesis and priming the B-lymphoid fate.

Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential hematopoietic progenitors

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture—the type and strength of regulatory interconnections—and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured.

The supply of blood cells is replenished by the maturation of hematopoietic progenitor cells into different cell types. Which cell type a progenitor cell develops into is determined by a complex network of genes whose protein products directly or indirectly regulate each others’ expression and that of downstream genes characteristic of the cell type. We inferred the nature and causality of the regulatory connections in a 12-gene network known to affect the decision between erythrocyte and neutrophil cell fates using a predictive machine-learning approach. Our analysis showed that the overall architecture of the network is densely interconnected and not hierarchical. Furthermore, the model inferred that PU.1, considered a master regulator of all white-blood cell lineages, is upregulated during neutrophil development by two other proteins, Cebpa and Gfi1. We validated this prediction by showing that Cebpa and Gfi1 expression precedes that of PU.1 in single-cell gene expression data from mouse bone marrow. These results revise the architecture of the gene network and the causality of regulatory events guiding hematopoiesis. The results also show that combining machine learning approaches with time course data can help resolve causality during development.

Nonequilibrium model of short-range repression in gene transcription regulation

Transcription factors are proteins that regulate gene activity by activating or repressing gene transcription. A special class of transcriptional repressors operates via a short-range mechanism, making local DNA regions inaccessible to binding by activators, and thus providing an indirect repressive action on the target gene. This mechanism is commonly modeled assuming that repressors interact with DNA under thermodynamic equilibrium and neglecting some configurations of the gene regulatory region. We elaborate on a more general nonequilibrium model of short-range repression using the graph formalism for transitions between gene states, and we apply analytical calculations to compare it with the equilibrium model in terms of the repression strength and expression noise. In contrast to the equilibrium approach, the new model allows us to separate two basic mechanisms of short-range repression. The first mechanism is associated with the recruiting of factors that mediate chromatin condensation, and the second one concerns the blocking of factors that mediate chromatin loosening. The nonequilibrium model demonstrates better performance on previously published gene expression data obtained for transcription factors controlling Drosophila development, and furthermore it predicts that the first repression mechanism is the most favorable in this system. The presented approach can be scaled to larger gene networks and can be used to infer specific modes and parameters of transcriptional regulation from gene expression data.

Fully interpretable deep learning model of transcriptional control

Motivation

The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent worksin the systems biology community to employDNNs to solve important problems in functional genomics and moleculargenetics. Typically, such investigations have taken a ‘black box’ approach in which the internal structure of themodel used is set purely by machine learning considerations with little consideration of representing the internalstructure of the biological system by the mathematical structure of the DNN. DNNs have not yet been applied to thedetailed modeling of transcriptional control in which mRNA production is controlled by the binding of specific transcriptionfactors to DNA, in part because such models are in part formulated in terms of specific chemical equationsthat appear different in form from those used in neural networks.

Results

In this paper, we give an example of a DNN whichcan model the detailed control of transcription in a precise and predictive manner. Its internal structure is fully interpretableand is faithful to underlying chemistry of transcription factor binding to DNA. We derive our DNN from asystems biology model that was not previously recognized as having a DNN structure. Although we apply our DNNto data from the early embryo of the fruit fly Drosophila, this system serves as a test bed for analysis of much larger datasets obtained by systems biology studies on a genomic scale. .

Availability and implementation

The implementation and data for the models used in this paper are in a zip file in the supplementary material.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analyzing a putative enhancer of optic disc morphology

Background

Genome-wide association studies have identified the CDC7-TGFBR3 intergenic region on chromosome 1 to be strongly associated with optic disc area size. The mechanism of its function remained unclear until new data on eQTL markers emerged from the Genotype-Tissue Expression project. The target region was found to contain a strong silencer of the distal (800 kb) Transcription Factor (TF) gene GFI1 (Growth Factor Independent Transcription Repressor 1) specifically in neuroendocrine cells (pituitary gland). GFI1 has also been reported to be involved in the development of sensory neurons and hematopoiesis. Therefore, GFI1, being a developmental gene, is likely to affect optic disc area size by altering the expression of the associated genes via long-range interactions.

Results

Distribution of haplotypes in the putative enhancer region has been assessed using the data on four continental supergroups generated by the 1000 Genomes Project. The East Asian (EAS) populations were shown to manifest a highly homogenous unimodal haplotype distribution pattern within the region with the major haplotype occurring with the frequency of 0.9. Another European specific haplotype was observed with the frequency of 0.21. The major haplotype appears to be involved in silencing GFI1repressor gene expression, which might be the cause of increased optic disc area characteristic of the EAS populations.

The enhancer/eQTL region overlaps AluJo element, which implies that this particular regulatory element is primate-specific and confined to few tissues.

Conclusion

Population specific distribution of GFI1 enhancer alleles may predispose certain ethnic groups to glaucoma.

Robust Normalization of Luciferase Reporter Data

Transient Luciferase reporter assays are widely used in the study of gene regulation and intracellular cell signaling. In order to control for sample-to-sample variation in luminescence arising from variability in transfection efficiency and other sources, an internal control reporter is co-transfected with the experimental reporter. The luminescence of the experimental reporter is normalized against the control by taking the ratio of the two. Here we show that this method of normalization, “ratiometric”, performs poorly when the transfection efficiency is low and leads to biased estimates of relative activity. We propose an alternative methodology based on linear regression that is much better suited for the normalization of reporter data, especially when transfection efficiency is low. We compare the ratiometric method against three regression methods on both simulated and empirical data. Our results suggest that robust errors-in-variables (REIV) regression performs the best in normalizing Luciferase reporter data. We have made the R code for Luciferase data normalization using REIV available on GitHub.

The regulatory control of Cebpa enhancers and silencers in the myeloid and red-blood cell lineages

Cebpa encodes a transcription factor (TF) that plays an instructive role in the development of multiple myeloid lineages. The expression of Cebpa itself is finely modulated, as Cebpa is expressed at high and intermediate levels in neutrophils and macrophages respectively and downregulated in non-myeloid lineages. The cis-regulatory logic underlying the lineage-specific modulation of Cebpa's expression level is yet to be fully characterized. Previously, we had identified 6 new cis-regulatory modules (CRMs) in a 78kb region surrounding Cebpa. We had also inferred the TFs that regulate each CRM by fitting a sequence-based thermodynamic model to a comprehensive reporter activity dataset. Here, we report the cis-regulatory logic of Cebpa CRMs at the resolution of individual binding sites. We tested the binding sites and functional roles of inferred TFs by designing and constructing mutated CRMs and comparing theoretical predictions of their activity against empirical measurements in a myeloid cell line. The enhancers were confirmed to be activated by combinations of PU.1, C/EBP family TFs, Egr1, and Gfi1 as predicted by the model. We show that silencers repress the activity of the proximal promoter in a dominant manner in G1ME cells, which are derived from the red-blood cell lineage. Dominant repression in G1ME cells can be traced to binding sites for GATA and Myb, a motif shared by all of the silencers. Finally, we demonstrate that GATA and Myb act redundantly to silence the proximal promoter. These results indicate that dominant repression is a novel mechanism for resolving hematopoietic lineages. Furthermore, Cebpa has a fail-safe cis-regulatory architecture, featuring several functionally similar CRMs, each of which contains redundant binding sites for multiple TFs. Lastly, by experimentally demonstrating the predictive ability of our sequence-based thermodynamic model, this work highlights the utility of this computational approach for understanding mammalian gene regulation.

A sequence level model of an intact locus predicts the location and function of nonadditive enhancers

Metazoan gene expression is controlled through the action of long stretches of noncoding DNA that contain enhancers-shorter sequences responsible for controlling a single aspect of a gene's expression pattern. Models built on thermodynamics have shown how enhancers interpret protein concentration in order to determine specific levels of gene expression, but the emergent regulatory logic of a complete regulatory locus shows qualitative and quantitative differences from isolated enhancers. Such differences may arise from steric competition limiting the quantity of DNA that can simultaneously influence the transcription machinery. We incorporated this competition into a mechanistic model of gene regulation, generated efficient algorithms for this computation, and applied it to the regulation of Drosophila even-skipped (eve). This model finds the location of enhancers and identifies which factors control the boundaries of eve expression. This model predicts a new enhancer that, when assayed in vivo, drives expression in a non-eve pattern. Incorporation of chromatin accessibility eliminates this inconsistency.